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    Item type:Publication,
    Genetic electro-search optimization for optimum energy consumption in edge computing-based internet of healthcare things
    (Springer Nature, 2024)
    Köse, Utku
    ;
    Marmolejo Saucedo, José Antonio
    ;
    Rodríguez Aguilar, Román  
    ;
    Marmolejo-Saucedo, Liliana
    ;
    Rodriguez-Aguilar, Miriam
    Energy consumption is a vital issue when optimum usage and carbon footprint are all considered in today’s Internet of Things (IoT) environments. Considering edge computing, that becomes too critical in terms of wireless devices with limited battery power. Especially in healthcare applications, the defined IoHT approach requires sustainability while future massive solutions may result negative outputs in terms of carbon footprint. So, optimum energy consumption seems positive in terms of multiple ways. In the literature, one trendy method is using clustering for lowering the energy consumption within the Internet of Healthcare Things (IoHT) environment on edge computing. In this study, optimization of energy consumption in IoHT was done via improved Genetic Electro-Search Optimization (GESO) algorithm. According to the obtained findings in the performed applications, GESO was effective enough in finding optimum conditions of energy consumption for an active IoHT setup. © 2024 Springer Nature
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    Implementation of a SVM on an Embedded System: A Case Study on Fall Detection
    (2020)
    Márquez Ordaz, Luis Eusebio
    ;
    Ponce, Hiram  
    Edge Computing seeks to bring Machine Learning as close as possible to the source events of interest, providing an almost instant interpretation to data acquired by sensors giving sense to raw data while addressing concerns of particular applications such as latency, privacy and server stress relieve. Due to a lack of research on this particular type of application, we are faced with difficulties both in software and hardware as embedded systems are known to possess serious limitations on its available processing resources. To address this, we make use of the concepts of edge computing and offline programming to accomplish a reliable machine learning model deployment on the microprocessor. By studying real case problem, we can get measurements on the resources required by such an application as well as its performance. In this study, we address the implementation of such an application in an embedded system focusing on the detection of human falls. © 2020, Springer Nature Switzerland AG.
    Scopus© Citations 1  15  2