Context Awareness in Mobile Computing Environments

Christos B. Anagnostopoulos, Athanasios Tsounis, Stathes Hadjiefthymiades
2006 Wireless personal communications  
In this article, we report software architectures for context awareness in mobile computing environments, sensor centric systems and discuss context modeling issues. Defining an architecture for supporting context-aware applications for mobile devices explicitly implies a scalable description of how to represent contextual information and which are the abstraction models capable of handling such information. Using sensors to retrieve contextual information (e.g., user location) leads to a
more » ... network scheme that provides services to the applications level. Operations for capturing, collating, storing, and disseminating contextual information at the lowest level and aggregating it into increasingly more abstract models qualify the context-aware systems. In this article, we introduce context aware systems in mobile computing environments, review the basic mechanisms underlying the operation of such systems, and discuss notable work and important architectures in the area. Context Dependen Context-aware computing Mobile computing Context Modeling Pervasiveness System Behavior Figure 1. Context-aware logic space. entities describing a physical or conceptual object, respectively. The definition of a context model may range from unstructured raw-data models (e.g., location, spatial data, network measurement, Quality of Services (QoS) indicators, and environmental state) to logic-based models (e.g., spatiotemporal models, object-oriented models, relational models, and propositional logics). The latter, logic-based models could relate to actual activities of specific entities (e.g., human activities, such as meeting, working, sleeping, and walking). Additionally, sensor-based systems use sensors, in order to capture low-level context. Such systems couple the collected data with a compatible data model. Obviously, high-level representation of context (i.e., context modeled by logic-based data representations) cannot be directly acquired or disseminated from sensors. Specifically, in mobile computing a well known technique for capturing contextual information is called proximity selection. Proximity selection is primarily based on the user location context. Such context can be (i) resources and devices in the vicinity of the user, (ii) places of interests closest to the user current position, and (iii) computational objects with which the mobile user is currently interacting. It is, also, the reasoning process that produces, or, even, entails (i.e., assertion of consistent knowledge) such new context derived from the existing and consistent one. One could view that kind of capability (i.e., reasoning) as an aspect of pervasiveness in a context-aware system. The more a system can extend (i.e., assert knowledge) and infer (i.e., entail such knowledge) knowledge for a specific world (e.g., application domain), the more pervasive could it be considered, as it can reason and learn behaviors, or, estimate values for that context. Obviously, CAS should be aware of the very specific context that falls in its management responsibility including, context storage, dissemination, adaptation, provision, and reasoning. System behavior, with respect to context management, means the ability of the system to adapt and, consequently, react to the expected dynamic changes of the context (e.g., from the fact that a sensor broke down, to a mobile user changing direction, to finding inconsistencies in the knowledge base). Moreover, context-aware systems target to the provision of context-aware
doi:10.1007/s11277-006-9187-6 fatcat:5buup4kstrhindvy74ddbpdyoe