Game theoretic analysis of age of information in multi-source scenarios

Scheduling updates from sensors is an important task for various network systems, especially for Internet of Things (IoT) scenarios where resources are scarce. The freshness of received data is often described using the mathematical concept of Age of Information (AoI). Therefore, this research represents nodes of networks as players in a game-theoretic framework to find an optimal schedule and achieve equilibrium, which is the optimized value of Age of Information (AoI). The players have a common goal to decrease the average AoI by transmitting over the network, but they cannot communicate with each other, and they are not sure if one of them has updated the information. We investigate what happens in the system when N players transmit with or without coordination. Further, we apply Harsanyi’s equilibrium selection principle to identify strategies that collectively minimize AoI in the network. We evaluate the Price of Anarchy, which quantifies the inefficiency of selfish management of the sources. We also propose practical implementations to improve the distributed management of status updates by multiple IoT nodes.