Advertisement

Modeling Internet of Things Data for Knowledge Discovery

  • Mudasir Shafi
  • Syed Zubair Ahmad ShahEmail author
  • Mohammad Amjad
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 35)

Abstract

Internet of Things (IoT) is a budding field. It finds its base in the science of electronic equipments, communication technologies and computing algorithms. It is the network of physical devices, vehicles, home appliances and other items embedded with electronics, software, sensors, actuators, and connectivity which enable these objects to connect and exchange data. All things on the IoT may develop a data overflow that encompasses various types of relevant information. Data can be generated as a result of communication between humans, between human and systems, and between systems themselves. This data can be used to improve the services offered by IoT and thus it becomes important to work on the IoT generated data. This paper presents a model for implementing an IoT system, collecting data from it and performing data analytics on the collected data with the intent of deducing knowledge from this data. This paper also proposes some new areas where IoT can be put to use, thus bringing in sight a ground-breaking view of what IoT can and will do. This, in turn, will change the way we live, work and communicate. The hurdles that may come in the way of implementing IoT have also been discussed in this paper. Finally, the methods of analyzing IoT data have also been discussed with focus on frequent pattern mining. Implementation and results of our work have been shown vividly.

Keywords

Internet of Things Knowledge discovery of data IoT reference model IoT data 

References

  1. Aggarwal, C.: Data Classification: Algorithms and Applications. CRC Press, Boca Raton (2014)CrossRefGoogle Scholar
  2. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proceedings of the 20th VLDB Conference, pp. 487–499 (1994)Google Scholar
  3. Anumba, C., Wang, X.: Mobile and Pervasive Computing in Construction. Wiley, Hoboken (2012)CrossRefGoogle Scholar
  4. Atzori, L., Iera, A., Morabito, G.: The internet of things: a survey. Comput. Netw. 54(15), 2787–2805 (2010)CrossRefGoogle Scholar
  5. Bandyopadhyay, D., Sen, J.: Internet of Things: applications and challenges in technology and standardization. Wireless Pers. Commun. 58(1), 49–69 (2011)CrossRefGoogle Scholar
  6. Bin, S., Yuan, L., Xiaoyi, W.: Research on data mining models for the Internet of Things. In: Proceedings of the International Conference on Image Analysis and Signal Processing, pp. 127–132. IEEE (2010)Google Scholar
  7. Bonomi, F., Milito, R., Zhu, J., Addepalli, S.: Fog computing and its role in the Internet of Things. In: Proceedings of the First Edition of the MCC Workshop on Mobile Cloud Computing, pp. 13–16. ACM (2012)Google Scholar
  8. Cantoni, V., Lombardi, L., Lombardi, P.: Challenges for data mining in distributed sensor networks. In: Proceedings of 18th International Conference on Pattern Recognition, vol. 1, pp. 1000–1007. IEEE (2006)Google Scholar
  9. Domingo, M.: An overview of the Internet of Things for people with disabilities. J. Netw. Comput. Appl. 35(2), 584–596 (2012)CrossRefGoogle Scholar
  10. Erlich, Y.: A vision for ubiquitous sequencing. Genome Res. 25(10), 1411–1416 (2015)CrossRefGoogle Scholar
  11. Haller, S., Karnouskos, S., Schroth, C.: The Internet of Things in an enterprise context. In: Future Internet Symposium, pp. 14–28. Springer, Heidelberg (2008)Google Scholar
  12. Han, J., Pei, J., Kamber, M.: Data Mining: Concepts and Techniques. Elsevier, Amsterdam (2011)zbMATHGoogle Scholar
  13. Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. ACM Sigmod Rec. 29(2), 1–12 (2000)CrossRefGoogle Scholar
  14. Keller, T.: Mining the Internet of Things-detection of false-positive RFID tag reads using low-level reader data (2011)Google Scholar
