A Time Efficient Approach for Detecting Errors in Big Sensor Data on Cloud - 2015
Huge sensor information is prevalent in each business and scientific analysis applications where the information is generated with high volume and velocity it's troublesome 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 because it provides a versatile stack of large computing, storage, and software services in an exceedingly scalable manner at low price. Some techniques are developed in recent years for processing sensor data on cloud, such as sensor-cloud. But, these techniques do not provide economical support on quick detection and locating of errors in massive sensor information sets. For fast knowledge error detection in huge sensor information sets, in this paper, we have a tendency to develop a unique data error detection approach that exploits the full computation potential of cloud platform and therefore the network feature of WSN. Firstly, a collection of sensor information error types are classified and defined. Based mostly on that classification, the network feature of a clustered WSN is introduced and analyzed to support fast error detection and placement. Specifically, in our proposed approach, the error detection is predicated on the scale-free network topology and most of detection operations can be conducted in limited temporal or spatial knowledge blocks instead of a complete huge data set. Hence the detection and site method can be dramatically accelerated. Furthermore, the detection and placement tasks will be distributed to cloud platform to fully exploit the computation power and large storage. Through the experiment on our cloud computing platform of U-Cloud, it's demonstrated that our proposed approach will significantly reduce the time for error detection and placement in big data sets generated by massive scale sensor network systems with acceptable error detecting accuracy.
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