A Comprehensive Digital Safety Framework for Travelers Using Multi-Stage AI and Decentralized Communication

Abstract

Traditional traveller safety systems are fundamentally reactive, relying on manual SOS triggers or \"overdue\" reports that often fail to initiate response efforts within the critical \"Golden Hour.\" This operational gap is most severe in remote, \"off-grid\" environments where the lack of cellular and road infrastructure renders standard navigation and safety tools functionally non-existent. To address these limitations, this paper proposes a proactive monitoring ecosystem designed for high-altitude, unstructured terrains. The framework moves away from human-initiated alerts toward an autonomous behaviour analysis engine that identifies distress signals in real-time. The core of the system is a dual-model machine learning pipeline: Isolation Forest is utilized to detect individual point-anomalies, such as falls or medical incapacitation, while DBSCAN (Density-Based Spatial Clustering of Applications with Noise) identifies collective group-level hazards like trail blockages or landslides. To ensure resilience in signal-free \"dead zones,\" the system utilizes a decentralized verification mesh employing Bluetooth Low Energy (BLE) \"data mule\" logic to propagate distress packets. Furthermore, a blockchain-backed Self-Sovereign Identity (SSI) layer is integrated to manage temporary, tamper-proof digital IDs, ensuring data sovereignty and privacy while facilitating automated legal reporting (E-FIR). By utilizing a five-stage progressive verification protocol to mitigate alert fatigue, this framework aims to reduce emergency response times from hours to minutes, transforming the wilderness into a context-aware smart safety corridor.

Introduction

The text presents the Safe Yatra framework, a proactive AI-based tourist safety system designed to reduce delays in search-and-rescue operations in remote mountainous regions.

Current tourist safety systems suffer from a major “reactive lag”, where rescue begins only after a delay or missed checkpoint, which is dangerous in high-altitude environments where medical conditions (like AMS or HAPE) and terrain challenges require rapid response. These delays are worsened by poor connectivity in “dead zones,” making traditional mobile-based SOS systems ineffective.

A second major issue is alert fatigue, where existing monitoring systems generate too many false alarms, causing users and authorities to ignore alerts or disable systems altogether. To address this, the paper emphasizes human-centered design, using progressive, tiered verification instead of immediate emergency alerts.

To solve these problems, the Safe Yatra system is proposed. It uses unsupervised machine learning and multimodal sensing to detect distress proactively without user input. The system combines:

• Isolation Forest for detecting anomalies in individual traveler movement (e.g., sudden stops, falls)
• DBSCAN for identifying group-level risks like landslides or group dispersal

A 5-stage progressive verification protocol filters false alarms:

1. Background monitoring
2. Subtle haptic check-in
3. Active user confirmation
4. Escalation via peer-to-peer mesh alerts
5. Full rescue dispatch with medical data sharing

The system operates through a three-layer architecture:

• Mobile device layer (sensor-based edge detection)
• Backend coordination layer (regional clustering and analytics)
• Authority dashboard layer (rescue monitoring using risk visualization)

To function in no-network regions, it uses a Bluetooth Low Energy (BLE) mesh network, allowing nearby devices to forward emergency data until it reaches connectivity.

Additional innovations include:

• An Itinerary Contract that defines safe movement corridors using GPS paths
• A Hiking Effort Index (HEI) to model terrain difficulty and human effort
• A blockchain-based identity system (SSI) for secure and private emergency data sharing
• Automated E-FIR generation for faster legal and rescue response

Conclusion

This study has presented Safe Yatra, a proactive monitoring framework designed to bridge the survival gap in remote, \"off-grid\" environments. By moving from a reactive \"overdue-based\" model to an autonomous \"sensing\" model, the system addresses the two primary failures of contemporary safety infrastructure: the \"Reactive Lag\" and \"Alert Fatigue.\" The integration of the Safety Matrix—utilizing Hypotenuse Velocity V_H, Cross-Track Error (XTE), and Sinuosity—enables the detection of distress with a degree of context-awareness previously missing in standard navigation tools [21, 23, 25]. Furthermore, by grounding the system in an SSI-based Privacy Layer and a Decentralized P2P Mesh Network, Safe Yatra ensures that safety does not come at the cost of personal privacy or infrastructure dependence. This framework provides Destination Management Organizations (DMOs) with a scalable blueprint to transform \"Dead Zones\" into \"Smart Safety Corridors,\" potentially reducing emergency response times from several hours to minutes [1, 12, 13].

References

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Copyright

Copyright © 2026 Dushyant Kumar, Bhumika Sharma, Naveen Sharma, Pradeep . 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.