Resolving and Mediating Ambiguous Contexts in Pervasive Environments

Resolving and Mediating Ambiguous Contexts in Pervasive Environments

Nirmalya Roy (Institute for Infocomm Research, Singapore), Sajal K. Das (University of Texas at Arlington, USA) and Christine Julien (University of Texas at Austin, USA)
DOI: 10.4018/978-1-60960-180-5.ch006
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Abstract

Pervasive computing applications envision sensor rich computing and networking environments that can capture various types of contexts of inhabitants of the environment, such as their locations, activities, vital signs, and environmental measures. Such context information is useful in a variety of applications, for example to manage health information to promote independent living in “aging-in-place” scenarios. In reality, both sensed and interpreted contexts are often ambiguous, leading to potentially dangerous decisions if not properly handled. Thus, a significant challenge facing the development of realistic and deployable context-aware services for pervasive computing applications is the ability to deal with these ambiguous contexts. In this chapter, the authors discuss a resource optimized quality assured ontology-driven context mediation framework for resource constrained sensor networks based on efficient context-aware data fusion and information theoretic sensor parameter selection for optimal state estimation. It has the ability to represent contexts according to the applications’ ontology and easily composable ontological rules to mediate ambiguous contexts.
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Pervasive computing applications, such as the Aware Home (Orr & Abowd, 2000), Intelligent Room (Coen, 1999) and House_n (Intille, 2006), do not provide explicit reusable support for users to manage uncertainty in the sensed data and its interpretation and thereby assume that sensed contexts are unambiguous. Toolkits enable the integration of context into applications (Dey, Salber & Abowd, 2001), however, they do not provide mechanisms for sensor fusion or reasoning about contexts’ ambiguity. Although other work has proposed mechanisms for reasoning about contexts (Vurgun, Philpose & Pavel, 2007), it does not provide well-defined context-aware data fusion models nor address the challenges associated with context ambiguity. Distributed mediation of ambiguous contexts in aware environments (Dey, Mankoff, Abowd & Carter, 2002) has, however, been used to allow the user to correct ambiguity in the sensed input.

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