Introduction
Ads in Society
Ads occupy a significant space of the contemporary life of the society because research studies show ads to positively correlate with the behavioral characteristics and influence the purchasing patterns of individuals, alter perceptions about product quality, and consumer tastes while creating brand loyalty and product differentiation in a pervasive advertising environment. In this case, pervasive advertising does not change consumer preferences but is an approach to rearrange the consumer valuations toward a product. In the modern business environment, pervasive advertising has gained dynamics and plays a central role in delivering content to the customer. That is because advertising is driven by the rapid advances and use of modern computing technology devices including PDAs and smartphones, which bring personalized ads to a more intimate level. A modern approach to advertising includes using ads sensing technologies such as AisleCaster, which deliver location-sensitive ads supported on different systems such as the Locadio system add value to pervasive advertising as shown in. The systems are infrastructure-based with content-driven acquisition tools implemented on hardware sensors based on context acquisition sensors, context awareness filtering, and fusion, and context-aware categorization applications. The systems enable advertising agencies to target responsive people to specific services and individual needs based on content congruency and ad responsiveness. The merge between pervasive advertising and modern technology to achieve the goal is evident in ubiquitous computing. Ubiquitous computing claims a significant share in propagating context sensitive ads based on time, location, and individual attitudes and behaviour. Many ubiquitous computing applications provide an enabling environment for the provision of context driven services in a pervasive computing environment envisioned in multimedia ubiquitous computing systems including mobile graphics, mobile ads and other applications. Inherent applications defined by context awareness applications, the latter are exemplified in modern trends of mobile advertising driven by the rapid growth in the acquisition of mobile phones by many users, reinforcing predictions that mobile ads will in future occupy a greater share than web advertising. Evidence is in the trend mobile operators in the UK and USA have shown in trading ads for services on cell phones, digital kiosks, among others. That is in addition to ubicomp’s real-time activity inferencing targeting specific ads implemented by querying advertising search engines based on aggregate demographic data and consumer sensitivity to relevant ads. The entire concept revolves around the significant share ads occupy in the pervasive computing environments drawing on situation aware, context sensitive, and ad hoc communication user applications. The overall significance of ads not only permeates society but also has had strong impact on the business community and the target audience with the ubiquitous computing applications used to achieve business goals and objectives.
Different Methods of Advertising
Different advertising methods for delivering content to the intended audience have evolved from the traditional to the current computer driven methods. Among the modern approaches to advertising is where ads use ubiquitous computing technologies to achieve the advertising goal. The underlying role and significance in business is to influence the social life and behavioral characteristics of customers and responses to content delivered on the ads. In the current business environment, powerful ubicomp behavioral inferencing tools generate context sensitive and location based ads to deliver to customers on modern computing devices. The traditional methods of advertising have been in the society for long causing different implications on the behavioral attitudes of the intended environment. This study is not going to assess in detail the attitudinal implications of ads, but the general benefits and disadvantages of ads based on traditional advertising methods including Television. The traditional methods to focus on include radio ads, TV ads, magazine ads, Newspaper Ads, mailing list ads, and tradeshow ads.
Television Advertising
As demonstrated in. Television advertising has its basis on the concept of delivering an audience to the people with ads tailored to bring information to influence behavioral characteristic of the people. In context, television ads designed toward achieving commercial goals based on the content and style of the ads depend on individual involvement. Often, the viewing audience for a particular ad never prepares in advance for a specific ad, but view the ads with specific content style and congruency unprepared. The psychological impact on the viewer and the response to the ad all depend on individual involvement and attitude toward the ad. Ads can be highly appreciated or negatively regarded by an audience drawing on advertising stimuli that hamper or enhance a particular response depending on the characteristics of the ad, situational factors including the environment and time of exposure of the ad, and the characteristics of the individuals in the audience. Context is a crucial situational factor affecting the response individuals in the audience develops toward an ad based on the characteristics of the ad and the perceptions individual have toward the channel providing content delivery. One can realize the importance of the content and the mode of delivery of the content, and as demonstrated in.
The whole concept draws on cognition, behavior and emotion. In this case, information communicated in the adverts, the level of formativeness of the ads, and the level of irritation of the TV ads, credibility, demographic targeted ads, and the level of interactivity of the ads bear a strong influence on the behavioral characteristics of the audience. In addition to television advertising, online advertising has had a significant share in the delivery of information to the target population.
Online advertising
Online ads play a significant role in delivering content to customers. The rapid rise in internet connectivity and users is a facilitating method for consumer involvement and attitude toward brand information based on search engine results and other directories available online.
Online ads generally come in different forms including text ads. Text ads do not include graphic images, but simple textual information. Typically, text ads can be served from individual websites and different methods provide different contextual information to the target individuals. Other forms of ads include display ads enabled on applications that place the ad on the top of a window of a web browser defined as graphical display through the display methods including banners, large boxes and many others. That is in addition to the use of video hosted on various online sites including YouTube among others. It is crucial to note that display ads employ the impact of visual effects in addition to the effects created by pop-up ads. Pop-up ads appear on the site an individual is browsing. A number of other online ads not discussed in this section generally characterized by targeting methods including contextual targeting, behavioral targeting, and local advertising.
Part 3
Ubiquitous Advertising the Killer Application for the 21st Century
A modern approach to killer applications is ubiquitous advertising. That is because many ubiquitous computing applications in the pervasive advertising environment seamlessly interwove into the lives of people in everyday lives. In context, ubiquitous computing delivers content to the customer executing on embedded applications capable of sensing, processing, activation, and communicating the right content to the right audience at the right time. Here, applications embedded into the devices have made computing an integral component of people’s lives.
To optimize the impact of ads on the target audience, derived benefits, and income generated from the ads, modern ubicomp practitioners use revenue generated from ads to enhance effective delivery by targeting specific audiences through segmentation strategies. Segmentation according to demographic and psychographic behavior and sexual orientation, values and life style (VAL) using embedded GeoVALS applications underlie the decision to deliver specific ads to a specific audience with effectiveness and accuracy. To target the audience better, modern applications integrated to into devices such as mobile phones play a significant role based on location sensing technologies. That is in addition to optimizing the use of dynamically changing ads as one move around in a shopping environment. Typical examples include indoor shopping carts with location sensitive ads, Alaris Media Network ads that are sensitive to changes in ones position as they move along, and Ticket2Talk that generates locations sensitive ads. While the technologies are growing, Ubicomp technologies provide superior advertising solutions based on integrated context sensitive technologies, successfully applied in inferring different behavioral characteristics and context sensitive objects based on location, time, and demographic estimates generated in real time.
Ubicomp in the context of capturing and generating contextual ads targeting the right audience, different advanced applications developed to address problems related to determining the causal relationship between ads and sales, ability to infer user behavioral characteristics, activity sensitive ads, and the ability to infer from exposure to ads and buyer behavior prevail. Toward generating behavioral related ads, there is a strong need to know the customer. Ubiquitous computing applications provide solutions by not only knowing what advertisers want, but also by using embedded capabilities such as simple context sensitive and behavioral targeting technologies. It is critical to assess the impact created because of context aware ads.
A survey on context awareness
As the dynamism in advertising changes with specialized focus on content specific ads, a rapid rise and integration of ubiquitous computing applications on hand held devices such as mobile phones, processing and delivery of information from any place at any time necessitates the need to assess context awareness. In this case, context awareness requires that ads capable generating applications be able to inference accurately displays that reflect the behavioral characteristics of the target audience which are location specific in the prevailing physical context of the customer. The context awareness inferred upon is the functionality of an application with inbuilt capabilities to sense and detect information and interpret that information to be able to respond to various environmental situational factors such as temperature, time, and location in a dynamically changing user environment. In this case, context awareness systems gather context aware information that is added to a context ware repository. The entire application consists of four components, which include context acquisition and sensing applications that enable context acquisition through sensed context, context explicitly provided, and derived context. Typical examples include the client based Global Positioning System (GPS) with embedded context acquisition capabilities in the form of artificial intelligence, machine vision, and user calendar driven applications options.
It is crucial to depict the surrounding environment and situation when conducting a context awareness survey. Context modeling and representation plays a significant role where generating context data using modified modeling techniques such as similar data, graphical data modeling, and ontology based models. Other context aware system components include context filtering and fusion to obtain semantic information generated from context sensitive data embedded applications. The entire context data collected should be saved in a database for later retrieval and analysis. Additional capabilities in context aware systems are context storage and retrieval. In this case, the storage and retrieval of context aware information is critical in establishing the relationship between objects and the relevance of retrieved information to a particular environment. If effectively used, the context aware application should address the relevance of information based on application functionalities that include context triggered actions, proximate selection, and automatic contextual configuration. The underlying strength of the context aware application is to present information to the user, automatically execute services, and tag such context data for later retrieval and use. That calls for integrated context aware applications to present user-friendly interfaces by eliminating weaknesses associated with the usage of such applications to facilitate solutions to user needs.
Knowledge Usability and its Characteristics for Pervasive Computing Environments
There is a challenge when users interact with context aware applications, which are used to generate content for providing solutions to problems associated with user needs. The challenge is the knowledge and usability of the applications characteristic of a pervasive computing envrio0nment. In context, pervasive computing focuses on communication, computing, and their relationship with the environment. Typically, to address the challenge paused in pervasive computing, it is critical the applications used in that environment be defined by three critical components. These include situation-awareness, context-sensitivity, and hoc Communication components. The applications are integrated into one system and use available resources to generate location specific information that is characterized by the behavioral movements of the people in a business environment. For the functionality of the system to enable users to gain the real benefits of using the system, it is crucial to gain knowledge on the usability of the system, which is supported on an understudying of the algorithms used to mine knowledge and data in the current state of pervasive computing.
Knowledge in this case involves a set of activity states retrieved to identify the best action to take when a particular situation arises. Typically, a system might have past knowledge about the daily activities of a particular user and is able to search and presenting the possible need of that user. User knowledge can be past, present or future depending on the situational requirements for that knowledge. The underlying algorithms for generating the required knowledge for use depend on arising situations, which includes the Pa accumulation algorithm where activity is arranged according to the clusters, which are stored according to each specific category. That is among other algorithms for generating knowledge from common repository. In this case, the algorithms generate knowledge stored in devices with large storage capacities. The knowledge is stored two-dimensional matrices with each element in the matrix representing a particular activity for a particular situation, day, and environment. Retrieving such knowledge is fast. It is crucial when integrating applications into any business models to address the challenges associated with the location sensitive ads when delivering location sensitive services.
Challenges and business models for mobile location-based services and advertising
In this case, location based services are defined as services delivered to the target environment depending on positional information generated by mobile devices. Delivering location-based services presents technical challenges using different applications and business models. One such is offering location based solutions with spatial and temporal information drawing on tracking and navigational systems and other mobile commerce applications. A typical example is the Mobile Location Protocol defined by the Open Geospatial Consortium (OGC), a standard for developing geospatial and location based services. Typically, the whole concept relies on portable mobile devices that enable mobile advertising market using portable and personalized devices and content. The devices should be characterized by location awareness, context awareness, mobility, and wireless mobility.
In the next chapter, a number of approaches related to some pervasive advertising systems, indoor tracking techniques and recommendation systems will be investigated. They will be investigated thoroughly to find the different methods that were used to analyze the user’s profile and the different paths used in indoor environments, in order to create a list of the user’s interests and preferences. Such a list will be used to determine which ads should be displayed on the digital screen, taking into account the impact of the -current- time and the user’s location on the dynamic list of ads
Literature Review
Approaches in Pervasive Advertising Systems
Activity-Based Advertising
Kurt and BegoleI have discussed in detail activity based advertising, one of the attributes of pervasive advertising that relies on context aware systems, which provide powerful tools to achieve pervasive advertising goals. In view of the problems traditional methods constitute by providing information to the customer often characterized by overwhelmingly unwelcome ads, customers find such ads delivery modes quite a nuisance. Studies show that customers do not appreciate and purchase products or services based on the numerous often-unwelcome adverts delivered on soft or hard selling techniques. While the intention is to provide the customer with accurate information about a product, research shows that not all customers embrace the idea because a significant number of consumers hold negative impressions about overwhelming adverts. It might be difficulty to determine the negative impact ads have on individuals because of lack of relative economic values to the adverts. The idea reduces to the findings that return on investment from the ads is low and adverts do not pay the full costs incurred in advertising. To address the problem and minimize the advertising costs incurred and other wastes because of the negative implications adverts have on the customer and the company, it is crucial to understand the customer by personalizing content using new technologies and activity based advertising.
Kurt and BegoleI figure out a system that provides accurate information on activity-based advertising targeting customers with specialized focus on user needs and information availability. The concept relies on the rapid advances and availability of technology driven devices such as smartphones and systems to implement the four elements of successful activity based advertising. The four elements of good targeting include “find”, “filter”, “time”, and “tailor” designed toward adequately addressing a consumer need to stimulate a need to start a search for something in an individual. That is in addition to the need to filter information about the location and availability of the product. The most crucial element is to reach the customer using the right technologies in the context of demographic targeting and viewing which is a weak form of targeting customers. While demographic targeting provides approximate information about the interest of an individual, Psychographic Targeting is a substitute detailed in the context of personal life such as political and religious life of a person.
