AoI-Based Scheduling of Correlated Sources for Timely Inference

We consider a setting where multiple correlated sources send real-time observations over a wireless communication channel to a receiver. The receiver uses the delivered observations to infer multiple time-varying targets. Due to limited communication resources, these observations may not always be fresh. To quantify data timeliness, we utilize the Age of Information (AoI) metric. Our goal is to minimize realtime inference error by developing signal-agnostic scheduling policies that leverage AoI without requiring knowledge of the actual target values or the specific source observations. For the two-source case, we obtain an optimal cyclic policy with low computational complexity. For more than two-sources, we establish an information-theoretic lower bound on inference error. Building upon this lower bound, we approximate the scheduling problem and propose an approximate Whittle index policy that is asymptotically optimal as the number of sources increases and the correlation among sources decreases. Our scheduling policies hold for arbitrary target and source processes and loss functions. Finally, we conduct simulations of a network of cameras with overlapping field of views tracking multiple mobile objects to demonstrate the effectiveness of our policies.