An MDP Model for Censoring in Harvesting Sensors: Optimal and Approximated Solutions Articles uri icon

publication date

  • August 2015

start page

  • 1717

end page

  • 1729

issue

  • 8

volume

  • 33

international standard serial number (ISSN)

  • 0733-8716

electronic international standard serial number (EISSN)

  • 1558-0008

abstract

  • In this paper, we propose a novel censoring policy for energy-efficient transmissions in energy-harvesting sensors. The problem is formulated as an infinite-horizon Markov Decision Process (MDP). The objective to be optimized is the expected sum of the importance (utility) of all transmitted messages. Assuming that such importance can be evaluated at the transmitting node, we show that, under certain conditions on the battery model, the optimal censoring policy is a threshold function on the importance value. Specifically, messages are transmitted only if their importance is above a threshold whose value depends on the battery level. Exploiting this property, we propose a model-based stochastic scheme that approximates the optimal solution, with less computational complexity and faster convergence speed than a conventional Q-learning algorithm. Numerical experiments in single-hop and multi-hop networks confirm the analytical advantages of the proposed scheme.

keywords

  • batteries; sensors; stochastic processes; indexes; computational modeling; wireless sensor networks; equations; energy harvesting; markov processes; telecommunication power management; transmission lines; wireless sensor networks