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A Novel Experimental Prototype for Assessing IoT Performance on Real-Time Analytics

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Software Engineering and Algorithms in Intelligent Systems (CSOC2018 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 763))

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Abstract

Internet-of-Things (IoT) is one of the stepping stone to future ubiquitous computing with the aid of cloud environment. We reviewed the existing literature to find that there are more theoretical-based study and less standard and established modeling approach to claim the efficiency of the IoT application. Therefore, we present simple and novel prototyping of our experimental framework that not only offers real-time analysis of heterogeneous and dynamic sensory data captured from different IoT nodes but also offer a very user-friendly experience to carry out any form of an analytical operation on the top of it. The study outcome shows good streaming of real-time data of different physical attributes with better capability to read and analyze the real-time information. The prototype will offer simpler experience to handle IoT-based data and open avenues of various researches on IoT.

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References

  1. Geng, H.: Internet of Things and Data Analytics Handbook. Wiley, Hoboken (2017)

    Book  Google Scholar 

  2. Greengard, S.: The Internet of Things. MIT Press, Cambridge (2015)

    Google Scholar 

  3. Kamila, N.K.: Handbook of Research on Wireless Sensor Network Trends, Technologies, and Applications. IGI Global (2016)

    Google Scholar 

  4. Mohanan, V., Budiarto, R., Aldmour, I.: Powering the Internet of Things With 5G Networks. IGI Global (2017)

    Google Scholar 

  5. Mukhopadhyay, S.C.: Internet of Things: Challenges and Opportunities. Springer, Heidelberg (2014)

    Chapter  Google Scholar 

  6. Acharjya, D.P., Kalaiselvi Geetha, M.: Internet of Things: Novel Advances and Envisioned Applications. Springer, Cham (2017)

    Google Scholar 

  7. Tripathy, B.K., Anuradha, J.: Internet of Things (IoT): Technologies, Applications, Challenges and Solutions. CRC Press, Boca Raton (2017)

    Book  Google Scholar 

  8. Tayeb, S., Latifi, S., Kim, Y.: A survey on IoT communication and computation frameworks: An industrial perspective. In: 2017 IEEE 7th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, pp. 1–6 (2017)

    Google Scholar 

  9. Sterbenz, J.P.G.: Smart city and IoT resilience, survivability, and disruption tolerance: Challenges, modelling, and a survey of research opportunities. In: 2017 9th International Workshop on Resilient Networks Design and Modeling (RNDM), Alghero, Italy, pp. 1–6 (2017)

    Google Scholar 

  10. Manujakshi, B.C., Ramesh, K.B.: SDaaS: framework of sensor data as a service for leveraging services in Internet of Things. In: International Conference on Emerging Research in Computing, Information, Communication, and Applications, pp. 351–363 (2017)

    Google Scholar 

  11. Akbar, A., Khan, A., Carrez, F., Moessner, K.: Predictive analytics for complex IoT data streams. IEEE Internet Things J. 4(5), 1571–1582 (2017)

    Article  Google Scholar 

  12. Cao, N., Nasir, S.B., Sen, S., Raychowdhury, A.: Self-optimizing IoT wireless video sensor node with in-situ data analytics and context-driven energy-aware real-time adaptation. IEEE Trans. Circ. Syst. I Regul. Pap. 64(9), 2470–2480 (2017)

    Article  Google Scholar 

  13. Conti, F., et al.: An IoT endpoint system-on-chip for secure and energy-efficient near-sensor analytics. IEEE Trans. Circ. Syst. I Regul. Pap. 64(9), 2481–2494 (2017)

    Article  Google Scholar 

  14. Marjani, M., et al.: Big IoT data analytics: architecture, opportunities, and open research challenges. IEEE Access 5, 5247–5261 (2017)

    Article  Google Scholar 

  15. Patel, P., Intizar Ali, M., Sheth, A.: On using the intelligent edge for IoT analytics. IEEE Intell. Syst. 32(5), 64–69 (2017)

    Article  Google Scholar 

  16. Plageras, A.P., et al.: Efficient large-scale medical data (eHealth Big Data) analytics in Internet of Things. In: 2017 IEEE 19th Conference on Business Informatics (CBI), Thessaloniki, pp. 21–27 (2017)

