Abstract
With the increasing need for public security and intelligent life and the development of Internet of Things (IoT), the structure and application of vision sensor network are becoming more and more complex. It is no longer a system with simple static monitoring, but a complex system that can be used for intelligent processing, such as target localization, identification, tracking and so on. In order to accomplish various tasks efficiently, it is important to determine the deployment plan of camera network in advance. Many researches discretize the optimal camera placement problem into a binary integer programming (BIP) problem, which is NP-hard, and put forward some approximate solutions including greedy heuristics, semi-definite programming, simulated annealing, etc. In practice, however, camera parameters include both continuous values (location and orientation) and discrete values (camera type). To get a much more accurate result, we do not discretize the continuous camera parameters any more, on the contrary, we handle the continuous values in continuous domain directly. Meanwhile, a Latin Hypercube based Resampling Particle Swarm Optimization (LH-RPSO) algorithm is proposed to effectively solve the problem. To validate the proposed algorithm, we compared it with standard Particle Swarm Optimization (PSO) and Resampling Particle Swarm Optimization (RPSO). Simulation results for an outdoor planar regions illustrated the efficiency of the proposed algorithm.
Highlights
Internet of Things (IoT) is the extension and expansion of Internet technology
Experimental results of both steps have shown that the Latin Hypercube based Resampling Particle Swarm Optimization (LH-Resampling Particle Swarm Optimization (RPSO)) performs better than the Particle Swarm Optimization (PSO) and the RPSO, which fully demonstrated that the LH-RPSO can be used in practical large-scale camera placement problems
SIMULATION EXPERIMENT AND RESULTS ANALYSIS in order to verify the effectiveness of the LH-RPSO algorithm in the domain of camera placement problem, we did a simulation with a real-world map of a campus (Fig. 4)
Summary
Internet of Things (IoT) is the extension and expansion of Internet technology. The IoT connects objects and realizes information sharing by using technologies such as recognition, perception, and communication. Some researchers argue that the continuous-based formulations are not suitable for large-scale problems because of the dramatically increased complexity when practical considerations are incorporated We believe this situation will be improved with increased computing power and new approaches [7], [8]. As a kind of swarm intelligence optimization algorithm, the PSO needs a great deal of computation time, so improving efficiency is a key issue. In the second step, we further improve the coverage of the network based on the solution of the previous step Experimental results of both steps have shown that the LH-RPSO performs better than the PSO and the RPSO, which fully demonstrated that the LH-RPSO can be used in practical large-scale camera placement problems
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