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The Neural Knowledge DNA Based Smart Internet of Things

Abstract

ABSTRACT The Internet of Things (IoT) has gained significant attention from industry as well as academia during the past decade. Smartness, however, remains a substantial challenge for IoT applications. Recent advances in networked sensor technologies, computing, and machine learning have made it possible for building new smart IoT applications. In this paper, we propose a novel approach: the Neural Knowledge DNA based Smart Internet of Things that enables IoT to extract knowledge from past experiences, as well as to store, evolve, share, and reuse such knowledge aiming for smart functions. By catching decision events, this approach helps IoT gather its own daily operation experiences, and it uses such experiences for knowledge discovery with the support of machine learning technologies. An initial case study is presented at the end of this paper to demonstrate how this approach can help IoT applications become smart: the proposed approach is applied to fitness wristbands to enable human action recognition.

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Category:
Articles
Type:
artykuły w czasopismach
Published in:
CYBERNETICS AND SYSTEMS no. 51, pages 258 - 264,
ISSN: 0196-9722
Language:
English
Publication year:
2020
Bibliographic description:
Zhang H., Li F., Wang J., Wang Z., Shi L., Sanin C., Szczerbicki E.: The Neural Knowledge DNA Based Smart Internet of Things// CYBERNETICS AND SYSTEMS -Vol. 51,iss. 2 (2020), s.258-264
DOI:
Digital Object Identifier (open in new tab) 10.1080/01969722.2019.1705545
Bibliography: test
  1. Al-Ali, A. R. , Zualkernan, I. A. , Rashid, M. , Gupta, R. , & Alikarar, M. . (2018). A smart home energy management system using iot and big data analytics approach. IEEE Transactions on Consumer Electronics, 63(4), 426-434. open in new tab
  2. Burton, L., Dave, N., Fernandez, R. E., Jayachandran, K., & Bhansali, S. (2018). Smart gardening iot soil sheets for real-time nutrient analysis. Journal of The Electrochemical Society, 165(8), B3157-B3162. open in new tab
  3. Gubbi, J. , Buyya, R. , Marusic, S. , & Palaniswami, M. . (2013). Internet of things (iot): a vision, architectural elements, and future directions. Future Generation Computer Systems, 29(7), 1645-1660. open in new tab
  4. Lecun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436. open in new tab
  5. Lin, J., Yu, W., Zhang, N., Yang, X., Zhang, H., & Zhao, W. (2017). A survey on internet of things: architecture, enabling technologies, security and privacy, and applications. IEEE Internet of Things Journal, 4(5), 1125-1142. open in new tab
  6. Lokshina, I. , & Lanting, C. . (2019). A Qualitative Evaluation of IoT-Driven eHealth: Knowledge Management, Business Models and Opportunities, Deployment and Evolution. Hicss. open in new tab
  7. Mohammadi, M. , & Al-Fuqaha, A. . (2018). Enabling cognitive smart cities using big data and machine learning: approaches and challenges. IEEE Communications Magazine, 56(2), 94-101. open in new tab
  8. Sanin, C. , Haoxi, Z. , Shafiq, I. , Waris, M. M. , Caterine, S. D. O. , & Szczerbicki, E. . (2018). Experience based knowledge representation for internet of things and cyber physical systems with case studies. Future Generation Computer Systems, S0167739X17316965. open in new tab
  9. Sharma, N. , Singh, K. , & Goyal, D. P. . (2012). Is technology universal panacea for knowledge and experience management? answers from indian it sector. Communications in Computer & Information Science, 285, 187-198. open in new tab
  10. Siddiqui, I. F. , Qureshi, N. M. F. , Shaikh, M. A. , Chowdhry, B. S. , Abbas, A. , & Bashir, A. K. , et al. (2018). Stuck-at fault analytics of iot devices using knowledge-based data processing strategy in smart grid. Wireless Personal Communications. open in new tab
  11. Siow, E. , Tiropanis, T. , & Hall, W. . (2018). Analytics for the internet of things: a survey. Acm Computing Surveys, 51(4). open in new tab
  12. Siryani, J., Tanju, B., & Eveleigh, T. J. (2017). A machine learning decision-support system improves the internet of things' smart meter operations. IEEE Internet of Things Journal, 4(4), 1056-1066. open in new tab
  13. Tsai, C. W., Lai, C. F., Chiang, M. C., & Yang, L. T. (2014). Data mining for internet of things: a survey. IEEE Communications Surveys & Tutorials, 16(1), 77-97. open in new tab
  14. Wu, S. , Rendall, J. B. , Smith, M. J. , Zhu, S. , Xu, J. , & Wang, H. , et al. (2017). open in new tab
  15. Survey on prediction algorithms in smart homes. IEEE Internet of Things Journal, 4(3), 636-644. open in new tab
  16. Zhang, H., Sanin, C., & Szczerbicki, E. (2016). Towards neural knowledge dna. Journal of Intelligent & Fuzzy Systems, 32(2), 1575-1584. open in new tab
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Gdańsk University of Technology

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