# A Markov Game of Age of Information From Strategic Sources With Full Online Information

## TL;DR

This paper models strategic sources updating information in a network as a Markov game, deriving Nash equilibria and practical policies, and compares centralized and distributed approaches through simulations.

## Contribution

It introduces a Markov game framework for age of information updates with full online awareness, deriving equilibrium policies for strategic sources.

## Key findings

- Nash equilibrium strategies are characterized for the game.
- Practical threshold policies are derived from the equilibrium analysis.
- Distributed approaches with different objectives are compared to centralized optimal policies.

## Abstract

We investigate the performance of concurrent remote sensing from independent strategic sources, whose goal is to minimize a linear combination of the freshness of information and the updating cost. In the literature, this is often investigated from a static perspective of setting the update rate of the sources a priori, either in a centralized optimal way or with a distributed game-theoretic approach. However, we argue that truly rational sources would better make such a decision with full awareness of the current age of information, resulting in a more efficient implementation of the updating policies. To this end, we investigate the scenario where sources independently perform a stateful optimization of their objective. Their strategic character leads to the formalization of this problem as a Markov game, for which we find the resulting Nash equilibrium. This can be translated into practical smooth threshold policies for their update. The results are eventually tested in a sample scenario, comparing a centralized optimal approach with two distributed approaches with different objectives for the players.