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The Role of Machine Learning in Internet-of-Things (IoT) Research: A Review

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2018
Authors:
Aneri M. Desai, Rutvij H. Jhaveri
10.5120/ijca2018916609

Aneri M Desai and Rutvij H Jhaveri. The Role of Machine Learning in Internet-of-Things (IoT) Research: A Review. International Journal of Computer Applications 179(27):36-44, March 2018. BibTeX

@article{10.5120/ijca2018916609,
	author = {Aneri M. Desai and Rutvij H. Jhaveri},
	title = {The Role of Machine Learning in Internet-of-Things (IoT) Research: A Review},
	journal = {International Journal of Computer Applications},
	issue_date = {March 2018},
	volume = {179},
	number = {27},
	month = {Mar},
	year = {2018},
	issn = {0975-8887},
	pages = {36-44},
	numpages = {9},
	url = {http://www.ijcaonline.org/archives/volume179/number27/29131-2018916609},
	doi = {10.5120/ijca2018916609},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Rapid developments in hardware and software based connected devices with communication technologies introduce Internet-of-things (IoT). In recent year, IoT gained enormous popularity and used vastly in a variety of applications. IoT represents the new forms of communication such as between human and things, and between two things. At present era IoT concepts are highly influence by creating a new dimension in the internet world. Intelligent processing and analysis of big data is the key to developing smart IoT applications. Such applications are logistic, transportation, agriculture, healthcare, and environment. Increasing the use of internet and external factor of environment are caused dynamic change in IoT system. The use of smart environments in the delivery of a pervasive care is the research topic that has witnessed increasing interest in recent years. These smart environments aim to deliver pervasive care through ubiquitous sensing by monitoring the occupant's activities. In order to provide smarter environment, their need to be implement IoT with machine learning. In recent year, machine learning technique have been used widely because of its technologies such that identification, extraction, classification, regression and forecasting. Machine learning exploring historical data from camera and sensors and perform techniques which improve the lifespan of network. In this paper, we build survey on existing research work carried out for various applications of machine learning to IoT. We summarize techniques and tools for IoT and also their benefits and limitations. Using a summarized data we also look for different future challenges. The aim of this paper is to survey different IoT technologies to assist the people to live in a smart environment.

References

  1. Aneri M Desai and Rutvij H Jhaveri. A Review on Applications of Ambient Assisted Living. International Journal of Computer Applications 176(8):1-7, October 2017.
  2. S. Li, L. Da Xu, and S. Zhao, “The internet of things: a survey,” Inf. Syst. Front., vol. 17, no. 2, pp. 243–259, 2015.
  3. A. Al-fuqaha, M. Guizani, M. Mohammadi, M. Aledhari, and M. Ayyash, “Internet of Things : A Survey on Enabling Technologies , Protocols and Applications Internet of Things : A Survey on Enabling Technologies , Protocols and Applications,” vol. 17, no. JANUARY, pp. 2347–2376, 2015.
  4. R. Michalski, J. Carbonell and T. Mitchell, Machine Learning. Berlin: Springer Berlin, 2013.
  5. F. Ganz, D. Puschmann, P. Barnaghi, and F. Carrez, “A Practical Evaluation of Information Processing and Abstraction Techniques for the Internet of Things,” IEEE Internet Things J., vol. 2, no. 4, pp. 340–354, 2015.
  6. Y. Meidan et al., “ProfilIoT: A Machine Learning Approach for IoT Device Identification Based on Network Traffic Analysis,” SAC 2017 32nd ACM Symp. Appl. Comput., pp. 506–509, 2017.
  7. A. Ferdowsi and W. Saad, “Deep Learning-Based Dynamic Watermarking for Secure Signal Authentication in the Internet of Things,” 2017.
  8. P. M. Kumar, U. Gandhi, R. Varatharajan, G. Manogaran, J. R., and T. Vadivel, “Intelligent face recognition and navigation system using neural learning for smart security in Internet of Things,” Cluster Comput., 2017.
  9. T. Truong, A. Dinh, and K. Wahid, “An IoT environmental data collection system for fungal detection in crop fields,” Can. Conf. Electr. Comput. Eng., pp. 0–3, 2017.
  10. J. Joshi et al., “Machine Learning Based Cloud Integrated Farming,” Proc. 2017 Int. Conf. Mach. Learn. Soft Comput. - ICMLSC ’17, pp. 1–6, 2017.
  11. P. M. Kumar and U. Devi Gandhi, “A novel three-tier Internet of Things architecture with machine learning algorithm for early detection of heart diseases,” Comput. Electr. Eng., vol. 0, pp. 1–14, 2017.
  12. Y. W. Chen, C. Torro, S. Tanaka, R. J. Howlett, and L. C. Jain, “Innovation in medicine and healthcare 2015,” Smart Innov. Syst. Technol., vol. 45, 2016.
  13. M. Chen, Y. Ma, J. Song, C. F. Lai, and B. Hu, “Smart Clothing: Connecting Human with Clouds and Big Data for Sustainable Health Monitoring,” Mob. Networks Appl., vol. 21, no. 5, pp. 825–845, 2016.
  14. X. Luo, J. Liu, D. Zhang, and X. Chang, “A large-scale web QoS prediction scheme for the Industrial Internet of Things based on a kernel machine learning algorithm,” Comput. Networks, vol. 101, pp. 81–89, 2016.
  15. F. Firouzi et al., “Internet-of-Things and big data for smarter healthcare: From device to architecture, applications and analytics,” Futur. Gener. Comput. Syst., vol. 78, pp. 583–586, 2018.
  16. A. Ilapakurti and C. Vuppalapati, “Building an IoT framework for connected dairy,” Proc. - 2015 IEEE 1st Int. Conf. Big Data Comput. Serv. Appl. BigDataService 2015, pp. 275–285, 2015.
  17. H. Ghayvat, J. Liu, S. C. Mukhopadhyay, and X. Gui, “Wellness Sensor Networks: A Proposal and Implementation for Smart Home for Assisted Living,” IEEE Sens. J., vol. 15, no. 12, pp. 7341–7348, 2015.
  18. A. R. Elias, N. Golubovic, C. Krintz, and R. Wolski, “Where’s The Bear?,” Proc. Second Int. Conf. Internet-of-Things Des. Implement. - IoTDI ’17, pp. 247–258, 2017.
  19. M. S. Norouzzadeh et al., “Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning,” pp. 1–17, 2017.
  20. F. Anjomshoa, M. Aloqaily, B. Kantarci, M. Erol-Kantarci, and S. Schuckers, “Social Behaviometrics for Personalized Devices in the Internet of Things Era,” IEEE Access, vol. 5, no. c, pp. 12199–12213, 2017.
  21. V. Soundarya, U. Kanimozhi, and D. Manjula, “Recommendation System for Criminal Behavioral Analysis on Social Network using Genetic Weighted K-Means Clustering,” J. Comput., vol. 12, no. 3, pp. 212–220, 2017.
  22. Y. Zhang, B. Song, and P. Zhang, “Social behavior study under pervasive social networking based on decentralized deep reinforcement learning,” J. Netw. Comput. Appl., vol. 86, pp. 72–81, 2017.
  23. J. Chin, V. Callaghan, and I. Lam, “Understanding and personalising smart city services using machine learning, the Internet-of-Things and Big Data,” IEEE Int. Symp. Ind. Electron., pp. 2050–2055, 2017.
  24. S. Devi and T. Neetha, “Machine Learning based traffic congestion prediction in a IoT based Smart City,” pp. 3442–3445, 2017.

Keywords

Internet-of-Things (IoT), Machine Learning, Intelligent Processing, Smart Environment