Age-driven Joint Sampling and Non-slot Based Scheduling for Industrial Internet of Things

TL;DR

This paper proposes a joint sampling and scheduling scheme for industrial IoT sensors that minimizes the maximum average age of information, balancing energy costs and queue stability, with demonstrated significant MAoI reduction.

Contribution

It introduces a novel joint sampling and non-slot based scheduling method for IoT sensors, converting a complex CMDP into an MDP for optimization, and offers a low-complexity semi-distributed scheme for multiple sensors.

Findings

Significant reduction in MAoI achieved by the proposed scheme.

Effective balance between sampling rate and service rate for multiple sensors.

The scheme outperforms existing methods in simulation results.

Abstract

Effective control of time-sensitive industrial applications depends on the real-time transmission of data from underlying sensors. Quantifying the data freshness through age of information (AoI), in this paper, we jointly design sampling and non-slot based scheduling policies to minimize the maximum time-average age of information (MAoI) among sensors with the constraints of average energy cost and finite queue stability. To overcome the intractability involving high couplings of such a complex stochastic process, we first focus on the single-sensor time-average AoI optimization problem and convert the constrained Markov decision process (CMDP) into an unconstrained Markov decision process (MDP) by the Lagrangian method. With the infinite-time average energy and AoI expression expended as the Bellman equation, the single-sensor time-average AoI optimization problem can be approached through the steady-state distribution probability. Further, we propose a low-complexity sub-optimal sampling and semi-distributed scheduling scheme for the multi-sensor scenario. The simulation results show that the proposed scheme reduces the MAoI significantly while achieving a balance between the sampling rate and service rate for multiple sensors.