SafeHer: A Smart Solution for Women Safety

Abstract

The increasing incidents of harassments and violence against women underscores the urgent need for the technology driven safety solution. This study focuses on the design and implementation of a wearable IoT-enabled safety device integrated with a mobile application to provide real-time tracking and rapid emergency alerts. This system is built on an ESP32-S3 microcontroller, leveraging the dual connectivity of Bluetooth Low Energy and the Wi-Fi to maintain seamless communication between the device and the companion app. In critical situations users are allowed to press the SOS button which sends the notification to nearby connected devices and the registered guardians with live GPS coordinates. Continuous real-time location monitoring ensures that the user’s movements are tracked and updated dynamically within the network. The proposed design emphasizes low latency, high reliability, and user-centred functionalities establishing a community-based safety network capable of immediate response. By integrating the IoT communication and real-time geolocation the system provides an efficient, scalable, and accessible safety mechanism that enhances women security and the confidence in public environment.

Introduction

Women’s safety remains a critical issue, especially in areas with limited access to immediate help. Traditional safety devices often rely on manual activation or stable network connections, causing delays in emergencies. To address this, the SafeHer system implements an IoT-based real-time safety solution using the ESP32-S3 microcontroller, which integrates Wi-Fi and BLE for fast, low-power communication.

The system combines hardware reliability, mobile app integration, and cloud support to ensure rapid alert delivery. When a user presses the SOS button, the device sends real-time location and alert signals to nearby users via BLE mesh and, if needed, escalates to server-based notifications to trusted contacts and authorities. The layered architecture includes:

• Hardware Layer: ESP32-S3, GPS module, microphone, SOS button, buzzer.

• Network Layer: BLE for proximity alerts, Wi-Fi Direct for larger data, HTTP, push notifications, and SMS for redundancy.

• Server Layer: Centralized database, alert processing, logging, and analytics.

• Mobile Application Layer: User management, alert monitoring, manual SOS triggering, and communication with trusted contacts.

The system ensures low latency alerts (≈1.15 s for BLE proximity alerts) and reliable detection even in weak network conditions. Device design emphasizes compactness, wearability, and durability (IP67-certified enclosure). Overall, SafeHer provides a fast, reliable, and community-driven safety network, improving the chances of immediate help during emergencies.

Conclusion

The SafeHer system demonstrates the effectiveness of a multi-layered architecture combining Proximity and Server Alerts, ensuring both rapid and reliable emergency notification. The Proximity Alert, using BLE communication between the ESP32 and nearby mobile devices, provides low-latency local response, making it highly effective in urban or indoor areas. However, its performance is sensitive to physical obstructions and RF interference, which can reduce acknowledgement rates in indoor or crowded environments. The Server Alert serves as a reliable backup, using HTTP-based cloud communication and push notifications or SMS to guarantee alert delivery to trusted contacts and nearby app users, even when local BLE fails. The ESP32’s dual-core processor and integrated BLE and Wi-Fi modules enable simultaneous local and cloud operations, while its audio recording capability provides contextual evidence. The relational database structure, including fields such as ack_count and auto_escalated, allows systematic tracking of alert outcomes and enhances security by separating authentication and user profile data. The mobile application further improves usability, offering manual Trigger Alerts, Receiver Mode, and geo-fencing for danger zones, with potential for future enhancements using machine learning to adapt thresholds and predict high-risk areas. Overall, the dual-alert mechanism and thoughtful hardware-software integration make SafeHer a robust, responsive, and user-friendly personal safety system. The SafeHer system demonstrates a robust and reliable approach to personal safety by integrating hardware (ESP32), network protocols (BLE, HTTP), and cloud services into a low-latency, high-reliability emergency alert framework. The dual-alert mechanism ensures immediate local response through the Proximity Alert (average latency ? 1.15 s) while automatically escalating to the Server Alert (average latency ? 3.52 s for push notifications) when local wireless communication fails due to obstructions or high interference, minimizing the risk of dropped alerts. The relational database schema supports efficient querying and post-incident analysis by storing critical fields such as ack_count, auto_escalated status, and voice_file_path. Integration of GPS and voice recording further enhances emergency response by providing accurate location data (average error ? 4.5 m) and contextual audio evidence. Overall, the implemented system effectively balances speed, reliability, and actionable information, providing a resilient framework for personal safety. Future enhancements aim to improve the system’s intelligence, adaptability, and integration with external safety infrastructure. Adaptive thresholding and geo-fencing using machine learning could dynamically adjust the Proximity Alert threshold based on environmental conditions and historical ACK data, ensuring timely escalation in high-risk or low-population areas. Response tracking can create a closed-loop feedback system by logging responder actions such as “On the Way,” improving situational awareness. Low-power communication options like LoRaWAN or NB-IoT could provide long-range, energy-efficient alternatives when Wi-Fi is unavailable. Finally, a secure API connection to regional emergency services (e.g., 911 or 112) would enable automated transmission of validated alert data and precise location information, further enhancing the system’s utility and effectiveness in real-world emergency scenarios.

References

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Copyright

Copyright © 2025 Suprabha B N, Sriraksha K R, Navile Nageshwara Naveen, Arya K, Bhavana M J. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.