Energy-efficient reprogramming in wsn using constructive neural networks.

Abstract

In this paper, we propose the use of neural based technologies to carry out the dynamic reprogramming of wireless sensor networks as an alternative to traditional methodology. An analysis an comparison of the energy cost involved in reprogramming wireless sensor networks was done using rule-based programming (TP) standard feeforward neural networks (FF), and the C-Mantec (CM) algorithm, a novel method based on constructive neural networks. The simulation results, first performed on array of sensor networks under COOJA simulator (considering best, medium and worst case scenarios for three benchmark problems) and finally evaluated on a case os study with identical conditions, show that the use of neural network based methodoligies (FF & CM) produces a significant saving in resources, measured by the number of packets transmitted, the energy consumed and the time needed to reprogram the sensors.