A Wheel Graph Structured Associative Memory for Single-Cycle Pattern Recognition within P2P Networks

A novel and efficient associative-memory-based pattern recognition scheme within P2P networks is proposed and implemented. The proposed scheme, known as the multi-wheel Graph Neuron, is adapted from Graph Neuron-based algorithms which are single-cycle, light-weight, and scalable associative-memory-based pattern recognition algorithms for wireless sensor networks, and has been implemented over a structured P2P Chord overlay network. The proposed approach promotes collaboration among peers during the detection process within the P2P networks. Since the scheme only required single cycle learning, the communication cost amongst peers is minimized. The preliminary results show that the proposed single-cycle recognition scheme guarantees high detection accuracy.