Title: Track My Device With Ai Assistant System
Authors: Fadhili Michael Clever, Said Haruna Shabani, Benjamin Fredy Nkanem, John Simon, Paul Masanja, Mpanduji Juma, Goodluck Mangula, Logatho Benedict
Volume: 10
Issue: 4
Pages: 77-83
Publication Date: 2026/04/28
Abstract:
The increasing reliance on mobile devices such as smartphones, tablets, and laptops has intensified concerns regarding device security, tracking, and recovery. The loss of a mobile device may result in serious consequences, including data exposure, financial loss, interruption of communication, and violation of privacy. Although existing tracking and security solutions provide certain levels of protection, many of them are limited by weak predictive capability, fragmented functionality, and inadequate support for critical events such as SIM card replacement, device shutdown, unauthorized movement, and low battery conditions. Consequently, there is a need for an intelligent and integrated device tracking system capable of offering effective monitoring and recovery support. This review paper examines existing device tracking and security systems with the aim of identifying key research trends, limitations, and gaps that affect their usability and effectiveness. The review focuses on challenges related to poor performance in low-connectivity environments, limited real-time intelligence, lack of conversational interaction, insufficient multi-device management, and weak integration of anti-theft mechanisms. Based on these observations, the study motivates the development of a Track My Device with AI Assistant system that integrates real-time GPS tracking, machine learning-based movement prediction, geofencing alerts, SIM change notifications, remote lock, alarm activation, low battery alerts, and an AI chatbot within a unified platform. The proposed system is intended to strengthen device security, improve recovery prospects, support real-time monitoring, and provide users with a practical and intelligent means of managing multiple devices.