Machine learning algorithms for distributed operations in internet of things IoT

Qusay Abdullah Abed, Mohammed Thajeel Abdullah, Huda Jalil Dikhil


Generally, the things that have the great role in facilitating the emergence of internet-connected sensory devices can be embodied in the developments that happen in the sphere of software, hardware, and communication technologies. The internet-connected sensory devices present perceptions and measurements of data from the real world. It is suggested that nearly through 2020, the total use of internet-connected devices may reach to 25 to 50 billion. Actually, the relation between technologies and the volume of data being published is kept in one line. That is, if there is growth in the technologies, the volume of the data will be increased. Such technology, i.e. internet-connected devices, can be called as Internet of Things (IoT). Its role is to connect the real world with the cyber one. Furthermore, generating great data with velocity as its main characteristic will help in increasing the volume of IoT. To develop smart IoT applications, one can use such intelligent processing and analyzing such big data. In this paper, we tend to study the impact of implementing machine learning (ML) algorithms and methods and their efficiency in the IoT domain. As well as explore how these algorithms help in founding efficient backbone solutions to analyze and estimate the huge amounts of data that are expected to arise in the coming few years due to the rapid growth on demands for IoT based applications.


Machine Learning, Internet of Things (IoT), Distributed Operations, Learning Algorithms.

Full Text:



M. H. Sharif, I. Despot, and S. Uyaver, "A proof of concept for home automation system with implementation of the internet of things standards," Periodicals of Engineering and Natural Sciences, vol. 6, pp. 95-106, 2018.

M. H. Miraz, M. Ali, P. S. Excell, and R. Picking, "A review on Internet of Things (IoT), Internet of everything (IoE) and Internet of nano things (IoNT)," in 2015 Internet Technologies and Applications (ITA), 2015, pp. 219-224.

S. Jagtap and S. Rahimifard, "Unlocking the Potential of the Internet of Things to Improve Resource Efficiency in Food Supply Chains," in International Conference on Information and Communication Technologies in Agriculture, Food & Environment, 2017, pp. 287-301.

H. Kargupta, B.-H. Park, S. Pittie, L. Liu, D. Kushraj, and K. Sarkar, "MobiMine: Monitoring the stock market from a PDA," ACM SIGKDD Explorations Newsletter, vol. 3, pp. 37-46, 2002.

H. H. Nasereddin and M. FAQIR, "The impact of internet of things on customer service: A preliminary study," Periodicals of Engineering and Natural Sciences, vol. 7, pp. 148-155, 2019.

M. Fernández-Delgado, E. Cernadas, S. Barro, and D. Amorim, "Do we need hundreds of classifiers to solve real world classification problems?," The Journal of Machine Learning Research, vol. 15, pp. 3133-3181, 2014.

T. G. Dietterich, "Machine learning in ecosystem informatics and sustainability," in Twenty-First International Joint Conference on Artificial Intelligence, 2009.

P. L. Silsbee, A. C. Bovik, and D. Chen, "Visual pattern image sequence coding," IEEE Transactions on Circuits and Systems for Video Technology, vol. 3, pp. 291-301, 1993.

U. S. Shanthamallu, A. Spanias, C. Tepedelenlioglu, and M. Stanley, "A brief survey of machine learning methods and their sensor and IoT applications," in 2017 8th International Conference on Information, Intelligence, Systems & Applications (IISA), 2017, pp. 1-8.

E. G. Ularu, F. C. Puican, G. Suciu, A. Vulpe, and G. Todoran, "Mobile Computing and Cloud maturity-Introducing Machine Learning for ERP Configuration Automation," Informatica Economica, vol. 17, 2013.

J. L. Berral-García, "A quick view on current techniques and machine learning algorithms for big data analytics," in 2016 18th international conference on transparent optical networks (ICTON), 2016, pp. 1-4.

I. Muhic and M. Hodzic, "Internet of Things: Current Technological Review," Periodicals of Engineering and Natural Sciences, vol. 2, 2014.

M. Rouse, "Supervised learning," ed, 2019.

P. Sedgwick, "Simple linear regression," BMJ, vol. 346, 2013.

S. Sperandei, "Understanding logistic regression analysis," Biochemia medica: Biochemia medica, vol. 24, pp. 12-18, 2014.

H. P. Singh, C. A. Bailer-Jones, and R. Gupta, "Introduction to artificial neural networks," 2001.

L. Wu, C. Shen, and A. Van Den Hengel, "Deep linear discriminant analysis on fisher networks: A hybrid architecture for person re-identification," Pattern Recognition, vol. 65, pp. 238-250, 2017.

"Decision Trees — A simple way to visualize a decision," 02-Oct-2019 2019.

N. V. Chawla, "Data mining for imbalanced datasets: An overview," in Data mining and knowledge discovery handbook, ed: Springer, 2009, pp. 875-886.

J. P. Gee, "Learning and games," The ecology of games: Connecting youth, games, and learning, vol. 3, pp. 21-40, 2008.

Y. Yang, S. Liao, Z. Lei, and S. Z. Li, "Large scale similarity learning using similar pairs for person verification," in Thirtieth AAAI Conference on Artificial Intelligence, 2016.

G. P. Nguyen, M. Worring, and A. W. Smeulders, "Interactive search by direct manipulation of dissimilarity space," IEEE Transactions on Multimedia, vol. 9, pp. 1404-1415, 2007.

E. J. Sommerfeldt, "The dynamics of activist power relationships: A structurationist exploration of the segmentation of activist publics," International Journal of Strategic Communication, vol. 6, pp. 269-286, 2012.

“What is Bayesian logic? - Definition from” [Online]. Available:, [Accessed: 04-Oct-2019].

Y. OUASSIT, S. ARDCHIR, and M. AZOUAZI, "How Machine Learning Potentials are transforming the Practice of Digital Marketing: State of the Art," Periodicals of Engineering and Natural Sciences, vol. 6, pp. 373-379, 2018.

M. Khanum, T. Mahboob, W. Imtiaz, H. A. Ghafoor, and R. Sehar, "A survey on unsupervised machine learning algorithms for automation, classification and maintenance," International Journal of Computer Applications, vol. 119, 2015.

S. Logeswari, K. Premalatha, and D. Sasikala, "A Survey on Text Mining in Clustering," International Journal of Advanced Research in Computer Science, vol. 2, 2011.

I. Goodfellow, Y. Bengio, and A. Courville, Deep learning: MIT press, 2016.



  • There are currently no refbacks.

Copyright (c) 2019 Qusay Abdullah Abed, Mohammed Thajeel Abdullah, Huda Jalil Dikhil

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

ISSN: 2303-4521

Digital Object Identifier DOI: 10.21533/pen

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License