    Google Scholar 

  17. Ricciardi, S., Amazonas, J.R., Palmieri, F., Bermudez-Edo, M.: Ambient intelligence in the Internet of Things. Mob. Inf. Syst. (2017)

    Google Scholar 

  18. Sharma, S.K., Wang, X.: Live data analytics with collaborative edge and cloud processing in wireless IoT networks. IEEE Access 5, 4621–4635 (2017)

    Article  Google Scholar 

  19. Silva, B.N., Khan, M., Han, K.: Big data analytics embedded smart city architecture for performance enhancement through real-time data processing and decision-making. Wirel. Commun. Mob. Comput. (2017)

    Google Scholar 

  20. Yang, S.: IoT stream processing and analytics in the Fog. IEEE Commun. Mag. 55(8), 21–27 (2017)

    Article  Google Scholar 

  21. Zhu, M., Liu, C., Wang, J., Su, S., Han, Y.: Service hyperlink: modeling and reusing partial process knowledge by mining event dependencies among sensor data services. In: 2017 IEEE International Conference on Web Services (ICWS), Honolulu, HI, pp. 902–905 (2017)

    Google Scholar 

  22. Bhuiyan, M.Z.A., Wu, J.: Event detection through differential pattern mining in Internet of Things. In: 2016 IEEE 13th International Conference on Mobile Ad Hoc and Sensor Systems (MASS), Brasilia, pp. 109–117 (2016)

    Google Scholar 

  23. Hwang, I., Kim, M., Ahn, H.J.: Data pipeline for generation and recommendation of the IoT rules based on open text data. In: 2016 30th International Conference on Advanced Information Networking and Applications Workshops (WAINA), Crans-Montana, pp. 238–242 (2016)

    Google Scholar 

  24. Kumarage, H., Khalil, I., Alabdulatif, A., Tari, Z., Yi, X.: Secure data analytics for cloud-integrated Internet of Things applications. IEEE Cloud Comput. 3(2), 46–56 (2016)

    Article  Google Scholar 

  25. Sun, Y., Song, H., Jara, A.J., Bie, R.: Internet of Things and big data analytics for smart and connected communities. IEEE Access 4, 766–773 (2016)

    Article  Google Scholar 

  26. Mishra, N., Lin, C.-C., Chang, H.-T.: A cognitive adopted framework for IoT big-data management and knowledge discovery prospective. Int. J. Distrib. Sens. Netw. (2015)

    Google Scholar 

  27. Mishra, N., Chang, H.-T., Lin, C.-C.: An IoT knowledge reengineering framework for semantic knowledge analytics for BI-services. Math. Prob. Eng. (2015)

    Google Scholar 

  28. Ganz, F., Puschmann, D., Barnaghi, P., Carrez, F.: A practical evaluation of information processing and abstraction techniques for the Internet of Things. IEEE Internet Things J. 2(4), 340–354 (2015)

    Article  Google Scholar 

  29. Mikusz, M., Clinch, S., Jones, R., Harding, M., Winstanley, C., Davies, N.: Repurposing web analytics to support the IoT. Computer 48(9), 42–49 (2015)

    Article  Google Scholar 

  30. Zhu, X., Kui, F., Wang, Y.: Predictive analytics by using bayesian model averaging for large-scale Internet of Things. Int. J. Distrib. Sens. Netw. (2013)

    Google Scholar 

  31. ThinkSpeak. https://thingspeak.com/pages/learn_more. Accessed 06 Dec 2017

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Acharya Institute of Technology, Bengaluru, India

    B. C. Manujakshi

  2. Department of Electronics and Instrumentation Engineering, RV College of Engineering, Bengaluru, India

    K. B. Ramesh

Authors
  1. B. C. Manujakshi
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  2. K. B. Ramesh
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Corresponding author

Correspondence to B. C. Manujakshi .

Editor information

Editors and Affiliations

  1. Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlín, Czech Republic

    Radek Silhavy

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Manujakshi, B.C., Ramesh, K.B. (2019). A Novel Experimental Prototype for Assessing IoT Performance on Real-Time Analytics. In: Silhavy, R. (eds) Software Engineering and Algorithms in Intelligent Systems. CSOC2018 2018. Advances in Intelligent Systems and Computing, vol 763. Springer, Cham. https://doi.org/10.1007/978-3-319-91186-1_6

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