Enhancing Border Surveillance Efficiency through Edge Computing and Anomaly Detection

This research work focuses on enhancing the efficiency of border surveillance through the integration of edge computing and advanced anomaly detection algorithms. By processing data locally at the edge, the system reduces the latency and bandwidth requirements associated with transmitting large volumes of sensor data to centralized servers. Furthermore, the research examines the impact of edge computing on energy consumption in Wireless Sensor Networks (WSNs). This reduction is achieved by minimizing data transmission and leveraging local computation, thereby prolonging the lifespan of the network and improving the overall efficiency of the surveillance system. The findings of this study underscore the importance of integrating edge computing with advanced anomaly detection algorithms like Isolation Forest in border surveillance systems. This approach not only enhances detection capabilities but also optimizes energy consumption, making it a viable solution for long-term, real-time monitoring of borders.