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Decentralized Learning in Wireless Sensor Networks Authors: M. Emilov Mihaylov, K. Tuyls and A. Nowé Publication Date: Oct. 2009 Pages: 345-347
Abstract: In this paper we use a reinforcement learning algorithm with
the aim to increase the autonomous lifetime of a Wireless
Sensor Network (WSN) and decrease latency in a decen-
tralized manner. WSNs are collections of sensor nodes that
gather environmental data, where the main challenges are
the limited power supply of nodes and the need for decen-
tralized control. To overcome these challenges, we make each
sensor node adopt an algorithm to optimize the efficiency of
a small group of surrounding nodes, so that in the end the
performance of the whole system is improved. We compare
our approach to conventional ad-hoc networks of different
sizes and show that nodes in WSNs are able to develop an
energy saving behaviour on their own and signicantly re-
duce network latency, when using our reinforcement learning
algorithm. External Link.
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