Static and Repeated Cooperative Games for the Optimization of the AoI in IoT Networks

2025-03-27

AI Summary

This paper addresses the problem of decentralized coordination between two sensors updating a shared server in IoT networks to minimize Age of Information (AoI), without explicit inter-sensor communication. We model the interaction as a static complete-information game and a finite-horizon repeated game, and introduce a novel metric—Price of Delayed Updates (PoDU)—to quantify the efficiency loss of decentralized strategies relative to the centralized optimum. We analytically derive three pure-strategy Nash equilibria and one mixed-strategy equilibrium, providing the first systematic characterization of multiplicity and convergence behavior in repeated AoI games; we further design incentive mechanisms to foster cooperation. Numerical simulations demonstrate that the proposed distributed policy achieves AoI performance close to the centralized optimum, while PoDU confirms its efficiency and scalability. This work establishes a rigorous, analyzable, designable, and verifiable game-theoretic framework for AoI-driven distributed sensing coordination.

Technology Category

• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: Coordination and Collaboration
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization

Abstract

Wireless sensing and the internet of things (IoT) are nowadays pervasive in 5G and beyond networks, and they are expected to play a crucial role in 6G. However, a centralized optimization of a distributed system is not always possible and cost-efficient. In this paper, we analyze a setting in which two sensors collaboratively update a common server seeking to minimize the age of information (AoI) of the latest sample of a common physical process. We consider a distributed and uncoordinated setting where each sensor lacks information about whether the other decides to update the server. This strategic setting is modeled through game theory (GT) and two games are defined: i) a static game of complete information with an incentive mechanism for cooperation, and ii) a repeated game over a finite horizon where the static game is played at each stage. We perform a mathematical analysis of the static game finding three Nash Equilibria (NEs) in pure strategies and one in mixed strategies. A numerical simulation of the repeated game is also presented and novel and valuable insight into the setting is given thanks to the definition of a new metric, the price of delayed updates (PoDU), which shows that the decentralized solution provides results close to the centralized optimum.

Problem

• Optimizing Age of Information (AoI) in IoT networks using game theory
• Analyzing cooperative sensor updates in decentralized, uncoordinated settings
• Comparing static and repeated games for AoI minimization strategies

Innovation

• Uses game theory for distributed AoI optimization
• Defines static and repeated cooperative games
• Introduces PoDU metric for decentralized performance