An Adaptive Coverage Strategy for WSNs With Dynamic Energy Decay Based on Multiobjective Dingo Optimization Algorithm

A significant challenge faced by wireless sensor networks (WSNs) is how to maintain a high sensing coverage rate with dynamic energy decay. In this study, we investigate methods to extend the duration of high sensing coverage rate in WSNs under limited power supply circumstances. In contrast to the sensing model presented earlier, we consider the relationships between a sensor node’s sensing capacity and the decay of supply energy. Based on the decay of supply energy with time, a dynamic sensing model is established to analyze the sensing ability. Moreover, an adaptive coverage strategy with dynamic energy decay based on the multiobjective dingo optimization algorithm (MODOA) is proposed. And the Pareto multiobjective strategy is integrated to this algorithm to optimize the sensing coverage. The experimental result points out that during the initial operation, our approach can increase the sensing coverage rate by 4.56% compared to the multiobjective artificial vulture optimization algorithm (MOAVOA) and by 6.84% compared to the multiobjective particle swarm algorithm (MOPSO) and by the 20th week, it increases by 7.46% and 3.71% compared to MOPSO algorithm and MOAVOA algorithm.