Advertisement

Intelligence in Embedded Systems: Overview and Applications

  • Paul D. Rosero-Montalvo
  • Vivian F. López Batista
  • Edwin A. Rosero
  • Edgar D. Jaramillo
  • Jorge A. Caraguay
  • José Pijal-Rojas
  • D. H. Peluffo-Ordóñez
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 880)

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.

Keywords

Decision making Embedded systems Internet of things Machine learning 

References

  1. 1.
    Parameswaran, S., Wolf, T.: Embedded systems security-an overview. Des. Autom. Embed. Syst. 12, 173–183.  https://doi.org/10.1007/s10617-008-9027-xCrossRefGoogle Scholar
  2. 2.
    Noergaard T.: Embedded Systems Architecture. Chemistry, p. 657.  https://doi.org/10.1016/B978-0-12-382196-6.00006
  3. 3.
    Kadionik, P.: Introduction to Embedded Systems. Communicating Embedded Systems (2013).  https://doi.org/10.1002/9781118557624.ch1Google Scholar
  4. 4.
    Levy, M., Conte, T.M.: Embedded multicore processors and systems. IEEE Micro, 7–9 (2009).  https://doi.org/10.1109/MM.2009.41CrossRefGoogle Scholar
  5. 5.
    Toulson, R., Wilmshurst, T.: Embedded Systems, Microcontrollers, and ARM (2017).  https://doi.org/10.1016/B978-0-08-100880-5.00001-3CrossRefGoogle Scholar
  6. 6.
    Gu, C.: Building Embedded Systems. O’Reilly & Associates (2016).  https://doi.org/10.1007/978-1-4842-1919-5CrossRefGoogle Scholar
  7. 7.
    Edwards, S., Lavagno, L., Lee, E.A., Sangiovanni-Vincentelli, A.: Design of embedded systems: formal models, validation, and synthesis. Proc. IEEE (1997).  https://doi.org/10.1109/5.558710CrossRefGoogle Scholar
  8. 8.
    Chin, J., Callaghan, V.: Educational living labs: a novel internet-of-things based approach to teaching and research. In: 2013 9th International Conference Intelligent Environments (IE), pp. 92–99 (2013)Google Scholar
  9. 9.
    Alippi, C.: Intelligence for embedded systems. In: Intelligence for Embedded Systems: A Methodological Approach (2014).  https://doi.org/10.1007/978-3-319-05278-6CrossRefGoogle Scholar
  10. 10.
    Kortuuem, G., Keynes, M., Bandara, A.: Educating the internet of things generation. Computer, 53–61 (2013)Google Scholar
  11. 11.
    Zhao, G.X., Bei, Q.: Application of the IOT technology in the intelligent management of university multimedia classrooms. Appl. Mech. Mater., 2050–2053 (2014)CrossRefGoogle Scholar
  12. 12.
    Thangavel, D., Ma, X., Valera, A.: Performance evaluation of MQTT and CoAP via a common middleware. In: 2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), pp. 4–6 (2014)Google Scholar
  13. 13.
    Alwakeel, S., Alhalabi, B., Aggoune, H., Alwakeel, M.: A machine learning based wsn system for autism activity recognition. In: 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA), pp. 771–776 (2015)Google Scholar
  14. 14.
    Knickerbocker, J., Patel, C., Andry, P., Cornelia, T.: Through-Vias: 3-D silicon integration and silicon packging technology using silicon. IEEE J. Solid-State Circuits, 1718–1725 (2006)CrossRefGoogle Scholar
  15. 15.
    Singh, K.: WSN LEACH based protocols: a structural analysis. In: International Conference and Workshop on Computing and Communication (IEMCON). Vancouver, BC, pp. 1–7 (2015).  https://doi.org/10.1109/IEMCON.2015.7344478
  16. 16.
    Sudheendran, S., Bouachir, O., Moussa, S., Dahmane, A.O.: Review - challenges of mobility aware MAC protocols in WSN. In: Advances in Science and Engineering Technology International Conferences (ASET), Dubai, Sharjah, Abu Dhabi, United Arab Emirates, pp. 1–6 (2018).  https://doi.org/10.1109/ICASET.2018.8376831
