دانلود رایگان مقاله انگلیسی شناسایی و اصلاح خطای سنسور کلان داده در شبکه حسگر بی سیم مبتنی بر WFCM - اشپرینگر 2018

Abstract

In WSN the requested data is collected from the initial node i.e. sender and the information are uploaded on a cloud platform. Only numeric Data type is considered in this error detection and correction technique. Map Reduce algorithm is applied on clusters made by big data and Weighted Fuzzy C-Means Clustering (WFCM) technique is used for clustering. Completely different operations are performed on the cloud platform like error detection, location finding, data cleansing and error recovery. Throughout the filtering of big data sets, whenever an abnormal knowledge is encountered, detection rule has to perform two tasks. ‘‘fd (n/e,t)’’ is decision making function. It is used to determine whether the detected anomalous data is a true error. In other words, fd (n/e,t) has two outputs, ‘‘false negative’’ for detecting a true error and ‘‘false positive’’ to select non-error data. ‘‘fl (n/e,t)’’ is a function for tracking and returning original error source.

6 Conclusion

Various implementation strategies of error detection techniques in WSN are presented. The main aim is at particular implementation strategy for error detection and correction is discussed with the aid of Weighted FCM and KSVM. In order to detect and find the location of error in big data set, a sensor network system is mainly used and a novel approach is developed with cloud computing. First, the classification of error in big data sets is presented. Second, the correlation comparison between sensor network systems and the scale-free complex networks are introduced. Accordingly the error types are defined. Different strategies for detecting and locating errors in big data sets on cloud are used.