A Lightweight, Fast and Efficient Distributed Hierarchical Graph Neuron-based Pattern Classifier

A lightweight, fast and efficient pattern classifier based on Distributed Hierarchical Graph Neuron (DHGN) is proposed and implemented. A classifier with such features is essential for the emerging networks such as wireless sensor networks and mobile ad hoc networks, where resources such as bandwidth and energy are limited. The proposed classifier adopts an in-network processing algorithm and has a one cycle learning capability. DHGN network is a new form of neural networks which consist of a hierarchical graph-based representation of input patterns. This paper compares the proposed solution with the well known Self-Organizing Map (SOM) classifier, in relation to accuracy and computational complexity. The results show that our solution offers lower computational complexity than SOM while guaranteeing satisfactory accuracy.