ArticleSensors (Basel, Switzerland)2024
Wireless Sensor Network Coverage Optimization Using a Modified Marine Predator Algorithm.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Enhancing network longevity in WSNs via a two-layer hierarchical routing protocol with dual-hexagonal topology.Scientific reports · 2026Article
- An intelligent federated learning boosted cyberattack detection system for Denial-Of-Wallet attack using advanced heuristic search with multimodal approaches.Scientific reports · 2025Article
- Self-Organizing Wireless Sensor Networks Solving the Coverage Problem: Game-Theoretic Learning Automata and Cellular Automata-Based Approaches.Sensors (Basel, Switzerland) · 2025Article
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2 authors.
Funding
Abstract
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.
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