Search targeting substitutes both approaches by drawing on commercial querying of the systems, which uses behavioral targeting and cookies as evidently used by Google and yahoo. The main except inherent weaknesses in this case is substitution and use of technology driven context sensitive devices. A typical activity-based technology is mobile advertising using context sensitive location ads defined by consumer location. The weakness with the approach includes incurring costs that do not reflect on the profits earned through the adverts. In addition, there is need to target specific location and content for the target customers based on history that might not be easily attained.
To address the weaknesses inherent in the above activity based advertising, inferential advertising using ubiquitous computing technologies based on daily activity patterns such as activity inferencing provide a fall back solution. Typical technologies for implementing the solutions include GPS, orientation sensors, etc., and sensors in the environment. The technologies provide a range of desirable solutions when integrated with the right. Here, the benefits of integrating technology to execute pervasive advertising on ubiquitous computing devices include the flexibility and ability to provide location specific ads based on activity inferencing algorithms. That is in addition to the capabilities that provide activity forecasting based on probabilistic machine learning, user social groups, and the correlational effects of the activities by each individual customer.
The algorithms for search based activity forecasting embedded into ubiquitous computing applications use content-based bundles to address the latent need of a customer for a product the consumer is unaware. The algorithm generates activity content bundles, location based content, and timely presented ads to optimize exposure to the ads. The weaknesses with activity-based advertising are large memory requirements and the difficulty to identify and implement efficiently the algorithm for classifying user patterns. The solutions to the identified weaknesses include integrating the use of software applications to collect activity-targeted data, user context data, based on opt-in policies as a countermeasure for privacy requirements.
Critically, the overall element of pervasive advertising drawing on activity based advertising techniques and applications follows the perceptions customers have toward traditional advertising methods, which target customers without the inherent characteristics of identifying user needs and experiences. In context, activity based advertising provides context specific customer ads based on modern computing applications and systems including hand held devices are crucial to successful communication of information to target customer drawing on activity based advertising.
Managing Advertising Context
A successful delivery of context sensitive ads to the appropriate customer draws on ubiquitous computing applications integrated into pervasive advertising systems with location context sensitive applications and other attributes to enable the right management of ads context [11]. Embedded applications such as Sensor and Context Information that rely on environmental knowledge provide technical challenges defined in the sensors capabilities to detect customers’ proximity to gain appropriate information. The systems have inherent problems. The underlying problems include distribution problems, acquisition of customer specific context information, distribution and use of the sensing applications to industry Practitioners, and Sensor Technology Providers. One crucial element is to define context and additional parameters related to structured data and by relating it to the information acquired from the use of sensors.
The entire pervasive advertising component uses applications such as Context-Aware Digital Signage based on context information sources as depicted in the diagram 1 below.
The entire diagram represents a typical content targeting scenario based on user profiles and other personalized characteristics. The content is technically augmented on near field codes and two-dimensional code attributes to the QR codes. That is in addition to the applications that provide support services for personal activities such as retrieving user information and other activities based on context information that is a problem to find and use. In that context, it remains crucial for application developers integrate into pervasive advertising to develop applications reflecting the discovery of mobile context sources, changes in content conditions, handling distributed content, providing unified high levels of abstraction, and for the discovery of context information to drive pervasive advertising.
Successful use of the applications integrated into pervasive advertising systems requires effective context management for the whole process of identifying and gathering appropriate information to target the customers. Existing frameworks targeting context-information management systems includes applications such as android sensors API, which employ low sensor class capabilities. Other application sensor managers driven include the location manager used to discover the location of other sensors. The underlying applications providing context management services (CMS) rely on context data and sources. The technical functionality of the applications and supporting environment not discussed in this section include the OGC Sensor Web Enablement (SWE) and other applications.
The technical process of developing the context aware applications are not discussed in detail but deserve a brief overview in the context of what the application developer should bear in mind when developing applications to reflect the underlying goal of pervasive adverting. Typical elements include context sinks, information attributes, related models, sensors, and content information access mechanisms. The entire framework of application development and management that enable the delivery of context information for real time appropriate information delivery in ads to the customer rely on an embodied management mechanism embodied in a management framework as illustrated in figure 2 below.
The following is an illustration of the coordinating agents clustered with specialized roles based on entity relationship model with Meta data and contextual attributes as shown in the figure 3 below.
It is crucial to manage information effectively using a successful pervasive advertising system working in a ubiquitous computing environment by drawing on contextual information modeled under audience measurement information in view of well-defined relationship between information content, product, shopping list, and personal attributes. Context sources used toward that end, context sources used modeled on the UML diagram shown in figure 4 below.
Context information sources should outline above that include content, product, shopping list among others in the above diagram illustrated in the information flow figure 5 shown below. A crucial element of pervasive advertising is information flow for appropriate identification of the relationship between activities, context information, and location information. The entire system that supports pervasive advertising enables mapping of contextual information for mapping specific system attributes and information.
Critically, the whole process of managing context is crucial to succeeding in pervasive advertising based on integrated modern technologies embedded into pervasive advertising applications. Different context sensitive applications and technologies underlie the successful use of the applications in pervasive advertising to address weaknesses inherent in personalized advertising.
Advertising in a pervasive computing environment
The use of appropriate applications is crucial to the successful delivery of the right ads to the right customers. The delivery of the right ads to the right audience at the right time narrows to specific ads with particulars content to the right audience. The key element is to use the right method of delivering ads with a specific focus on modern computing technologies integrated into the pervasive computing environment from an array of ads delivering methods. Each of the methods used are defined by typical attributes and characteristics specific to a specific need in the delivery of specific ads. Organizations use methods such as Serendipitous advertising aimed at interesting people to develop sufficient interest in product to purchase a product. In context, the system does not provide a means for following the impact by ads on the target audience.
To address the above weakness, each of the methods for delivering ads should embraces systems that provide a means for user follow-ups, enable identification of customer location, and generate ads content that address user specific needs. Such measures lead to effective and profitable ads that generate revenue. It is crucial for organizations to implement appropriate measures for collecting the available revenue. The solution to the challenges related to revenue collection from the delivery of ads to the target customer includes engaging advertising companies. These advertising companies are normally given the task of marketing or promoting products via the use of media, for instance, television, radios, newspapers, etc. Of late, many companies have resorted to the use of emails and the internet to advertise their products. Several studies have pointed out to the fact that the adoption of online advertising has proved to be so effective due to the fact that it provides a better avenue of reaching a significant number of many customers. It is crucial to note that the studies categorically show that product knowledge increases rapidly with every single exposure to any advertising platform.
The effective use of ads to deliver content to the appropriate customers follows a strategy that includes identifying the right methods of delivering ads to the customers, the best time and delivery mechanisms, and the right kind of audience for a specific ad. To address the challenges, the right kind of adverts to the right customer is enables on a pervasive computing environment with the flexibility of providing users with the interactive interfaces to enable follow ups and usage of the devices.
It is curial to note that pervasion advertising environments using pervasive computing applications provide organizations and customers with the right combination of applications that enable effective ads delivery. In addition, the use of pervasive computing environment provides the capability to successfully deliver the right ads to the right audience based on use of the right content based on the right customer behavioural patterns. There are indicators that show the successful use of computing applications and methods of delivery, such as outsourcing services to deliver high quality ads to the customer.
Pervasive Symbiotic Advertising
Delivering the right content to the right people is a crucial challenge pervasive symbiotic advertising should address. The challenges include ensuring adequate privacy with effective levels to result in positive impact on the response from the target audience using a particular ad with an aggregate right content for the customers. Other challenges include capability ads applications have for retrieving information from mobile devices and other ubiquitous computing applications, the type of ads and applications that provide simultaneous display of ads, live ads, and provision of personalized ads. To address the above challenges, a critical component integrated into the symbiotic pervasive advertising environment includes the use of Live sensing of collective user context based on user preferences, intent, and interests.
Critical to addressing the challenges includes implementing sensing and aggregating user context components. One of the solutions is to identify accurately the time and location of the target customer. A solution-based approach is analytical and creating a user profile reflective of the sensing and aggregating user context. Here the acquisition of user information creates user profile that accurately reflects user needs and profile as an important component for the study. A typical solution includes using cameras and other methods that edge on individual privacy for aggregating information from different users on individual behavioral characteristics. The whole concept revolves around effective use of ads to deliver content designed for each particular customer for specific environments. An effective use and delivery of ads to the target environment draws on identifying the right impact symbiotic ads have on the targeted individuals relative to the uniqueness of the target environment.
The methods include use of cameras and mobile phone location sensing technologies and inherent user behavioral characteristics to ads displays such as viewing an ad when walking. That is in addition to ensuring adequate privacy by providing acceptable and reasonable intrusion upon the privacy of an individual. While aggregating information from the people, it is crucial to ensure zone brokers and interested parties ensure adequate anonymity of the information they have collected regarding the behavioral characteristic of individuals. That could inspire users of the confidence of personal information and behavioral characteristics toward ads and buying behavior. The entire symbiotic pervasive advertising eco system consists of interlinked players that include customers for whom the ads are delivered using the applications already discussed, advertisers, zone brokers, consumers, advertisement brokers, and consumer brokers with each playing specific roles in symbiotic pervasive advertising.
A critical review of the success of symbiotic pervasive advertising is exemplified in prototypes that works through an ecosystem of players mentioned above. Evidence of the level of success of use of symbiotic pervasive advertising is shown in the active involvement customers have in searching for a particular ad suiting a specific need, manufactures who bid for ads based on available demographic information, agent based ads, and consumer brokers who generate user context information including location. Additional success factors include interest and use of zone brokers among other facilitators in the mechanism of symbiotic pervasive advertising. The success of symbiotic pervasive advertising is inherently evident in the discussions that show the level of involvement of different players in the symbiotic pervasive advertising environment. The weaknesses inherent in the approach the author uses is failure to provide a clear link between the use of ads in the real world environment and associated implications on pervasive symbiotic advertising with little focus on the practicability of applicable technologies.
Conceptualizing Context for Pervasive Advertising
Complete implementation and realization of the capabilities and benefits accruing from the use of modern pervasive computing technologies relies on the use of profile driven context ads. Toward that end, appropriate conceptualization of the right content delivering mechanisms to influence customer-purchasing decision specifically focusing on personalized context is valuable. One crucial component is to a comprehensive understanding of context adaptivity [14]. The conceptualization process is critical to identifying appropriate user driven content in developing appropriate content adaptivity as shown in figure 6 below.
The framework embeds various activities at the levels of conceptualizing context, computing the context, adapting the context, and identifying the responses toward the developed context. In each case, the system provides relationships and dependencies beginning with identifying context variables, identification of the right sources of content, intelligible adaptability to context, and storing the content for use.
The main component is to conceptualize the right content which the right expert identifies into common information categories based on user location and objects in the right environment systematized into mobile systems. That is in addition to integrating content with a multidisciplinary approach from the domain of distinguishing the user from the applications. A critical assessment of grouping criteria by physical, social, temporal, and task, cognition, and application context, despite being disputed provides the information necessary for executing adaptive service delivery when the elements are integrated into the methodology of adaptive pervasive advertising.
The conceptualization methodology draws on the context of the advertiser, inventory levels, and brand promotion. That is in addition to factoring the buying process and information taxonomies.
One of the methodologies for context conceptualization for pervasive advertising is shown figure 9 below. Each of the domains indicated consist of subsystems with a bottom up approach of base elements comprising the entire system. The system draws on context computing for the “identification, collection, transformation, interpretation, provision and delivery of context information” using integrated context sensing technologies for contextual augmentation.
The process requires personalizing information for individual customers from a large body of contextual information sources, by drawing on customer information, which includes preferences, buyer behavior, and historical patterns as evidently applied on the web. The criticality of using responsive interfaces for context sensitive systems to input data and interact with the systems uses user-friendly systems. The whole process is to identify the right information for the customer and establish customer behavioral patterns critical for developing ads to target the right people at the right time and delivered in the right environment. The advertiser’s environment, the customer, and the applications used in pervasive advertising environment are crucial in this case. Developing the elements relies on the perceptions based on different hierarchies of information in the advertiser’s environment. The hierarchy includes the micro, macro, and situational levels. At each level, the critical elements are data categories used for specific purposes and how to apply the data to enable the development of an efficient system for pervasive advertising. A complete list of elements integrated into the system includes advertising campaigns based on adaptive contextual information. Additional factors include consumer environment envisaging all individual primary tasks at different levels and purchase objectives, the social environment that defines individual interactions with others and emotional responses to implications ads, and ultimately the consumer profile. Consumer profile provides details the personalization categories drawn from different behavioral traits that include stable segment traits, which are unchangeable in an individual and which define the typical audience of a person. That is in addition to dynamic traits relating to mood and individual traits such as feelings, influenced in an individual, revealing a particular buying behavior, which can be either stable or unstable.
A critical review of the paper shows successful conceptualizing context for pervasive advertising is profile driven using context-adaptive systems based on a process that deconstructs personalization into measurable units. The weakness with this paper is the failure of the author to link ads with the pervasive computing environment with the technologies and the link between the implications of the ads on pervasive advertising environment.
Adaptive pervasive advertisement- scenarios and strategies
Ads play a significant role in pervasive advertising where companies use different advertising methods to deliver messages specific to the audience aimed at generating interest to engage the target audience.
In pervasive advertising environments, different types of pervasive gadgets (such as Smartphones or PDAs) play a significant role in delivering content to the right destination. PDAs or Smartphones have lately considerable impact when integrated into everyday human life or business operations by enabling instant access to large number of people. These new advertising techniques present different challenges for advertisers to look for the best options to address the problems. The challenges can be overcome by channelling the correct advertisements to the right people using the right media at the time. Advertising effectively has the potential to raise the quality of the ads and to access a wide target audience increasing the total revenue to the business.
