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@article{202990,
        author = {Ruturaj Shankar Bharade and Ritesh Laxman Gavade and Prathamesh Dilip Kumbhar and Satyajit Vitthal Redekar and Bhushan Haridas Ukarande and Prof. Pallavi D. Patil},
        title = {Smart Tourist Safety Monitoring and Incident Response System Using Artificial Intelligence, Geo-Fencing, and Blockchain-Based Digital Identity},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {9670-9676},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=202990},
        abstract = {Tourist safety in the remote terrains of northeastern India — particularly in Meghalaya, Manipur, and Arunachal Pradesh — remains a serious and largely unresolved operational challenge. Conventional policing and manual tracking mechanisms are inadequate for ensuring real-time tourist safety across expansive and difficult terrains. We developed Smart Tourist Safety Monitoring and Incident Response System — combining AI anomaly detection, geo-fencing, and blockchain digital identity — to directly address this gap in tourist safety infrastructure. The proposed system encompasses a mobile application for tourists, a web-based command dashboard for law enforcement and tourism authorities, a blockchain-secured digital identity platform, AI-driven anomaly detection for behavioural pattern analysis, and optional IoT wearable integration. Core features include a panic button with live GPS dispatch, automated geo-fence violation alerts, real-time family tracking, automated E-FIR generation, and a multilingual interface. The system architecture employs React Native for the mobile frontend, Node.js for the backend API, PostgreSQL with Firebase for data storage, Polygon blockchain for tamper-proof identity records, and a Python-based FastAPI microservice for AI anomaly detection. We tested on 2,400 synthetic GPS trajectories from northeastern Indian terrain. The LSTM anomaly detector achieved 91.4% precision and 88.7% recall, and panic alerts reached police dashboards within 2.3 seconds on average. Results are simulation-based; a real-world pilot deployment is planned as the next validation step, in collaboration with tourism and police authorities.},
        keywords = {Tourist Safety; Geo-Fencing; Blockchain; Digital Identity; Artificial Intelligence; Anomaly Detection; Panic Button; E-FIR; IoT; React Native; Multilingual Support},
        month = {May},
        }