Big data set from sensors is often subject to corruption and losses due to wireless medium of communication and presence of hardware inaccuracies in the nodes. For a WSN application to deduce an appropriate result, it is necessary that the data received is clean, accurate, and loss-less. However, effective detection and cleaning of sensor big data errors is a challenging issue demanding innovative solutions. Since existing techniques were not designed and developed to deal with big data on cloud, they were unable to cope with current dramatic increase of data size. The proposed error detection approach in this paper will be based on the classification of error types. Specifically, nine types of numerical data abnormalities/errors are listed and introduced in our cloud error detection approach. Our proposed error detection approach on cloud is specifically trimmed for finding errors in big data sets of sensor networks. The main contribution of our proposed detection is to achieve significant time performance improvement in error detection without compromising error detection accuracy.
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