Ads play a significant role in creating awareness to promote various products to the potential customers, compelling companies to spend a significant portion of budgetary allocations for advertising. Research shows that conventional methods for advertising were considerably expensive did not produce the desired outcome as many of them were considered to be less productive. That compelled many advertising companies to shift from traditional advertising methods and adopt the latest advertising strategies that come with the latest technological advancements.
Adaptive pervasive advertisement can be best applied in areas with high density population with a high concentration of shopping malls, football stadiums, exhibition shops, airports or railway stations. In that context, the use of pervasive devices such as Bluetooth and installed Wi-Fi connections, when enabled provide instant access to information. Embedding the devices is likely to benefit owners of the buildings because of ads generated revenues. The benefits and technical capabilities and functionality of the devices for adaptive pervasive advertisement provide evidence of their overwhelming use in digital advertising environment. That is addition to the significant revenue generated for advertising agencies related to excellent quality of advertisement targeting potential clients. In the future, related research papers are expected to analyse a more structured scenario that supports the successful implementation of pervasive advertising. Fig 8 illustrates a typical scenario for pervasive advertising architecture.
Adaptive pervasive advertisements are customized with appropriate information that fits into the interests and needs of the target customers by sensing the type and needs of the customers shown in the above architecture. This is due to the fact that the consumers’ profiles are already known and integrated into the applications using sophisticated solutions which are capable of short range communication. In that context, customers are provided with access to efficient and reliable information that is appropriate for the specific audience. In addition, the advertisers are able to plan and provide specifically designed ads for specific audiences that fit target audience profiles. Here short range communication using short range communicating devices that collect user profiles are able to analyse aggregate profiles to display, using the right screens, the right information based on the right content searching and display engines. The screens provide the users with the potential capabilities of interacting with the applications to gain the right information and content critical in deciding what to buy. The whole scenario provides the basis and rationale for pervasive advertising.
In context, pervasive advertising is the most valuable instrument exploit by both the advertising agencies and the customers. That is because, pervasive advertisement dwells so much on location based strategies, which enables the advertisers to customize their advertisements as per the targeted locations.
To make pervasive advertising effective, a number of adaptive advertising strategies characterise the system. One such characteristic is the capability of the system in the screen environment to sense and examine the profiles of the kind of customers close to the screen at any particular time. The screens should embed application capabilities to determine the type of advertisement to display based on the screen for the person nearest to the screen. That makes it critical to seek for refined remedies because personalised customer profiles will be automatically stored and updated in PDAs or Smartphones, and other devices in use. For instance, if a Bluetooth connection device is installed next to the advertising screen, the screens will be able to receive the profiles of the customers who come close to the screens.
Toward achieving effectiveness, various adaptive strategies associated with pervasive advertisement have been implemented. The adaptive strategies help in determining which kind of advertisement to present to which customer profiles and the inherent value of the advertisement. The value of the advertisement is critical to consider before coming up with an appropriate strategy for the advertisement. Different advertising agencies have divergent values due to the variation in the customer responds to the advertisements. With regard to this, there are strategies that the advertising companies have adopted with the main reason of addressing the irregularities expected in a pervasive advertising environment. The three main strategies that are considered include: the round robin strategy, the non-competitive adaptive strategy and the competitive adaptive strategy.
The most frequently used strategy is the round robin strategy. The round robin strategy constructs a surge of advertisements based on a specific sequence that is repeated over and over again. Here, the round robin strategy forms the baseline to assess the merits of pervasive advertisement strategies. The non-competitive adaptive strategy is regarded as a very simple strategy due to the fact that it generates a series of advertisements with regard to the customers’ preferences. The customers considered are the ones within the periphery of the screen. In this strategy, the advertisement screen has the capability of detecting the profiles of the customers who are around. The screen then calculates the expected value of the advertisement to the customers. Lastly, the screen relays the advertisement that maximizes the advertisement’s value as in.
The third strategy, which is the competitive strategy, enables the advertising agencies to compete when it comes to relaying of commercials. In this strategy, the advertisement screen detects the profiles of the consumers who are around it. The screen then analyses the advertised values that are connected to the potential customers. The value of the advertisement from the various companies is evaluated and the company with the best value is awarded the tender to advertise.
The continuous use of digital wide screens in the present modern world with the aim of advertising creates a new aspect of modern advertising in addition to providing education with regard to social and cultural issues. When the present advertising technologies are infused with pervasive paradigms, the screens will have the ability to sense the profiles of the customers who are close to them and adjust relevantly so as to relay the right advertisements to the right customers. This enables the advertising agencies to provide efficient and reliable services that are widely accepted by all the customers. Adaptive advertising strategies have been proved to be so much more productive and effective in the advertising world as it helps the advertising agencies to be in a very good position to compete with others.
Dynamic user profiling approach for services discovery in mobile environments
The discovery of user profile is crucial for content personalization in pervasive advertising in response to dynamic user mobile environments because of the amount of unwelcome and welcome ads pushed to users in a small amount of time. Many of the ads pushed to the customers are unwelcome and customers could do well to avoid the ads. User profiling is crucial for advertisers and concerned stakeholders in designing and delivering ads specifically targeting each audience to address with accuracy the needs of the target audience. Toward that end, a solution lies in the use of UPM. The entire architecture includes identifying and integrating the use of a user-profiling model (UPM) based on complete user profile knowledge. The model constitutes user interactive support for capturing behaviour learner input to provide accurate service environment crucial to user preferences to represent the user accurately. Here, user personalized information is not private as the framework one has signed to allows for information sharing. Modifying user profile contents based on the learning procedures ensuring the contents remain consistent and safe from external manipulations.
User behavior learning (UBL) is another fundamental component identified for user profiling based on third party user interactions and behavioral learning to inference user trends. The inferencing mechanism for clustering users based on the SCAR algorithm provides user profiles for old and new customers described by a set of attributes unique to each group. The behavioral learning techniques draw on different behavioral characteristics depending on the amount of information available on each individual, user setting transactions, and other intervening components when creating the user profile. The Apriori algorithm is a crucial tool used in this context.
Successful user profile building relies on the use of user profile building engines for continued updating and management of user profiles based on two intrinsic components that include the profile learner and the user modeler. It is crucial for users to enter personal information through web based and other computing applications including social networking sites. The information collected from the user is stored and inferring engines based on specific algorithms provide the required level of personalized information for targeting the right audience with the right information at the right time. Other crucial components integrated into the system include the profile learner, behavioral leaner, in addition to presentation and graphics components that gather information from the external requester component.
A learning point for the usage of the dynamic user profiling approach for services discovery in mobile environments is the case study application of the system based on user location and content. System components integrated into the tourism-profiling environment provided appropriate methods for accepting user input according to user personalization including preferences and user behavioral characteristics supported on dynamic profiling. That was in addition to sensors integrated into the system enabling the identification of the target customer’s location in relation to personalized information on individual preferences. Identification of the location enables automatic provision of services based on the user’s profile. Successful provision of services based on user profile and user-sensing applications was evident of the effective use of applications in delivering the right ads to the right person based.
The paper has detailed the use of applications based on user profiling but falls short of technically detailing applications for user profiling and correlating the applications to convince one of a cost effective and rationale for integrating the systems in daily business transactions.
Adaptive User Profiles in Pervasive Advertising Environments
User profile is crucial in pervasive advertising. Crucial elements in pervasive advertising include identifying adaptive user profiles based on self-assessment generated from target content about the customer. User profile provides accurate reflection of user buying behavior and interests in addition to individual activities based on user interactions. Consumer profile based on the level of user activities based on weighted parameters in response to the impact of a response. Methods used to calculate individual activities include regression analysis depending on specific categories and individual intent.
Examples explicitly exemplified in the enabling information used by advertisers in campaigning profiles. Advertisers characterize advertisements and deliver them to the customer on a pervasive computing environment using based on user interaction types.
Profiling and Targeting Opportunities in Pervasive Advertising
Crucial to pervasive advertising are adaptive advertising systems defined by user responsiveness. That is built on the awareness of user characteristics enabling the delivery of ads to the people in the context of the user’s environment. The underlying component is pervasive user profile defined by population aware ads tailored toward the needs of the customer’s environment. Profiling in the context of the user environment enables marketers and other stakeholders target the customers precisely through user-generated content using applications that support activities that occur anywhere on the interment. Facebook is one such example with applications integrated into the system that allows for the sharing of activities related to customer behavior and ads response patterns. The user patterns provide an opportunity for the advertisers to identify user interest and other patterns through social networking sites as already such as Facebook already mentioned above.
The applications integrated into social networking sites such as Facebook combine methods for collecting profile information. The applications enable the acquisition and identification of data using unique applications such as Blue-tooth enabled devices based on the movement of a customer in a sensitive applications running nearby. The problem with the approach includes challenges related to privacy issues because of the visibility of user information that might be shown to a wider audience.
The solution to the concerns includes identifying and integrating applications and systems to display information without the necessity to display individual content to the public. Such situations lead to the development of applications and ads that respond to user needs by reacting to repeat customers, people who are hubs in the wider networking activities, and basing it on the number of people with similar interests in any environment. In that context, specific ads based on specific applications that capture and analyze user-generated content based on personalized profiles are resources to optimize.
A critical analysis of the above article shows a successful approach of creating user profile targeting opportunities available on the internet with Facebook as a typical example. Facebook consists of integrated applications that support the generation of needed user information. Successful approach are countered with weaknesses that includes wider range of applications exemplified on Facebook, success factors related to the applications, and typical case study benefits associated with the use of the applications.
Low-cost-personalized-location based service for delivering advertisements to mobile users
Context aware location based advertising services with personalized advertisements based on location aware shopping seamlessly fit into the delivery of adverts to target destinations in a cost effective framework. The main problem experienced in such a framework includes identifying standardized services, designing and delivering advertisements to the target destination. That is because users are constantly in need of information and context aware applications and support service devices have already gained entry into the market today. In addition to that, context aware applications have experienced a rise because of the rise in the use of portable devices such as mobile phones and the low cost associated with the usage of such devices. One critical component for context aware applications is the Location Aware Shopping Advertisement (LASA). To optimize the applications to fit the provision of services into the entire framework while ensuring low costs, privacy, and seamless integration, the services standardized based on the Ontology of each domain, displaying specific ads for specific audience, and implementing low cost deployment of the applications. One such system uses the OSGi technology.
OSGi technology has portability and integration capabilities and its use is based on platforms defined by a service-oriented architecture. The functionality of the entire system depends on a database, which stores user information, products, and product categories classified into Trees. The Tree provides an edges and nodes for integrating a methods and a search strategy for accessing the nodes into categories. The categories follow a predefined path using an algorithm for generating information about products based on each target group needs.
To achieve the best from such products, it is crucial for the appropriate party to develop a user profile with carefully formulated ontologies to achieve the most accurate details about the needs of the customer. Crucial elements integrated into the system include collecting information about the user to provide the user with information about their needs. Data retrieved and generated this way provides information about the location of the user and the appropriate time to send an advert to the user. The location of the customer is critical because when combined with the user profile, it is possible to predict the type of advert and the right time to send or present the advert to the customer. One method as already mentioned for profiling is the use of profiling Ontologies. The ontologies are implemented at the server level though it requires feeder information from the client. It is possible to identify a particular user from the behavioral characteristics of an individual without the need of using a user ID, which is not supplied at the time of providing personal information. Once the user has been characterized in the Ontology, it is possible to provide the user with the right information at the right time about a specific product. That is in addition to the capabilities provided in limiting the amount of time for displaying a particular advert while reducing the computational complexity of processing an ad by enabling the server to perform the computations.
PerAd-Service- a middleware service for pervasive advertisement in m-business
Different applications used to achieve pervasive advertising provide different services on the ubiquitous computing environment. Toward affecting the use of the applications, a number of challenges are presented from the user’s view and the commercial world. Typically, the user is not paid by viewing the ads; neither do they pay by viewing the ads online. However, the costs come as indirect costs incurred by the user who pays internet service providers and mobile service providers for the services offered. Additionally, the user is faced with challenges to their privacy, ubiquitous access to the ads, ad viewing, and the ability to make follow-ups for any particular ad. One critical approach to addressing the challenges includes integrating the PerAd-Service.
The PerAd-Service can be used on mobile phones and other smaller portable PCS. The architectural framework of the application consists of different interdependent applications used capture the user profile and provides appropriate information to the client when needed to address their specialized needs. Typical solutions provided to address the challenges.
BlueMall a Bluetooth-based advertisement system for commercial areas
A BlueMall system is an example of a pervasive application that can be used in a big commercial place. The system uses the information generated from the customer profiles to relay adverts or shipping information. With this system, appropriate information will be sent to the right clients at the right time and in the right manner. With regard to this, the Blue Mall system is considered to be flexible as it prevents the relaying of unwanted or unexpected adverts to the customers. The blue Mall system has been tailor made for specific areas where they have been installed, for instance, a Blue Mall system for a hospital is much different from a Blue Mall system for a museum or say for games in. Fig 2.7 shows the structure of a typical BleMall system.
