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

arXiv: https://arxiv.org/abs/2310.18895 · HTML: https://mlsanity.com/paper/2310.18895/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Oct 2023.

## Built on by (most cited first, 3 of 3)

- [Age-Based Scheduling for Mobile Edge Computing: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2312.00279/) (2023)
- [Timeliness-Oriented Scheduling and Resource Allocation in Multi-Region Collaborative Perception](https://mlsanity.com/paper/2601.04542/) (2026)
- [TALSC: Timeliness-Aware Large-Small VLM Collaboration for Infrastructure-Assisted Autonomous Driving](https://mlsanity.com/paper/2608.01998/) (2026)

## Abstract (arXiv)

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.