An Efficient Deployment Scheme with Network Performance Modeling for Underwater Wireless Sensor Networks

A high-performance network deployment strategy supports fundamental network services, such as topology controls, protocol designs, and boundary detections in underwater wireless sensor networks (UWSNs). Existing deployment methods treat nodes within the communication range as connected. However, in...

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Veröffentlicht in:IEEE internet of things journal 2024-03, Vol.11 (5), p.1-1
Hauptverfasser: Xu, Cangzhu, Song, Shanshan, Liu, Jun, Xu, Yuanbo, Che, Shouheng, Lin, Bin, Xu, Gaochao
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Sprache:eng
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Zusammenfassung:A high-performance network deployment strategy supports fundamental network services, such as topology controls, protocol designs, and boundary detections in underwater wireless sensor networks (UWSNs). Existing deployment methods treat nodes within the communication range as connected. However, in addition to internode distance, packet errors and collisions are also significant factors for point-to-point connectivity. Furthermore, when allocating node locations, deployment strategies focus on maximizing coverage, ignoring the trade-off between coverage and network performance (reliability, latency, and energy efficiency). To this end, an Efficient Deployment scheme with Network Performance Modeling (EDNPM) is proposed, to provide reliable data transmission in a time-aware and energy-efficient way for UWSNs. Specifically, we first explore sensor locations' impact on communication and network factors, to improve the point-to-point connectivity and network performance. A Network Performance Evaluation Model (NPEM) is established to quantify performance metrics for guiding network deployment. Based on NPEM, network deployment is formulated as a multi-objective optimization problem, and we propose a novel Network Connection-constraint Particle Swarm Optimization algorithm (NCPSO) to solve this problem. Notably, EDNPM is a unified network deployment framework for various underwater applications. Extensive experiments demonstrate that EDNPM outperforms other deployment algorithms in terms of network performance, and robustness with different network settings.
ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2023.3318222