Bluetooth can be described as a multipurpose and adaptable technology of connection which is wireless in nature without high consumption of power. Bluetooth is capable of identifying devices that are located within a close range and check out what kind of services they can put forward. With regard to the BlueMall system, the use of the Bluetooth device is highly recommended because it has the ability to reach out to a large group of customers at the same time. In the present environment all phones always have a Bluetooth device installed in them, thus, making them to be prospective clients. In addition, Bluetooth has been picked as the best device for a BlueMall system because of the fact that it conserves power.
The BlueMall system is a prototype that provides advertisement based on Bluetooth technology. The system is developed using Java application. Through the BlueMall system, it has been proved that Bluetooth is a powerful networking connection and can be used for various uses. The BlueMall system has been seen to function very efficiently based on the evaluation of its performance in.
There is a relevant application customized for mobile phone advertisement in business environments. AdNext is one application, which allows for a comprehensive analysis of the customers’ interests and needs. The application has the potential to predict when the targeted customer will visit the business complex again. It does this by assessing the patterns of the customers’ behaviour. With regard to this, it becomes easier to provide the needs of the consumers effectively and efficiently. Fig 2.8 shows the structure of a typical AdNext system. The AdNext system is composed of a client and a server. The client is mainly the mobile phone, while the server is advertising server. The mobile phone’s role is to collect the users’ information with regard to the frequency of visit and then relays the collected information to the advertising server. The mobile phones also inform the customers of the latest advertisements in a timely manner.
Visit-pattern-aware mobile advertising system for urban commercial complexe
Visiting a shopping mall or any public place relying on technology helps facilitate the ability of the visitor to identify and access particular places and products depending on the user’s preferences. The challenge is to provide the customers with the right information for targeting the right place to purchase the right product leading the user to identify the location of the product in real time. That is in addition to modeling offline user behavior. Typically, user’s location is crucial when generating location-based ads based on analyzing sequential visit patterns with underlying location-based applications (LBA). Different applications such as the B-MAD system enabled on Blue Tooth Mobile advertising for detecting the position of users to generate ads based on location proximity with and without the sending requests. One of the highly compatible applications for addressing the location finding and content generating applications is AdNext. AdNext is one of the technologies, a mobile advertising system for providing a visit-pattern-aware to relevant to users at specific places. The underlying concept is service integration provided in the visit-pattern-aware applications used in mobile advertising.
Probabilistic reasoning is one option that underlies effective modeling of human behavior that is difficult to predict and model because of the uncertainty of human behavior. While different applications modeled on GPS to predict human behavior have been developed and tested, AdNext provides reliable performance based results because of ease of integration to mobile phone devices.
The client, who is the customer, connects with the server using the AdNext application hosted on the mobile phone when in the shopping environment. The AdNext client collects user information and provides the phone with the capability to collect place-in and place-out events using integrated Wi-Fi technology. Collecting the user’s history uses store-level localization accuracy cost effectively.
In this case, the advertiser operates on online and offline modes. Offline advertising is designed and developed using data collected from commercial complexes while in offline advertising mode, the server generate user history from the owner’s mobile phones when one enters the shopping environment. The server then predicts the next place the owner visits using a trained probabilistic prediction model to deliver relevant location specific ads. All information collected from the user is encrypted to enforce information integrity and confidentiality. To collect accurately user information and be precise about product location, AdNext requires capturing accurately information about each movement of the customer. To identify and deliver location specific ads, AdNext uses location changer-validation checks validated using Elekspot ID using confidence values generated to indicate situations such as standing, passing by, and many other human behavioral characteristics. The most crucial element in this case is to identify accurately the customer’s location to generate appropriately designed ads with the right content for a specific customer. In addition to that, predicting the next customer’s position, movements, and visit patterns underlies the strength of the AdNext application.
To address the problems associated with predicting the user’s history, movements, and other behavioral characteristics in commercial complexes, AdNext integrates core components to learn the attitude and behavior of the customers. The underlying concepts include Visit Causality for predicting sequential visits and the Common Visit Pattern for analyzing common visit patterns for individual visitors. The context of the prediction is being collective, where collective place visits provide information about the behavioral characteristics and visit patterns. The most critical elements underlying the prediction model includes visit time, visit place, visit duration, gender, and age. Using the E-M algorithm, to predict the next position and predict the likelihood of making the next move.
To provide the customer the right ads with the right content delivered to the right place at the right time requires selecting the right ad from among different candidate ads. AdNext assigns score to different ads and the capability to generate ads based on the right classifications and categories. The probabilistic model mentioned elsewhere provides AdNext the functionality to predict the next movement, the next visit and the frequency of visiting the commercial place.
A critical challenge here is to attain an acceptable level of accuracy to predictthe events and other actions. The most appropriate method AdNext uses is the Bayesian networks. The prediction accuracy of the of the probabilistic model using the AdNext application shows a more than 70% level of accuracy and as practically exemplified in shopping malls and other public places. However, a number of issues include privacy concerns when information is stored in a centralized server.
A number of architectural mechanisms proposed to address privacy issues include us of a proxy server, which anonymizes the user in addition to providing authentication and encryption services for each user. Anonymizing applications such as k-anonymity integrated into the AdNext application provides real time and reliable solutions based on path confusion techniques. While the problem of energy consumption rises because of several applications running in the background in addition to other integrated applications on the AdNext platform, it is recommended that the Wi-Fi application be used periodically.
Trust Context Spaces An Infrastructure for Pervasive Security in Context-Aware Environments
Trust is crucial in pervasive advertising environments when executing services on ubiquitous and pervasive computing environments, which execute effectively when integrated with interfacing technologies and real world oriented tasks. Here security issues arise because of the difficulty of coordinating the physical and virtual entities involved. To ensure secure pervasive advertising environments and secure and appropriate organization of data, efficient collaborations, and effective generation of information for decision-making, a risk analysis model for pervasive context aware applications are crucial. Pervasive context aware applications enable “proactive triggering of events, streamlining interaction, memory for past events, reminders for future events, optimizing patterns of behaviour, and sharing experience”. The applications should integrate features that support privacy by striking a balance between sharing of interactions and when not to share any interactions specifically focusing on information derived from personal behavior. That is in addition to the applications providing security not only at individual levels, but also collectively for the resources and knowledge when different people are interacting.
It is crucial for the features to provide a balance within a shared environment as demonstrated in the following diagram.
In figure 11 above, privacy should guarantee the privacy, integrity, confidentiality, and non-repudiation of data when moving between different sections using reliable encryption and authentication mechanisms. It is crucial at the design, development, and implementation phases to ensure continuous identification and isolation of vulnerable points within the systems to allow for implementation of appropriate security patches. That ensures a threat related to personalized information when neither on transit can be shared with third party eavesdroppers nor simply shared if accidentally destine wrongly.
Toward addressing potential sources of threats to information, a balance between the virtual and physical applications provides solutions using technologies such as smartcards, virtual identity management applications, ensuring effective privacy and policy management, and enforcing context security using an authorization and access.control model. The model integrates applications that enforce information security using “Transient Authentication” for user authentication in a short-lived communication between parties.
Toward enforcing and implementing a context secure environment, a trust-context space architecture with defined goals based on a technical definition of trust and security such as PKI (public Key Infrastructure), Certificate Authorities, and use of a context key define the whole architecture and process. Embedded into the architecture are components that include communication using proprietary RF protocols such as SPOT, context management, policy management, entity mapping, and trust service management components. That is in addition to enforcing security on software applications that are difficulty for cryptanalysts to decrypt defined on trap door functions using encryption algorithms such as elliptic curve cryptographic algorithms.
Don’t kill my ads- balancing privacy in an ad-supported mobile application market
The need to ensure a secure pervasive computing environment lays demand for the integration of security measures including policies and applications come along with additional overheads. While integrated security measures increase the level of security and privacy, privacy and exposure to particular ads compete for the available space. The underlying need is to balance the level of privacy and the exposure of information to others in the ubiquitous computing environment. That is because ubiquitous computing is the way to go. Many of the hand held devices have become overwhelmingly available to almost everyone wanting to use the device. A typical device is the mobile phone, which provides a platform for installing applications, freely provided in the market today.
A significant share of the ads runs on a mobile adverting business model. The business model has accelerated the rise in mobile advertising using advertising networks such as ad-network, which targets audiences, which positively regards the ad and who find it very useful. It is crucial to seek permission to support target advertising because of the tight coupling of the application used with the ad-support widget. While the coupling and permission requirements drive the functionality of the application, the level of privacy is compromised in this case. The need to ensure and enforce privacy demands for the integration of applications such as MockDroid and Apex that block the flow of information based on the location and target environment.
The above environment is susceptible to starving revenue generation from ads because of the tight controls because of privacy enforcements. To address the issue, a proposed model that decouples privacy control and advertisement support elements allows for the flow of information as two separate entities. Decoupling underlies the two distinct privacy entities in the ad-network framework. Among the entities defining the model, include a causal link between privacy and revenue and Incentivizing Developers. The framework identifies with a market aware privacy control by allowing individual involvement in controlling the flow of information, in addition to the use of integrated firewalls for controlling information flow. The model further allows for a degree of control by allowing users to minimize the level of control they have on the flow of information using applications such as privacy aware applications implemented based on access permission without the need to specify the permission requirements. That is in addition to providing dynamically control exposed data and real time monitoring tools. The solutions provide a wide range of security and privacy enforcements while ensuring a sustained flow of revenue and ads information to the target audience.
Shaping how advertisers see me- user views on implicit and explicit profile capture
While ensuring security and privacy are maintained in the advertising environment, it is crucial to shape the perceptions viewers have about an ad. The need is embedded in the new and modern approaches of ubiquitous computing and the significant share hand held devices such as mobile phones have taken as communication tools. That is in addition to the reductions in costs associated with advertising and the type of displays, which include interactive mobile application interfaces, use of Bluetooth technology to share information between devices and other applications, and the features integrated into the devices have with the abilities to provide interactive collection of user profile. While ensuring features for collecting user profile are integrated into the systems, it is crucial to factor personal privacy depending on user perceptions about individual privacy.
Summary
In this section, activity based advertising is one of the attributes of pervasive advertising where ads are delivered to the customers using specialized focus on user needs and information detailing their behavioral purchasing patterns and movement characteristics. This is one approach of overcoming the weakness in traditional advertising methods. The ads are context specific and are enable on ubiquitous computing applications and devices such as GPS, orientation sensors using the four filtering elements such as find”, “filter”, “time”, and “tailor”. The ads are user content specific using effective content management systems such as management services (CMS), and context sensitive applications. To advertise content in such environments requires using pervasive computing applications the right combination of ads specific to customer needs. In this case, the problem of privacy especially is critical when delivering the right content to the right audience on a pervasive symbiotic advertising, which has been successful by using zone brokers and personalized ads. The best approach is to use domains of subsystems of base elements comprising the entire system which runs on draws on the “identification, collection, transformation, interpretation, provision and delivery of context information” using integrated context sensing technologies for contextual augmentation. To generate the most appropriate context aware ad, the user profile, context aware, and location based applications such as Location Aware Shopping Advertisement (LASA) and OSGi technology, B-MAD system enabled on Blue Tooth Mobile, and AdNext are integrated into the systems for generating the ads. To ensure the privacy of information, security such as PKI (public Key Infrastructure), and Certificate Authorities capabilities form an integral component of the entire system.
Approaches in the Interaction between Users and Public Displays
How to evaluate public displays
Ads play a significant role in the way advertisers and target customers view content and the response they have toward the ads in public displays. The motivation to develop interactive ads seen prevailing in public places underlies the need to understand the best methods to display the ads toward achieving effectiveness, meeting user needs and expectations, effective assessment of the impact on the behavior of the target customer, user experiences and acceptance, and implications on individual privacy and other social behavior.
Key elements to factor include audience behavior toward a display based on the type of display, user experience related to the flexibility of allowing interactivity with the user, user experience that is a motivational factor, and user acceptance. Other elements include user performance from the perspective of flexibility in supporting user interaction techniques, display effectiveness, level of privacy, and the social impact facilitated by use of the ads.
The article details the research paradigms employed in assessing public displays including ethnographic methods, field and lab studies, and use of deployment based research. The underlying methods and tools to capture data included interviews, questionnaires, focus groups, and observations. Toward ensuing data consistency and validity of the results, internal and external consistency measures were used in addition to using measures to achieve ecological validity.
Interviews with predefined guidelines provided deeper and interesting information about individual attitudes, questionnaires provided qualitative data on public perceptions about the ads, and focus groups provided the best answers on a one-on-one basis. That was in addition to the use of observations using cameras and appropriate tools to gather information on the behavioral perceptive of the customers toward public displays. The underlying rationale was to develop interactive displays to optimize user involvement.
Requirements and design space for interactive public displays
Toward assessing and developing public displays with specialized focus on interactive screens deployed in different public places such as malls, inner-city areas among others, the need for requirements analysis plays a crucial role. It is crucial to note the technical aspects of requirements analysis not addressed in detail in this study undergo the interaction phase modeled in figure 10 below. Crucial in the phases is that people never have prior information about the ads. The phases constitute unplanned viewing of the interactive ads. The model in figure 10 details the requirements from the behavioral perspectives of the customers, with observations made on the number of people passing through each phase as modeled in figure 9 below. The most crucial elements in this case include usability, the likeability, utility, and the ability to grab attention from passers-by. In the context the model consists of identifying the reaction and involvement of passersby, number of people viewing and reacting to the ad, subtle interaction by the viewer, direction of interaction, multiple interactions, and follow up actions.
