Radial and Sigmoid Basis Function Neural Networks in Wireless Sensor Routing Topology Control in Underground Mine Rescue Operation Based on Particle Swarm Optimization

Abstract

The performance of a proposed compact radial basis function was compared with the sigmoid basis function and the gaussian-radial basis function neural networks in 3D wireless sensor routing topology control, in underground mine rescue operation. Optimised errors among other parameters were examined in addition to scalability and time efficiency. To make the routing path efficient in emergency situations, the sensor sequence and deployment as well as transmission range were carefully considered. In times of danger and unsafe situations, data-mule robot with Through The Earth (TTE) radio would be used to carry water, food, equipments, and so forth to miners underground and return with information. Using Matlab, the optimised vectors with high survival rate and fault tolerant, based on rock type, were generated as inputs for the neural networks. Particle swarm optimisation with adaptive mutation was used to train the neurons. Computer simulation results showed that the neural network learning algorithm minimized the error between the neural network output and the desired output such that final error values were either the same as the error goal or less than the error goal. Thus, the proposed algorithm shows high reliability and superior performance.