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Two Timescale Convergent Q-learning for Sleep--Scheduling in Wireless Sensor Networks
[article]
2014
arXiv
pre-print
In this paper, we consider an intrusion detection application for Wireless Sensor Networks (WSNs). We study the problem of scheduling the sleep times of the individual sensors to maximize the network lifetime while keeping the tracking error to a minimum. We formulate this problem as a partially-observable Markov decision process (POMDP) with continuous state-action spaces, in a manner similar to (Fuemmeler and Veeravalli [2008]). However, unlike their formulation, we consider infinite horizon
arXiv:1312.7292v2
fatcat:ktdruc6fpzerjfalepev576zxm