Skip to main content

Recent Research on Data Analytics Techniques for Internet of Things

  • Conference paper
  • First Online:
Advances in Computing and Data Sciences (ICACDS 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1045))

Included in the following conference series:

  • 1012 Accesses

Abstract

The factor driving the lucrative IoT (Internet of Things) domain is the amalgamation of diverse technologies and their associated solution strategies. This research paper reports the recent review work on IoT for data analytics methods and applications. It also work IoT data classification holding diverse dimensions. In addition, a recent and intense survey of different kinds of data analytics techniques pertaining to different applications with datasets picked up from diverse domains are unfolded and packed into domain categories for usage in IoT in this work. In the present heterogeneous IoT scenario, this recent survey is dedicated to the ones who wish to loom towards this complex domain and dream to bestow upon its research and development. A wide spectrum of visions related to data techniques for IoT is presented along with the intense review of the allied enabling technologies. Open research issues and future directions of research are also presented.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Yan, Z., Zhang, P., Vasilakos, A.V.: A survey on trust management for Internet of Things. J. Netw. Comput. Appl. 42, 120–134 (2014)

    Article  Google Scholar 

  • Díaz, M., Martín, C., Rubio, B.: State-of-the-art, challenges, and open issues in the integration of Internet of things and cloud computing. J. Netw. Comput. Appl. 67, 99–117 (2016)

    Article  Google Scholar 

  • Alaba, F.A., Othman, M., Hashem, I.A.T., Alotaibi, F.: Internet of Things security: a survey. J. Netw. Comput. Appl. 88, 10–28 (2017)

    Article  Google Scholar 

  • https://www.postscapes.com/internet-of-things-protocols/

  • Darshan, K.R., Anandakumar, K.R.: A comprehensive review on usage of Internet of Things (IoT) in healthcare system. In: 2015 International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT), pp. 132–136. IEEE, December 2015

    Google Scholar 

  • Martis, R.J., Gurupur, V.P., Lin, H., Islam, A., Fernandes, S.L.: Recent advances in big data analytics, Internet of Things and machine learning. Future Gener. Comput. Syst. 88, 696 (2018)

    Article  Google Scholar 

  • Ravi, D., Wong, C., Lo, B., Yang, G.Z.: A deep learning approach to on-node sensor data analytics for mobile or wearable devices. IEEE J. Biomed. Health Inform. 21(1), 56–64 (2017)

    Article  Google Scholar 

  • Li, P., Chen, Z., Yang, L.T., Zhang, Q., Deen, M.J.: Deep convolutional computation model for feature learning on big data in Internet of Things. IEEE Trans. Industr. Inf. 14(2), 790–798 (2018a)

    Article  Google Scholar 

  • Li, H., Ota, K., Dong, M.: Learning IoT in edge: deep learning for the Internet of Things with edge computing. IEEE Netw. 32(1), 96–101 (2018b)

    Article  Google Scholar 

  • Fekade, B., Maksymyuk, T., Kyryk, M., Jo, M.: Probabilistic recovery of incomplete sensed data in IoT. IEEE Internet Things J. 5(4), 2282–2292 (2018)

    Article  Google Scholar 

  • Diro, A.A., Chilamkurti, N.: Distributed attack detection scheme using deep learning approach for Internet of Things. Future Gener. Comput. Syst. 82, 761–768 (2018)

    Article  Google Scholar 

  • Mohammadi, M., Al-Fuqaha, A., Guizani, M., Oh, J.S.: Semisupervised deep reinforcement learning in support of IoT and smart city services. IEEE Internet Things J. 5(2), 624–635 (2018)

    Article  Google Scholar 

  • Zhang, Q., Yang, L.T., Chen, Z., Li, P.: High-order possibilistic c-means algorithms based on tensor decompositions for big data in IoT. Inf. Fusion 39, 72–80 (2018)

    Article  Google Scholar 

  • Höller, J., Boyle, D., Karnouskos, S., Avesand, S., Mulligan, C., Tsiatsis, V.: From Machine-to-Machine to the Internet of Things, pp. 1–331. Academic Press, Cambridge (2014)

    Book  Google Scholar 

  • Soldatos, J., et al.: OpenIoT: Open source Internet-of-Things in the cloud. In: Podnar Žarko, I., Pripužić, K., Serrano, M. (eds.) Interoperability and Open-Source Solutions for the Internet of Things. LNCS, vol. 9001, pp. 13–25. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-16546-2_3

