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

TL;DR

This paper introduces a threshold-based censoring policy for energy-harvesting sensors, optimizing message importance transmission to enhance energy efficiency, and proposes a computationally efficient approximation method validated through numerical experiments.

Contribution

It formulates the censoring problem as an MDP, proves the optimal policy is a threshold function, and develops a faster approximation scheme compared to traditional Q-learning.

Findings

Optimal threshold policy depends on battery level.

Proposed scheme achieves similar performance with less computation.

Numerical results confirm analytical advantages in network scenarios.

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.