Balancing the trade-off between cost and reliability for wireless sensor networks: a multi-objective optimized deployment method

• URL: http://arxiv.org/abs/2207.09089v1
• Date: Tue, 19 Jul 2022 05:53:55 GMT
• Title: Balancing the trade-off between cost and reliability for wireless sensor networks: a multi-objective optimized deployment method
• Authors: Long Chen, Yingying Xu, Fangyi Xu, Qian Hu, Zhenzhou Tang
• Abstract summary: We propose an optimal deployment method for practical wireless sensor networks (WSNs) We develop a novel multi-objective optimization algorithm known as the competitive multi-objective optimization algorithm (CMOMPA) The results show that the optimized deployment can balance the trade-off among deployment cost, sensing reliability, and network reliability.
• Score: 4.031433260365659
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The deployment of the sensor nodes (SNs) always plays a decisive role in the system performance of wireless sensor networks (WSNs). In this work, we propose an optimal deployment method for practical heterogeneous WSNs which gives a deep insight into the trade-off between the reliability and deployment cost. Specifically, this work aims to provide the optimal deployment of SNs to maximize the coverage degree and connection degree, and meanwhile minimize the overall deployment cost. In addition, this work fully considers the heterogeneity of SNs (i.e. differentiated sensing range and deployment cost) and three-dimensional (3-D) deployment scenarios. This is a multi-objective optimization problem, non-convex, multimodal and NP-hard. To solve it, we develop a novel swarm-based multi-objective optimization algorithm, known as the competitive multi-objective marine predators algorithm (CMOMPA) whose performance is verified by comprehensive comparative experiments with ten other stateof-the-art multi-objective optimization algorithms. The computational results demonstrate that CMOMPA is superior to others in terms of convergence and accuracy and shows excellent performance on multimodal multiobjective optimization problems. Sufficient simulations are also conducted to evaluate the effectiveness of the CMOMPA based optimal SNs deployment method. The results show that the optimized deployment can balance the trade-off among deployment cost, sensing reliability and network reliability. The source code is available on https://github.com/iNet-WZU/CMOMPA.

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