Abstract
Large-scale wireless sensor networks (WSNs) require significant resources for event recognition and classification. We present a light-weight event classification scheme, called Identifier based Graph Neuron (IGN). This 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 proposed approach not only conserves the power resources of sensor nodes but is also effectively scalable to large scale WSNs. In addition, our scheme overcomes the issue of false-positive detection -(which existing associated memory based solutions suffers from) and hence promises to deliver accurate results. We compare Identifier based Graph Neuron with two of the existing associated memory based event classification schemes and the results show that IGN correctly recognizes and classifies the incoming events in comparative amount of time and messages.
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Imran, N., Khan, A. (2010). Identifier Based Graph Neuron: A Light Weight Event Classification Scheme for WSN. In: Wong, K.W., Mendis, B.S.U., Bouzerdoum, A. (eds) Neural Information Processing. Models and Applications. ICONIP 2010. Lecture Notes in Computer Science, vol 6444. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17534-3_37
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DOI: https://doi.org/10.1007/978-3-642-17534-3_37
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