  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.
