A Cost-Optimized, Edge-Based Smart CCTV Surveillance System for Sustainable Security in Resource-Limited Environments

This paper provides the design and implementation of a smart CCTV surveillance system, which combines the use of Internet of Things (IoT) connectivity, edge computing, and wireless communication to resolve the constraints of the conventional surveillance systems. The suggested architecture uses a mathematical model in optimal positioning of cameras, which involves field-of-view, tilt angle, and spacing formulae, and uses low-power elements to improve energy efficiency and scalability. Such functionalities as real-time motion detection, automated alert generation and hybrid cloud-edge storage (consuming less bandwidth) are key. A pilot was used in a controlled office scenario, and its performance compared to a baseline system based on traditional DVR. Quantitative findings reveal that there was a 40% reduction in manual monitoring interventions and also the amount of energy used during the normal working situation was reduced by 30%. The motion detection accuracy of the system was 92% in the field of view that the camera was configured to cover. Nevertheless, the execution of the system is determined by the stability of the network connection when operating remotely, and the edge-processing latency rises as the video stream is of high resolution. Despite these shortcomings, the framework is a cheaper and modular method of sustainable security check in urban and limited resource settings.