ArticleEntropy (Basel, Switzerland)2022
Node Deployment Optimization for Wireless Sensor Networks Based on Virtual Force-Directed Particle Swarm Optimization Algorithm and Evidence Theory.
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 5 papers.
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Who cites it
5 citing papers in PubMed.
- Sunn Pest Optimization (SPO): A Phase-Differentiated Bio-Inspired Metaheuristic for Global Optimization with Application to Precision Agriculture Monitoring Networks.Biomimetics (Basel, Switzerland) · 2026Article
- Energy efficient transactions for blockchain networks using adaptive global best-worst particle swarm optimization.Scientific reports · 2026Article
- An Enhanced Particle Swarm Optimization-Based Node Deployment and Coverage in Sensor Networks.Sensors (Basel, Switzerland) · 2024Article
- Gate-Level Circuit Partitioning Algorithm Based on Clustering and an Improved Genetic Algorithm.Entropy (Basel, Switzerland) · 2023Article
- Remora Optimization Algorithm with Enhanced Randomness for Large-Scale Measurement Field Deployment Technology.Entropy (Basel, Switzerland) · 2023Article
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Authors and funding
4 authors.
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Abstract
Wireless sensor network deployment should be optimized to maximize network coverage. The D-S evidence theory is an effective means of information fusion that can handle not only uncertainty and inconsistency, but also ambiguity and instability. This work develops a node sensing probability model based on D-S evidence. When there are major evidence disputes, the priority factor is introduced to reassign the sensing probability, with the purpose of addressing the issue of the traditional D-S evidence theory aggregation rule not conforming to the actual scenario and producing an erroneous result. For optimizing node deployment, a virtual force-directed particle swarm optimization approach is proposed, and the optimization goal is to maximize network coverage. The approach employs the virtual force algorithm, whose virtual forces are fine-tuned by the sensing probability. The sensing probability is fused by D-S evidence to drive particle swarm evolution and accelerate convergence. The simulation results show that the virtual force-directed particle swarm optimization approach improves network coverage while taking less time.
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Registered trials
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