Centralized and Distributed Age of Information Minimization with non-linear Aging Functions in the Internet of Things

TL;DR

This paper introduces novel centralized and distributed resource allocation schemes for IoT systems that minimize non-linear age of information metrics, effectively managing heterogeneity and ensuring timely data delivery.

Contribution

It proposes new resource management schemes that incorporate non-linear AoI metrics and device heterogeneity, with proven convergence and improved performance over baseline methods.

Findings

Centralized scheme significantly reduces average AoI compared to non-learning approaches.

Distributed scheme outperforms random allocation in AoI reduction.

Centralized approach generally outperforms distributed, but distributed is more scalable for large IoT systems.

Abstract

Resource management in Internet of Things (IoT) systems is a major challenge due to the massive scale and heterogeneity of the IoT system. For instance, most IoT applications require timely delivery of collected information, which is a key challenge for the IoT. In this paper, novel centralized and distributed resource allocation schemes are proposed to enable IoT devices to share limited communication resources and to transmit IoT messages in a timely manner. In the considered system, the timeliness of information is captured using non-linear age of information (AoI) metrics that can naturally quantify the freshness of information. To model the inherent heterogeneity of the IoT system, the non-linear aging functions are defined in terms of IoT device types and message content. To minimize AoI, the proposed resource management schemes allocate the limited communication resources considering AoI. In particular, the proposed centralized scheme enables the base station to learn the device types and to determine aging functions. Moreover, the proposed distributed scheme enables the devices to share the limited communication resources based on available information on other devices and their AoI. The convergence of the proposed distributed scheme is proved, and the effectiveness in reducing the AoI with partial information is analyzed. Simulation results show that the proposed centralized scheme achieves significantly lower average instantaneous AoI when compared to simple centralized allocation without learning, while the proposed distributed scheme achieves significantly lower average instantaneous AoI when compared to random allocation. The results also show that the proposed centralized scheme outperforms the proposed distributed scheme in almost all cases, but the distributed approach is more viable for a massive IoT.