Storage of Mobile Sensor Data in Clouds using Information Classification Algorithms

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 three popular 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.