Task Offloading Strategy for Mobile Edge Computing in Industrial Internet of Things

Abstract

The rapid advancement of the Industrial Internet of Things (IIoT) has facilitated real-time information exchange and accelerated data analysis and processing. Nevertheless, the progress of IIoT is significantly impeded by its constrained resources and other inherent limitations. To address these challenges, Mobile Edge Computing (MEC) technology has been integrated into the IIoT environment to decrease data processing latency and enhance the network's computational capacity. In this study, we propose a joint task offloading and user association optimization algorithm to reduce the system's average information age and improve data timeliness. Initially, we present a multi-constraint problem that combines task offloading and user association to minimize the Age of Information (AoI). To tackle this issue, we develop an iterative algorithm based on Block Coordinate Descent (BCD) techniques. Subsequently, we provide simulations to demonstrate the efficacy of the proposed algorithm in this study.