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Intelligent real-time applications, such as video surveillance, demandintensive computation to extract status information from raw sensing data. Thisposes a substantial challenge in orchestrating computation and communicationresources to provide fresh status information. In this paper, we consider ascenario where multiple energy-constrained devices served by an edge server. Toextract status information, each device can either do the computation locallyor offload it to the edge server. A scheduling policy is needed to determinewhen and where to compute for each device, taking into account communicationand computation capabilities, as well as task-specific timeliness requirements.To that end, we first model the timeliness requirements as general penaltyfunctions of Age of Information (AoI). A convex optimization problem isformulated to provide a lower bound of the minimum AoI penalty given systemparameters. Using KKT conditions, we proposed a novel scheduling policy whichevaluates status update priorities based on communication and computationdelays and task-specific timeliness requirements. The proposed policy isapplied to an object tracking application and carried out on a large videodataset. Simulation results show that our policy improves tracking accuracycompared with scheduling policies based on video content information.

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