@inproceedings{7c96d6aab7704f92845cef9947d9d06b,
title = "QL-MAC : a Q-learning based MAC for wireless sensor networks",
abstract = "WSNs are becoming an increasingly attractive technology thanks to the significant benefits they can offer to a wide range of application domains. Extending the system lifetime while preserving good network performance is one of the main challenges in WSNs. In this paper, a novel MAC protocol (QL-MAC) based on Q-Learning is proposed. Thanks to a distributed learning approach, the radio sleep-wakeup schedule is able to adapt to the network traffic load. The simulation results show that QL-MAC provides significant improvements in terms of network lifetime and packet delivery ratio with respect to standard MAC protocols. Moreover, the proposed protocol has a moderate computational complexity so to be suitable for practical deployments in currently available WSNs.",
author = "S. Galzarano and A. Liotta and G. Fortino",
year = "2013",
doi = "10.1007/978-3-319-03889-6_31",
language = "English",
isbn = "ISBN 978-3-319-03888-9",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "267--275",
editor = "R. Aversa and J. Kolodziej and J. Zhang and F. Amato and G. Fortino",
booktitle = "Algorithms and Architectures for Parallel Processing - Proceedings of the 13th International Conference, ICA3PP 2013, Vietri sul Mare, Italy, December 18-20, 2013. Part II",
address = "Germany",
}