2023/10/29 by Jingzhou Sun, Lehan Wang, Sun, Jingzhou +9 · 1 citation
Computer Science · Engineering · Psychology · #Age of Information Optimization #Cognitive Functions and Memory #FOS: Computer and information sciences #Information Theory (cs.IT) #IoT Networks and Protocols #Networking and Internet Architecture (cs.NI)
paper · pdf · doi:10.48550/arxiv.2310.18895
openalex publication_date 2023/10/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Intelligent real-time applications, such as video surveillance, demand intensive computation to extract status information from raw sensing data. This poses a substantial challenge in orchestrating computation and communication resources to provide fresh status information. In this paper, we consider a scenario where multiple energy-constrained devices served by an edge server. To extract status information, each device can either do the computation locally or offload it to the edge server. A scheduling policy is needed to determine when and where to compute for each device, taking into account communication and computation capabilities, as well as task-specific timeliness requirements. To that end, we first model the timeliness requirements as general penalty functions of Age of Information (AoI). A convex optimization problem is formulated to provide a lower bound of the minimum AoI penalty given system parameters. Using KKT conditions, we proposed a novel scheduling policy which evaluates status update priorities based on communication and computation delays and task-specific timeliness requirements. The proposed policy is applied to an object tracking application and carried out on a large video dataset. Simulation results show that our policy improves tracking accuracy compared with scheduling policies based on video content information.