| With the rapid development of wireless communications,sensing,and micro-electromechanical technologies,wireless sensor networks with low power consumption,high degree of autonomy,have been widely used in environmental monitoring and protection,medical care,military,and intelligent transportation.In these applications,improving the positioning accuracy is one of the key technologies of wireless sensor networks.In this paper,a new meta-heuristic moth-flame optimization algorithm is applied to node localization.The main contents and achievements of the thesis are as follows:(1)A node localization algorithm based on moth-flame optimization is improved.After measuring the distance between nodes,the communication range of the node is approximately expressed as a square.Then based on the idea of iterative positioning,a search space determination method with different number of positioning parameter points is given,and each unknown node then autonomously adopts the moth-flame optimization algorithm to estimate its own position.(2)The two important parameters of the moth-flame optimization algorithm are given,namely the selection strategy of the number of moths(also the number of flames)and the number of iterations.Ather determining the parameters,including communication range,a number of anchor nodes,ranging error,a number of test are generated.Finally,the best parameter selection strategy in different scenarios is determined by comparing the positioning accuracy.(3)A simulation experiment was conducted on the proposed algorithm.Within a deployment area,many anchor nodes and unknown nodes were randomly deployed,and various experimental parameters such as ranging error and communication radius were set,the positioning performance is compared by analyzing the mean value and mean square error of the positioning error.The simulation results show that the moth-flame optimization can greatly improve the positioning accuracy,which is higher than least square method. |
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