Construction and simulation of performance evaluation index system of Internet of Things based on cloud model Abstract   Due to the lack of corresponding scientific evaluation standards and effective evaluation methods for the overall performance of the Internet of Things system, the Internet of Things industry and the market are in a chaotic state, and the quality of various products is uneven. It is difficult for users to select a more suitable application system, which seriously restricts the development process of the industrialization of Internet of Things technology. Therefore, it is very necessary for us to conduct research on the performance evaluation methods of IoT systems. In this paper, the performance of IoT system is selected as the performance evaluation index of the system, and the evaluation system is constructed. The performance evaluation method of the Internet of Things system based on the cloud model is proposed. The comprehensive evaluation of the reliability of the IoT system for the cloud model will be based on the AHP. Based on the combination of attribute decisionmaking methods, a comprehensive evaluation method based on indicators is proposed, and the systemxe2x80x99s indicator model is established. The reliability of the Internet of Things system can be comprehensively evaluated by the value of the indicators. Secondly, the probability density function of tracking error is studied, and its optimal probability density distribution is given by optimal control. Based on the optimal probability density distribution, the performance evaluation index of the system is constructed. In order to compare the current probability density function with the reference probability density function, the tracking error function is first vector, then the performance evaluation index is constructed by gray correlation, and the general steps of performance evaluation based on this index are given. Finally, the effectiveness of the proposed system performance evaluation algorithm is verified by simulation comparison. The data processing and experimental simulation are used to achieve the purpose of evaluating the performance of the Internet of Things system.

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.