A Distributed Gradient-Based Deployment Strategy for a Network of Sensors with a Probabilistic Sensing Model
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arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866911136111132672 |
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| author | Mosalli, Hesam Aghdam, Amir G. |
| author_facet | Mosalli, Hesam Aghdam, Amir G. |
| contents | This paper presents a distributed gradient-based deployment strategy to maximize coverage in hybrid wireless sensor networks (WSNs) with probabilistic sensing. Leveraging Voronoi partitioning, the overall coverage is reformulated as a sum of local contributions, enabling mobile sensors to optimize their positions using only local information. The strategy adopts the Elfes model to capture detection uncertainty and introduces a dynamic step size based on the gradient of the local coverage, ensuring movements adaptive to regional importance. Obstacle awareness is integrated via visibility constraints, projecting sensor positions to unobstructed paths. A threshold-based decision rule ensures movement occurs only for sufficiently large coverage gains, with convergence achieved when all sensors and their neighbors stop at a local maximum configuration. Simulations demonstrate improved coverage over static deployments, highlighting scalability and practicality for real-world applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_02869 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Distributed Gradient-Based Deployment Strategy for a Network of Sensors with a Probabilistic Sensing Model Mosalli, Hesam Aghdam, Amir G. Systems and Control This paper presents a distributed gradient-based deployment strategy to maximize coverage in hybrid wireless sensor networks (WSNs) with probabilistic sensing. Leveraging Voronoi partitioning, the overall coverage is reformulated as a sum of local contributions, enabling mobile sensors to optimize their positions using only local information. The strategy adopts the Elfes model to capture detection uncertainty and introduces a dynamic step size based on the gradient of the local coverage, ensuring movements adaptive to regional importance. Obstacle awareness is integrated via visibility constraints, projecting sensor positions to unobstructed paths. A threshold-based decision rule ensures movement occurs only for sufficiently large coverage gains, with convergence achieved when all sensors and their neighbors stop at a local maximum configuration. Simulations demonstrate improved coverage over static deployments, highlighting scalability and practicality for real-world applications. |
| title | A Distributed Gradient-Based Deployment Strategy for a Network of Sensors with a Probabilistic Sensing Model |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2509.02869 |