Technical Note: \cal Q-Learning
Machine Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Ad-hoc On-Demand Distance Vector Routing
WMCSA '99 Proceedings of the Second IEEE Workshop on Mobile Computer Systems and Applications
Distributed CDMA-based MAC Protocol for Underwater Sensor Networks
LCN '07 Proceedings of the 32nd IEEE Conference on Local Computer Networks
R-MAC: An Energy-Efficient MAC Protocol for Underwater Sensor Networks
WASA '07 Proceedings of the International Conference on Wireless Algorithms,Systems and Applications
Adaptive Energy Reservation MAC Protocol for Underwater Acoustic Sensor Networks
EUC '08 Proceedings of the 2008 IEEE/IFIP International Conference on Embedded and Ubiquitous Computing - Volume 02
C-MAC: A TDMA-Based MAC Protocol for Underwater Acoustic Sensor Networks
NSWCTC '09 Proceedings of the 2009 International Conference on Networks Security, Wireless Communications and Trusted Computing - Volume 01
IEEE Transactions on Mobile Computing
VBF: vector-based forwarding protocol for underwater sensor networks
NETWORKING'06 Proceedings of the 5th international IFIP-TC6 conference on Networking Technologies, Services, and Protocols; Performance of Computer and Communication Networks; Mobile and Wireless Communications Systems
Shallow water acoustic networks
IEEE Communications Magazine
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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.