DCC-IACJS: A novel bio-inspired duty cycle-based clustering approach for energy-efficient wireless sensor networks

Abstract

Clustering routing is one of the most significant mechanisms applied to extend the lifetime of battery-powered wireless sensor networks (WSNs). However, most existing clustering approaches fail to exploit the feature of node redundancy in WSNs, which as a result leads to unnecessary energy waste. In this regard, a new clustering model named DCCM, based on the duty cycle method, was proposed to reduce the number of working nodes to save energy. This model enables nodes to work alternately to slow energy depletion by offering a novel designed coverage relationship matrix (CRM) and cover sets (CSs). Furthermore, an improved adaptive clone jellyfish search (DCC-IACJS) algorithm was designed to optimize the proposed model to obtain the most desirable clustering scheme. To increase its superiority over the clustering schemes, a new adaptive parameter strategy, as well as a new clone scheme, was devised in the DCC-IACJS. Subsequently, to verify the effectiveness of the proposed clustering approach, comprehensive simulation experiments were conducted to compare it with other state-of-the-art counterparts, namely, GA-CSO-LBCM, FGF, and O-LEACH. Simulation results showed that DCC-IACJS can provide 10.24%, 7.04%, and 8.54% longer network lifetime than O-LEACH, FGF, and GA-CSO-LBCM, respectively.