An Efficient Intrusion Detecting Method Using Multiple Sensors and Edge Computing

Critical facilities need a security system to protect against intruders. Most security systems use CCTV cameras to monitor the boundary areas of critical facilities. We propose an edge computing system that uses multiple sensors to detect intruders in a wider area. The proposed system uses light detection and ranging (LiDAR) and radar sensors to detect intruding objects in critical facilities. The sensors are widely used to scan distant objects in real time and monitor the outside of facility from the inside. These sensors can monitor a wider area in detail than existing video management systems that use video cameras. However, the output data are 3D point clouds, and the number of data is larger compared to video data. The larger the area to be monitored, the more sensors are required, which creates a bottleneck in the network of the monitoring system. We have solved this problem through edge computing. The proposed edge computing service compresses point cloud data to centralize all data to the main server without bottlenecks. This makes it possible to monitor a larger area in real time. This distributed most of the computing to edge computers, allowing the existing GPU-based high-performance server to be replaced with a low-end cloud server.