A TIME EFFICIENT APPROACH FOR DETECTING ERRORS IN BIG SENSOR DATA ON CLOUD

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A TIME EFFICIENT APPROACH FOR DETECTING ERRORS
IN BIG SENSOR DATA ON CLOUD
ABSTRACT
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 alable 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, 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. Hence the detection and location process can be ramatically accelerated.
Furthermore, the detection and location tasks can be distributed to cloud platform to fully exploit
the computation power and massive storage. Through the experiment on our cloud computing
platform of U-Cloud, it is demonstrated that our proposed approach can significantly reduce the
time for error detection and location in big data sets generated by large scale sensor network
systems with acceptable error detecting accuracy

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1. A TIME EFFICIENT APPROACH FOR DETECTING ERRORS IN BIG SENSOR DATA ON CLOUD ABSTRACT 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 alable 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, 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. Hence the detection and location process can be ramatically accelerated. Furthermore, the detection and location tasks can be distributed to cloud platform to fully exploit the computation power and massive storage. Through the experiment on our cloud computing platform of U-Cloud, it is demonstrated that our proposed approach can significantly reduce the time for error detection and location in big data sets generated by large scale sensor network systems with acceptable error detecting accuracy

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