Improved Marine Predator Algorithm for Wireless Sensor Network Coverage Optimization Problem

Qing He (), Zhouxin Lan, Damin Zhang (), Liu Yang and Shihang Luo
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Qing He: College of Big Data and Information Engineering, Guizhou University, Guiyang 550025, China
Zhouxin Lan: College of Big Data and Information Engineering, Guizhou University, Guiyang 550025, China
Damin Zhang: College of Big Data and Information Engineering, Guizhou University, Guiyang 550025, China
Liu Yang: School of Public Administration, Guizhou University, Guiyang 550025, China
Shihang Luo: College of Big Data and Information Engineering, Guizhou University, Guiyang 550025, China

Sustainability, 2022, vol. 14, issue 16, 1-19

Abstract: A wireless sensor network (WSN) is a distributed network system composed of a great many sensor nodes that rely on self-organization. The random deployment of WSNs in city planning often leads to the problem of low coverage of monitoring areas. In the construction of smart cities in particular, a large number of sensor nodes need to be deployed to maintain the reception, processing, and transmission of data throughout the city. However, the uneven distribution of nodes can cause a lot of wasted resources. To solve this problem, this paper proposes a WSN coverage optimization model based on an improved marine predator algorithm (IMPA). The algorithm introduces a dynamic inertia weight adjustment strategy in the global exploration and local exploitation stages of the standard marine predator algorithm to balance the exploration and exploitation capabilities of the algorithm and improve its solution accuracy. At the same time, the improved algorithm uses a multi-elite random leading strategy to enhance the information exchange rate between population individuals and improve the algorithm’s ability to jump out of the local optimum. The optimization experiment results of 11 benchmark test functions and part of the CEC2014 test functions show that the optimization performance of the improved algorithm is significantly better than the standard marine predator algorithm and other algorithms in the literature. Finally, the improved algorithm is applied to the WSN coverage optimization problem. The simulation results demonstrate that the IMPA has a better coverage rate than other metaheuristic algorithms and other improved algorithms in the literature for solving the WSN coverage optimization problem.

Keywords: wireless sensor network; coverage optimization; marine predator algorithm; inertia weight; multi-elite random leading (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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