Industrial Wireless Sensor Networks: Protocols and Applications

Yoo, Seong-eun; Kim, Taejoon

doi:10.3390/s20205809

Cited by 4 publications

(1 citation statement)

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“…They use wireless communication to complete the target monitoring and information transmission of the environment. WSNs are widely used in aviation [3], environmental [4], medical [5], industrial [6], and other fields due to their advantages of low cost, versatility and easy deployment. In recent years, the fields of smart transportation [7], smart homes [8], and smart cities [9] have also achieved large-scale applications.…”

Section: Introductionmentioning

confidence: 99%

Improved Marine Predator Algorithm for Wireless Sensor Network Coverage Optimization Problem

Lan

Zhang

et al. 2022

Sustainability

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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.

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