We consider the design of a fair sensor schedule for a number of sensors monitoring different linear time-invariant processes. The largest average remote estimation error among all processes is to be minimized. We first consider a general setup for the max-min fair allocation problem. By reformulating the problem as its equivalent form, we transform the fair resource allocation problem into a zero-sum game between a "judge" and a resource allocator. We propose an equilibrium seeking procedure and show that there exists a unique Nash equilibrium in pure strategy for this game. We then apply the result to the sensor scheduling problem and show that the max-min fair sensor scheduling policy can be achieved.
@article{arxiv.1902.03594,
title = {Max-Min Fair Sensor Scheduling: Game-theoretic Perspective and Algorithmic Solution},
author = {Shuang Wu and Xiaoqiang Ren and Yiguang Hong and Ling Shi},
journal= {arXiv preprint arXiv:1902.03594},
year = {2025}
}