Optimizing Task-Specific Timeliness With Edge-Assisted Scheduling for Status Update

TL;DR

This paper develops an edge-assisted scheduling policy for real-time status updates in energy-constrained devices, optimizing timeliness and improving application accuracy through a convex optimization framework and practical evaluation.

Contribution

It introduces a novel scheduling policy based on AoI penalty functions, considering communication, computation delays, and task requirements, with a convex optimization approach and real-world application.

Findings

The proposed policy reduces Age of Information penalties effectively.

Scheduling based on delays and requirements improves tracking accuracy.

Simulation confirms the policy's advantage over content-based scheduling.

Abstract

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.