A Time Efﬁcient Approach for Detecting Errors in Big Sensor Data on Cloud
Abstract— A Time Efﬁcient Approach for Detecting Errors in Big Sensor Data on Cloud. Big sensor data is prevalent in both industry and scientiﬁc research applications where the data is generated with high volume and velocity it is difﬁcult to process using on-hand database management tools or traditional data processing applications. Cloud computing provides a promising platform to support the addressing of this challenge as it provides a ﬂexible stack of massive computing, storage, and software services in a scalable manner at low cost. Some techniques have been developed in recent years for processing sensor data on cloud, such as sensor-cloud. However, these techniques do not provide efﬁcient support on fast detection and locating of errors in big sensor data sets. For fast data error detection in big sensor data sets, in this paper, we develop a novel data error detection approach which exploits the full computation potential of cloud platform and the network feature of WSN. Firstly, a set of sensor data error types are classiﬁed and deﬁned. Based on that classiﬁcation, the network feature of a clustered WSN is introduced and analyzed to support fast error detection and location. Speciﬁcally, < Final Year Projects 2016 > in our proposed approach, the error detection is based on the scale-free network topology and most of detection operations can be conducted in limited temporal or spatial data blocks instead of a whole big data set.
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