Wireless Sensor Network Coverage Optimization Using a Modified Marine Predator Algorithm
To solve the coverage problem caused by the random deployment of wireless sensor network nodes in the forest fire-monitoring system, a modified marine predator algorithm (MMPA) is proposed. Four modifications have been made based on the standard marine predator algorithm (MPA). Firstly, tent mapping...
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MDPI AG
2024-12-01
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| author | Guohao Wang Xun Li |
| author_facet | Guohao Wang Xun Li |
| author_sort | Guohao Wang |
| collection | DOAJ |
| description | To solve the coverage problem caused by the random deployment of wireless sensor network nodes in the forest fire-monitoring system, a modified marine predator algorithm (MMPA) is proposed. Four modifications have been made based on the standard marine predator algorithm (MPA). Firstly, tent mapping is integrated into the initialization step to improve the searching ability of the early stage. Secondly, a hybrid search strategy is used to enhance the ability to search and jump out of local optimum. Thirdly, the golden sine guiding mechanism is applied to accelerate the convergence of the algorithm. Finally, a stage-adjustment strategy is proposed to make the transition of stages more smoothly. Six specific test functions chosen from the CEC2017 function and the benchmark function are used to evaluate the performance of MMPA. It shows that this modified algorithm has good optimization capability and stability compared to MPA, grey wolf optimizer, sine cosine algorithm, and sea horse optimizer. The results of coverage tests show that MMPA has a better uniformity of node distribution compared to MPA. The average coverage rates of MMPA are the highest compared to the commonly used metaheuristic-based algorithms, which are 91.8% in scenario 1, 95.98% in scenario 2, and 93.88% in scenario 3, respectively. This demonstrates the superiority of this proposed algorithm in coverage optimization of the wireless sensor network. |
| format | Article |
| id | doaj-art-56cdccd2e1f245ee89ad417c89fee74c |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-56cdccd2e1f245ee89ad417c89fee74c2025-01-10T13:20:46ZengMDPI AGSensors1424-82202024-12-012516910.3390/s25010069Wireless Sensor Network Coverage Optimization Using a Modified Marine Predator AlgorithmGuohao Wang0Xun Li1School of Electronics and Information Engineering, Hebei University of Technology, Tianjin 300401, ChinaSchool of Electronics and Information Engineering, Hebei University of Technology, Tianjin 300401, ChinaTo solve the coverage problem caused by the random deployment of wireless sensor network nodes in the forest fire-monitoring system, a modified marine predator algorithm (MMPA) is proposed. Four modifications have been made based on the standard marine predator algorithm (MPA). Firstly, tent mapping is integrated into the initialization step to improve the searching ability of the early stage. Secondly, a hybrid search strategy is used to enhance the ability to search and jump out of local optimum. Thirdly, the golden sine guiding mechanism is applied to accelerate the convergence of the algorithm. Finally, a stage-adjustment strategy is proposed to make the transition of stages more smoothly. Six specific test functions chosen from the CEC2017 function and the benchmark function are used to evaluate the performance of MMPA. It shows that this modified algorithm has good optimization capability and stability compared to MPA, grey wolf optimizer, sine cosine algorithm, and sea horse optimizer. The results of coverage tests show that MMPA has a better uniformity of node distribution compared to MPA. The average coverage rates of MMPA are the highest compared to the commonly used metaheuristic-based algorithms, which are 91.8% in scenario 1, 95.98% in scenario 2, and 93.88% in scenario 3, respectively. This demonstrates the superiority of this proposed algorithm in coverage optimization of the wireless sensor network.https://www.mdpi.com/1424-8220/25/1/69wireless sensor networkmarine predator algorithmcoverage optimization |
| spellingShingle | Guohao Wang Xun Li Wireless Sensor Network Coverage Optimization Using a Modified Marine Predator Algorithm Sensors wireless sensor network marine predator algorithm coverage optimization |
| title | Wireless Sensor Network Coverage Optimization Using a Modified Marine Predator Algorithm |
| title_full | Wireless Sensor Network Coverage Optimization Using a Modified Marine Predator Algorithm |
| title_fullStr | Wireless Sensor Network Coverage Optimization Using a Modified Marine Predator Algorithm |
| title_full_unstemmed | Wireless Sensor Network Coverage Optimization Using a Modified Marine Predator Algorithm |
| title_short | Wireless Sensor Network Coverage Optimization Using a Modified Marine Predator Algorithm |
| title_sort | wireless sensor network coverage optimization using a modified marine predator algorithm |
| topic | wireless sensor network marine predator algorithm coverage optimization |
| url | https://www.mdpi.com/1424-8220/25/1/69 |
| work_keys_str_mv | AT guohaowang wirelesssensornetworkcoverageoptimizationusingamodifiedmarinepredatoralgorithm AT xunli wirelesssensornetworkcoverageoptimizationusingamodifiedmarinepredatoralgorithm |