Lion Optimized K-Means Support Vector Machine for Clustering Problems in Cloud Internet of Things Environment

Abstract

In the recent past Cloud Internet of Things (CIoT) normally affords information about environmental conditions which are associated to the internet using numerous sensors. As the sensors are resource obliged devices, productive topology is required to deal with the cluster information from the base station to the cloud administration in the network layer which stays away from information clustering issues in CIoT. Different scenarios are considered with different gadgets and communication technologies in order to minimize the bandwidth, the energy consumption, delay latency and maximize the lifetime of the massive CIoT environment In this research, to reduce the cluster overhead issues in CIoT environment and to increase the scalability a digitized framework has been developed using Lion Optimized K-means Support Vector Machine (LKSVM) clustering approach to identify various sensor data patterns which helps to detect the environmental condition of smart cities according using data mining approach In this Research Experimental analysis proved that LKSVM algorithm reduces latency , Consumption of Energy with more data accuracy of the nodes and improved network lifetime compared with the other existing works.