CRAM: A Conditioned Reflex Action Inspired Adaptive Model for Context Addition in Wireless Sensor Networks

Abstract

In Wireless Sensor Networks (WSNs), Context Awareness is typically realized through Context Aware Systems (CASs). Although almost each CAS follows sense-decide-adapt cycle, the notion of context is hardwired into the applications; that is, when an event is triggered, the sense-decide-actuate cycle runs and performs required actuation. In situations, for instance, whenever the same event is triggered, the cycle produces the same actuation through mechanical use of the same resources, posing the same processing and time. In this paper, we propose CRAM, a context added system in which actuations once performed by the system help it 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, the system reaches a juncture where recurrence of each event proves to be a stimulus for the system to respond impulsively, through priming memory of introspective contexts, to achieve an imitation of learned reflex action resulting into reduced time and energy expenditure.