Performance-Aware Wi-Fi Problem Diagnosis and Mitigation through Peer-to-Peer Data Sharing

Large-scale, high-density Wi-Fi networks use hundreds of access points to serve thousands of closely-packed users within a large physical space, such as within a stadium or arena. It is difficult to predict when and where problems will occur in these Wi-Fi networks, due to the constant movement of mobile devices within the network and the constantly-changing workload as users switch between applications. In this paper, we describe a unique approach to detecting, diagnosing, and mitigating problems in Wi-Fi networks using Wi-Fi performance data collected from mobile devices and shared between nearby peers. Our approach draws upon 3 years of production performance data that we have collected from 35 production mobile applications used in 25 professional and collegiate sports venues in the US. We also present an evaluation of the effectiveness of our diagnostic and mitigation approach in a real-world high-density Wi-Fi environment, showing that our approach outperforms standard driver-based problem detection and mitigation on several common Wi-Fi faults.