Age-of-Information Oriented Scheduling for Multichannel IoT Systems With Correlated Sources

Abstract

Age-of-information (AoI) based minimization problems have been widely considered in Internet-of-Things (IoT) networks with the settings of multi-source single-channel systems and multi-source multi-channel systems. Most existing works are limited to either the case of identical multi-channel or independent sources. In this paper, we study this problem under the identical and non-identical multi-channel, as well as the correlated sources setting. This correlation defines the case when updating a source’s AoI; others correlated to this one will also reveal partial information. To tackle this AoI-based minimization problem, we formulate it as a correlated restless multi-armed bandit (CRMAB) problem. By decoupling the CRMAB problem into <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> independent single-armed bandit problems, we derive the closed-form expressions of the generalized Whittle index (GWI) and the generalized partial Whittle index (GPWI) under the identical channel and the non-identical channel settings, respectively. Then, we put forth the GWI-based and GPWI-based scheduling policies to solve this AoI-based minimization problem. In addition, we provide two lower numerical performance bounds for the proposed policies by solving the relaxed Lagrange problem of the decoupled CRMAB. Numerical results show that the proposed policies can achieve these lower bounds and outperform the state-of-the-art scheduling policies. Compared with the case of independent sources, the performance of the proposed policies in the case of correlated sources improves significantly, especially in high-density networks.