Locally Adaptive Scheduling Policy for Optimizing Information Freshness\n in Wireless Networks

Optimization of information freshness in wireless networks has usually been\nperformed based on queueing analysis that captures only the temporal traffic\ndynamics associated with the transmitters and receivers. However, the effect of\ninterference, which is mainly dominated by the interferers' geographic\nlocations, is not well understood. In this paper, we leverage a spatiotemporal\nmodel, which allows one to characterize the age of information (AoI) from a\njoint queueing-geometry perspective, and design a decentralized scheduling\npolicy that exploits local observation to make transmission decisions that\nminimize the AoI. Simulations results reveal that the proposed scheme not just\nlargely reduces the peak AoI but also scales well with the network size.\n