In recent years VANets have come up as new information promulgation technology. To enhance vehicle and road safety, traffic efficiency, and ease as well as comfort to both drivers and passengers, VANets become an active area of research. In VANets, vehicle work as a node. Vehicle collect different types of information like road traffic and environmental information and transmit them to intended entities but it become a challenging task to route the information to destination because of sparse distribution and high mobility of vehicles in road. To address this issue, clustering has been widely used in many existing proposals in articles. In this paper various challenges for clustering in VANets are discussed briefly but main emphasis laid on the current technique which is widely used nowadays i.e Token-based Clustered data Gathering Protocol (TCDGP).

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The issue of SVMs parameter optimization with particle swarm optimization (pso) provide the optimum solution. This new classification approach may be an efficient alternative, in existing paradigms. PSO technique work with high dimensional datasets and mixed attribute data. The structure of the image is recognized through PSO technique which provide optimized parameter for SVM. This approach determines the performance of image classification after structural recognition based on content of image and comparing the obtained results with those reported for various other classification approaches. PSO-SVM technique can be applied mixed-attribute, hyperspectral data, hyperdimension spaces & problem description spaces and it can also be a competitive alternative to well established classification techniques. The optimized process of data reduces the unclassified region of support vector machine and improves the performance of image classification. The feature of region of image is classified by PSO-SVM technique in inside the image. Cassified features are increase recogniztion ratio because the feature of image is optimized. General TermsPattern Recognition, high dimensional image classification et. al.

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