SummaryA wireless visual sensor network is a collective network of directional and battery‐operated sensor nodes equipped with cameras. The field of view of these nodes depends on the camera opening angle, its direction, and its depth of view. Therefore, coverage and object detection in this type of networks are more challenging compared with the traditional wireless sensor networks. Thus, many researchers propose algorithms and solutions in this field that need tests and simulations. In this paper, we focus on network simulator 3 (ns‐3), which is an open‐source and discrete‐event tool suitable for wireless network simulation targeted primarily for research and educational use. The lack of models that can simulate visual sensor nodes in this simulator motivated us to design and develop a new visual node module as an extension of the ns‐3 core libraries and also to adapt the NetAnim tool to present these nodes graphically. This module will help researchers to simulate, test, and visualize their solutions in wireless visual sensor networks field. In this paper, we present the design and implementation of the proposed module. Furthermore, we show how it can be used in ns‐3 to simulate different scenarios of object detection and visualize the results in NetAnim tool.
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.
In recent years, wireless sensor networks have been used in a wide range of applications such as smart cities, military, and environmental monitoring. Target tracking is one of the most interesting applications in this area of research, which mainly consists of detecting the targets that move in the area of interest and monitoring their motions. However, tracking a target using visual sensors is different and more difficult than that of scalar sensors due to the special characteristics of visual sensors, such as their directional limited field of view, and the nature and amount of the sensed data. In this paper, we first present the challenges of detection and target tracking in wireless visual sensor networks, then we propose a scheme that describes the basic steps of target tracking in these networks, we focus then on the tracking across camera nodes by presenting some metrics that can be considered when designing and evaluating this type of tracking approaches.
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