This paper presents a survey on the adoption of Reinforcement Learning (RL) approaches for power management in Wireless Sensor Networks (WSNs). The survey has been carried out after a review expressly focused on the most relevant and the most recent contributions for the topic. Moreover, the analysis encompassed proposals at every methodological level, from dynamic power management to adaptive autonomous middleware, from self learning scheduling to energy efficient routing protocols.

A bird's eye view on reinforcement learning approaches for power management in WSNs

RUCCO, LUIGI;BONARINI, ANDREA;BRANDOLESE, CARLO;FORNACIARI, WILLIAM
2013

Abstract

This paper presents a survey on the adoption of Reinforcement Learning (RL) approaches for power management in Wireless Sensor Networks (WSNs). The survey has been carried out after a review expressly focused on the most relevant and the most recent contributions for the topic. Moreover, the analysis encompassed proposals at every methodological level, from dynamic power management to adaptive autonomous middleware, from self learning scheduling to energy efficient routing protocols.
Wireless and Mobile Networking Conference (WMNC), 2013 6th Joint IFIP
978-1-4673-5614-5
978-1-4673-5615-2
978-1-4673-5616-9
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11311/733004
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