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

TL;DR

This paper introduces a novel multi-objective optimization algorithm, CMOMPA, for deploying sensor nodes in wireless sensor networks to optimize coverage, connection, and cost in heterogeneous 3D scenarios, outperforming existing methods.

Contribution

It proposes a new swarm-based multi-objective optimization algorithm, CMOMPA, tailored for complex, non-convex deployment problems in heterogeneous 3D wireless sensor networks.

Findings

CMOMPA outperforms ten state-of-the-art algorithms in convergence and accuracy.

The optimized deployment balances cost, coverage, and reliability effectively.

Simulations confirm the method's effectiveness in practical scenarios.

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.