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Big Data Analysis of Internet of Things System

Published:30 March 2021Publication History
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Abstract

The study aims at exploring the Internet of things (IoT) system from the perspective of data and further improving the performance of the IoT system. The IoT data energy collection and information transmission system model is constructed by combining IoT and wireless relay cooperative transmission technology. Moreover, the energy efficiency, outage probability (OP), and accuracy of the model are evaluated by simulation experiments. The results show that, in the energy efficiency analysis, with the increase of power split factor ρ, the information transmission ability of the system increases. Whereas, the energy collection ability decreases, so the energy efficiency is reduced. Thus, choosing a more suitable power split factor for the energy efficiency of IoT is important. By analyzing OP and bit error rate (BER), as the values of m (Nakagami, the fading index of the fading distribution) and multi-hop paths increase, the OP and BER are reduced while the system performance is increased. Therefore, this article uses wireless relay cooperative transmission technology to integrate big data analysis into the IoT system. Finally, by adding multi-hop path and other methods to reduce the OP and BER of system, the system performance is improved. It provides experimental basis for the development of IoT systems.

References

  1. S. Yu, C. Wang, K. Liu, and A. Y. Zomaya. 2016. Editorial for IEEE access special section on theoretical foundations for big data applications: Challenges and opportunities. IEEE Access 4 (2016), 5730–5732.Google ScholarGoogle ScholarCross RefCross Ref
  2. S. K. Lakshmanaprabu, K. Shankar, et al. 2018. Effective features to classify big data using social internet of things. IEEE Access 6 (2018), 24196–24204.Google ScholarGoogle ScholarCross RefCross Ref
  3. F. Firouzi, B. Farahani, A. B. Kahng, J. M. Rabaey, and N. Balac. 2017. Guest editorial: Alternative computing and machine learning for internet of things. IEEE Trans. Very Large Scale Integr. Syst. 25, 10 (2017), 2685–2687.Google ScholarGoogle ScholarCross RefCross Ref
  4. Y. Mehmood, F. Ahmad, I. Yaqoob, A. Adnane, M. Imran, and S. Guizani. 2017. Internet-of-things-based smart cities: Recent advances and challenges. IEEE Commun. Mag. 55, 9 (2017), 16–24.Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. M. Gohar, S. H. Ahmed, M. Khan, N. Guizani, A. Ahmed, and A. U. Rahman. 2018. A big data analytics architecture for the internet of small things. IEEE Commun. Mag. 56, 2 (2018), 128–133. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Y. Zhang, H. Wang, M. Chen, J. Wan, and I. Humar. 2018. IEEE access special section editorial: Healthcare big data. IEEE Access 6 (2018), 50555–50558.Google ScholarGoogle ScholarCross RefCross Ref
  7. A. Alrawais, A. Alhothaily, C. Hu, and X. Cheng. 2017. Fog computing for the internet of things: Security and privacy issues. IEEE Internet Comput. 21, 2 (2017), 34–42. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. T. Samad. 2016. Control systems and the internet of things [technical activities]. IEEE Control Syst. Mag. 36, 1 (2016), 13–16.Google ScholarGoogle ScholarCross RefCross Ref
  9. Z. Lv, H. Song, J. Lloret, D. Kim, and J. N. De Souza. 2019. IEEE access special section editorial: big data analytics in the internet-of-things and cyber-physical systems. IEEE Access 7 (2019), 18070–18075.Google ScholarGoogle ScholarCross RefCross Ref
  10. J. Wan, S. Tang, Z. Shu, et al. 2016. Software-defined industrial internet of things in the context of industry 4.0. IEEE Sensors J. 16, 20 (2016), 7373–7380.Google ScholarGoogle ScholarCross RefCross Ref
  11. I. Yaqoob, E. Ahmed, I. A. T. Hashem, et al. 2017. Internet of things architecture: Recent advances, taxonomy, requirements, and open challenges. IEEE Wireless Communications 24, 3 (2017), 10–16.Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. C. Zhu, J. J. P. C. Rodrigues, V. C. M. Leung, et al. 2018. Trust-based communication for the industrial internet of things. IEEE Commun. Mag. 56, 2 (2018), 16–22. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. X. Vilajosana, T. Watteyne, M. Vučinić, et al. 2019. 6tisch: Industrial performance for ipv6 internet-of-things networks. Proc. IEEE 107, 6 (2019), 1153–1165.Google ScholarGoogle ScholarCross RefCross Ref
  14. K. Wang, Y. Wang, Y. Sun, et al. 2016. Green industrial internet of things architecture: An energy-efficient perspective. IEEE Commun. Mag. 54, 12 (2016), 48–54.Google ScholarGoogle Scholar
  15. M. Wollschlaeger, T. Sauter, and J. Jasperneite. 2017. The future of industrial communication: Automation networks in the era of the internet of things and industry 4.0[J]. IEEE Industr. Electron. Mag. 11, 1 (2017), 17–27.Google ScholarGoogle ScholarCross RefCross Ref
  16. K. Gai, K. K. R. Choo, M. Qiu, et al. 2018. Privacy-preserving content-oriented wireless communication in internet-of-things. IEEE Internet Things J. 5, 4 (2018), 3059–3067.Google ScholarGoogle ScholarCross RefCross Ref
