Sleep–Wake Sensor Scheduling for Minimizing AoI-Penalty in Industrial Internet of Things
Jia Wang, Xianghui Cao, Bo Yin, Yu Cheng
Abstract
Ensuring data freshness is important for Industrial Internet of Things (IIoT). In this article, we consider a typical IIoT application where multiple sensors monitor some time-varying physical processes and report measurements to a central base station through an unreliable wireless channel. In order to save energy, each sensor may switch to sleep mode for a while after successfully transmitting a packet. Since only awake sensors are able to transmit data, we propose a novel function of Age of Information with penalty (or AoI-penalty) to capture the eagerness of the active sensors to provide fresh information. We formulate a new AoI-penalty minimization problem for scheduling the sensors’ transmissions. We theoretically derive a necessary condition for the system’s AoI-penalty to converge to a finite value, and further obtain a lower bound of the AoI-penalty. Moreover, we develop a max-weight-based scheduling policy and theoretically prove that it is the optimal policy when the network is symmetric and the channel is error-free. Simulation results demonstrate that the proposed policy achieves AoI performance near to the lower bound and that with such sleep–wake sensors, the achieved AoI performance is close to that with nonsleeping sensors but at a much lower energy cost.