Extending the Lifetime of a Wireless Sensor Network Using Fuzzy-Chinese Remainder Theorem

Abstract

Improvements in wireless communication and hardware miniaturization have fostered increased utilization of Wireless Sensor Networks (WSN) in hazardous, low-human presence environments. Unfortunately, energy constraints prevented application of other adhoc networks’ protocols to improving WSN nodes, which are designed to operate continuously on a single charge, constantly communicate with the network Base Station (BS) and forward data. This makes battery life crucial. Existing WSN algorithms do not consider battery charge levels, which determine affordable data packet sizes. Therefore, insufficient battery charge levels poses a significant constraint to the general performance of the WSN. Hence, to prolong battery life, this study
implemented data-centric Fuzzy Chinese Remainder Theorem (CRT) to save energy and used Fuzzy logic for WSN data collection, node location, and battery charge monitoring with Python. Results show that the Fuzzy-  CRT algorithm extended WSN battery life, reduced the number of dead nodes by 40% over 8000 rounds, and thus, surpassed Low Energy Adaptive Clustering Hierarchy (LEACH) and CRT-LEACH algorithms in overall performance. Data-centric Fuzzy-CRT is therefore recommended for prolonged WSN battery life, improved network service, extended data collection and packet transfer durations.