Women Safety Mobile Application Using ESP32 Camera and Real-Time Crime Data Analysis

The rising cases of harassment and assault against women have become a significant global concern, emphasizing the urgent need for effective, technology- driven safety solutions. This research presents a novel Women Safety Mobile Application integrated with real- time location tracking, crime data analytics, emergency communication, and an IoT-based ESP32 camera module for visual evidence collection. The system combines Android-based application design, Google Maps API, Firebase cloud storage, and ESP32-CAM hardware to provide a comprehensive safety ecosystem. It enables users to send automated SOS alerts containing live GPS coordinates and captures time-stamped images during emergencies. Furthermore, the system analyzes area-wise crime intensity using historical and real-time data, classifying zones into low, medium, or high-risk categories. The integration of IoT and mobile computing provides a cost-effective, efficient, and scalable model that enhances women’s personal security and situational awareness. Experimental results demonstrate high system reliability, rapid SOS response (under six seconds), and accurate crime-zone classification with 93% precision. This research contributes to the field of smart safety technologies by combining IoT, cloud computing, and data analytics to create an intelligent, real-time personal protection framework.