A slotted CSMA based reinforcement learning approach for extending the lifetime of underwater acoustic wireless sensor networks

被引:30
作者
Jin, Lu [1 ]
Huang, Defeng [1 ]
机构
[1] Univ Western Australia, Sch Elect Elect & Comp Engn, Crawley, WA 6009, Australia
基金
澳大利亚研究理事会;
关键词
Underwater acoustics; Wireless sensor networks; Reinforcement learning; Slotted CSMA; Energy efficient;
D O I
10.1016/j.comcom.2012.10.007
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Underwater acoustic wireless sensor networks (UA-WSNs) are capable of supporting underwater missions. Due to the harsh environment, replacing or recharging battery for underwater sensors are difficult or costly, thus UA-WSN systems must be energy efficient. Although a large number of energy efficient schemes have been proposed for terrestrial wireless sensor networks, the fundamental differences between underwater acoustic channel and its terrestrial counterparts make those schemes perform poorly in underwater acoustic communications. In this work, we present an energy efficient architecture for UA-WSNs, which employs a reinforcement learning algorithm and a slotted Carrier Sensing Multiple Access (slotted CSMA) protocol. Due to the reinforcement learning algorithm, the proposed system is capable of optimising its parameters to adapt to the underwater environment after having been deployed. Simulation results show that the lifetime of the network is extended significantly with the proposed architecture by lowering the number of collisions and retransmissions of data packets. Crown Copyright (C) 2012 Published by Elsevier B.V. All rights reserved.
引用
收藏
页码:1094 / 1099
页数:6
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