Balancing Current and Historical State Information in Remote Tracking Systems: A Randomized Update Approach

The traditional goal in remote tracking of a dynamic source is to keep the current estimate at the destination as close as possible to the true state. However, in domains such as surveillance applications, the destination is also interested in reconstructing the past trajectory of states for further processing. This requires striking a balance between providing current versus past state information so that the destination can optimize the trade-off between the metrics of freshness and reconstruction queue length. In this work, we propose a randomized update policy that decides between head-of-line versus tail-of-line packets in the update queue. As such, our policy combines the strength of Last-Come-First-Serve (LCFS) service discipline (which aims at reducing the age) with the strength of First-Come-First-Serve (FCFS) service discipline (which aims at reducing the reconstruction delay). We evaluate the performance of our proposed policy in terms of its randomization parameter, which can be optimized given the system parameters to achieve a better trade-off.