水下
计算机科学
弹道
实时计算
遥控水下航行器
数据收集
节点(物理)
航程(航空)
水声通信
能量(信号处理)
利用
运动规划
能源消耗
人工智能
移动机器人
工程类
机器人
海洋学
电气工程
物理
地质学
统计
航空航天工程
结构工程
计算机安全
数学
天文
作者
Mingyue Cheng,Quansheng Guan,Fei Ji,Julian Cheng,Yankun Chen
出处
期刊:IEEE Internet of Things Journal
[Institute of Electrical and Electronics Engineers]
日期:2022-08-01
卷期号:9 (15): 13168-13178
被引量:7
标识
DOI:10.1109/jiot.2022.3141402
摘要
Marine science and Internet of Underwater Things applications rely significantly on collecting data from underwater sensors. Data collection using long-distance underwater acoustic communications consumes a lot of energy in underwater sensor nodes, which are powered by batteries. To achieve low-energy consumption, we can use the autonomous underwater vehicle (AUV) to move close to sensor nodes and exploit the short-range and high-rate communications. Most of the existing AUV-based data collection schemes consider the scenarios having the knowledge of node positions, where the cruising trajectory can be computed before the AUV’s departure. These schemes cannot apply to some scenarios such as turtle tracking for a certain sea area having no position information. To this end, we first propose a planning-while-detecting approach to dynamically detect the sensors on turtles and adjust the AUV cruising direction to collect data. To further improve data efficiency under the energy limit of the AUV, we group the sensors that can share the same trajectory using their detected directions. A grouping-based dynamic trajectory planning (GDTP) is then proposed to determine the next cruising direction that can visit the group of sensors having the largest amount of data and demanding the least cruising energy at the risk of detection errors. Simulation results show that GDTP achieves significantly higher data collection efficiency than the existing trajectory planning algorithms in dynamic scenarios, and as the communication range increases, it can even outperform the existing algorithms with node locations.
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