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Title: Context Addition in Wireless Sensor Networks
Authors: Hussain, Majid
Keywords: Computer Sciences
Computer & IT
Issue Date: 2016
Publisher: University of Engineering & Technology, Lahore.
Abstract: The property of any artifact for being aware of its physical environment or situation and responding in a proactive and intelligent manner is the Context Awareness typically realized through Context Aware Applications (CAA)—an exceptional genus of WSNs. Although almost each CAA follows sense-decide-adapt cycle, the notion of context is hardwired into the applications. In such sort of mechanism when an event is triggered the sense-decide-actuate cycle runs and performs the required actuation. In the situations, for instance, whenever the same event is triggered, the cycle produces same actuation by using same context semantics, situational recognition, perception and adaptation, posing same processing load, delay and mechanical use of resources for acquiring same amount of actuation. In this thesis we propose CRAM, a context added system in which the actuations once performed by the system help the system to internally evolve by serving as new contexts. As the system is exposed to more situations overtime, its context repository is enriched through such retrospective contexts, gradually letting it perform internal actuation through improved introspective contexts. This internal actuation leads the system towards the evolution of intelligent processing by reducing the independent function of decision in sense-decide-actuate cycle and merging it with new context. Finally it reaches at a juncture where the recurrence of each event proves to be a stimuli that stimulates the system to respond through primed memory of introspective contexts achieving an imitation of learned reflex action and a cut through processing paradigm along with reduced time and energy expenditure. Through analytical modeling and prototype implementation, the performance of CRAM is substantiated. The implementation of reflex arc is shown to provide the same level of accuracy for visual context processing as that of contemporary context aware systems with more than 10 times improvement in responsiveness.
Gov't Doc #: 16830
Appears in Collections:PhD Thesis of All Public / Private Sector Universities / DAIs.

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