The full text of this article
Decentralised reinforcement learning for energy-efficient scheduling in wireless sensor networks
by Mihail Mihaylov; Yann-Aël Le Borgne; Karl Tuyls; Ann Nowé
International Journal of Communication Networks and Distributed Systems (IJCNDS), Vol. 9, No. 3/4, 2012
Abstract: We present a self-organising reinforcement learning (RL) approach for scheduling the wake-up cycles of nodes in a wireless sensor network. The approach is fully decentralised, and allows sensor nodes to schedule their active periods based only on their interactions with neighbouring nodes. Compared to standard scheduling mechanisms such as SMAC, the benefits of the proposed approach are twofold. First, the nodes do not need to synchronise explicitly, since synchronisation is achieved by the successful exchange of data messages in the data collection process. Second, the learning process allows nodes competing for the radio channel to desynchronise in such a way that radio interferences and therefore packet collisions are significantly reduced. This results in shorter communication schedules, allowing to not only reduce energy consumption by reducing the wake-up cycles of sensor nodes, but also to decrease the data retrieval latency. We implement this RL approach in the OMNET++ sensor network simulator, and illustrate how sensor nodes arranged in line, mesh and grid topologies autonomously uncover schedules that favour the successful delivery of messages along a routing tree while avoiding interferences.
is only available to individual subscribers or to users at subscribing institutions.
Go to Inderscience Online Journals to access the Full Text of this article.
Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.
Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Communication Networks and Distributed Systems (IJCNDS):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable).
See our Orders page to subscribe.
If you still need assistance, please email firstname.lastname@example.org