Multitask Age of Federated Information via Game Theoretic Distributed Control

Internet of things (IoT) applications require up-todate information about the system conditions. This can often be provided from multiple alternative sources that sense the environment, but act without centralized coordination. In this paper, we consider a scenario where multiple sources can provide information for a number of tasks of the IoT application, assuming that the information content of multiple sources is generally redundant, yet one single source is generally insufficient for all tasks. In so doing, we seek for the minimization of the age of federated information (AoFI), a metric describing the age of information from multiple sources, considering that the epochs of successful updates are only those where all tasks are covered. At the same time, we would like to contain the number of active sources for cost reasons. To this end, we tackle the problem through a game-theoretic approach, where individual sources act as players minimizing a linear combination of AoFI and activation cost. We prove that this framework identifies efficient Nash equilibria very close to the optimum performance. However, the latter can only be achieved through centralized control, whereas the former allows for distributed implementation, which is key in IoT scenarios.