SEAL: Self-adaptive AUV-based localization for sparsely deployed Underwater Sensor Networks
| Autor: | Sudip Misra, Mohammad S. Obaidat, Tamoghna Ojha |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
SIMPLE (military communications protocol)
Computer Networks and Communications Computer science Node (networking) Real-time computing 020206 networking & telecommunications 02 engineering and technology Energy consumption Network topology Seal (mechanical) Transmission (telecommunications) 0202 electrical engineering electronic engineering information engineering Overhead (computing) 020201 artificial intelligence & image processing |
| Zdroj: | Computer Communications. 154:204-215 |
| ISSN: | 0140-3664 |
| Popis: | In this paper, we propose a Se lf-adaptive A UV-based L ocalization (SEAL) scheme, which is specifically designed to provide network-wide localization service to sensor nodes in sparsely deployed Underwater Sensor Networks (UWSN) using a high-speed Autonomous Underwater Vehicle (AUV). Even though the sparse nature of node deployment in UWSN is cost-effective, it creates a new challenge for the existing UWSN localization schemes. Moreover, due to the effect of passive node mobility owing to oceanic waves and currents, the network topology experiences partitioning. In such a sparse deployment scenario, the existing static anchor-based schemes of node localization exhibit low localization coverage, high localization error, and high message overhead. On the contrary, mobile anchor-based schemes are able to maintain low message overhead. However, these schemes achieve low localization coverage only or result in higher average energy consumption. In SEAL, we excogitate a simple and self-adaptive scheme, which empowers the AUV to select deployment-aware transmission range and maintain energy-efficiency. Simulations in NS-3 indicate that SEAL achieves significantly improved localization coverage while maintaining the energy-efficiency of the AUV when compared to the schemes from the existing literature that were considered as benchmarks in this study. |
| Databáze: | OpenAIRE |
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