The University of Connecticut supports thousands of students and faculty who rely on its campus WiFi network every day. With over 1,000 wireless access points (APs) deployed across campus, monitoring network performance and coverage is a major challenge for administrators. One current monitoring method is wardriving, where administrators move around campus with wireless devices to collect signal strength and connectivity data. While useful, this approach is labor-intensive, time-consuming, and only collects data at specific times. Because of this, wardriving does not provide continuous monitoring and makes it difficult to quickly detect network issues. To improve this process, our team proposes a mobile monitoring application that collects network measurement data through users’ smartphones. The app would periodically record network performance information and send it to a backend server for analysis. Running in the background, the app would allow continuous and scalable data collection across campus without requiring administrators to manually gather measurements. However, collecting accurate data often requires location information, which raises privacy and security concerns. If raw location data were stored or transmitted without protection, users could be re-identified, sensitive locations could be exposed, malicious actors could access the data, and individual movement patterns could potentially be reconstructed. To address these risks, the system incorporates privacy protections designed to safeguard sensitive user information. By protecting location data while still allowing analysis of general campus areas, the system enables effective network monitoring without compromising individual privacy.