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.
|Title of host publication||Algorithms and Architectures for Parallel Processing - Proceedings of the 13th International Conference, ICA3PP 2013, Vietri sul Mare, Italy, December 18-20, 2013. Part II|
|Editors||R. Aversa, J. Kolodziej, J. Zhang, F. Amato, G. Fortino|
|ISBN (Print)||ISBN 978-3-319-03888-9|
|Publication status||Published - 2013|
|Name||Lecture Notes in Computer Science|
Galzarano, S., Liotta, A., & Fortino, G. (2013). QL-MAC : a Q-learning based MAC for wireless sensor networks. In R. Aversa, J. Kolodziej, J. Zhang, F. Amato, & G. Fortino (Eds.), Algorithms and Architectures for Parallel Processing - Proceedings of the 13th International Conference, ICA3PP 2013, Vietri sul Mare, Italy, December 18-20, 2013. Part II (pp. 267-275). (Lecture Notes in Computer Science; Vol. 8286). Springer. https://doi.org/10.1007/978-3-319-03889-6_31