학술논문

An Efficient Deployment Scheme With Network Performance Modeling for Underwater Wireless Sensor Networks
Document Type
Periodical
Source
IEEE Internet of Things Journal IEEE Internet Things J. Internet of Things Journal, IEEE. 11(5):8345-8359 Mar, 2024
Subject
Computing and Processing
Communication, Networking and Broadcast Technologies
Reliability
Sensors
Optimization
Underwater acoustics
Three-dimensional displays
Network topology
Internet of Things
Network deployment
performance evaluation model
performance optimization
underwater wireless sensor networks (UWSNs)
Language
ISSN
2327-4662
2372-2541
Abstract
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 tradeoff 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 multiobjective optimization problem, and we propose a novel network connection-constraint particle swarm optimization (NCPSO) algorithm 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.