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Yatheesh KC,
Poorvi Raghav,
Abhyuday Singh,
Srinivas Pandey,
- Associate Professor, Department of Electronics & Communication Engineering, JSS Academy of Technical Education, Noida, Uttar Pradesh, India
- Student, Department of Electronics & Communication Engineering, JSS Academy of Technical Education, Noida, Uttar Pradesh, India
- Student, Department of Electronics & Communication Engineering, JSS Academy of Technical Education, Noida, Uttar Pradesh, India
- Student, Department of Electronics & Communication Engineering, JSS Academy of Technical Education, Noida, Uttar Pradesh, India
Abstract
The safety of women in urban settings is an urgent societal concern. Most existing safety solutions typically focus on a single aspect – either emergency notifications or environmental awareness; often failing to combine both into a unified system. This paper presents a comprehensive women’s safety platform that includes an Internet of Things (IoT) wearable device, a cloud-based web application, and data analytics. The wearable device is designed around the ESP32-CAM microcontroller, a NEO-6M GPS module, and a panic button to obtain location data and visual evidence in case of emergencies. The backend is developed using the MERN stack (MongoDB, Express.js, React.js, Node.js) and integrates real-time notifications, crowdsourced safety information, and historical crime data to create safety heatmaps and suggest safer routes. Additionally, the system features machine learning-based prediction of crime hotspots by utilizing spatial and temporal data. Experimental evaluation demonstrates low-latency alert transmission, accurate GPS tracking, and informative dynamic safety dashboards. The proposed platform delivers both reactive emergency support and proactive risk awareness, constituting a scalable architecture for smart urban safety systems. Furthermore, the integration of geospatial information enables the visualization of location-based safety patterns, helping users identify potentially high-risk areas. The proposed system also highlights the role of remote sensing and GIS-based analysis in developing location-aware safety applications. Its combined hardware and software architecture provides a foundation for future improvements in urban safety monitoring and emergency response.
Keywords: Women Safety; Internet of Things; MERN Stack; Crime Prediction; Geospatial Analytics
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Journal of Remote Sensing & GIS
| Volume | 17 | |
| 03 | ||
| Received | 14/09/2026 | |
| Accepted | 21/10/2026 | |
| Published | 30/10/2026 | |
| Publication Time | 46 Days |