Broadcast scheduling in wireless sensor networks using fuzzy Hopfield neural network

In this paper we describe fuzzy Hopfield neural network (FHNN) technique to solve the TDMA (time division multiple access) broadcast scheduling problem in wireless sensor networks (WSN). We formulate it as discrete energy minimization problem and map it into Hopfield neural network with the fuzzy c-means strategy to find the TDMA schedule for nodes in a communication network. The broadcast scheduling problem for wireless sensor networks is an NP-complete problem. Each time slot is regarded as a data sample and every node is taken as a cluster. Time slots are adequately distributed to the dedicated node while satisfying the constraints. The aim is to minimize the TDMA cycle length and maximize the node transmissions avoiding both primary and secondary conflicts. The FHNN reduces the processing time and increases the convergence rate for Broadcast Scheduling Problem. Simulation results show that the FHNN improves performance substantially through solving well-known benchmark problems.