Intelligent IoT and AI ‐Based Framework for Remote Area Security: A Multi‐Mote Approach With LoRa ‐ GSM Integration

ABSTRACT Remote and high‐risk sectors such as mining, oil refineries, and power grids face growing security threats due to limited connectivity, sparse surveillance, and delayed incident response. Conventional monitoring approaches dependent on CCTV or internet connectivity are often ineffective in these environments. To address these challenges, this paper proposes an AI‐driven multi‐mote framework comprising the AI Vision Monitoring Mote, Routing Receiver Mote, and Gateway Receiver Mote. The AI Vision Mote, built on a Raspberry Pi with camera, motion, sound, and RFID sensors, performs real‐time detection of human intrusion, heavy vehicle movement, drones, and weapons using lightweight AI models such as TensorFlow Lite, OpenCV, and PyTorch. Routing and Gateway Motes extend coverage through LoRa‐based communication and provide sequential alerts via GSM and cloud integration. A prototype deployment was evaluated under simulated industrial conditions, demonstrating low latency, < 1% packet loss, and reliable alert delivery. The framework enhances decision‐making and operational safety in disconnected regions, offering a scalable and cost‐effective solution for remote‐area security and surveillance. The proposed system demonstrated improved detection accuracy, reduced latency, and reliable communication in offline environments, making it suitable for deployment in remote‐area security applications.