Abstract
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.
Highlights
The ocean is rich in biological and mineral resources, making the exploitation and usage of ocean resources receive more and more attention [1,2,3]
The underwater vehicle is more suitable for practical applications, because energy efficiency is a significant objective in underwater data collection missions
In [4], underwater acoustic sensor networks (UASNs) were divided into two layers and different algorithms were employed: in the lower layer was a modified path planning scheme; while in the upper layer, the autonomous underwater vehicles (AUVs) dove from top to bottom and collected the data from gateway nodes, which received data packages from other nodes based on a multi-hop algorithm, and in this layer, the energy consumption increased with the diving path increase
Summary
The ocean is rich in biological and mineral resources, making the exploitation and usage of ocean resources receive more and more attention [1,2,3]. In [4], UASNs were divided into two layers and different algorithms were employed: in the lower layer was a modified path planning scheme; while in the upper layer, the AUV dove from top to bottom and collected the data from gateway nodes, which received data packages from other nodes based on a multi-hop algorithm, and in this layer, the energy consumption increased with the diving path increase. The aforementioned problems pose two challenges to the system: collecting underwater data given the special movement characteristics of the glider and optimizing the data collection performance under the fast-changing environment. Different from algorithms for AUVs, we propose a sensor scheduling algorithm for gliders and design an indexable threshold policy to optimize the data collection performance.
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call