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IoT Analytics Architectures: Challenges, Solution Proposals and Future Research Directions

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Research Challenges in Information Science (RCIS 2020)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 385))

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Abstract

The Internet of Things (IoT) presents an extensive area for research, based on its growing importance in a multitude of different domains of everyday life, business and industry. In this context, different aspects of data analytics, e.g. algorithms or system architectures, as well as their scientific investigation play a pivotal role in the advancement of the IoT. Therefore, past research has presented a multitude of architectural approaches to enable data processing and analytics in various IoT domains, addressing different architectural challenges. In this paper, we identify and present an overview of these challenges as well as existing architectural proposals. Furthermore, we categorize found architectural proposals along various dimensions in order to highlight the evolution of research in this field and pinpoint architectural shortcomings. The results of this paper show that several challenges have been addressed by a large number of IoT system architectures for data analytics while others are either not relevant for certain domains or need further investigation. Finally, we offer points of reference for future research based on the findings of this paper.

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Notes

  1. 1.

    The full list of publications can be found at https://github.com/zsco/IoT-Analytics-Architectures-Challenges/blob/master/publications.md.

  2. 2.

    The data collection form of the challenges can be found at: https://github.com/zsco/IoT-Analytics-Architectures-Challenges/blob/master/challenges.md.

  3. 3.

    The data collection form of the classification can be found at: https://github.com/zsco/IoT-Analytics-Architectures-Challenges/blob/master/classification.md.

  4. 4.

    The data collection form of the mapping of architectures versus challenges can be found at: https://github.com/zsco/IoT-Analytics-Architectures-Challenges/blob/master/architecturesVsChallenges.md.

References

  1. Statista: Size of the Internet of Things (IoT) market worldwide from 2017 to 2025 (2019). https://www.statista.com/statistics/976313/global-iot-market-size/. Accessed 27 Jan 2020

  2. Siow, E., Tiropanis, T., Hall, W.: Analytics for the Internet of Things. ACM Comput. Surv. 51(4), 1–35 (2018)

    Article  Google Scholar 

  3. Simmhan, Y., Perera, S.: Big data analytics platforms for real-time applications in IoT. In: Pyne, S., Rao, B., Rao, S. (eds.) Big Data Analytics, pp. 115–135. Springer, New Delhi (2016). https://doi.org/10.1007/978-81-322-3628-3_7

    Chapter  Google Scholar 

  4. Webster, J., Watson, R.T.: Analyzing the past to prepare for the future: writing a literature review. MIS Q. 26, xiii–xxiii (2002)

    Google Scholar 

  5. vom Brocke, J., Simons, A., Niehaves, B., Riemer, K., Plattfaut, R., Cleven, A.: Reconstructing the giant: on the importance of rigour in documenting the literature search process. In: Proceedings of the 17th European Conference on Information Systems, Verona, Italy (2009)

    Google Scholar 

  6. Vom Brocke, J., Simons, A., Riemer, K., Niehaves, B., Plattfaut, R., Cleven, A.: Standing on the shoulders of giants: challenges and recommendations of literature search in information systems research. Commun. Assoc. Inf. Syst. 37, 205–224 (2015)

    Google Scholar 

  7. Cooper, H.M.: Organizing knowledge syntheses: a taxonomy of literature reviews. Knowl. Soc. 1, 104–126 (1988)

    Google Scholar 

  8. Mouradian, C., Naboulsi, D., Yangui, S., Glitho, R.H., Morrow, M.J., Polakos, P.A.: A comprehensive survey on fog computing state-of-the-art and research challenges. IEEE Commun. Surv. Tutor. 20(1), 416–464 (2018)

    Article  Google Scholar 

  9. Saleem, T.J., Chishti, M.A.: Data analytics in the Internet of Things: a survey. Scalable Comput. Pract. Exp. 20(4), 607–630 (2019)

    Article  Google Scholar 

  10. Hasan, T., Kikiras, P., Leonardi, A., Ziekow, H., Daubert, J.: Cloud-based IoT analytics for the smart grid: experiences from a 3-year pilot. In: Michelson, D.G., Garcia, A.L., Zhang, W.-B., Cappos, J., Darieby, M.E. (eds.) Proceedings of the 10th International Conference on Testbeds and Research Infrastructures for the Development of Networks & Communities (TRIDENTCOM), Vancouver, Canada (2015)

    Google Scholar 

  11. Stolpe, M.: The Internet of Things: opportunities and challenges for distributed data analysis. SIGKDD Explor. Newsl. 18(1), 15–34 (2016)

    Article  Google Scholar 

  12. Ahmed, E., et al.: The role of big data analytics in Internet of Things. Comput. Netw. 129, 459–471 (2017)

    Article  Google Scholar 

  13. Kaur, M., Aslam, A.M.: Big data analytics on IOT challenges open research issues and tools. IJSRCSE 6(3), 81–85 (2018)

