Dynamic clustering method based on power demand and information volume for intelligent and green IoT Abstract   The Internet of Things (IoT) is an integrated part of the future network which requires comprehensive awareness, reliable delivery and intelligent processing. Therefore, it faces the challenge of energy efficiency during realtime implementations based on respective applications. The work proposes an efficient and green dynamic clustering mechanism based on power demand and information volume. The machine learning approach is based on dynamic cluster formation by avoiding any information loss. Initially, among all the available Poisson distributed nodes, the master node divides them into two temporary clusters based on their power requirements. Then, the power requirements within these two clusters are equalized by a set threshold and based on application requirements, the dynamic clusters are formed by measuring the amount of information in each cluster. The simulation results presents that the proposed method equalizes the power demand of the network and maximizes the information in the cluster, which further improves the energy efficiency of the whole network. The results are also compared with traditional methods to justify the proposed work.

Energy analysis of Internet of things data mining algorithm for smart green communication networks Abstract   With the continuous development of Internet technology and electronic information technology, big data technology and cloud computing technology also rise and develop, and have a positive impact on peoplexe2x80x99s lives. Data mining system can deeply mine the value information contained in big data, so as to assist users to solve practical problems and help users to make correct decisions and judgments. This paper presents an energy analysis of data mining algorithm based on cloud platform for Internet of things (IoT). First of all, an improved Apriori algorithm is proposed, which is based on Boolean matrix and sorting index rules. Then Boolean matrix is obtained after scanning the dataset and the Boolean matrix is preprocessed to delete the useless transactions and the item set, which are combined with sorting index to produce other item sets, effectively improving the efficiency of frequent item mining, which effectively reduce the memory usage. Secondly, the densitybased spatial clustering of applications with noise (DBSCAN) clustering algorithm needs human intervention in the global parameter selection, and the process of regional query is complex and the query is easy to lose objects. An improved parameter adaptive and regional query density clustering algorithm is proposed, which can effectively delete the redundant data in the highlevel complex data space on the premise of retaining the internal nonlinear structure of the IoT data. The efficiency of clustering is also improved accordingly Finally, the simulation based on cloud platform verifies the effectiveness and superiority of the algorithm.