International Journal of Science, Engineering and TechnologyAuthors: Pawar Sakshi Ajinath, Bangar Abhishek Popat, Bhosale Sachin Bajirao, Khatri Anand A.
Abstract: The rapid expansion of smart cities and urban populations has created an urgent demand for real-time, highly secure, and privacy-preserving public surveillance systems. Traditional CCTV surveillance architectures heavily rely on centralized cloud servers, making them vulnerable to single points of failure, network latency, data tampering, and severe privacy breaches. To address these critical limitations, this paper proposes a secure, decentralized surveillance framework that integrates Edge AI with private blockchain technology and InterPlanetary File System (IPFS). The proposed system utilizes lightweight deep learning models—such as YOLOv8 for high-precision real-time object detection, DeepSORT for multi-object tracking, and ArcFace for robust facial recognition— operating locally on edge devices to achieve low-latency processing without relying on centralized cloud infrastructure. To ensure strict data confidentiality and integrity, a hybrid cryptography model combining AES-256 for video metadata encryption and RSA-2048 for secure key management within a Trusted Execution Environment (TEE) is implemented. Furthermore, a private Ethereum blockchain leveraging Quorum Byzantine Fault Tolerance (QBFT) consensus and smart contracts is deployed to enforce role-based access control (RBAC) and maintain tamper-proof, immutable audit trails. Experimental results demonstrate that the proposed edgeblockchain integration significantly improves detection precision, ensures real-time performance at 60 FPS, and provides robust resistance against data tampering, offering an efficient and secure solution for next-generation intelligent urban surveillance.
