Abstract
The use of electronic systems and devices has become widely spread and is reaching several fields as well as indispensable for many daily activities. Such systems and devices (here termed embedded systems) are aiming at improving human beings’ quality of life. To do so, they typically acquire users’ data to adjust themselves to different needs and environments in an adequate fashion. Consequently, they are connected to data networks to share this information and find elements that allow them to make the appropriate decisions. Then, for practical usage, their computational capabilities should be optimized to avoid issues such as: resources saturation (mainly memory and battery). In this line, machine learning offers a wide range of techniques and tools to incorporate “intelligence” into embedded systems, enabling them to make decisions by themselves. This paper reviews different data storage techniques along with machine learning algorithms for embedded systems. Its main focus is on techniques and applications (with special interest in Internet of Things) reported in literature about data analysis criteria to make decisions.
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Rosero-Montalvo, P.D. et al. (2019). Intelligence in Embedded Systems: Overview and Applications. In: Arai, K., Bhatia, R., Kapoor, S. (eds) Proceedings of the Future Technologies Conference (FTC) 2018. FTC 2018. Advances in Intelligent Systems and Computing, vol 880. Springer, Cham. https://doi.org/10.1007/978-3-030-02686-8_65
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