In-network data reduction approach based on smart sensing
Author | Awad, Alaa |
Author | Saad, Amal |
Author | Jaoua, Ali |
Author | Mohamed, Amr |
Author | Chiasserini, Carla-Fabiana |
Available date | 2021-09-05T05:40:07Z |
Publication Date | 2016 |
Publication Name | Global Communications Conference, GLOBECOM 2016 - Proceedings |
Resource | Scopus |
Abstract | The rapid advances in wireless communication and sensor technologies facilitate the development of viable mobile-Health applications that boost opportunity for ubiquitous real- time healthcare monitoring without constraining patients' activities. However, remote healthcare monitoring requires continuous sensing for different analog signals which results in generating large volumes of data that needs to be processed, recorded, and transmitted. Thus, developing efficient in-network data reduction techniques is substantial in such applications. In this paper, we propose an in-network approach for data reduction, which is based on fuzzy formal concept analysis. The goal is to reduce the amount of data that is transmitted, by keeping the minimal-representative data for each class of patients. Using such an approach, the sender can effectively reconfigure its transmission settings by varying the target precision level while maintaining the required application classification accuracy. Our results show the excellent performance of the proposed scheme in terms of data reduction gain and classification accuracy, and the advantages that it exhibits with respect to state-of-the-art techniques. |
Language | en |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Subject | Conceptual learning EEG signals Fuzzy data reduction Mobile-Health system Wavelet compression |
Type | Conference Paper |
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Computer Science & Engineering [2287 items ]