Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China.
College of Computer Science & Technology, Jilin University, Changchun 130012, China.
Sensors (Basel). 2018 Nov 23;18(12):4110. doi: 10.3390/s18124110.
In this paper, we target solving the data gathering problem in underwater wireless sensor networks. In many underwater applications, it is not quick to retrieve sensed data, which gives us the opportunity to leverage mobile autonomous underwater vehicles (AUV) as data mules to periodically collect it. For each round of data gathering, the AUV visits part of the sensors, and the communication between AUV and sensor nodes is a novel high-speed magnetic-induction communication system. The rest of the sensors acoustically transmit their sensed data to the AUV-visit sensors. This paper deploys the HAS 4 (Heuristic Adaptive Sink Sensor Set Selection) algorithm to select the AUV-visited sensors for the purpose of energy saving, AUV cost reduction and network lifetime prolonging. By comparing HAS 4 with two benchmark selection methods, experiment results demonstrate that our algorithm can achieve a better performance.
在本文中，我们旨在解决水下无线传感器网络中的数据收集问题。在许多水下应用中，快速检索感测数据并不容易，这为我们利用移动自主水下机器人（AUV）作为数据骡定期收集数据提供了机会。在每一轮数据收集过程中，AUV 会访问部分传感器，而 AUV 和传感器节点之间的通信是一种新颖的高速磁感应通信系统。其余的传感器通过声传输将其感测数据传输到 AUV 访问传感器。本文部署了 HAS 4（启发式自适应汇聚传感器集选择）算法来选择 AUV 访问传感器，以达到节能、降低 AUV 成本和延长网络寿命的目的。通过将 HAS 4 与两种基准选择方法进行比较，实验结果表明，我们的算法可以实现更好的性能。
