Institute of Meteorology and Oceanography, National University of Defense Technology, Nanjing 211101, China.
Sensors (Basel). 2019 Jan 10;19(2):256. doi: 10.3390/s19020256.
Data forwarding for underwater wireless sensor networks has drawn large attention in the past decade. Due to the harsh underwater environments for communication, a major challenge of Underwater Wireless Sensor Networks (UWSNs) is the timeliness. Furthermore, underwater sensor nodes are energy constrained, so network lifetime is another obstruction. Additionally, the passive mobility of underwater sensors causes dynamical topology change of underwater networks. It is significant to consider the timeliness and energy consumption of data forwarding in UWSNs, along with the passive mobility of sensor nodes. In this paper, we first formulate the problem of data forwarding, by jointly considering timeliness and energy consumption under a passive mobility model for underwater wireless sensor networks. We then propose a reinforcement learning-based method for the problem. We finally evaluate the performance of the proposed method through simulations. Simulation results demonstrate the validity of the proposed method. Our method outperforms the benchmark protocols in both timeliness and energy efficiency. More specifically, our method gains 83.35% more value of information and saves up to 75.21% energy compared with a classic lifetime-extended routing protocol (QELAR).
在过去十年中，水下无线传感器网络的数据转发引起了广泛关注。由于通信的水下环境恶劣，水下无线传感器网络（UWSN）的主要挑战是实时性。此外，水下传感器节点的能量有限，因此网络寿命是另一个障碍。此外，水下传感器的被动移动导致水下网络的动态拓扑变化。在 UWSN 中，考虑数据转发的实时性和能量消耗，以及传感器节点的被动移动性是很重要的。在本文中，我们首先在水下无线传感器网络的被动移动模型下，通过联合考虑实时性和能量消耗来形式化数据转发问题。然后，我们为该问题提出了一种基于强化学习的方法。最后，我们通过仿真评估了所提出方法的性能。仿真结果验证了所提出方法的有效性。与经典的生命周期延长路由协议（QELAR）相比，我们的方法在实时性和能效方面都有更好的表现。具体来说，与经典的生命周期延长路由协议（QELAR）相比，我们的方法在信息价值方面提高了 83.35%，在能量消耗方面节省了高达 75.21%。
