Information Cofreshness-Aware Grant Assignment and Transmission Scheduling for Internet of Things

Abstract

The proliferation of Internet of Things (IoT) applications has prompted the continuous increase of research efforts in recent years. In light of the diversified use cases and service requirements, the information freshness [or Age of Information (AoI)] of IoT data is key for latency-sensitive IoT applications (e.g., industrial automation and intelligent transportation) because stale information may lead to delayed responses and catastrophic outcomes. In addition, various types of IoT applications require the analytics of IoT data collected from their constituent IoT devices. While previous AoI-related works have analyzed or optimized the information freshness of various communication systems, the problem that the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">information cofreshness</i> [or Coage of Information (CoI)] of an IoT application is determined by the maximum AoI of the constituent IoT devices has been rarely investigated. In this article, we address the grant assignment and transmission scheduling (GATS) problem for IoT and formulate it as an integer linear program (ILP) to minimize a weighted sum of CoI. Due to the intractability of the original GATS problem, we transform it to an equivalent problem of the maximization of the number of the eliminated age blocks. Then, we propose the CoI-aware age block elimination (CABEL) algorithm in which information updates are selected progressively according to their coage efficiency (CE) values and prove that the achieved approximation factor depends on the relative service costs and uplink delays. Our simulation results demonstrate that the proposed solution can effectively perform information updates and utilize service budgets, thereby achieving low CoI compared with the existing solutions under various parameter settings.