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The advancement of smartphones with various type of sensors enabled us toharness diverse information with crowd sensing mobile application. However,traditional approaches have suffered drawbacks such as high battery consumptionas a trade off to obtain high accuracy data using high sampling rate. Tomitigate the battery consumption, we proposed low sampling point of interest(POI) extraction framework, which is built upon validation based stay pointsdetection (VSPD) and sensor fusion based environment classification (SFEC). Westudied various of clustering algorithm and showed that density based spatialclustering of application with noise(DBSCAN) algorithms produce most accurateresult among existing methods. The SFEC model is utilized for classifying theindoor or outdoor environment of the POI clustered earlier by VSPD. Real worlddata are collected, bench-marked using existing clustering method to denoteeffectiveness of low sampling rate model in high noise spatial temporal data.

Extracting Point of Interest and Classifying Environment for Low Sampling Crowd Sensing Smartphone Sensor Data