ArticleEntropy (Basel, Switzerland)2022
An Optimal WSN Node Coverage Based on Enhanced Archimedes Optimization Algorithm.
Article in Entropy (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Research on Coverage Optimization in Wireless Sensor Networks Based on an Improved Sparrow Search Algorithm.Sensors (Basel, Switzerland) · 2026Article
- Designing Effective Drug Therapies Using a Multiobjective Spider-Wasp Optimizer.Biomimetics (Basel, Switzerland) · 2025Article
- Self-Organizing Wireless Sensor Networks Solving the Coverage Problem: Game-Theoretic Learning Automata and Cellular Automata-Based Approaches.Sensors (Basel, Switzerland) · 2025Article
- An Optimization Coverage Strategy for Wireless Sensor Network Nodes Based on Path Loss and False Alarm Probability.Sensors (Basel, Switzerland) · 2025Article
- Multi-Strategy Bald Eagle Search Algorithm Embedded Orthogonal Learning for Wireless Sensor Network (WSN) Coverage Optimization.Sensors (Basel, Switzerland) · 2024Article
- Article
- Application of an Enhanced Whale Optimization Algorithm on Coverage Optimization of Sensor.Biomimetics (Basel, Switzerland) · 2023Article
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Authors and funding
5 authors.
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Abstract
Node coverage is one of the crucial metrics for wireless sensor networks' (WSNs') quality of service, directly affecting the target monitoring area's monitoring capacity. Pursuit of the optimal node coverage encounters increasing difficulties because of the limited computational power of individual nodes, the scale of the network, and the operating environment's complexity and constant change. This paper proposes a solution to the optimal node coverage of unbalanced WSN distribution during random deployment based on an enhanced Archimedes optimization algorithm (EAOA). The best findings for network coverage from several sub-areas are combined using the EAOA. In order to address the shortcomings of the original Archimedes optimization algorithm (AOA) in handling complicated scenarios, we suggest an EAOA based on the AOA by adapting its equations with reverse learning and multidirection techniques. The obtained results from testing the benchmark function and the optimal WSN node coverage of the EAOA are compared with the other algorithms in the literature. The results show that the EAOA algorithm performs effectively, increasing the feasible range and convergence speed.
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