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Optimization of information freshness in wireless networks has usually beenperformed based on queueing analysis that captures only the temporal trafficdynamics associated with the transmitters and receivers. However, the effect ofinterference, which is mainly dominated by the interferers' geographiclocations, is not well understood. In this paper, we leverage a spatiotemporalmodel, which allows one to characterize the age of information (AoI) from ajoint queueing-geometry perspective, and design a decentralized schedulingpolicy that exploits local observation to make transmission decisions thatminimize the AoI. Simulations results reveal that the proposed scheme not justlargely reduces the peak AoI but also scales well with the network size.

Locally Adaptive Scheduling Policy for Optimizing Information Freshness in Wireless Networks