LoRaWAN-Enabled AI-Based Smart Surveillance System for Critical Infrastructure Protection
Keywords:
Critical infrastructure, Edge AI, Event-driven monitoring, IoT sensors, LoRaWAN, Low-power communication, Smart surveillanceAbstract
This article presents a LoRaWAN-enabled AI-based smart surveillance system designed to enhance the protection of critical infrastructure in remote and resource-constrained environments. Conventional surveillance systems often rely on continuous video streaming, which leads to high bandwidth consumption, large storage requirements, significant power usage, and dependence on reliable communication networks. These limitations make them unsuitable for geographically dispersed sites such as power substations, industrial plants, transportation facilities, and border areas. To address these challenges, the proposed system adopts an event-driven architecture that combines low-power IoT sensor nodes, LoRaWAN communication, edge artificial intelligence, and cloud-based monitoring. The sensor nodes continuously monitor environmental and security-related parameters such as motion, vibration, smoke, gas leakage, temperature, and unauthorized entry. When an abnormal event is detected, the information is transmitted through the LoRaWAN gateway to the edge AI controller, which classifies the event and activates the surveillance camera only when necessary. The captured video is then processed using AI models for object detection and threat verification, and alerts are sent to authorize personnel in real-time. This approach minimizes unnecessary video recording, reduces communication overhead, lowers storage and energy requirements, and improves response time. The proposed framework offers a scalable, cost-effective, and energy-efficient solution for intelligent surveillance in critical infrastructure protection.
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