Quantum Inspired Multiobjective Optimization in Clustered Homogeneous Wireless Sensor Networks for Improving Network Lifetime and Coverage

Abstract

The optimization technique in which many objectives are simultaneously optimized is called multiobjective optimization. A wireless sensor network (WSN) consists of many sensors forming a network. These sensor nodes mainly run on battery which deteriorates with time. Our aim is to optimize coverage and lifetime of the network. One of the most effective methods for minimizing energy and increasing lifetime of nodes is clustering. In this paper, we integrate the two objectives of improving network lifetime and increasing coverage. We use quantum bits or qubits in our representation instead of bits. A qubit can be in 0 state, 1 state or a super position of these two states at the same time. This is what makes quantum computing-based algorithms more powerful as we can have more diversity. The proposed algorithm, quantum inspired multiobjective evolutionary algorithm based on decomposition (QMOEAD) is compared with LEACH, SEP, NSGAII and MOEA/D on the basis of coverage and network lifetime. The results show that QMOEAD outperforms the other algorithms mentioned above. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.