A Grey Wolf Optimization Approach for Improving the Performance of Wireless Sensor Networks

Optimizing the energy consumption of sensor nodes have been a big design issue in wireless sensor networks (WSNs). Energy efficient WSN usually compromise with network stability which is a crucial factor in ensuring full, lasting and reliable coverage of the network. Connected dominating set (CDS) based virtual backbone and traditional cluster based approach are two most commonly used data delivery protocols in a WSN. The paper proposes a distance based stable connected dominating set methodology using a meta-heuristic algorithm grey wolf optimization (DBSCDS-GWO) for achieving a stable, balanced and energy efficient CDS based WSN. We also propose a distance based stable clustering algorithm using GWO (DBSC-GWO) for improving the performance of cluster based WSN. DBSCDS-GWO performs better than RMCDS-GA and SAECDS-GA by 70.5% and 67.7% respectively and DBSC-GWO performs better than LEACH and DRESEP by 74.7% and 50.6% respectively in terms of both network stability and energy efficiency. Performance of the proposed algorithm is validated using Matlab simulation and Netsim Emulator.