Modern mobile pervasive applications focus on context awareness that monitors a diverse range of personal domains. In order to infer contextual information, most of these applications require the collection of raw data from sensors which are either embedded in personal smartphones or worn by the user. Critical context-aware applications rely on continuous accurate monitoring of the user's current context. Continuous sensing mechanisms in sensors cost high energy consumption to support accurate contextual detection. Hence, there is a trade-off between the classification accuracy and the energy consumption. In this paper, we exploit the advantages of Deep Neural Network (DNN) with ensemble classification of other complementary machine learning approaches to determine the best sensor sampling frequency for the recognition of a given context. DNN relies on raw data for classification while the other complementary methods (such as Decision Tree and Naive Bayes) use feature recognition to classify data. Therefore, our approach provides a range of granularity from raw data. We prove the robustness of our approach in experiments which show high accuracy in context recognition. In addition, real experiments demonstrate the energy gains of the proposed algorithm which reach 87% reduction in energy consumption when compared to continuous sensing.
['Sirine Taleb', 'Ahmad A. Al Sallab', 'Hazem M. Hajj', 'Zaher Dawy', 'Rahul Khanna', 'Anil S. Keshavamurthy']
Deep learning with ensemble classification method for sensor sampling decisions