# Locally Adaptive Scheduling Policy for Optimizing Information Freshness in Wireless Networks

## TL;DR

This paper introduces a decentralized scheduling policy for wireless networks that uses local observations and a spatiotemporal model to effectively minimize the age of information, accounting for interference effects.

## Contribution

It presents a novel joint queueing-geometry model and a decentralized scheduling policy that significantly reduces AoI and scales efficiently with network size.

## Key findings

- Reduces peak AoI significantly
- Scales well with network size
- Outperforms existing methods in simulations

## Abstract

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