LSDR-ALOHA-Q:基于强化学习适用于大范围远距离的水下无线传感器网络MAC协议  

LSDR-ALOHA-Q:MAC Protocol for Wide Range and Long Distance Underwater Wireless Sensor Networks Based on Reinforcement Learning

作　　者：温立坤 WEN Likun(Department of Software Engineering,School of Computer Science and Technology,China University of Petroleum(East China),Qingdao 266580)

机构地区：[1]中国石油大学(华东)计算机科学与技术学院软件工程系,青岛266580

出　　处：《计算机与数字工程》2024年第6期1776-1782,1787,共8页Computer & Digital Engineering

摘　　要：随着对海洋开发需求的日益增长,水下无线传感器网络成为研究的热点。但由于水下环境信道可用带宽有限,传输速度和传播速度都慢,多普勒效应和多径效应严重等众多不利因素,导致水下网络性能低下。这主要是因为缺少在大范围远距离的水下网络中表现良好的媒体访问控制协议(MAC协议)。提出的LSDR-ALOHA-Q协议以ALOHA-Q协议为基础,该协议采用加入强化学习的方法,以提高信道利用率,同时结合强化学习使得该协议能够适应多变和复杂的水下环境。提出的LSDR-ALOHA-Q协议包括对时隙和帧结构的改造,使其更适合于大范围网络,通过优化时隙数,主动寻找空间复用的机会来增加信道利用率,同时通过提出一个新的退避算法来避免因为优化时隙数量而带来的Q-learning可能无法收敛的问题,即可能存在节点一直无法找到无冲突发送时间的问题。仿真表明当LSDR-ALOHA-Q协议被应用到大规模远距离的网络时可以显著提升信道利用率,同时降低冲突率和平均端到端时延。With the increasing demand for ocean development,underwater wireless sensor network(WSN)has become a re-search hotspot.However,due to many unfavorable factors such as limited available channel bandwidth,slow transmission speed and propagation speed,serious Doppler effect and multipath effect,underwater network performance is low.This is mainly due to the lack of Media Access Control protocol(MAC protocol)that performs well over large distances in underwater networks.The pro-posed LSDR-ALOHA-Q protocol is based on ALOHA-Q protocol,which adopts reinforcement learning method to improve channel utilization and adapt to changeable and complex underwater environment.The proposed LSDR-ALOHA-Q protocol includes the modification of time slots and frame structure to make it more suitable for large-scale networks.By optimizing the number of time slots,it actively looks for opportunities of spatial reuse to increase channel utilization.Meanwhile,a new backoff algorithm is pro-posed to avoid the problem that Q-learning may not converge due to the optimization of the number of time slots.That is,there may be a problem that the node has been unable to find the conflict-free send time.Simulation results show that when LSDR-ALOHA-Q is applied to large-scale play-distance networks,channel utilization can be significantly improved,and the collision rate and aver-age end-to-end delay can be reduced.

关 键 词：MAC协议 强化学习 水下无线声学传感器网络 ALOHA-Q 空间复用 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]