ABSTRACT
Mobiles are equipped with sensors like accelerometer, magnetic subject, and air strain meter, which assist within
the system of extracting context of the person like area, scenario and so on. But, processing the extracted sensor
facts is generally an aid intensive assignment, which can be offloaded to the general public cloud from mobiles.
Mobile devices have become an essential part of our day to day life by which the user is able to access, create and
share information at any location. This design especially objectives at extracting beneficial statistics from the
accelerometer sensor records. The design proposes the utilization of parallel computing to the use of Map Reduce
at the cloud for spotting human behavior primarily based on classifiers and ultimately calculating its accuracy.
The sensor facts is extracted from the cellular, sent to the cloud and processed using threepopular classifier
algorithms namely, Kernel Naïve Bayes, Naive Byes Classifier and K-Nearest-Neighbors. The results are verified
at different scenarios of human activity and finally the accuracy is calculated using the classification algorithms.
Keywords: - Sensor nodes, Cloud Computing, Information Classification, Sensor Data, K-NN Classifier, Naïve Bayes
Classifier