A multi-layer deep learning framework for intelligent surveillance via edge-based big data analytics in IoT systems

Abstract

The rapid spread of IoT-enabled surveillance systems has led to an exponential growth in video data, posing significant challenges in real-time processing, scalability, and intelligent decision-making. Traditional CCTV-based systems remain largely passive, relying on human monitoring and lacking the capability to extract meaningful insights from large-scale distributed data streams. In this paper, we propose ELISA (Edge-based Layered Intelligent Surveillance Architecture), a multi-layer deep learning framework for intelligent surveillance via edge-based big data analytics in IoT environments. ELISA is structured into six layers: data collection, network communication, edge processing, face detection, behavior detection, and decision-making. The edge layer performs data preprocessing and filtering close to the source, reducing latency and bandwidth consumption while enabling real-time analytics. The face detection module identifies individuals using deep feature embeddings, while the behavior detection module recognizes activities such as falling, hugging, loitering, and phone use through a deep learning-based classifier. The decision layer integrates these outputs to generate context-aware alerts and actionable insights. Experimental evaluation on diverse datasets for face recognition and behavior classification demonstrates the effectiveness of ELISA in terms of accuracy, scalability, and computational efficiency. The results confirm its ability to process large-scale distributed video data while maintaining near real-time performance. Overall, ELISA transforms conventional surveillance into an intelligent, edge-driven, and data-centric system suitable for next-generation IoT applications.