    Chapter  Google Scholar 

  • Hromic, H., et al.: Real time analysis of sensor data for the Internet of Things by means of clustering and event processing. In: 2015 IEEE International Conference on Communications (ICC), pp. 685–691. IEEE, June 2015

    Google Scholar 

  • Dastjerdi, A.V., Buyya, R.: Fog computing: helping the Internet of Things realize its potential. Computer 49(8), 112–116 (2016)

    Article  Google Scholar 

  • Qin, Y., Sheng, Q.Z., Falkner, N.J., Dustdar, S., Wang, H., Vasilakos, A.V.: When things matter: a survey on data-centric Internet of Things. J. Netw. Comput. Appl. 64, 137–153 (2016)

    Article  Google Scholar 

  • Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: iFogSim: a toolkit for modeling and simulation of resource management techniques in the Internet of Things, edge and fog computing environments. Softw. Pract. Exp. 47(9), 1275–1296 (2017)

    Article  Google Scholar 

  • Malek, Y.N., et al.: On the use of IoT and big data technologies for real-time monitoring and data processing. Procedia Comput. Sci. 113, 429–434 (2017)

    Article  Google Scholar 

  • Tao, M., Zuo, J., Liu, Z., Castiglione, A., Palmieri, F.: Multi-layer cloud architectural model and ontology-based security service framework for IoT-based smart homes. Future Gener. Comput. Syst. 78, 1040–1051 (2018)

    Article  Google Scholar 

  • Rodríguez-Valenzuela, S., Holgado-Terriza, J.A., Gutiérrez-Guerrero, J.M., Muros-Cobos, J.L.: Distributed service-based approach for sensor data fusion in IoT environments. Sensors 14(10), 19200–19228 (2014)

    Article  Google Scholar 

  • Su, X., et al.: Distribution of semantic reasoning on the edge of Internet of Things. In: 2018 IEEE International Conference on Pervasive Computing and Communications (PerCom), pp. 1–9. IEEE, March 2018

    Google Scholar 

  • Ploennigs, J., Ba, A., Barry, M.: Materializing the promises of cognitive IoT: how cognitive buildings are shaping the way. IEEE Internet Things J. 5(4), 2367–2374 (2018)

    Article  Google Scholar 

  • Li, C.S., Darema, F., Chang, V.: Distributed behavior model orchestration in cognitive Internet of Things solution. Enterp. Inf. Syst. 12(4), 414–434 (2018c)

    Article  Google Scholar 

  • Ali, F., et al.: Type-2 fuzzy ontology–aided recommendation systems for IoT–based healthcare. Comput. Commun. 119, 138–155 (2018)

    Article  Google Scholar 

  • 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 

  • Liu, J., Shen, H., Narman, H.S., Chung, W., Lin, Z.: A survey of mobile crowdsensing techniques: a critical component for the internet of things. ACM Trans. Cyber-Physical Syst. 2(3), 18 (2018)

    Article  Google Scholar 

  • Chen, X., Ma, M., Liu, A.: Dynamic power management and adaptive packet size selection for IoT in e-Healthcare. Comput. Electr. Eng. 65, 357–375 (2018)

    Article  Google Scholar 

  • Santoro, G., Vrontis, D., Thrassou, A., Dezi, L.: The Internet of Things: building a knowledge management system for open innovation and knowledge management capacity. Technol. Forecast. Soc. Chang. 136, 347–354 (2018)

    Article  Google Scholar 

  • Dabas, C., Gupta, J.P.: A cloud computing architecture framework for scalable RFID. In: Proceedings of the International MultiConference of Engineers and Computer Scientists, vol. 1, March 2010

    Google Scholar 

  • Ning, Z., Huang, J., Wang, X.: Vehicular fog computing: enabling real-time traffic management for smart cities. IEEE Wirel. Commun. 26(1), 87–93 (2019)

    Article  Google Scholar 

  • Zhou, Y., Tuzel, O.: Voxelnet: End-to-end learning for point cloud based 3d object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4490–4499 (2018)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Chetna Dabas .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Dabas, C. (2019). Recent Research on Data Analytics Techniques for Internet of Things. In: Singh, M., Gupta, P., Tyagi, V., Flusser, J., Ören, T., Kashyap, R. (eds) Advances in Computing and Data Sciences. ICACDS 2019. Communications in Computer and Information Science, vol 1045. Springer, Singapore. https://doi.org/10.1007/978-981-13-9939-8_41

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-9939-8_41

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-9938-1

  • Online ISBN: 978-981-13-9939-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics