Minimizing deep sea data collection delay with autonomous underwater vehicles
| Autor: | Huanyang Zheng, Jie Wu, Ning Wang |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Schedule
Data collection Computer Networks and Communications Computer science Real-time computing 020302 automobile design & engineering 020206 networking & telecommunications 02 engineering and technology Deep sea Theoretical Computer Science Set (abstract data type) 0203 mechanical engineering Artificial Intelligence Hardware and Architecture 0202 electrical engineering electronic engineering information engineering Underwater Software |
| Zdroj: | Journal of Parallel and Distributed Computing. 104:99-113 |
| ISSN: | 0743-7315 |
| DOI: | 10.1016/j.jpdc.2017.01.006 |
| Popis: | As a special application of delay tolerant networks (DTNs), efficient data collection in the deep sea poses some unique challenges, due to the need for timely data reporting and the delay of acoustic transmission in the ocean. Autonomous underwater vehicles (AUVs) are deployed in the deep sea to surface frequently to transmit collected data from sensors (in a 2-dimensional or 3-dimensional search space) to the surface stations. However, additional delay occurs at each resurfacing. In this paper, we want to minimize the average data reporting delay, through optimizing the number and locations of AUV resurfacing events. We also study the AUV trajectory planning using an extended Euler circuit, where the search space is a set of segments (e.g., oil pipes) in the deep sea. To further reduce the data reporting delay, several schemes, which schedules multiple AUVs cooperatively, are also explored. Finally, experiments in both the synthetic and real traces validate the efficiency and effectiveness of the proposed algorithms. |
| Databáze: | OpenAIRE |
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