Optimal Sleep Scheduling for Energy-Efficient AoI Optimization in Industrial Internet of Things

Abstract

Keeping sensor data fresh is desired for industrial Internet of Things (IIoT) especially in real-time monitoring applications. However, this may require sensors always in active mode and thus incur low energy efficiency. In this paper, we consider that a wireless sensor monitors a dynamical system and reports real-time measurements to a processing center through an unreliable wireless channel. We study the problem of optimizing the sensor data freshness in terms of Age of Information (AoI) while saving energy by scheduling the sensor to sleep when needed. The problem is formulated as a Markov decision process that takes both AoI and energy consumption into account, to which we theoretically prove that the optimal scheduling policy forms a cyclic sleep-wake pattern. The optimal sleep period is also analyzed. Simulation results demonstrate that the proposed scheduling policy outperforms other existing policies.