Context awareness

About: Context awareness is a research topic. Over the lifetime, 5790 publications have been published within this topic receiving 119944 citations.

Context management for m-commerce applications: determinants, methodology and the role of marketing

Abstract: Studying consumer behaviour and usage of environmental determinants in the mobile services domain contributes to the identification of context information which is critical for the effective operation of mobile commerce applications. Exploiting this information towards providing enhanced and innovative mobile services offers a competitive advantage within the highly demanding domain of m-commerce applications. However, in order to effectively exploit such context information, there is a need to design the necessary methods, software tools and information systems that will be employed for collecting, processing and disseminating this information. In this paper we develop a theoretical framework which defines the context information necessary for m-commerce applications, taking into account relevant marketing dimensions as well as privacy protection perspectives. Then, this framework is operationalized through the design of an appropriate software architecture which enables the standardization and management of context information.

A service-oriented middleware for context-aware applications

Abstract: Context awareness has emerged as an important element in distributed computing. It offers mechanisms that allow applications to be aware of their environment and enable these applications to adjust their behavior to the current context. Considering the dynamic nature of context, the data flow of relevant contextual information can be significant. In order to keep track of this information flow, a flexible service mechanism should be available for the client applications. In this document we present a service-oriented middleware for context-aware applications. This middleware provides support to leverage the development of context-aware applications by providing a scripting-like approach for context-aware application development; allowing the subscription of rules containing context-based events and conditions and a notification to be sent when the specified context holds. Moreover, a domain-specific language has been developed to express these context-based rules.

The Praxis of Cognitive Assistance in Smart Homes.

Abstract: The current and prospective situation of cognitively impaired people entails great human, social, and economical costs. Smart homes can help to maintain at home cognitively impaired people, to improve their autonomy, and accordingly to alleviate the burden put on informal and professional caregivers. This chapter will provide a comprehensive view of the research performed at DOMUS lab. This research aims at turning the whole home into a cognitive prosthetic, especially by providing cognitive assistance. The first part of the chapter presents research on the infrastructure, both sensors networks and middleware. Research work on autonomic computing, multi-person localization, context awareness, and personalization are presented. The next part of the chapter illustrates by means of DOMUS research prototypes how cognitive assistance can help to address four kinds of cognitive deficits: initiation, attention, planning, and memory. Studies involving cognitively impaired people are also be presented. In the final part of the chapter, the role of AI, context awareness and behavior tracking are questioned. To what extend are they compulsory? Does design can provide smart and simple solutions to complex issues?

Context-aware music recommender systems: workshop keynote abstract

Abstract: Recommender Systems (RSs) are software tools and techniques providing suggestions for items to be of use to a user [20]. In the music domain recommender systems can support information search and discovery tasks by helping the user to find relevant music items, for instance, new music tracks, or artists that the user may not even know [18, 9]. Several techniques have been proposed but most of the available systems use either contentor collaborativeor social-based approaches, or even more often, a hybrid combination of these three basic approaches [9, 7]. The recommendation algorithm is content-based, when the features of the music tracks that are liked by the user are considered when the system predicts what else the target user may like. Music features can be extracted directly from the music content, with signal processing techniques, or can be based on metadata (e.g., genre, year, author). Conversely, in collaborative-based approaches the system ignores the items’ descriptions, i.e., their features. It tries to find users with music preferences that are similar to those of the target user. Two users are estimated as similar by observing only the cooccurrences of the items in the sets of items liked/purchased by the two users. Then, the system recommends to the target user items liked by these similar users and novel to the target user. A third approach, which is called social-based, is emerging in the music domain. It is based on computing similarities among the items to be recommended (music songs or artists) through web mining techniques, or on exploiting social tagging information [8]. Social-based recommendations can be generated by using the similarities of artists that in turn can be computed using the social activity of the users, for instance by analyzing: the songs played by a community of users in the same listening sessions, or the tags assigned by users to songs or artists. The rationale of this approach is that items similar to those that the user liked will also probably be relevant to the user. However, notwithstanding the fact that music recommender systems are among the most common applications of recommendation techniques, there are very few music recommender systems that are capable to adapt their suggestions to contextual conditions important to predict the user’s preferences at a particular moment or situation. This is an important issue for a music recommender system, since people often seek music for a contextual situation like an occasion,

Fuzzy logic based algorithm for Context Awareness in IoT for Smart home environment

Abstract: In Internet of Things, Context Awareness plays important role. Also it is important in Smart home environment where data from various sensors and input from user or user action must be interpreted and corresponding commands to actuators must be generated. There are various approaches available for context awareness, like supervised, unsupervised, rule based systems etc. But, these approaches have their own pros and cons depending on the need of an application. In the proposed approach we discuss the fuzzy logic based algorithm with architecture. Various Smart home scenarios are identified and implemented. Two scenarios are discussed in detail by applying proposed algorithm. For the performance measurement of proposed algorithm two parameters Response time and Accuracy are considered. Our implementation results show reasonable improvement in response time and accuracy, while keeping our approach light weight on resources.