A complete list of elements included in requirements and design space for interactive public displays includes modeling attention to enable interactive activity with the people and stimulating motivation in the ads. Generally, when designing the ads for public displays, it is crucial to note with clarity, the models of attentions embedded in the human brain based on visual attentions and other metaphors through a bottom up process of external stimuli and internal stimuli [27]. A computational model constituting sensory input, as already mentioned about sensor driven applications embedded into systems for collecting personalized data, define the devices with inbuilt capabilities for stimulating attention as characterized in saliency maps. That is in addition to integrating neuro-computational models with the underlying attention generating computational models such as the behavioral urgency to capture personal attention to an ad. Other underlying elements include identifying the need for the ads to stimulate choice and curiosity in the ad because it is the precursor to stimulating explorative behavior in individuals.
Ads technically designed with interactive interfaces motivate interactive use by the public, which at times are susceptible to hampering interactions in public places for the front and back face of the public if poorly designed. That is in addition to factoring the personality traits of different groups of people. It is crucial to provide selective control of self, privacy data in view of the impact on individual perceptions and attitudinal behavior stimulate in an individual. Typical elements when making specific designs includes control over personal data, characterizing social behavior and the nature of public space defining the underlying functionality of the mental models.
The typical design space characterizing public displays includes identifying how users institutively identify posters and the relationship developed with attitudinal behavior of each person. Links established with remote locations locating capabilities, and applications with embedded capabilities of mirroring the target audience with the potential to make users part of the display provide further stimulation and effectiveness to ads displays in public places. It is crucial when designing and implement the public displays to consider interaction modalities defined in body language, body posture, facial expressions, personal speech characteristics, and gaze. The unique attributes of the embedded applications that react to a specific environmental action or activities and movements provide further attraction to passersby without the need for manual controls.
When does the public really look at public displays
To design the right ads to develop public interest and capture personal involvement in the public ads is critical when identification of the factors underlying public interest in the displays [28]. A case study research report shows that the public included the positioning of displays based on the eye position, the size of the display, and the type of content. That is in addition to content format typically including different formats and dynamic displays, and the intent of the content under display [28].
Opportunities and Challenges of Interactive Public Displays as an Advertising Medium
Communicating content contained in ads to the public using public displays plays a significant role in generating revenue from advertising based on ubiquitous computing applications leveraging on strong attention availability. That is because advertising serves a significant role in promoting public information about a company besides informing and persuading. The key challenges attributed to addressing and attaining the goal due to advertising includes measuring the effectiveness of an adverts and correlating the advert to positive buyer behavior, the challenges of identifying what to present to whom. That is in addition to the inherent ability of the applications used to provide public displays to direct the right messages to the right audience with the right content and context to make any campaign cost effective.
In context of the challenges and in view of the opportunities provided on interactive advertising applications, there is in need to identify and integrate content specific ads. The underlying elements to address the challenges and opportunities presented in public displays include identifying the primary targets and potential credibility of the audience that influence the advertising model to employ. The core to advertising includes different advertising models, which use different media to reach the audience including the web, television as discussed earlier, and social networking sites.
The elements integrated into different advertising models should reflect the core elements integrated into pervasive advertising systems and specifically digital systems as is evident in the digital signage systems. Typical examples include digital SeeSaw, BookingDOOH, and Argo Digital Solution or AdCentricity among other applications. The underlying characteristics of the above applications include sensing and interaction capabilities for public displays. It is crucial to note the design and development of the mentioned digital applications in addition to the deployment for public advertising to have the capability of sensing the proximity of the intended object targeted in public displays. That is because the target objects in the environment for determining the communication context includes identifying physical and social proximity of the elements using integrated sensor technology in the electronic computing devices as discussed in other sections of this paper. In addition, the applications and devices for public displays should be location sensitive and attract general engagement form the public, advertisers, and specific target audiences.
It is critical for the applications using sensing technologies embedded into different electronic and other computing devices to provide the capabilities for presence identification of unique entities within the target environment as one of the key elements in public displays. The technologies rely on the digital footprints of individual characteristic collections based on self-exposure of user profile, other personal characteristics discussed elsewhere in this paper, and user generated content. The link is in the technologies such as Bluetooth that provide the capabilities for supporting user interactions.
Using Bluetooth device names to support interaction in smart environments
The rise in the use of mobile devices in pervasive advertising environments requires the user to have the capability to control interactions with the content presented on public displays based on user locations and other identifying characteristics. Toward identifying one’s location and enabling interactions with the environment, a range of devices including Bluetooth technology provide the capabilities. The devices are defined with a set of requirements that support their use in public display environments. The defining characteristics include that the devices are free to use, are designed to support hands free commands and support persistent commands.
The Bluetooth technology critically qualifies for use in the above environment because of the unique attributes and application capabilities such as being user friendly and being addressable by using globally unique 48 bit Bluetooth Device Address. That is in addition to the device being hardware independent and a low barrier to entry allowing simplified integration into the different mobile devices with different hardware architectures.
Among the application, specific characteristics identified in the Bluetooth that allows for easy integration into different hardware devices include easy usability in identifying devices. That is in addition to the ability to listen and respond to devices Bluetooth seeking devices without the authority of the owner as long as the device is turned on, ability to enable the device establish a logical link with different computing and electronic devices. Studies show the capability of the Bluetooth devices to provide information about the user and user location in addition to the registration status of the devices. The complete system architecture of prototype tests includes one used in a University campus consisting of a Bluetooth scanner and PHP applications for generating web pages from users querying the system.
It is possible for each of the Bluetooth devices to continuously scan the environment and generate information about any requested application from a shared database to display to the public. The rate of displaying information to the public depends on the requesting application with Bluetooth device queries given higher priority. A sample of the system architecture prototype is illustrated in figure 11 below. It is clear from the system architecture that displaying content is based on querying the system with the right parameters supplied as input. The application processes run in response based on the scheduler that continuously evaluates input information using the APIs to display information of the targeted content to the public display component.
Bluetooth technology that seamlessly embeds into any hardware device provides the capabilities required for retrieving information from a common database on identifiable input parameters and other data repositories for displaying to the public. The underlying rationale is to use the technology to display information to the public to influence attitudinal behavior and a positive response toward the ad. That is despite the experience of using Bluetooth technology in public places with associated consequences of adverse security risks. That is in addition to the difficulty associated with the use of Bluetooth scanners to collect appropriate data, the difficulties of developing applications specifically designed for responding to user queries, and embedding user interface metaphors to enable ease of interactions.
The various changing wants of consumers have made it so hard for the existing way-finding systems to be operational. This is due to the fact that these ways-finding systems come in the form of printed maps or signs that are posted on the walls. The introduction of digital gadgets like mobile phones or PDAs has made everything to be a walk in the park. These digital gadgets, however, have various limitations with regard to their ease of access, level of accuracy, small surface area of the screen, and the inability of many users to navigate the gadgets. The digital way-finding systems normally require the full attention of the users during their navigation process. With every interruption of the user, the level of accuracy can be so much compromised. Actually, the natural environment, such as mountains, buildings, etc. can help in giving out precise clues to the users with regard to the needed direction.
Many researchers have dwelt a lot in the area of interactive advertising system. These researches have mainly brought out new ideas with regard to how interactive advertisement should be developed to suit the modern advertising world. The use of wide screens for advertising is the main element of interactive advertising. The use of several connected screens has also been adopted and is viewed as another element of interactive advertising in the modern world. The big screens serve the purpose of relaying the communicated message to the consumers in an appropriate time
Adaptive Navigation Support with Public Displays
Inbuilt adaptive navigational systems provide the user with the capabilities to find their way in unfamiliar environments enabling them formulate their action plans based on way finding events rather than people. Among the devices and applications used in this case include the position aware unique way finding systems that have inbuilt capability to identify and adapt to the present environment. The functionality of the system relies on the interface descriptions generated from the server. That is in addition to the fact that Adaptive navigation support enables users to develop and adopt signs that they will use as a guide to their destined place. These signs or displays can be used digitally with the possibility that they can be managed by a single server enabling enhanced formulation of a reliable pervasive computing environment. With time the adaptive navigation support system is expected to have the capability of showing various targets with regard to the location of the various users. In addition, more research should be carried on ways to make the navigation support system more effective.
The functionality of the system draws on the server’s ability to generate information defining the locations of a specific node in two dimensional models. The model consists of nodes linking different point on the location and direction with respective distances, which are calculated using the A* algorithm.
While the displays provide any individual with the required information displayed in the public, it is crucial to have appropriate display management in place. Toward that end, the applications are written in languages such as java with the specific GAUDI application and the server written in java. Communicating between the devices follows the TCP/IP protocol using standard TCP/IP sockets. In this case, the XSmiles XML rendering engine is used.
In this case, pervasive advertising draws on a range of applications and in this case the GAUDI pervasive system is used. It is possible using the system to display the whole network of displays from a single server to trigger the automatic adaptation of all connected displays as required.
Evaluating BluScreen: Usability for Intelligent Pervasive Displays
It is critical to select the most appropriate material to display to the customer to address their needs. One of the critical methods suggested in a ubiquitous computing environment is a market-based approach. The market-based approach is driven by public electronic displays that provide the required information to the customers via different display mechanisms. The displays generated should be appropriate to specific audiences to address the needs of the target audience. The market driven approach provides information about the attitude users develop when subjected to the displays and the role the user plays in the ubiquitous advertising environment. One such a tool used for the displays is the blue screen.
The blue screen is a market driven approach in ubiquitous computing that maximizes the customer’s exposure to as many exposures as possible. The target is to maximize the prior knowledge the audience have on a particular product and to stimulate a response in the user toward performing a certain actions. In this case, the BlueScreen relies on implicit input from the environment and users. Typically, the BluScreen functionality is based on systematic interrelated factors in accumulating user information and experience, which is used in the longer term to generate appropriate ads at the right time and in the right environment. An evaluation of the BluScreen however shows a number of challenges that include cognitive constraints, environmental constraints that depend on the level of exposure to the advert and the configuration of the environment in which the ads were deliversed to the people.
An interaction model is a problem specific solution to the problem, which provides interaction interfaces, which support distance interactions with displays, and using devices that capture the gesture of an individual. In this case, the methodology for addressing the challenges in the study could be both qualitative and quantitative. Each of the research paradigms provide solutions based on environmental factors, interaction context, user profile. Each of the elements contributes to the performance of the system based on both internal and external factors. Internal factors constitute the frequency with which displays change with time, direction, and location besides the response rate to devices detected in a specified environment. Other additional factors to consider include user goals, which define the specific functionality of each device and the level of interaction and flexibility it provides to the customer. In addition, the behavioral aspects of the user and system characteristics provide a further support for the usage of the device.
Other factors to consider in the evaluation process are the cognitive factors defined by the user’s level of attention, distractions, focus, and interest. In addition, physical factors considered include physical factors that define the rate at which users interact with the environment with a typical example demonstrated in the way people walk in an environment. In a typical environment, the interaction models proposed in the study to provide solutions to the challenges of evaluating the BluScreen, which displays ads based on information stored and retrieved from the database to make intelligent displays.
Interaction models provide solutions to the problems associated with displaying ads based on effective BluScreen in heavily populated public places. In this case, the factors mentioned above provide the parameters for the evaluation process and provide further rationale for conducting the study. In this case, it is critical to consider both qualitative and quantitative paradigms and factor in each of the benefits gained from each method when conducting the study.
Mobile Interaction with the Real World
It is critical to consider in the study the kind of interactions and technologies that support the interactions in the pervasive computing environment. One such kind of interactions is provided using the mobile phone. That is because the mobile phone has inherently becomes a pervasive component in the everyday life of people. It is critical, when considering a study in this area to consider a number of issues, by identifying the kind of interactions that mobile phones provide to the people. That is in addition to identifying the characteristics of the user interfaces, the design component of the mobile phones, task descriptions and typical methods of describing objects such as WSDL and UPnP, and establishing if the interfaces can be generated automatically and if the real world services can be standardized. This study is a result of the MIRW 2006 conference on human computer interactions.
In answer to the challenges presented to the user of the pervasive computing device, mobile phones designed and integrated with peripheral devices and applications including GPS, which are modules that capture support gesture recognition activities. These are, displayed on the mobile phone. That is in addition to the use of applications embedded in the mobile phone with the ability to enlarge a display and provide additional functionalities.
Among the solutions is the mobile pointing device and input systems with a large virtual screen, which enables the user to surf the internet while performing other tasks in the device. In addition, the mobile phone integrates an Active Marker, to enable controlled visual effects. The active marker-tracking module is enabled on the mobile phone with the source code provided by the ARToolkit Library and ARToolkitPlus with impressive performance results. That is in addition to integrating alphanumeric capabilities on the mobile phone to help users manipulate the device in the real mobile environment. In this case, both letters and numbers are printed on the surface of the physical buttons of the mobile phone to allow for various inputs into the device.
Studies conducted to provide the solutions to the challenges mentioned prior based on Fitts’ law mostly used to study the functionality of pointing devices, a mathematical model describing the use of the laws is as follows.
In the above case, the variables of interest include T which is a measure of the average movement between two pointing devices in a given environment, W is the width of the movement, A is the amplitude of the movement, and coefficient a and b can be empirically determined in the study.
There are a number of devices that obey the above mathematical relation in the world today, which can be used to generate ads depending on the environment and the movement of the device bearer of the device. Typical examples include PDAs in addition to other application, which provide information to the user in case of an exceptional circumstance. That is in addition to user interface provision while other applications provide user generated information based on technologies such as Radio Frequency Identification (RFID). Typical examples include BlueScreen, which, as already discussed above, is a distributed advertising framework that generates adverts according to the needs of an individual in the environment.
