International Journal of Advanced Networking and Applications

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Storage of Mobile Sensor Data in Clouds using Information Classification Algorithms


Affiliations
1 Department of Electronics and Communication Engineering, BKIT Bhalki, India
2 Department of Electronics and Communication Engineering, BEC Bagalkot, India
 

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.
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  • Storage of Mobile Sensor Data in Clouds using Information Classification Algorithms

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Authors

Prashant Sangulagi
Department of Electronics and Communication Engineering, BKIT Bhalki, India
Ashok V. Sutagundar
Department of Electronics and Communication Engineering, BEC Bagalkot, India
S. Stelvarani
Department of Electronics and Communication Engineering, BKIT Bhalki, India

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.

References