Physical layer driven protocol and algorithm design for energy-efficient wireless sensor networks
Proceedings of the 7th annual international conference on Mobile computing and networking
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Medium access control with coordinated adaptive sleeping for wireless sensor networks
IEEE/ACM Transactions on Networking (TON)
PMAC: An Adaptive Energy-Efficient MAC Protocol for Wireless Sensor Networks
IPDPS '05 Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Workshop 12 - Volume 13
R-MAC: An Energy-Efficient MAC Protocol for Underwater Sensor Networks
WASA '07 Proceedings of the International Conference on Wireless Algorithms,Systems and Applications
RL-MAC: a reinforcement learning based MAC protocol for wireless sensor networks
International Journal of Sensor Networks
Traffic adaptive IEEE 802.15.4 MAC for wireless sensor networks
EUC'06 Proceedings of the 2006 international conference on Embedded and Ubiquitous Computing
Reinforcement learning models for scheduling in wireless networks
Frontiers of Computer Science: Selected Publications from Chinese Universities
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In wireless sensor networks, it is an important problem to adjust the work time window in each working/sleeping period to save energy under light network loads and decrease the packet delay under heavy network loads. In this paper, we introduce reinforcement learning method into this problem. We discuss the algorithm design method in a simple IEEE 802.15.4 network, where an RL-based adaptive algorithm is proposed. Simulation results show that this RL-based algorithm can adapt to the change of data flow and make a good tradeoff between the energy-saving performance and the packet delay performance.