Sensor Placement Optimization and Rapid Deployment of a Class of Visual Sensor Networks

To achieve the purpose of large-scale visual tracking of targets based on Visual Sensor Networks (VSNs), this paper proposes a sensor placement optimization method to provide a suitable sensor configuration that satisfies the requirements of maximal network coverage and minimal reconstruction error as closely as possible. In the proposed method, some necessary models are established as the mathematical basis of the optimization process. Then, a multi-objective optimization function is designed to balance the coverage and reconstruction error, followed by an intelligent optimization algorithm that combines the advantages of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). Additionally, a dimensionality reduction technique is used to represent the sensor location information using a one-dimensional forward distance, which makes the search process simpler and increases execution speed. Finally, comprehensive comparison experiments are carried out in various settings to show the reliability and effectiveness of the optimization algorithms.