Image-Oriented Fuzzy C-Means Clustering Algorithm For Wireless Sensor Networks

Wireless sensor networks(WSNs)are wireless networks that distribute a large number of sensor nodes randomly in a certain area.In practice,the wireless sensor network will receive massive data and there is a lot of redundant data,the use of data clustering mechanism,you can delete redundant data,while reducing the amount of data transmitted in wireless sensor networks,thereby reducing the energy consumed in the network.So that the network's life cycle can be extended.As a kind of soft clustering method,fuzzy C-means clustering algorithm is widely used and the technology is more mature.However,the general fuzzy C-means clustering algorithm is still faced with the problem of lack of spatial correlation and easy to fall into the local optimal solution when analyzing the image data of nodes in wireless sensor networks.In order to solve these problems,the main work of this paper is as follows:Firstly,a fuzzy C-Means clustering algorithm(MFCM)is proposed to improve the fuzzy C-Means clustering algorithm,and the fuzzy C-means algorithm is used to improve the fuzzy C-means clustering algorithm.The clustering algorithm can preserve the spatial correlation information between neighboring pixels when analyzing the data,so that the information of the image can be taken into account during the clustering iterative process,so as to enhance the image processing ability of the algorithm and reduce the influence of noise.Effectively correct the wrong classification of pixels.Secondly,based on fuzzy C-means clustering algorithm MFCM which is easy to fall into the local optimal solution,an improved fuzzy C-means clustering algorithm based on the firefly algorithm is proposed.Class algorithm(FAMFCM).The algorithm uses the firefly algorithm to find the global optimal solution,and uses the firefly algorithm to replace the iterative search process of the cluster center in the fuzzy C-means clustering algorithm.This algorithm reduces the number of iterations and reduces the energy consumption of the wireless sensor network.And make the cluster center location more accurate,the accuracy of clustering algorithm is improved,in dealing with wireless sensor network image data has better performance.