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Dynamic AI-Geofencing: Secure and Efficient Edge-Cloud Frameworks

Authors:

Kaushal Pratap Singh, Vishesh Singh, Rounak Kumar, Hirdesh Sharma

Abstract – This study presents a novel framework integrating geofencing with edge artificial intelligence (AI) to address latency, adaptability, and ethical challenges in real-time security and operational systems. By leveraging reinforcement learning (RL) for dynamic geofence optimization and TinyML models for localized anomaly detection, the proposed three-tier architecture reduces decision-making latency to ≤230 ms, a 35% improvement over traditional cloud-dependent systems. Case studies in urban logistics and predictive policing demonstrate 18–25% reductions in operational costs through AI-driven resource allocation, validated via field trials with GPS-enabled fleets and crime datasets from high-risk zones. Ethical considerations are embedded into the design, employing differential privacy (ε=0.5) for location anonymization and SHAP-based audits to mitigate demographic bias in patrol allocation. The framework adheres to IEEE’s Ethically Aligned Design principles and India’s DPDP Act (2023), ensuring compliance with emerging data protection norms. Results underscore the viability of adaptive geofencing systems for smart cities while providing actionable guidelines for balancing efficiency with ethical responsibility.

Index Terms – Adaptive Geofencing, Edge AI, Reinforcement Learning, Ethical AI, Operational Efficiency