@inproceedings{8b0b326dc04b4e599ad93c17698e3727,
title = "A learning-based MAC for energy efficient wireless sensor networks",
abstract = "Designing energy-efficient communication protocols is one of the main challenges in wireless sensor networks. This work presents an adaptive radio scheduling schema employing a reinforcement learning algorithm for reducing the energy consumption while preserving the other network performances. By means of a decentralized on-line approach, each nodes determines the most beneficial radio schedule by dynamically adapting to its own traffic load and to the neighbors{\textquoteright} communication activities. We compare our approach with other learning-based MAC protocols as well as conventional MAC approaches and show that, under different simulating scenarios and traffic conditions, our protocol achieves better trade-offs in terms of energy consumption, latency and throughput.",
author = "S. Galzarano and G. Fortino and A. Liotta",
year = "2014",
doi = "10.1007/978-3-319-11692-1_34",
language = "English",
isbn = "978-3-319-11691-4",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "396--406",
editor = "G. Fortino and {Di Fatta}, G. and W. Li and S. Ochoa and A. Cuzzocrea and M. Pathan",
booktitle = "Proceedings of the 7th International Conference on Internet and Distributed Computing Systems, IDCS 2014, September 22-24, 2014, Calabria, Italy",
address = "Germany",
}