Summary
It is critical to evaluate public displays to assess and provide customers with specific ads targeting specific needs. When assessing the display, the key components to consider include the behaviour of the audience, user experience and interactions with a product and the ad, interaction techniques, display effectiveness, level of privacy, and the social impact because of the ads. The usability, likeability, utility, and the ability to grab attention from passers-by are elements critical in the modeling process. A system computational model constituting sensory input, as already mentioned about sensor driven applications embedded into systems for collecting personalized data, define the devices with inbuilt capabilities for stimulating attention as in saliency maps. The models should function with clear guidelines on factors such as eye position, the size of the display, and the type of content that determine when the public looks at a display. The customer is critical at this point and identifying user characteristics includes using integrated applications such as digital SeeSaw, BookingDOOH, and Argo Digital Solution or AdCentricity for interactive public displays. Devices such as the BlueTooth support interaction with the environment with applications linked to a server that generates content according to user behavioral characteristics. In this case, content specific location specific ads generated provide the customer with personalized ads.
Approaches in recommendation systems
Personalized Recommendation over a Customer Network for Ubiquitous Shopping
With time, many users demand for more services provided on ubiquitous computing environment in the context of ubiquitous personalization services computing. Such needs can be met by the information providers using applications and devices that target the needs of the customer on a ubiquitous computing environment. In this case, advanced information systems have been developed to address the problem. The critical component is information seeking. Information seeking has grown significantly with the use of the internet environment because of the use of search engines. That has led to the introduction of popular sites, such as Amazon, which has necessitated the preference of divergent category of products. The search engines are configured and customized to provide relevant information to the customers based on their behavioural characteristics and past their past interests.
The mechanisms for information provision are provided by use of the right technology. Consumers are provided with efficient and effective information any time using technologies such as collaborative filtering (CF). The application automates the word of mouth and transform what one is looking at into data that can be stored and used to generate patterns for providing user information. The information received by the consumers assist them in deciding to purchase goods and services to address individual needs. The technologies have similarities because of the mechanisms of information provision. However, there are enough challenges that are exhibited by the mechanisms of information provision. These challenges can be tackled through the provision of customized information that satisfies the interests of the consumers. In this case, the functionality of the applications is based on the k-NN (k-nearest neighbor) model, where statistical machine learning is used to find a customer based on the similarities and differences between different neighboring customers. One of the applications that has been adopted and which provides solutions based on personalized customer network of the problems associated with the old method of generating customer oriented content is the Buying-net application. The Buying-net application operates on the ubiquitous computing environment to provide customers with appropriate location based content. The Buying-net Architecture consists of the client side of the application to manage customer based learning by compiling information about the customer and generating content that is specific to the customer’s shopping characteristics. In this case, the application provides functionalities for manipulating information on behalf of the customer. The customer model of the application is modeled after the centroid of personal information as depicted below. The personal information from the customer consists of the items that the customer has purchased represented by the letter u, with each item represented by a vector, which provides a description of the properties of a product such as the price and the value of the brand.
In this case, the aggregate features and properties can be expressed in the mathematical relation developed and illustrated below.
In the above case, the relation consists of variables
demoting the number of elements in the set 
, where the centroid is a clear representation of the customer preferences and buying behaviour [34]. In this case, the privacy of information obtained from the customer is protected by limiting the number of communication channels referred to as information push. To complete the entire system, the systems have an integrated Buying-net Server, which provides connectivity to the client side of the application. The functionality of the system depends on the buying behavior of the customer. In context, after a customer has connected to the system using the client side of the application, bought an item, information is generated about the product and any other times related to the search. That is in addition to the application filtering information about the customer’s buying behavior based on the functionality of the information dissemination module (IDM) [34]. The module collaboratively functions with the personal information management module (PIMM) to generate user information by receiving information and pushing it to the personal information management module (PIMM) for privacy purposes and specifically sent to a specific customer with specific user needs and shopping characteristics.
The sequence of events and the functioning of the application are indicated in the following flow chart that demonstrates the overall procedure of the Buying-net application.
The flow chart as can be seen in the flow chart, once a customer enters a shopping place and connects with the -customer network formation module–(CNFM) initializes the process and connects to the systems. The personal history of the customer is analyzed, relevant information generated, and recommendations made on the specific item to present to the customer [34]. The entire process is described in the following pseudo code.
The whole process is able to yield information that is specific to the customer and which can be updated based on the frequency the customer visits the place. In addition to the above functionality, the application integrates an Information Dissemination Module (IDM) purposely to forward information to the recommend element based on the referral certification-based push strategy. The functionality of the module and other modules has shown a significant contribution to the functionality of the Buying-net systems that has contributed positively by providing viable solutions to selling systems such as the CF-based system.
Information Seeking Convergence of Search and Recommendations and Advertising
In the above case, information seeking, retrieval, and presentation underlies the effectiveness of any advertising mechanism. In this case, any search mechanism accepts an input as a query, which is matched against information in a database, which is then returned because of the search. Unique to the systems used in ubiquitous computing, a recommendation usually generates information from searching the content in the system in the form of the description of an object such as books, people, and movies. Typically, the recommendation mechanism plays a greater role in the analysis of the contexts of the users. This mechanism offers the users with recommended information that targets the consumers’ interests. In context, the system through which customers are fed with relevant information that meet their interests and needs is called the recommendation system. This method of information provision has helped the customers to make informed decisions with regard to the purchase of products. The recommendation system helps in mitigating the challenge of information overload due to the fact that the only information that targets the interests of the consumers is provided for. The quality of mobile service can be highly improved by the introduction of a recommendation system that filters information relayed in a mobile commerce framework. The framework relies so heavily on the feedback generation process from the customers. This feedback is much relevant in designing advertisements that are not a nuisance to the intended consumers. It is projected that in the future, researchers will come up with a more precise framework for assessing the performance of the system implementation.
A number of challenges and problems associated with information seeking are based on the complexity of the targets. That is because the objects targeted in this case include items that can be purchased on discount and other objects that cannot be purchase on discount. In that case, it becomes more complex because the set of object can either be homogeneous or heterogeneous. That is in addition to the challenges of keeping track of all moving objects within an indoor system. Tracking enhances the use of many connected applications, for instance, security system, or movement and navigation systems. The use of a graph model tracking system necessitates the homogenous management of data services in order to support the various connected technologies, example, Bluetooth application and Radio Frequency ID application. Very many people spend more of their time indoors as compared to the time they spend outdoors. This is evident in the fact that during the day, people go to work, schools, shop, etc. and at night people sleep, all of which take place indoors.
Typical constraints in the systems to be addressed to overcome the above challenges and make the system effective include the package, filter prohibitive and sequencing restrictions. Filtering targets the ability of the application to minimise the search into specific objects and are typically oriented using traditional search methods. On the other hand, package approach constrains the user to specific objects using numerical methods with typical examples including constraints. On the other hand, prohibitive constraints demonstrate the number of items such as triples typical of objects that cannot be provided at the same time. Such constraints in addition to the sequence constraint that depicts the order in which items are consumed depending on an individual’s consumer preferences provide a significant level of problems associated with identifying the best method to address user preferences.
One of the solutions provided is the personalized recommendations. In this case, personalized recommendations have an inbuilt capability to suggest to the customer the type and category of items to purchase once in the shopping environment. That is in addition to the solutions provided by the personalized search results. In this case, search results are generated depending on the location of an individual because of different consumer patterns and location. One typical example are the results generated using Google for different people in different locations. A search in USA yields different results for a search in the UK for the same search. In this case, the search results are location sensitive and user needs sensitive. Other solutions include personalized ads such as yahoo generates based on the demographic and behavioral patterns and characteristics of an individual.
An effective search strategy is driven by a high level of accuracy of personalized information that targets user information and based on user searching behavioral characteristics. Such searches and ads generated for the customer always infringe upon the privacy for the customer. To address the problem, some of the applications provide solutions such as options for the user to opt out of the system. It is critical to note that users get the best out of the adverts and the systems they have embedded into their devices in addition to securing a high degree of privacy.
Applying customer-centered recommendation on an on-line shopping system
In this case, it is important for the system to provide customer centered services based on ubiquitous computing capabilities. One such solution are provided on the online shopping systems that provide solutions to avoid problems such as information overload, and the provision of adequate information about and to the customer. That is in addition to the problem of managing personalized information, the level of privacy provided to the customer, and increasing the capability of recommendations for a particular product to a particular individual. In the case, the solutions provided using the Recommendation system (RS) are based on the key features defined in RS and a number of functionalities. The RS capabilities include “popularity-based, demographics-based, expertise, content-based, and collaborative filtering recommendation”. In this case, RS consists of module such as Content-based (CB) method module that is used to generate specific objects that the user might have interest buying, by analyzing the description of the times and generating the appropriate time. To deliver the most appropriate ads to the right audience, it is important to link different people with the same interests. One approach is to use the Collaborative filtering (CF) method. In this case, different customers are put together depending on the purchasing characteristics that are common to the group to enable others recommend the product to other customers in the same group. Both applications are integrated into the same RS architecture that is designed to be a Customer-centered RS to optimize the capabilities inherent in the application. That is in addition to the capability of providing personalized information. In this case, it has been realized that RS provides customers with the ability to handle their own information including their purchasing history and the type of products they frequently purchase. That is in addition to the capabilities to provide the cross-stores recommendation for the purchasing behavior. Here, RS also supports online advertising. Here, online advertising has proved to be so effective because it provides a better avenue of reaching out to many clients with the same purchasing behaviours and history. Indeed, the studies categorically point out that the product knowledge increases rapidly with every single exposure through online advertising. In this case by using a central server component, user profiles, advertisements, and how the users connect with the system are stored in an effective and efficient way in the system [36]. With regard to this information, it becomes easier for the system to determine the profiles of the users. The determined profiles are then compared with the matching advertisements through an online client in [9]. The online client enables the advertising agencies to choose the target groups with regard to the content of the advertisement. , the accuracy of the tracking devices should be enhanced so that more positioning technological devices can be incorporated in the system in [32]. The enhancement of online tracking services can help in providing accurate forecasts with regard to the movement of the individuals who are enclosed within the indoor system. Graph model based indoor tracking system therefore is an efficient way of installing tracking devices within an indoor framework.
Mining traveling and purchasing behaviors of customers in electronic commerce environment
It is very essential to generate the link that exists between purchasing items in the field of electronic commerce. In, the availability of this information will highly boost the act of cross selling of products. With regard to this, it is imperative that the website administrators should give much interest to the mobility of the consumers. In addition, this information can be of help to the consumers who can get navigation ideas. The website administrators should not just pay attention to the navigation patterns of the consumers; they should also pay attention the purchasing patterns in.
Intelligent Recommender System using Shopper’s Path and Purchase Analysis
It is critical when designing and implementing systems that generate context and location specific ads to consider that shoppers follow a predetermined path if they have a shopping list already prepared [38]. Such systems designs rely on information obtained by studying the behavioral characteristics of shoppers in a shopping area. The motivation for the study is the shopping patterns of the customers based on studying the data on the number of people who visit shopping points who purchase and who do not purchase any products. That is in addition to the current trends based on product placing products according to product categories, intuition or experience, brand, promotion, product promotions, and product brands.
The procedure for creating an artificial shopping pattern includes collecting data and conducting data cleaning by extracting relevant data for the study, categorizing the data according to different data zones. In this case, the algorithmic steps of data leaning includes identifying and scanning appropriate data, entering the date and time for each output data, matching the words accordingly, and continuing the process until the end of the process. In context, data should also be cleaned after categorizing the data to remain with the desirable categories of data. That is followed by creating paths based on the behavioral patterns of the users to enable the application create artificial paths based on assigning to each node a specific value ranging from zero to infinity. Here, the Bellman–Ford algorithm and is critical because the topologies of the nodes do not change and the path generation using this algorithm is real time.
An additional component in creating the artificial path based on the buying path patterns incudes clustering of products and customer based on the purchasing patterns and movements within the shopping mall. In this case, the clustering is based on extracting the number of zones visited by the customer, the number of products bought, and the type of transactions conducted at every stage of the visit. In this case, information on the frequency of the number of zones visited and the number of products and transactions conducted at each vertex of the node [38]. In this case, the algorithmic steps include identifying the number of categories belonging to each category of products and filling the files for storing information about the products. Each of the information categories are filled to the end of the file. Cone the file is competed, and then a new file is opened. After data cleaning, the following is a sample of the input and output data generated from the file:
In the above case, the elements to consider include the average path length and the analysis, by determining and identifying the number of paths and path lengths followed by the customers in their purchasing network. In this case, the information collected concerning he path lengths plays a significant role in determining type of product categories and where to each category is positioned for the customers to purchase. Statistical analysis of the results shows that the location attraction and product attraction are critical elements in determining in the layout of the products for sale. Typical characteristics of the intelligent system included categories of time variant data; the time spent by the customers in particular areas or zones in specific zones, and the multiple entry points to the store.
