Fuzzy modelling based energy aware clustering in wireless sensor networks using modified invasive weed optimization - Richa Sharma; Vasudha Vashisht; Umang Singh (Journal of King Saud University: Computer and Information Sciences)
Energy conservation in a highly resource restricted environment of Wireless Sensor Networks (WSNs) is a crucial task. This issue can be efficiently tackled by adopting the mechanism of clustering in sensor networks. The primary objective of any of the designed clustering protocols for WSNs is to obtain energy efficiency and to lengthen the network life duration. This achievement is made through the election of a single deserving node as a head node called cluster head. This paper suggested an evolutionary approach based clustering protocol named Modified-Invasive Weed Optimization Based Clustering Algorithm (M-IWOCA) for WSNs. M-IWOCA focuses on the selection of fittest node as a cluster head to enhance the network’s lifetime as well as to address the issue of energy conservation in sensor networks. A fuzzy inference model is designed to evaluate the fitness of each node in the network. Simulation results for M-IWOCA depict significant reduction in dead nodes per round and results in minimizing the energy utilization of the sensors. Moreover, M-IWOCA enhances the network stability period by 45% in comparison to Artificial Bee Colony (ABC) protocol and by 18% in comparison to Quantum Artificial Bee Colony (QABC) protocol.