Max-Min Fair Sensor Scheduling: Game-theoretic Perspective and Algorithmic Solution
Provides a game-theoretic framework for fair resource allocation in sensor networks, offering a principled solution for minimizing worst-case estimation error.
This paper addresses the max-min fair sensor scheduling problem for multiple linear time-invariant processes, aiming to minimize the largest average remote estimation error. By reformulating the problem as a zero-sum game, they prove the existence of a unique Nash equilibrium and derive a fair scheduling policy.
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.