Graph neuron and hierarchical graph neuron, novel approaches toward real time pattern recognition in wireless sensor networks

The capability to support plethora of new diverse applications has placed Wireless Sensor Network (WSN) technology at threshold of an era of significant potential growth. In this paper, an attempt is made to analyze effectiveness of various available approaches toward pattern recognition in WSNs while introducing a novel method using a highly distributed associative memory technique called Graph Neuron (GN). The proposed approach not only enjoys from conserving the limited power resources of resource-constrained sensor nodes but also can be scaled effectively to address scalability issues which are of primary concern in wireless sensor networks. In addition, to overcome the issues of crosstalk available in the GN algorithm, Hierarchical Graph Neuron (HGN) an extended model of GN is presented which not only promises to deliver accurate results but also can be used for diverse types of applications in a multidimensional domain.

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