A single shot associated memory based classification scheme for WSN

Authors:
Nomica Imran;Asad Khan
Affiliations:
School of Information Technology, Monash University, Clayton, Victoria, Australia;School of Information Technology, Monash University, Clayton, Victoria, Australia
Venue:
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part III
Year:
2011

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Abstract

Identifier based Graph Neuron (IGN) is a network-centric algorithm which envisages a stable and structured network of tiny devices as the platform for parallel distributed pattern recognition. The proposed scheme is based on highly distributed associative memory which enables the objects to memorize some of its internal critical states for a real time comparison with those induced by transient external conditions. The approach not only save up the power resources of sensor nodes but is also effectively scalable to large scale wireless sensor networks. Besides that our proposed scheme overcomes the issue of false-positive detection - (which existing associated memory based solutions suffers from) and hence assures accurate results. We compare Identifier based Graph Neuron with two of the existing associated memory based event classification schemes and the results show that Identifier based Graph Neuron correctly recognizes and classifies the incoming events in comparative amount of time and messages.