Abstract
In this work we present a reinforcement learning algorithm that aims to increase the autonomous lifetime of a Wireless Sensor Network (WSN) and decrease its latency in a decentralized 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 decentralized 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 significantly reduce network latency, when using our reinforcement learning algorithm.
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Mihaylov, M., Tuyls, K., Nowé, A. (2010). Decentralized Learning in Wireless Sensor Networks. In: Taylor, M.E., Tuyls, K. (eds) Adaptive and Learning Agents. ALA 2009. Lecture Notes in Computer Science(), vol 5924. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11814-2_4
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DOI: https://doi.org/10.1007/978-3-642-11814-2_4
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