Design of a Recommendation Filtering System in Mobile Commerce
Wan has researched on the impact electronic devices such as the developments of mobile phones have on e-commerce. That is in addition to the m-commerce applications including mobile phone applications, wireless networks, and middleware [38]. Many of the applications and electronic devices have integrated devices that enable them to function in a wireless environment. Many of the applications on the mobile devices have functionalities tied to information stored in databases which when retrieved provided the basis for supporting certain activities by any individual in a shopping environment. That is in addition to the mobile phone devices suggesting to the customer the type and category of products to purchase based on an analysis of their behavioral purchasing patterns. Typical applications include the Hybrid recommender systems, which combines behavioral and collaborative content-based elements. To address and provide real time solutions to the challenges of generating user ratings on a particular item for a specific user, and to filter other information related to user characteristics and attitude toward a particular product. In this case, it is critical to avoid messages and other ads that are not specific to the user, which when generated are regarded as spam. To address the problems, a spam filtering system is critical in this case.
According to the above diagram, the filtering is based on recommendations where the recommended messages are forwarded and the system filters all information about the product and the appropriate audience for the messages. Once the user has viewed the message, the implicit feedback provides the analysis for user profile and content. Implicit feedback is critical in conducting further analysis and research on the type of content and specific characteristics and is critical in creating personalized recommendations besides capturing user interest in short messages.
It is critical at this stage to model the user by anal using the characteristics inherent in short messages. One method is to use the vector space model defined by a n-D feature vector, with each dimension consisting of keywords with associated weights. The model is demonstrated as: {(k1,w1),…(ki,wi),…,(kn,wn)}, where each element represents a key word and information that has been extracted. The following mathematical equation illustrates the model effectively:
Upon receiving the implicit feedback, new changes in the weights occur based on the appropriate keywords. In this case, calculations using the above equation generate new interest for the users and reflect the changes that have occurred on customer interests in a product. To generate the right kind of interest and information to the user, new keywords are generated using new approaches such as commercial information extraction [38]. In this case, the extraction process is allows for an input of new short message. In each case, each short message sentences are split according to the syntax of the language of the message. The next phase is to segment the messages according to dictionary rules applicable on other inferencing techniques. Each entity is assigned a name according to the rules based on commercial information specific dictionary rules used to name and enter entities into the system. Then, the entities are classified according to the rules and dictionary requirements to generate the results of information extraction. The entire framework is a filtering system used to generate specific content to specific customers based on the model discussed above to support m-commerce transactions on mobile devices designed and integrated with location and context sensing technologies discussed elsewhere in this paper.
Summary
Personalized information is critical for a customer when making a decision to buy a product because the information content specifically addresses the needs of the target customer. Information about the customer that is personalised can be gathered from different points such as use of the search engines like Google. Typically, to generate personalized information targeting specific customers can be modeled after the centroid of personal information which consists of the value of a product and the price attached to the product. The whole process consists of capturing information about the customer and storing the information in a database using the Information Dissemination Module (IDM) purposely to forward information to the recommend component based on the referral certification-based push strategy, which underlies effective advertising. The system relies on keeping track of moving objects to analyze and generate location specific ads. In this case, the information generated from the ads are also specific to a network created using nodes with similar interests. One of the underlying capabilities is to create artificial shopping pattern by collecting data and conducting data cleaning and extracting relevant data, categorizing the data according to different data zones for use. The functionality if the system is to use an artificial information filtering system to generate content and location specific ads for the target customer.
Approaches in Indoor Tracking
An exploratory look at supermarket shopping paths
Pervasive advertising draws on the ubiquitous computing environment based on the behavioral attributes individuals have toward ads and their shopping characteristics. The applications draw on established schema for shoppers captured in their travel characteristic in shopping points such as supermarkets that provide case study analysis for collecting data sets for revealing individual patterns using embedded technologies such as radio frequency identification tags. The main problem is to identify the best paths to follow to reach the ragged array of items and the spatial constraints specific to the search for the data items.
The value derived from the datasets to establish individual behavioral patterns is crucial to pervasive advertising. Identifiable characteristics of the data are attained by determining the size of the dataset using a clustering algorithm that executes on different split paths. The critical element is to make distance comparisons using the k-medoids clustering technique on randomly selected items along the medoids based on the concept of ragged array path comparisons. The most effective distance traveling approach is to implement the algorithm using the Euclidean distance [40]. The characteristics of the algorithm has desirable properties, which include clustering customers according to their behavioral purchasing patterns and the way they travel within the shop in addition to identifying the most feasible and economical path to travel for the medoids.
Using the above algorithm enables the user of the applications to profile the shopping paths by bivariate store paths. The percentage time taken in traveling each path is recorded for the six mutually exclusive and exhaustive paths areas covered to access and retrieve an item using a k-means clustering algorithm. Each of the results can be clustered using different methods such as time clustering. The behavioral characteristics based on the clustering mechanisms indicate that different people have different search methods in a large or small building such as in a super market.
One of the main facets of any large building is the tendency of the visitors to find their way when they get lost. Normally, when people get lost in any building, there is always a particular direction they follow. As a result, it is very important for each visitor to know their exact location when in any big building. A large building complex, way-finding systems stationed within building with the main objective of helping the visitors to find their way in and out of the building plays a significant role. Using the system, visitors are in a safe zone as they move freely around the building without the potential of getting lost , and in.
Way-finding is more relevant to people more than it is relevant to the various locations. For instance, in colleges or universities, way-finding helps the new students to find their way through the institution. With this, they can access lecture halls, the library or even the halls of residence. In the same way, way-finding system is very relevant when the new changes to access have developed, for instance, when a particular road is under repair or blocked, the use of way-finding will help to direct the potential road users to use another way.
Understanding individual human mobility patterns
The entire system of delivering ads to the target environment to achieve pervasive advertising goals and to influence attention, perceptions, and activation requires establishing clear mobility patterns of the people. Establishing people’s mobility patterns requires identifying and understanding the highly volatile and temporal nature and low probability that a person might repeat the same pattern. A mechanism used to create time resolved trajectory of the people provides a solution to the problem. The mechanism relies on recording each instance individuals engage with their mobile phones to communicate to establish the statistical mobility patterns of the people based on displacements and the resulting distributions.
Studies indicate human motion obeys the law of a truncated Le´vy flight. This paper does not detail the mechanism and algorithm used on tracking human mobility patterns. The study shows that the movement of people from one point to another has very low probability of following a similar pattern for a similar period. The probability of following a similar pattern is possible when people go to shopping points or when going home from work. That is in addition to when searching for an item they had seen and frequently get from a specific location.
Results indicate that the movement of people is considered as being random, with individual trajectories described on the radius of gyration, not discussed in detail in this section. That makes it difficult to predict the next direction. Individual human mobility follows some specific patterns with a lot of studies conducted to examine the nature of human mobility with regard to the places they frequently visit and the rate at which they make their phone calls based on the fact that young adult and adults in the whole world own a mobile phone. The mobile phones have a wide variety of applications compatible to different uses to meet different user needs. The mobile phone users develop a very close association with the phones because of the fact that they carry the phone along with them for a long period of time. In addition, mobile phone users normally store a lot of information on the phones.
In public places such as shopping malls or train stations, big LCD screens erected for the main purpose of advertisement based on the frequency of visits paid to the places. This is an evidence of advancements in technology. Indeed in the recent past, advertisement is rapidly revolutionizing. Many businesses have shifted from the print media or televisions to using the internet. In line with this trend, many companies or business enterprises are quickly adopting the use of mobile phones. Many ads are nowadays customized to fit the specifications of mobile phones.
To comprehend the human mobility patterns to optimize on the movement of individuals within a public place as discussed above, a number of studies based on different algorithms have shown repeat patterns and use of the same route with a small probability. Typically, that is based on individual trajectories characterised by independence and two dimensional probability distributions of the chances to return to the specific point. That is also based on spatial proximity and nearness to social links. The link between the movement of individuals and the frequency of visiting a point can be tracked using moving objects using an indoor tracking system. Tracking enhances the use of many connected applications, like security system, or movement and navigations systems. The use of a graph model tracking system necessitates the homogenous management of data services in order to support the various connected technologies, like Bluetooth application and Radio Frequency ID application. Very many people spend more of their time indoors as compared to the time they spend outdoors.
Graph Model Based Indoor Tracking
Understanding the movement of people and any other objects in a restricted space provides capabilities to track and implement security measures and provide real time information when designing the delivery mechanisms for ads. Research shows the use of tracking devices functioning on underlying algorithms to establish the identity and position of the consumers at any time as contributing to the delivery of the right information to the right customer. Data regarding the personal profile of each consumer can be gathered using consumers’ mobile devices. This data is helpful for planning purposes as it will facilitate the delivery of appropriate adverts preferred to each specific consumer. In this case, the data collected on the behavioural patterns of the movement of people in public and shopping places can be refined and utilized to determine and predict the trajectories based on raw RFID readings. The set of devices and underlying functionalities based on different algorithms enables tracking customer and other functionalities to be achieved.
Tracking in an indoor environment is enhanced using different applications such as the GPS in addition to WiFi and Bluetooth. One new technology that has gained precedence recently is the RFID technology. The applications utilized in an indoor environment include indoor way-finding technology to aid in enhancing user navigation at individual levels. The navigation system has the potential of providing ideas with regard to the utilization of the indoor space. This information is very much important to advertisers as it will help with the pricing of their advertisements. The applications provide user navigation capabilities based on an algorithm where edges provide information about user relationships between different entities in the system. That is in addition to capturing the connectivity between different points in a user environment. The connectivity between edges and vertex points of the graph is defined in the relation (V, Ed, Σdoor), where V defines the set of vertices and Ed defines the edges. The edges and vertices define different paths and points in the shopping environment.
The graph model is very efficient for an indoor environment because of its ability to support positioning technologies very effectively. In addition, it enhances the wide adoption of tracking devices which help in keeping track of all the individuals who are within the system. Fig 15 illustrates a typical model of a base graph. The connectivity of a base graph originates from the floor plan of the indoor system.
Other capabilities embedded into the applications include geometric information mapping to help customer’s identify specific locations. The applications embed capabilities that include capturing topological information and building partitions when deployed in a building.
The base graph model is considered to be a homogeneous framework for an efficient indoor tracking. The functionality of the model different phases shown in the following algorithm:
The reading demonstrated above move through different phases that include refinement based on a data structure where adjacent vertices are recorded representing a region from which another trajectory is developed. That is in addition to the refinement phase where each RFID reader providing specific locations of the reader. The capability of the system provides online tracking to establish the trajectory for a future customer as one of the capabilities of using the system.
Low-Cost Bluetooth Mobile Positioning for Location-based Application
Location based application systems are continuously viewed as critical to different users because different systems can rely on different wireless connectivity applications to achieve pervasive advertising. Bluetooth technology has been shown to be a more cost efficient method of providing location based services. Bluetooth application is easily available in many mobile communication devices and in an indoor system, it is cheaper to install and operate. The technology has been boosted by the fact mobile advertising has been on the rise. The introduction of mobile based advertising has offered many opportunities for the growth of location based service provision. The services can reach the targeted customers anywhere they are at any time. The possibility of positioning has also brought about new insights with regard to location based services. Positioning has necessitated the ability to track the consumers whenever they are and provide them with efficient services in an accurate manner.
It is very important for an organization to reduce the operating costs of the connectivity applications. This will enable the organizations to maximize the revenue that they get from the provision of location based services. In the same manner, the targeted potential customers stand a good chance to gain from the location based services because of the existence of more effective and reliable services.
Towards a location model for indoor navigation support
In order to realize an efficient navigation system, it is very important to come up with an efficient location model to support navigation in an indoor setup. Many researchers have pointed out to the fact that there are many location models that can support navigation in an indoor system. Location of an individual goes together with the privacy of the same individual. In this sense, finding the location of an individual is like prying into his/her privacy. Many researchers have alluded to the fact that a person’s privacy should be handled carefully when it comes to positioning. Thus, in future it is very relevant to explore the link between a person’s location and his/her privacy.
An indoor system of navigation can either be incorporated in portable mobile devices, public displays, and/or a combination of both mobile devices and public displays. In some cases, infrared technology has been used to sense the locations. Indeed, there are some conditions to be met before coming up with a successful navigation model of an indoor system. Some of these conditions stem from the general framework of the applications; others are brought about by the field of networking, while some are brought about the technological constraints.
For an application to efficiently facilitate navigation there has to be a good location model in place to support it in an effective way. Moreover, the location model should be incorporated with the present environment format. Using the information model, data can be generated automatically with regard to the current environmental format. In as much as a location based model is complex, it should also be manageable. A location based model for indoor navigation support mainly relies on rich information with regard to the individuals so that it can function efficiently.
A location based model for any building should be in line with the architectural design of the buildings. It should also give regard to the environmental aspects. When a building has just a few rooms, the location based model will use a limited number of nodes, hence making it manageable. The location based model is efficient because it integrates the architectural design of the buildings. Future researchers have a challenge as they should come up with an efficient way to link the outdoor system and the indoor system in an efficient manner.
Using Mobiles for On Campus Location Tracking
Tracking through the use of mobile phones in a campus environment utilizes the IMEI information of the mobile phones in addition to the network connection. The tracking application system of a mobile is fed with reliable information from a server which enables it to provide the users with accurate information with other colleagues’ movements from one location to the other. In an indoor environment, it is quite challenging to use GPS application for the purposes of tracking as a result of the inability to reposition the satellite effectively. It is therefore more efficient in an outdoor system.