  17. Y. Zhang, X. Ma, J. Zhang, et al. 2019. Edge intelligence in the cognitive internet of things: Improving sensitivity and interactivity. IEEE Netw. 33, 3 (2019), 58–64.Google ScholarGoogle ScholarCross RefCross Ref
  18. C. Cheng, R. Lu, A. Petzoldt, and T. Takagi. 2017. Securing the internet of things in a quantum world. IEEE Commun. Mag. 55, 2 (2017), 116–120. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. B. Qolomany, A. Al-Fuqaha, A. Gupta, D. Benhaddou, S. Alwajidi, J. Qadir, and A. C. Fong. 2019. Leveraging machine learning and big data for smart buildings: A comprehensive survey. IEEE Access 7 (2019), 90316–90356.Google ScholarGoogle ScholarCross RefCross Ref
  20. P. Sundaravadivel, E. Kougianos, S. P. Mohanty, and M. K. Ganapathiraju. 2017. Everything you wanted to know about smart health care: Evaluating the different technologies and components of the internet of things for better health. IEEE Consum. Electron. Mag. 7, 1 (2017), 18–28.Google ScholarGoogle ScholarCross RefCross Ref
  21. D. Puthal, N. Malik, S. P. Mohanty, E. Kougianos, and C. Yang. 2018. The blockchain as a decentralized security framework [future directions]. IEEE Consum. Electron. Mag. 7, 2 (2018), 18–21.Google ScholarGoogle ScholarCross RefCross Ref
  22. J. H. Lee and H. Kim. 2017. Security and privacy challenges in the internet of things [security and privacy matters]. IEEE Consum. Electron. Mag. 6, 3 (2017), 134–136.Google ScholarGoogle ScholarCross RefCross Ref
  23. S. Yu, G. Wang, X. Liu, and J. Niu. 2018. Security and privacy in the age of the smart internet of things: An overview from a networking perspective. IEEE Commun. Mag. 56, 9 (2018), 14–18.Google ScholarGoogle ScholarCross RefCross Ref
  24. Y. Huang, Q. Zhao, Q. Zhou, and W. Jiang. 2018. Air quality forecast monitoring and its impact on brain health based on big data and the internet of things. IEEE Access 6 (2018), 78678–78688.Google ScholarGoogle ScholarCross RefCross Ref
  25. N. N. Chu. 2017. Surprising prevalence of electroencephalogram brain-computer interface to internet of things future directions]. IEEE Consum. Electron. Mag. 6, 2 (2017), 31–39.Google ScholarGoogle ScholarCross RefCross Ref
  26. D. E. Boubiche, A. S. K. Pathan, J. Lloret, H. Zhou, S. Hong, S. O. Amin, and M. A. Feki. 2018. Advanced industrial wireless sensor networks and intelligent IoT. IEEE Commun. Mag. 56, 2 (2018), 14–15. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. M. Zolanvari, M. A. Teixeira, L. Gupta, K. M. Khan, and R. Jain. 2019. Machine learning-based network vulnerability analysis of industrial internet of things. IEEE Internet Things J. 6, 4 (2019), 6822–6834.Google ScholarGoogle ScholarCross RefCross Ref
  28. M. Lopez-Martin, B. Carro, A. Sanchez-Esguevillas, and J. Lloret. 2017. Network traffic classifier with convolutional and recurrent neural networks for internet of things. IEEE Access 5 (2017), 18042–18050.Google ScholarGoogle ScholarCross RefCross Ref
  29. C. J. D'Orazio, K. K. R. Choo, and L. T. Yang. 2016. Data exfiltration from internet of things devices: iOS devices as case studies. IEEE Internet Things J. 4, 2 (2016), 524–535.Google ScholarGoogle ScholarCross RefCross Ref
  30. T. Qiu, J. Liu, W. Si, M. Han, H. Ning, and M. Atiquzzaman. 2017. A data-driven robustness algorithm for the internet of things in smart cities. IEEE Commun. Mag. 55, 12 (2017), 18–23. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. M. Elhoseny. 2020. Intelligent firefly-based algorithm with Levy distribution (FF-L) for multicast routing in vehicular communications. Expert Syst. Appl. 140 (2020), 112889.Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. M. Elhoseny and K. Shankar. 2019. Reliable data transmission model for mobile ad hoc network using signcryption technique. IEEE Trans. Reliabil. 69, 3 (2019) 1077–1086.Google ScholarGoogle ScholarCross RefCross Ref
  33. B. Cao, J. Zhao, P. Yang, P. Yang, X. Liu, J. Qi, and K. Muhammad. 2019. Multiobjective feature selection for microarray data via distributed parallel algorithms. Future Gen. Comput. Syst. 100 (2019), 952–981.Google ScholarGoogle ScholarCross RefCross Ref
  34. K. Muhammad, S. Khan, M. Elhoseny, S. H. Ahmed, and S. W. Baik. 2019. Efficient fire detection for uncertain surveillance environment. IEEE Trans. Industr. Inform. 15, 5 (2019), 3113–3122.Google ScholarGoogle ScholarCross RefCross Ref

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    • Published in

      cover image ACM Transactions on Internet Technology
      ACM Transactions on Internet Technology  Volume 21, Issue 2
      June 2021
      599 pages
      ISSN:1533-5399
      EISSN:1557-6051
      DOI:10.1145/3453144
      • Editor:
      • Ling Liu
      Issue’s Table of Contents

      Copyright © 2021 Association for Computing Machinery.

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 30 March 2021
      • Online AM: 7 May 2020
      • Revised: 1 March 2020
      • Accepted: 1 March 2020
      • Received: 1 January 2020
      Published in toit Volume 21, Issue 2

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