Digital twin-enabled age of information-aware scheduling for Industrial IoT edge networks

Abstract

Mobile Edge Computing (MEC) is a significant technology employed in the development of the Industrial Internet of Things (IIoT) as it allows the collection and processing of high volumes of data at the network edge to support industrial processes and improve operational efficiency and productivity. However, despite significant advances in MEC capabilities, the stringent latency requirement that may occur in computation-intensive tasks may affect the freshness of status information. Therefore, there are practical challenges in scheduling the tasks associated with computational efficiency in local computation and remote computation. In this context, we propose an Age of Information (AoI)-based scheduler to determine where to execute computational tasks in order to continuously track state data updates, where the AoI metric measures the time elapsed from the generation of the computation task at the source to the latest received update at the destination. The contributions of this paper are threefold: First, we propose a digital twin-enabled AoI-based scheduler model that collects real-time data from IIoT nodes and predicts the best task assignment in terms of local computation and remote computation. The digital twin environment allows monitoring of the state changes of the real physical assets over time and optimizes the scheduling strategy. Second, we formulate the average AoI problem with the M/M/1 queueing model and propose a genetic algorithm-based scheduler to minimize AoI and task completion time to efficiently schedule the computation tasks between IIoT devices and MEC servers. Third, we compare the performance of our digital twin-enabled model with the traditional strategies and make a significant contribution to IIoT edge network management by analyzing AoI, task completion time and MEC server utilization.