College of Information Science and Technology, Dalian Maritime University, Dalian 116026, China.
School of Electrical Engineering, Dalian University of Science and Technology, Dalian 116052, China.
Sensors (Basel). 2022 Feb 18;22(4):1628. doi: 10.3390/s22041628.
In this paper, we investigate how to efficiently utilize channel bandwidth in heterogeneous hybrid optical and acoustic underwater sensor networks, where sensor nodes adopt different Media Access Control (MAC) protocols to transmit data packets to a common relay node on optical or acoustic channels. We propose a new MAC protocol based on deep reinforcement learning (DRL), referred to as optical and acoustic dual-channel deep-reinforcement learning multiple access (OA-DLMA), in which the sensor nodes utilizing the OA-DLMA protocol are called agents, and the remainder are non-agents. The agents can learn the transmission patterns of coexisting non-agents and find an optimal channel access strategy without any prior information. Moreover, in order to further enhance network performance, we develop a differentiated reward policy that rewards specific actions over optical and acoustic channels differently, with priority compensation being given to the optical channel to achieve greater data transmission. Furthermore, we have derived the optimal short-term sum throughput and channel utilization analytically and conducted extensive simulations to evaluate the OA-DLMA protocol. Simulation results show that our protocol performs with near-optimal performance and significantly outperforms other existing protocols in terms of short-term sum throughput and channel utilization.
在本文中，我们研究了如何在异构混合光声水下传感器网络中有效地利用信道带宽，其中传感器节点采用不同的媒体访问控制（MAC）协议通过光或声信道将数据包传输到一个公共的中继节点。我们提出了一种基于深度强化学习（DRL）的新 MAC 协议，称为光声双通道深度强化学习多址接入（OA-DLMA），其中采用 OA-DLMA 协议的传感器节点称为代理，其余节点称为非代理。代理可以学习共存的非代理的传输模式，并在没有任何先验信息的情况下找到最佳的信道接入策略。此外，为了进一步提高网络性能，我们开发了一种差异化的奖励策略，对光和声信道上的特定动作给予不同的奖励，优先补偿光信道以实现更大的数据传输。此外，我们还推导出了最优短期和吞吐量和信道利用率的解析表达式，并通过广泛的仿真来评估 OA-DLMA 协议。仿真结果表明，我们的协议具有接近最优的性能，并且在短期和吞吐量和信道利用率方面明显优于其他现有协议。
