This study addressed the growing problem of mobile phone theft by developing an intelligent tracking system that utilizes the International Mobile Equipment Identity Code (IMEIC) and geofencing techniques. The system was designed to accurately identify and locate lost or stolen mobile devices in real time by combining hardware-level identification with geographic boundary enforcement. The IMEIC served as a unique fingerprint for each phone, allowing the system to trace the device across different networks regardless of changes in SIM cards or user accounts. Geofencing was employed to create virtual boundaries, enabling the system to monitor whether the device remained within a defined safe zone or had crossed into unauthorized areas. Once the IMEIC was submitted through a user-friendly web interface, the system retrieved the latest known location of the phone based on latitude and longitude data and plotted the results on Google Maps. This allowed the user to visually track the device’s location and receive timely alerts, even via an alternative phone number. The system’s performance was evaluated using classification metrics such as accuracy, precision, recall, and F1 score, along with spatial accuracy measures based on real GPS data. The results showed high reliability, with accurate detection of geofence breaches and minimal errors in classification. By integrating real-time geolocation tracking with secure IMEIC-based identification, the system offered an effective and responsive solution for mobile phone theft mitigation. This work contributes significantly to mobile security applications by demonstrating how geospatial intelligence and hardware-level recognition can be combined into a practical, real-world model for protecting personal and institutional mobile assets. The Object-Oriented System Development Methodology (OOSDM) was adopted to model system components and ensure modular implementation. The system architecture comprises a secure IMEI registration database, GPS-enabled geofencing engine, and an interactive map interface for visualizing device movement. Technologies deployed include HTML, CSS, and JavaScript for front-end development; PHP for backend logic; MySQL for database management; and the Google Maps API for location visualization. Performance evaluation of the system revealed high operational efficiency, with an average accuracy of 92.4%, precision of 90.6%, recall of 91.4%, and an F1 score of 91%. The system demonstrated resilience under varying network conditions and received positive usability feedback from test users. Key recommendations from the study include establishing a centralized national IMEI registry accessible to telecom operators and law enforcement agencies, implementing public sensitization campaigns on IMEI awareness and registration, and integrating the system with existing mobile infrastructure to improve response time and recovery rates. Future work should explore offline-capable tracking modules, machine learning-based movement prediction, and regional cooperation frameworks to address cross-border phone theft challenges.
Igbudu et al. (Thu,) studied this question.