Nowadays, public security is becoming an increasingly serious issue in our society and its requirements have been extended from urban centers to all remote areas. Therefore, surveillance and security cameras are being deployed worldwide. Wireless Visual Sensor Networks nodes can be employed as camera nodes to monitor in the city without the need for any cables installation. However, these cameras are constrained in processing, memory, and energy resources. Also, they generate a massive amount of data that must be analyzed in real-time to ensure public safety and deal with emergency situations. As a result, data processing, information fusion, and decision making have to be executed on-site (near to the data collection location). Besides, surveillance cameras are directional sensors, which makes the coverage problem another issue to deal with. Therefore, we present a new system for real-time video surveillance in a smart city, in which transportations equipped with camera nodes are used as the mobile part of the system and an architecture based on fog computing and wireless visual sensor networks is adopted. Furthermore, we propose an approach for selecting the camera nodes that will participate in the tracking process and we simulated three different use cases to test the effectiveness of our system in terms of target detection. The simulation results show that our system is a promising solution for smart city surveillance applications.
A New System for Real-time Video Surveillance in Smart Cities Based on Wireless Visual Sensor Networks and Fog Computing
