Li Xinbin, Xu Xianglin, Yan Lei, Zhao Haihong, Zhang Tongwei
Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China.
National Deep Sea Center, Qingdao 266237, China.
Sensors (Basel). 2020 Jul 4;20(13):3758. doi: 10.3390/s20133758.
The autonomous underwater glider has attracted enormous interest for underwater activities, especially in long-term and large-scale underwater data collection. In this paper, we focus on the application of gliders gathering data from underwater sensor networks over underwater acoustic channels. However, this application suffers from a rapidly time-varying environment and limited energy. To optimize the performance of data collection and maximize the network lifetime, we propose a distributed, energy-efficient sensor scheduling algorithm based on the multi-armed bandit formulation. Besides, we design an indexable threshold policy to tradeoff between the data quality and the collection delay. Moreover, to reduce the computational complexity, we divide the proposed algorithm into off-line computation and on-line scheduling parts. Simulation results indicate that the proposed policy significantly improves the performance of the data collection and reduces the energy consumption. They prove the effectiveness of the threshold, which could reduce the collection delay by at least 10% while guaranteeing the data quality.
自主水下滑翔器在水下活动中引起了极大的关注，特别是在长期和大规模的水下数据收集方面。在本文中，我们专注于滑翔器通过水下声学信道从水下传感器网络收集数据的应用。然而，这种应用面临快速时变的环境和有限的能量。为了优化数据收集性能并最大化网络寿命，我们提出了一种基于多臂赌博机公式的分布式、节能传感器调度算法。此外，我们设计了一种可索引阈值策略，以在数据质量和收集延迟之间进行权衡。而且，为了降低计算复杂度，我们将所提出的算法分为离线计算和在线调度部分。仿真结果表明，所提出的策略显著提高了数据收集性能并降低了能耗。它们证明了阈值的有效性，该阈值在保证数据质量的同时可将收集延迟至少降低10%。