  15. Kortuem, G., Kawsar, F., Sundramoorthy, V., Fitton, D.: Smart objects as building blocks for the internet of things. IEEE Internet Comput. 14(1), 44–51 (2010)CrossRefGoogle Scholar
  16. Kulkarni, R., Forster, A., Venayagamoorthy, G.: Computational intelligence in wireless sensor networks: a survey. IEEE Commun. Surv. Tutor. 13(1), 68–96 (2011)CrossRefGoogle Scholar
  17. LaDiega, G., Walden, I.: Contracting for the “Internet of Things”: looking into the Nest. Eur. J. Law Technol. 7(2) (2016)Google Scholar
  18. Lindner, T.: The supply chain: changing at the speed of technology. Connected WorldGoogle Scholar
  19. Lopez, T., Ranasinghe, D., Harrison, M., McFarlane, D.: Adding sense to the Internet of Things. Pers. Ubiquit. Comput. 16(3), 291–308 (2017)CrossRefGoogle Scholar
  20. Lopez, T., Ranasinghe, D., Patkai, B., McFarlane, D.: Taxonomy, technology and applications of smart objects. Inf. Syst. Front. 13(2), 281–300 (2011)CrossRefGoogle Scholar
  21. M&M Research Group: Internet of Things (IoT) & M2 M Communication Market: Advanced technologies, future cities & adoption trends, roadmaps & worldwide forecasts. Electronics.ca Publications, Technical report (2012)Google Scholar
  22. Masciari, E.: A framework for outlier mining in RFID data. In: Proceedings of the 11th International Symposium on Database Engineering and Applications, pp. 263–267. IEEE (2007)Google Scholar
  23. Mattern, F., Floerkemeier, C.: From the internet of computers to the Internet of Things. In: From Active Data Management to Event-Based Systems and More, pp. 242–259. Springer, Heidelberg (2010)Google Scholar
  24. Miorandi, D., Sicari, S., DePellegrini, F., Chlamtac, I.: Internet of Things: vision, applications and research challenges. Ad Hoc Netw. 10(7), 1497–1516 (2016)CrossRefGoogle Scholar
  25. Palattella, M., Accettura, N., Vilajosana, X., Watteyne, T., Grieco, L., Boggia, G., Dohler, M.: Standardized protocol stack for the internet of (important) things. IEEE Commun. Surv. Tutor. 15(3), 1389–1406 (2013)CrossRefGoogle Scholar
  26. Poslad, S.: Ubiquitous Computing: Basics and Vision, pp. 1–40. Wiley, Hoboken (2009)CrossRefGoogle Scholar
  27. Rouse, M., Wigmore, I.: Internet of Things (IoT) (2013)Google Scholar
  28. Shah, S., Amjad, M.: Lexical analysis of the Quran using frequent itemset mining. In: Proceedings of the 21st World Multi-Conference on Systemics, Cybernetics and Informatics, pp. 310–313 (2017)Google Scholar
  29. Siegemund, F.: A context-aware communication platform for smart objects. In: Proceedings of the International Conference on Pervasive Computing, pp. 69–86. Springer, Heidelberg (2004)Google Scholar
  30. Steinbach, M., Karypis, G., Kumar, V.: A comparison of document clustering techniques. In: KDD Workshop on Text Mining, vol. 400, no. 1, pp. 525–526 (2000)Google Scholar
  31. Hussain, A., Keshavamurthy, B.: An enhanced communication mechanism for partitioned social overlay networks using modified multi-dimensional routing. Clust. Comput. (2018)Google Scholar
  32. Tsai, C., Lai, C., Chiang, M., Yang, L.: Data mining for Internet of Things: a survey. IEEE Commun. Surv. Tutor. 16(1), 77–97 (2014)CrossRefGoogle Scholar
  33. Vermesan, O., Friess, P.: Internet of Things: Converging Technologies for Smart Environments and Integrated Ecosystems. River Publishers, Aalborg (2013)Google Scholar
  34. Zhang, L.: The business scale of communications between smart objects is tens of times the scale of communications between persons. Science Times (2009)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Mudasir Shafi
    • 1
  • Syed Zubair Ahmad Shah
    • 2
    Email author
  • Mohammad Amjad
    • 3
  1. 1.Maharishi Dayanand UniversityRohtakIndia
  2. 2.Islamic University of Science and TechnologyAwantiporaIndia
  3. 3.Jamia Millia IslamiaNew DelhiIndia

Personalised recommendations