Parallel Pattern Recognition Using a Single-Cycle Learning Approach within Wireless Sensor Networks

Pattern recognition applications such as natural phenomena detection and structural health monitoring have been widely applied using wireless sensor networks. These applications involve large amount of data to be analysed, and thus incur high computational time and complexity. In this paper, we present a parallel associative memory-based pattern recognition algorithm known as distributed hierarchical graph neuron (DHGN). It is a single-cycle learning algorithm with in-network processing capability; able to reduce computational loads by efficiently disseminates recognition processes throughout the network. Hence, suitable to be deployed in wireless sensor networks. The results of the accuracy and scalability tests show that our system performs with high accuracy and remains scalable for increases in pattern size and the number of stored patterns. The response time for pattern recognition remains within milliseconds irrespective of the size of the network.