Research On Wireless Mulimedia Sensor Networks Coverage Based On Improved Quantum Genetic Algorithm

With the higher requirement in monitoring capabilities and environment of Wireless Sensor Networks(WSNs), the monitoring capacity is required much better, So Wireless Multimedia Sensor Networks (WMSNs) have emerged. The Wireless multimedia sensor networks is a new research field, which combine wireless sensor network with multimedia technology. The coverage control is an important part of WMSNs research field, it reflects the perception of the physical world, whether the nodes can fully coverage the monitoring area or not directly affect the Quality of service (Qos) of entire network.Different with WSNs, the nodes in WMSNs have a viewing angle and directional perception. At the same time, the most nodes are randomly deployed in recently applications, so it may be lead to the blind spots and cover overlapping regions in WMSNs, the node deployment and coverage optimization is the very important issue in WMSNs. Based on perceptual model, the improved quantum genetic algorithm have been conducted to improve the coverage optimization problem in wireless multimedia sensor network. Genetic algorithm(GA) is a highly parallel adaptive random search algorithm, quantum genetic algorithm (QGA) is a method,which combine GA with quantum theory.Compared to GA, QGA has fast convergence,diverse populations and small-scale search capability, the efficiency performance of QGA is more outstanding. However, the QGA also has slow convergence speed, easy to converge to local optimal solution, poor global search capability and other defects. Based on QGA advantages and disadvantages, the improved QGA has been proposed to improved coverage optimization in WMSNs. the improved algorithm transform the traditional QGA from the following three aspects:Firstly, the traditional QGA only select the current optimal chromosome to guide iteration, this strategy is due to solely focus on a temporary optimal, it is easily lead to the solving process fall into the local optimal, the improved algorithm randomly select a member from the best chromosome collection as the target to guide the iteration. The situation of QGA easy converging to local optimum can be improved.The quantum rotation angle of traditional QGA is fixed, the angle can not be changed with the specific circumstances of the iteration. The improved algorithm using the adaptive rotation angle strategy, so that the appropriate angle can be selected according to the algorithm performs,this operation can speed up the convergence rate of the algorithm.Lastly, the quantum variation of QGA is relatively simple, it just swap the probability amplitude of the qubit.The improved algorithm uses a new quantum mutation strategy to accelerate the convergence speed of the algorithm, to avoid the search time delay brought by simple quantum variation process.The coverage performance of GA, QGA and improved QGA have been analyzed in experiment. Simulation results show the coverage results from the number of nodes, the perception of the radius and the iterations. The experiments show that QGA algorithm can improved network coverage optimization more excellently.