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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 regard, pattern recognition especially in real-time applications plays a paramount role in securing the network against malicious activity. In this paper, an attempt is made to introduce a novel method using a highly scalable and distributed associative memory technique, called hierarchical graph neuron (HGN), while its effectiveness is analyzed from different points of view. 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, the algorithm overcomes the issue of crosstalk available in the original GN algorithm, and thus not only promises to deliver accurate results, but also can be deployed for diverse types of applications in a multidimensional domain.
Date of Conference: 14-16 Sept. 2009