    Article  Google Scholar 

  14. Batool, S., Saqib, N.A., Khan, M.A.: Internet of Things data analytics for user authentication and activity recognition. In: 2017 Second International Conference on Fog and Mobile Edge Computing (FMEC), 8–11 May 2017, Valencia, Spain, pp. 183–187. IEEE (2017)

    Google Scholar 

  15. Cheng, B., Longo, S., Cirillo, F., Bauer, M., Kovacs, E.: Building a big data platform for smart cities: experience and lessons from santander. In: Carminati, B. (ed.) 2015 IEEE International Congress on Big Data (BigData Congress), 27 June–2 July 2015, New York, USA, pp. 592–599. IEEE, Piscataway (2015)

    Google Scholar 

  16. Ding, G., Wang, L., Wu, Q.: Big data analytics in future Internet of Things (2013)

    Google Scholar 

  17. 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 

  18. Kefalakis, N., Roukounaki, A., Soldatos, J.: A configurable distributed data analytics infrastructure for the industrial Internet of Things. In: 2019 15th International Conference on Distributed Computing in Sensor Systems (DCOSS), pp. 179–181 (2019)

    Google Scholar 

  19. ur Rehman, M.H., Yaqoob, I., Salah, K., Imran, M., Jayaraman, P.P., Perera, C.: The role of big data analytics in industrial Internet of Things. Future Gener. Comput. Syst. 99, 247–259 (2019)

    Article  Google Scholar 

  20. Verma, S., Kawamoto, Y., Fadlullah, Z., Nishiyama, H., Kato, N.: A survey on network methodologies for real-time analytics of massive IoT data and open research issues. IEEE Commun. Surv. 19(3), 1457–1477 (2017)

    Article  Google Scholar 

  21. Biswas, A.R., Dupont, C., Pham, C.: IoT, cloud and bigdata integration for IoT analytics. Build. Blocks IoT Anal. 11, 11–38 (2016)

    Google Scholar 

  22. Stojkoska, B.L.R., Trivodaliev, K.V.: A review of Internet of Things for smart home. Challenges and solutions. J. Clean. Prod. 143(3), 1454–1464 (2017)

    Article  Google Scholar 

  23. Schooler, E.M., Zage, D., Sedayao, J., Moustafa, H., Brown, A., Ambrosin, M.: An architectural vision for a data-centric IoT. Rethinking things, trust and clouds. In: 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS), 05–08 June 2017, Atlanta, GA, USA, pp. 1717–1728. IEEE (2017)

    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. Cao, H., Wachowicz, M.: Analytics everywhere for streaming IoT data. In: 2019 Sixth International Conference on Internet of Things: Systems, Management and Security (IOTSMS), pp. 18–25 (2019)

    Google Scholar 

  26. Tönjes, R., et al.: Real time IoT stream processing and large-scale data analytics for smart city applications. In: Poster Session, European Conference on Networks and Communications (2014)

    Google Scholar 

  27. Wich, M., Kramer, T.: Enrichment of smart home services by integrating social network services and big data analytics. In: Bui, T.X., Sprague, R.H. (eds.) Proceedings of the 49th Annual Hawaii International Conference on System Sciences, 5–8 January 2016, Kauai, Hawaii, USA, pp. 425–434. IEEE, Piscataway (2016)

    Google Scholar 

  28. Tsai, C.-W., Tsai, P.-W., Chiang, M.-C., Yang, C.-S.: Data analytics for Internet of Things: a review. WIREs Data Min. Knowl. Discov. 8(5), e1261 (2018)

    Google Scholar 

  29. ur Rehman, M.H., Ahmed, E., Yaqoob, I., Hashem, I.A.T., Imran, M., Ahmad, S.: Big data analytics in industrial IoT using a concentric computing model. IEEE Commun. Mag. 56(2), 37–43 (2018)

    Article  Google Scholar 

  30. Anand, P.: Towards evolution of M2M into Internet of Things for analytics. In: 2015 IEEE Recent Advances in Intelligent Computational Systems (RAICS), Trivandrum, Kerala, India, pp. 388–393 (2015)

    Google Scholar 

  31. Marjani, M., Nasaruddin, F., Gani, A.: Big IoT data analytics. Architecture opportunities, and open research challenges. IEEE Access 5, 5247–5261 (2017)

    Article  Google Scholar 

  32. Rozik, A.S., Tolba, A.S., El-Dosuky, M.A.: Design and implementation of the sense Egypt platform for real-time analysis of IoT data streams. AIT 6(4), 65–91 (2016)

