IoT-Enabled Smart Border Surveillance Systems Using Deep Learning and Edge AI: Architectures, Challenges, and Future Directions

Abstract

The increasing demand of smart border security systems has stimulated the creation of IoT-based surveillance systems that combine deep learning and edge computing. The paper includes a review and system-level architecture of smart border surveillance based on IoT, Edge AI, and multi-modal data fusion. The proposed architecture integrates distributed sensing, edge-based inference and cloud coordination to facilitate real-time suspicious activity detection at low latency and energy consumption. A mathematical model is developed to optimize the detection accuracy, latency and energy efficiency jointly in resource-constrained edge environments. The system is trained with lightweight deep learning models and tested on several benchmark data, such as FLIR Thermal, VisDrone, COCO, and AI City Challenge datasets. Experimental performance is shown to be high with a maximum of 93.5% precision and 92.4% mAP and low latency of 42 ms per frame on edge devices. Also, model optimization methods cut down on the computational cost by about 23% without much loss in accuracy. As it can be seen through comparative analysis, the proposed approach is more efficient and more precise than the conventional cloud-based and single-sensor systems. The results emphasize the success of the combination of IoT, edge intelligence, and deep learning to scale and real-time border surveillance. This publication offers essential information on the modern issues, trends and future research directions of the next generation intelligent security systems.