There are various applications that serve as alternative methods of connection. The use of Bluetooth is one such method. Bluetooth devices interact effectively and exchange information between the users who are located in a given area. The use of Wi-Fi has been tipped to be the most economical technique of networking due to the fact that it can be easily installed in many buildings consisting of many people. These public places include big hotels, train stations, airports, universities, etc. Through the use of Wi-Fi technology, the ability to track and locate consumers has been so much boosted. The users can be located in an accurate and precise manner. The mobile phone system consists of a web server that helps in registering new users and updating their records. It also has an application system that is running on Java 2. In addition, there is the database that keeps most of the information regarding the customer. Lastly, there is a GSM modem that relays messages via the web server [45]. Fig 16 illustrates the mobile system framework.
Researchers have proposed a tracking system which works efficiently in hospitals, universities, and other public places as far as the location of other colleagues are concerned. The proposed framework can be linked to social networking sites like Facebook or Twitter for the sake of efficient and effective process of locating individuals within an institution. In addition, the framework can be utilized by parents to monitor the movements or the whereabouts of their children within an indoor system. In fact, there is a possibility to adjust the system within an indoor system to sense whether the little children have gone beyond the range of the tracker zone. The system can also be used to address gender issues in the society because the patterns of movement for both male and female shoppers can be assessed separately so as to determine the shopping behaviour of each gender. The shopping store administrators can find this information to be more useful in terms of making adequate plans to raise the level of services in the malls.
The use of radio frequency identification has rapidly grown. This growth is mainly attributed to the latest developments of technology that has given rise to cost effective means of networking. Electronic means are used to collect information relating to the attributes of the targeted consumers. This has boosted the ability to gather information relating to the shopping patterns of a consumer. The movements of the consumers from one location to another are also captured. With this information in the system, it becomes so easy to assess the characters of the consumers with regard to their shopping paths.
A pervasive computing system has been widely adopted through the widespread use of mobile communication devices. Pervasive devices are composed of very small gadgets which can be attached on cars, or even peoples’ clothing. These small devices will enhance network sharing system and via a reliable wireless networking system, thus, enhancing user connectivity within a specific location. The ability to track and locate the movements of the individuals eases the efficient management of human mobility in any given setup. In addition, the ability to track human mobility encourages the widespread adoption of various applications, thus, enhancing connection systems.
It is very important to assess and analyse the movements of individuals within a system. One of the good reasons attributed to this is that more chances are created for connections with regard to the pervasive network systems. With such a framework in place, there is efficient flow of information among the interconnected individuals in a systematic manner. Tracking devices are used to assess human mobility. The use of tracking systems enables the shopping mall administrators to understand the nature of the customers’ needs. This is done by analysing the patterns of the consumers’ shopping paying attention to their movements within the shopping malls.
The use of radio frequency identification is therefore a new concept that will enable researchers to come up with more advanced techniques for analyzing the attributes of the customers with regard to their movements within a shopping complex. It is very important for shop owners understand the nature of consumer needs in a way that can enable them to provide advanced services with regard to the changing consumer needs.
The use of character string analysis however poses some challenges. One of the challenges is the inability to maintain time series information that is kept with regard to the visits made by customers in a shopping complex. It is very hard to quantify the amount of time that a customer spends in any particular section of the shopping mall, so most of this data is lost. A remedy to this setback is to utilize a graphical data in this analysis process. The use of graphical enhances the best capture of the time series information with regard to the length of time that the consumers take on a particular section of the shopping centre. In the future researchers are challenged to formulate a more advanced version of graphical data to enhance the best capture of time series information.
The use of radio frequency is tipped to offer new aspects of the consumer behaviour with regard to the consumption trends. Indeed, any technology that can bring out this new aspect of consumer behaviour is tipped to benefit from efficient competitive advantages. The present radio frequency identification technology is mainly aligned towards maintaining the units of store keeping instead of maintaining the personal goods. But it can still be pointed out that the present radio frequency identification is still efficient enough to produce sufficient amount of information. Many researchers have come up with the intensions of seeking to improve the efficiency of the current radio frequency identification technology. For an effective tracking system, an appropriate RFID model should be utilized. Fig 17 illustrates an effective RFID model.
It is very much essential to understand the behaviour of the customers as this will help in terms of planning or generating information with regard to the most efficient way to meet the needs and the interests of the customers. With this regard, it is possible to gain through high competitive advantage that comes with the adoption of the system. Basically in any given setup, the flow of information is very essential as it enhances a mutual understanding between the consumers and the service providers. With this level of understanding in place, the service providers will be much more aware of the consumers’ information, with regard to their profiles in the database and provide customized services.
When a customer enters into a shopping complex, he will tour many areas of the shop, but there is one specific area that will interest the customer which will make him/her to make repeated visits to that area from time to time. The shop administrators will have the information regarding the consumers’ pattern of behaviour and look for ways in which the level of services can be enhanced to safeguard the interests and the needs of the consumers.
Human Mobility in Shopping Mall Environments
To deliver effectively the ads to the right environment at the right person requires capturing information about human movement when developing databases to retrieve and deliver human mobility information. Pervasive advertising draws on the behavioral patterns and movement of the people established using applications, which provide a platform for executing the applications that provide real time information about the location of an individual. With ubiquitous computing applications and environment, applications running on ad hoc mobile devices provide capabilities to build people centric networks. Typical examples include shopping mall networks. The networks are characterized by a mobility structure with each of the points defined by different activities such as music festival among others. The entire system of delivering ads to the right environment and the right person is based on applications already discussed elsewhere with the capabilities based on the customer and product locations. The critical elements in this case are the movement of customers and the network structure within the environment. The connections form an arbitrary graph depending on the different patterns and applications hosting devices within the communication channel in the environment. The critical challenge in this case is the difficulty of characterizing different patterns and environments because of the inability to predict accurately the movement patterns of the customers within the shopping mall environment.
The distribution and relationship between the nodes do not show a similarity, but according to an ergodic mobility model distributed according to the identically distributed trajectories. The distribution of the elements show a significant variation based on the time of the day and the type or relationship between the nodes. To understand critically the path established from studying movement patterns of the people in a shopping environment. One critical component is to establish the string relationships between the nodes as discussed in the next section.
Character String Analysis and Customer Path in Stream Data
The path a customer takes when moving in a shopping mall can be created predictably using knowledge discovery systems based on technological advances such as the RFID in many business varieties. In the context of the knowledge for establishing the path taken by a customer, knowledge expressions can be presented in the form of character strings using a character string parsing application. In this case, the movement of the customer within a building or shopping mall based on character strings makes it possible to enhance the performance and efficiency of the store layout. That is in addition to providing the customers with the right information about a specific product to address their shopping needs. It is possible to obtain the raw data by observing and capturing data from different applications used to track the movement of the customer within the shopping mall. Using the RFID, different numbers or IDs unique to different people moving in the environment provide a further source of data for the study. It is critical to note the data obtained can be mapped into a table that shows the movement of a particular individual within the shopping mall. One critical component used in this case is character string parsing.
EBONSAI is one of the systems used for character parsing. In this case, the system has an embedded capability to use character strings to express any movements in a shopping mall and other public places. The EBONSA systems functions on the algorithm. The algorithm functions on negative and positive events by capturing and working on negative and positive data sets designated as P and N expressed as |P| and |N| number records of the parses. The whole expression upon which the algorithm runs is expressed as:
In the above case, the original data is partitioned according to α which denotes the number of records not contained in P and N. An alphabet indexing mechanism is used to give the character sets of the positive and negative integers depending on the position of the customer.in this case, the right combinations of the alphabet sets leads to a clear form of refined classification of the items. One critical advantage of the system is a clear output and efficient functionality of the system. The entire data obtained using the systems discussed above is used to generate exploratory attributes of the customers by characterizing their movement within the system. The methods and technologies underlying the functionality if the systems follow methods such as indexing functionality as exemplified in EBONSAI. One of the typical areas where EBONSAI can be used is to interpret the meaning of an action by a customer besides providing the meaning of actions such as when a customer decides to shift from purchasing one product in preference for another. In this case, the rules used to determine the behavioral characteristics of the customer when switching from one product to another could easily be interpreted using the EBONSAI system. Despite the rise in the number of calculation times because of increased indexing as one the basic parameters of the EBONSAI system, system efficiency is effected by further subdivisions of product categories into subcategories.
Shopping Path Analysis and Transaction Mining Based on RFID Technology
In the above case, RFID is the technology of interest in both the business environment and other areas because of the relatively low cost of deployment and popularity. That is in addition to when the application is used in capturing and analyzing customer behavior when moving and shopping in public places such as shopping malls. The underlying algorithm that enables the application effectively execute is the Customer Access Matrix (CAM) that enables the mining and presentation of customer behavioral data for analysis and for predicting their behavioral patterns when shopping. To capture effectively the functionality and benefits of using the RFID model, it is critical to consider an effective deployment models.
Summary
To generate personalized ads that are location and content specific for the customer, a critical comprehension of supermarket shopping patterns provides actionable information about the movement patterns. Here, indoor tracking using the Euclid algorithm with location and movement sensing applications provide a clear pattern of human movement characteristics. Human mobility patterns generate highly probabilistic data that cannot provide concrete data for predicting the next move. Studies conducted in this field provide findings which show that human mobility is repetitive such as based on spatial proximity and nearness to certain places such as social links. One of the solutions is to use indoor tracking using different technologies such as WiFi and Bluetooth and RFID that identify the nodes and the paths taken by the customer. A graph with nodes and edges of the graph is defined in the relation (V, Ed, Σdoor), where V defines the set of vertices and Ed. A graph model used in this case enables data to be generated for use to characterize the customer. In addition, raw data for use can be captured by making observations using different applications to track the movement of the customer within the shopping environment. Here, RFID, different numbers or IDs unique to different people moving in the environment are a further source of data for analyzing user patterns to generate the right content for the right audience. Applications integrated into the mobile phone devices and the shopping mall applications can analyze the data and provide real time user specific ads.
Summary of the Literature Review
This summary focuses on the literature review on pervasive advertising using ubiquitous computing applications embedded in every day mobile devices such as mobile phones with location and context sensing capabilities. The summary covers diverse technologies used to capture user location and behavioral characteristics in a public environment and approaches and methods used to conceptualize effective content and context ads for pervasive advertising. The user environments and application used to capture user characteristics and behavioral patterns into a database use algorithms to generate specific ads suitable for specific environments among other issues to influence specifically pervasive advertising are included.
Pervasive advertising relies on different attributes based on context aware systems that deliver ads to the customers at the right time and environment. Context aware applications embedded into mobile devices provide a range of desirable solutions that include GPS and orientation sensors among others. The underlying functionality is to collect activity-targeted data and user context data based on opt-in policies. In context, that is done using existing frameworks integrated into context-information management systems using applications such android sensors API, which use low sensor class abilities to discover the location of a device in a given environment. The whole process relies on contextual information content, product, shopping list captured from the customer in a shopping environment using behavioral learning of the client. Here, it is critical that the whole process be a success by managing context information effectively. Once the process is well managed, it is critical that the ads generated and delivered to be defined and characterized to reach the public through advertising methods such as of emails and the internet. The growth of ubiquitous computing provides support for advertising using mobile phone devices using applications that support the delivery of an ad to the right environment and the right person. Among the approaches adopted in ubiquitous computing is pervasive symbiotic advertising. In this case, the type of ads and applications, which simultaneously display ads, provide live ads, and provision personalized ads are integrated to provide advertising solutions in a shopping environment. The problem in this case is how to preserve the privacy of individuals. The main solution is for the individual to agree on the level of intrusion into their private lives when providing personal information and allowing access to private lives. It is critical that conceptualized ads fit appropriately into the advertising environment and address the challenges that arise with pervasive advertising. One such solution is conceptualizing context defined in a framework for identifying various activities at the levels of adapting the context, conceptualizing context, and computing the context. In this case, it has to be the right content for the right purpose for the right audience. Different methodologies have been used to conceptualize ads such as using integrated conceptualizing technologies. In this case, both the advertiser and the advertising environment provide adverts designed to address the needs of the customer and the advertiser. That is in addition to factoring the institution using the ads as a method of delivering content to influence the customer to buy from them. Successful delivery of appropriate ads to the right audience relies on capturing information about the user profile using location sensing technologies that are able to sense the shopping characteristics and movement of a person within a given environment. Here, users can dynamically be profiled based on behavior learning (UBL) applications, relying on the SCAR inferencing algorithm to create user profiles that describe different groups and their behavioral characteristics. Here, the user profile depends on user characteristics and available opportunities using Location Aware Shopping Advertisement (LASA) and OSGi technologies integrated into mobile electronic devices because they possess portability and integration capabilities. Other profiling approaches include Ontologies to generate user information with such functionality provided using integrated location sensing applications such as BlueMall systems, and the AdNext system for mobile advertising. The most important element is the way users interact with the ads and the systems, based on the most crucial capabilities, which include usability, likeability, utility, and the ability to grab attention from passers-by. The Key issues here are factors that influence the way people look at ads in public that drives the way ads generating applications are used in the public. The Bluetooth is primary used in capturing and detecting the user’s presence and movement within a public place and their behavioral characteristics to enable the applications capture and store information for profiling and personalizing ads. That is basically based on understanding the core human mobility characteristics and shopping analysis using different applications such as the RFID, which is the technology that executes with the underlying Customer Access Matrix (CAM) algorithm. Here, user mobility characteristics, real time databases, context sensitive and aware applications use algorithms to analyze and generate user audience specific ads, which draw on personalized profiles that are crucial components in a pervasive advertising. That makes pervasive advertising effective on modern computing mobile devices such as mobile phones.
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