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Reinforcement Learning for Self-Organizing Wake-Up Scheduling in Wireless Sensor Networks Host Publication: Agents and Artificial Intelligence Authors: M. Emilov Mihaylov, Y. Le Borgne, K. Tuyls and A. Nowé Publisher: Springer Publication Date: May. 2012 Number of Pages: 16 ISBN: 978-3-642-29965-0
Abstract: Wake-up scheduling is a challenging problem in wireless sen-
sor networks. It was recently shown that a promising approach for solv-
ing this problem is to rely on reinforcement learning (RL). The RL
approach is particularly attractive since it allows the sensor nodes to
coordinate through local interactions alone, without the need of cen-
tral mediator or any form of explicit coordination. This article extends
previous work by experimentally studying the behavior of RL wake-up
scheduling on a set of three dierent network topologies, namely line,
mesh and grid topologies. The experiments are run using OMNET++,
a the state-of-the-art network simulator. The obtained results show how
simple and computationally bounded sensor nodes are able to coordinate
their wake-up cycles in a distributed way in order to improve the global
system performance. The main insight of these experiments is to show
that sensor nodes learn to synchronize if they have to cooperate for for-
warding data, and learn to desynchronize in order to avoid interferences.
This synchronization/desynchronization behavior, referred to for short
as (de)synchronicity, allows to improve the message throughput even for
very low duty cycles.
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