    Article  Google Scholar 

  33. Biswas, A.R., Giaffreda, R.: IoT and cloud convergence. Opportunities and challenges. In: 2014 IEEE World Forum on Internet of Things (WF-IoT), 06–08 March 2014, Seoul, Korea (South), pp. 375–376. IEEE (2014)

    Google Scholar 

  34. Auger, A., Exposito, E., Lochin, E.: Sensor observation streams within cloud-based IoT platforms. Challenges and directions. In: 2017 20th Conference on Innovations in Clouds, Internet and Networks (ICIN), 07–09 March 2017, Paris, pp. 177–184. IEEE (2017)

    Google Scholar 

  35. Xu, Q., Aung, K.M.M., Zhu, Y., Yong, K.L.: Building a large-scale object-based active storage platform for data analytics in the Internet of Things. J. Supercomput. 72(7), 2796–2814 (2016)

    Article  Google Scholar 

  36. Din, S., Paul, A.: Smart health monitoring and management system: toward autonomous wearable sensing for Internet of Things using big data analytics. Future Gener. Comput. Syst. 91, 611–619 (2018)

    Article  Google Scholar 

  37. Almeida, A., Mulero, R., Rametta, P., Urošević, V., Andrić, M., Patrono, L.: A critical analysis of an IoT-aware AAL system for elderly monitoring. Future Gener. Comput. Syst. 97, 598–619 (2019)

    Article  Google Scholar 

  38. Manogaran, G., Varatharajan, R., Lopez, D., Kumar, P.M., Sundarasekar, R., Thota, C.: A new architecture of Internet of Things and big data ecosystem for secured smart healthcare monitoring and alerting system. Future Gener. Comput. Syst. 82, 375–387 (2018)

    Article  Google Scholar 

  39. Al-Jaroodi, J., Mohamed, N.: Service-oriented architecture for big data analytics in smart cities. In: Proceedings of the 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID), 1–4 May 2018, Washington, DC, pp. 633–640. IEEE, Piscataway (2018)

    Google Scholar 

  40. Taneja, M., Jalodia, N., Davy, A.: Distributed decomposed data analytics in fog enabled IoT deployments. IEEE Access 7, 40969–40981 (2019)

    Article  Google Scholar 

  41. Ge, Y., Liang, X., Zhou, Y.C., Pan, Z., Zhao, G.T., Zheng, Y.L.: Adaptive analytic service for real-time Internet of Things applications. In: 2016 IEEE International Conference on Web Services (ICWS), San Francisco, CA, USA, pp. 484–491 (2016)

    Google Scholar 

  42. Hochreiner, C., Vogler, M., Waibel, P., Dustdar, S.: VISP: an ecosystem for elastic data stream processing for the Internet of Things. In: Matthes, F., Mendling, J., Rinderle-Ma, S. (eds.) Proceedings of the 2016 IEEE 20th International Enterprise Distributed Object Computing Conference (EDOC), 5–9 September 2016, Vienna, Austria, pp. 1–11. IEEE, Piscataway (2016)

    Google Scholar 

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Acknowledgements

The work presented in this paper is partly funded by the European Regional Development Fund (ERDF) and the Free State of Saxony (Sächsische Aufbaubank - SAB).

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Authors and Affiliations

  1. Institute for Applied Informatics (InfAI), Goerdelerring 9, 04109, Leipzig, Germany

    Theo Zschörnig & Robert Wehlitz

  2. Information Systems Institute, Leipzig University, Grimmaische Str. 12, 04109, Leipzig, Germany

    Bogdan Franczyk

  3. Business Informatics Institute, Wrocław University of Economics, ul. Komandorska 118-120, 53-345, Wrocław, Poland

    Bogdan Franczyk

Authors
  1. Theo Zschörnig
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  2. Robert Wehlitz
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Correspondence to Theo Zschörnig .

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Editors and Affiliations

  1. Utrecht University, Utrecht, The Netherlands

    Fabiano Dalpiaz

  2. Stockholm University, Kista, Sweden

    Jelena Zdravkovic

  3. The Institute of Digital Innovation and Research, Dublin, Ireland

    Pericles Loucopoulos

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Zschörnig, T., Wehlitz, R., Franczyk, B. (2020). IoT Analytics Architectures: Challenges, Solution Proposals and Future Research Directions. In: Dalpiaz, F., Zdravkovic, J., Loucopoulos, P. (eds) Research Challenges in Information Science. RCIS 2020. Lecture Notes in Business Information Processing, vol 385. Springer, Cham. https://doi.org/10.1007/978-3-030-50316-1_5

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  • DOI: https://doi.org/10.1007/978-3-030-50316-1_5

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  • Online ISBN: 978-3-030-50316-1

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