  17. 17.
    Arya, S., Yadav, S.S., Patra, S.K.: WSN assisted modulation detection with maximum likelihood approach, suitable for non-identical Rayleigh channels. In: 2017 International Conference on Recent Innovations in Signal Processing and Embedded Systems (RISE), Bhopal, India, pp. 49–54 (2017).  https://doi.org/10.1109/RISE.2017.8378123
  18. 18.
    Khan, A.R., Rakesh, N., Bansal, A., Chaudhary, D.K.: Comparative study of WSN protocols (LEACH, PEGASIS and TEEN). In: 2015 Third International Conference on Image Information Processing (ICIIP), Waknaghat, pp. 422–427 (2015).  https://doi.org/10.1109/ICIIP.2015.7414810
  19. 19.
    Rosero-Montalvo, P., et al.: Prototype reduction algorithms comparison in nearest neighbor classification for sensor data: empirical study. In: IEEE Second Ecuador Technical Chapters Meeting (ETCM), Salinas, pp. 1–5 (2017).  https://doi.org/10.1109/ETCM.2017.8247530
  20. 20.
    Restuccia, F., D’Oro, S., Melodia, T.: Securing the internet of things in the age of machine learning and software-defined networking. IEEE Internet Things J.  https://doi.org/10.1109/JIOT.2018.2846040
  21. 21.
    Garcia, S., Derrac, J., Cano, J., Herrera, F.: Prototype selection for nearest neighbor classification: taxonomy and empirical study. IEEE Trans. Pattern Anal. Mach. Intell. 34(3), 417–435 (2012)CrossRefGoogle Scholar
  22. 22.
    Cano, J.R., Herrera, F., Lozano, M.: Using evolutionary algorithms as instance selection for data reduction in KDD: an experimental study. IEEE Trans. Evol. Comput. 7(6), 561–575 (2003)CrossRefGoogle Scholar
  23. 23.
    Simes, A., Costa, E.: CHC-based algorithms for the dynamic traveling salesman problem. In: Applications of Evolutionary Computation: EvoApplications (2011)Google Scholar
  24. 24.
    Peña-Unigarro, D.F., et al.: Interactive data visualization using dimensionality reduction and dissimilarity-based representations. In: Intelligent Data Engineering and Automated Learning–IDEAL 2017, pp. 461–469.  https://doi.org/10.1007/978-3-319-68935-7_50Google Scholar
  25. 25.
    Rosero-Montalvo, P.D., et al.: Data visualization using interactive dimensionality reduction and improved color-based interaction model. In: Biomedical Applications Based on Natural and Artificial Computing - IWINAC 2017, pp. 289–298.  https://doi.org/10.1007/978-3-319-59773-7_30CrossRefGoogle Scholar
  26. 26.
    Nuñez-Godoy, S., et al.: Human-sitting-pose detection using data classification and dimensionality reduction. In: IEEE Ecuador Technical Chapters Meeting (ETCM), Guayaquil, pp. 1–5 (2016).  https://doi.org/10.1109/ETCM.2016.7750822
  27. 27.
    Rosero-Montalvo, P.D., et al.: Elderly fall detection using data classification on a portable embedded system. In: IEEE Second Ecuador Technical Chapters Meeting (ETCM), Salinas, pp. 1–4 (2017).  https://doi.org/10.1109/ETCM.2017.8247529

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Paul D. Rosero-Montalvo
    • 1
    • 2
  • Vivian F. López Batista
    • 1
  • Edwin A. Rosero
    • 2
  • Edgar D. Jaramillo
    • 2
  • Jorge A. Caraguay
    • 2
  • José Pijal-Rojas
    • 3
  • D. H. Peluffo-Ordóñez
    • 4
    • 5
  1. 1.Departamento Informática y AutomáticaUniversidad de SalamancaSalamancaSpain
  2. 2.Universidad Técnica del NorteIbarraEcuador
  3. 3.Intituto Tecnológico Superior 17 de JulioIbarraEcuador
  4. 4.Yachay TechUrcuquíEcuador
  5. 5.Corporación Universitaria Autónoma de NariñoPastoColombia

Personalised recommendations