Multi-channel communication protocols have been developed to alleviate the effects of interference and consequently improve the network performance in Wireless Sensor Networks requiring high bandwidth. In this paper, we propose a contention-free multi-channel protocol to maximize network throughput while ensuring energy-efficient operation. Arguing that routing decisions influence to a large extent the network throughput, we formulate route selection and transmission scheduling as a joint problem and propose a Reinforcement Learning based scheduling algorithm to solve it in a distributed manner. The results of extensive simulation experiments show that the proposed solution not only provides a collision-free transmission schedule but also minimizes energy waste, which makes it appropriate for energy-constrained Wireless Sensor Networks.
Phung, TKH, Lemmens, B, Mihaylov, ME, Tran, L & Steenhaut, K 2013, 'Adaptive Learning Based Scheduling in Multichannel Protocol for Energy-Efficient Data-Gathering Wireless Sensor Networks', International Journal of Distributed Sensor Networks, no. vol. 2013, Article ID 345821, doi:10.1155/2013/34. <http://dx.doi.org/10.1155/2013/345821>
Phung, T. K. H., Lemmens, B., Mihaylov, M. E., Tran, L., & Steenhaut, K. (2013). Adaptive Learning Based Scheduling in Multichannel Protocol for Energy-Efficient Data-Gathering Wireless Sensor Networks. International Journal of Distributed Sensor Networks, (vol. 2013, Article ID 345821, doi:10.1155/2013/34). http://dx.doi.org/10.1155/2013/345821
@article{f6849ec8d2224a47bd85d76058952860,
title = "Adaptive Learning Based Scheduling in Multichannel Protocol for Energy-Efficient Data-Gathering Wireless Sensor Networks",
abstract = "Multi-channel communication protocols have been developed to alleviate the effects of interference and consequently improve the network performance in Wireless Sensor Networks requiring high bandwidth. In this paper, we propose a contention-free multi-channel protocol to maximize network throughput while ensuring energy-efficient operation. Arguing that routing decisions influence to a large extent the network throughput, we formulate route selection and transmission scheduling as a joint problem and propose a Reinforcement Learning based scheduling algorithm to solve it in a distributed manner. The results of extensive simulation experiments show that the proposed solution not only provides a collision-free transmission schedule but also minimizes energy waste, which makes it appropriate for energy-constrained Wireless Sensor Networks.",
keywords = "Wireless Sensor Networks, multi channel protocol, Energy efficiency, multi-agent learning",
author = "Phung, {Thi Kieu Ha} and Bart Lemmens and Mihaylov, {Mihail Emilov} and Lan Tran and Kris Steenhaut",
year = "2013",
month = feb,
day = "25",
language = "English",
journal = "International Journal of Distributed Sensor Networks",
issn = "1550-1329",
publisher = "Hindawi Publishing Corporation",
number = "vol. 2013, Article ID 345821, doi:10.1155/2013/34",
}