Dynamic Age Minimization With Real-Time Information Preprocessing for Edge-Assisted IoT Devices With Energy Harvesting

Abstract

For many Internet of Things (IoT) applications, the freshness of status information is of great importance, and age of information (AoI) is a newly proposed metric to quantify the freshness of system status. However, in many cases, the original raw data collected by IoT devices needs to be preprocessed in real-time to extract the hidden effective information, which is usually computationally intensive and time consuming. To this end, we promote an edge computing assisted approach and aim to reduce the AoI by flexibly offloading the raw IoT data to the edge server for information preprocessing. We consider that the IoT devices can opportunistically collect extra energy through energy harvesting for sustainable operations, and propose a novel timely system status update model that consists of multiple IoT devices with energy harvesting and edge-assisted information preprocessing. The objective is to minimize the system-wide average AoI under a fixed energy cost budget. To tackle the key challenges due to the unpredictability of the stochastic energy harvesting process and the long-term energy constraints, we propose a Lyapunov-based average AoI Minimization (LAoIM) algorithm to derive an approximate optimal solution, and further quantify the performance gap from the optimal solution. Extensive numerical evaluations demonstrate that LAoIM can take full advantages of local and edge computation resources and achieve superior performance gain over existing schemes.