Green Security and Servicing Provisioning
Privacy and security is the crucial factor of IoT deployment. Really, a significant amount of processing is required from devices to implement the security algorithms [43].
1.12 Future of G-IoT
IoT has changed our lives in a big manner. We can feel it everywhere. It has brought a digital revolution around the globe. It collects the real-time data with the help of smart sensors then this data is analyzed to extract valuable information from it which indeed helps in the decision making. In this way, it has improved transparency and reduced the processing time. It has created a wide and new market for sensors, and day by day, it is booming. IoT is improving our lives every day whether it is home, workplace, or playground. Soon, we will see automated door locks, intelligent street lights, industrial robots, smart cars, artificial hearts, etc. The upcoming generation is the world of IoT.
1.13 Conclusion
Ecological issues are obtaining more devotion as the universal public come to be more aware of the significances that atmosphere deprivation causes. We need to focus on the field of authority, safety, and standardization for the smooth operation of IoT which can help the people entirely. This research highlights several related tools, technologies, and worries about G-IoT for a smarter sphere. IoT characterizes an important pattern change in ICT which gives smooth growth of smart cities around the globe. The G-IoT is likely to take in remarkable revolutions in daily routine and would assist the dream of a green ambient world. This research also focused on ML and its various applications which give the ability to the machines to think logically, using training data. A remarkable contribution to the various areas has been made by the AI techniques from the last some decades. This article is focused on various applications based on AI and ML with IoT, that lead to providing various facilities to human lives. Some areas where AI algorithm used to detect intrusion in the network to defend our private network from invaders, AI also focused on the field of medicine, where medical image classification helps to predict disease in advance. AI algorithms are also involved to design the various level of computer gaming where people enjoyed a loT. It is also used to control the traffic with smart devices and dropped misshaping on the road, etc. Such latest technologies are offered more easiness, reliability, effectiveness, and efficiency in human life.
References
1. Tzanis, G. et al., Modern Applications of Machine Learning. Proceedings of the 1st Annual SEERC Doctoral Student Conference–DSC, 2006.
2. Horvitz, E., Machine learning, reasoning, and intelligence in daily life: Directions and challenges. IEEE Proceedings, vol. 360, 2006.
3. Mitchell, T.M., The discipline of machine learning, Carnegie Mellon University, School of Computer Science, Machine Learning Department, Pittsburgh, July 2006.
4. Ball, G.R. and Srihari, S.N., Semi-supervised learning for handwriting recognition. Document Analysis and Recognition, ICDAR’09. 10th International Conference on IEEE, 2009.
5. Valenti, R. et al., Machine learning techniques for face analysis. Mach. Learn. Techniques Int. J. Comput. Appl. (0975 – 8887), 115, 9, Springer Berlin Heidelberg, 159–187, 2008.
6. Al-Hmouz, A., An adaptive framework to provide personalisation for mobile learners, Doctor of Philosophy thesis, School of Information Systems & Technology, University of Wollongong, Australia.
7. Al-Hmouz, A., Shen, J., Yan, J., A machine learning based framework for adaptive mobile learning. Advances in Web Based Learning–ICWL 2009, Springer Berlin Heidelberg, pp. 34–43, 2009.
8. Graepel, T., Machine Learning Applications in Computer Games. ICML 2008 Tutorial, Helsinki, Finland, 5 July 2008.
9. Gabrilovich, E., Josifovski, V., Pang, B., Introduction to Computational Advertising. Association for Computational Linguistics Columbus, Ohio, USA, June 2008.
10. Cunningham, S.J., Littin, J., Witten, I.H., Applications of machine learning in information retrieval. University of Waikato, Department of Computer Science, Hamilton, New Zealand, 1997.
11. Bratko, A. et al., Spam filtering using statistical data compression models. J. Mach. Learn. Res., 7, 2673–2698, 2006.
12. Kaur, H., Singh, G., Minhas, J., A Review of Machine Learning based Anomaly Detection Techniques., Int. J. Comput. App. Technol. Res., 2, 2, 2(2), 185–187, 2013.
13. Gao, J. and Jamidar, R., Machine Learning Applications for Data Center Optimization, Google, 2014. Retrieve: https://docs.google.com/a/google.com/viewer?rl=www.google.com/about/datacenters/efficiency/internal/assets/machine-learning-applicationsfor-datacenter-optimization-finalv2.pdf
14. Haider, P., Chiarandini, L., Brefeld, U., Discriminative clustering for market segmentation. Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, 2012.
15. Kononenko, I., Machine learning for medical diagnosis: history, state of the art and perspective. Artif. Intell. Med., 23, 1, 23(1), 89–109, 2001.
16. Sadjadi, S.O. and Hansen, J.H.L., Unsupervised Speech Activity Detection Using Voicing Measures and Perceptual Spectral Flux. IEEE Signal Proc. Let., 20, 3, March 2013.
17. Hwang, K.E., Cho, D. Y., Park, S.W., Kim, S.D., Zhan, B. T., Applying machine learning techniques to analysis of gene expression data: cancer diagnosis, Methods of Microarray Data Analysis, Kluwer Academic Publishers, Springer US, pp. 167–182, 2002.
18. Pang, B., Lee, L., Vaithyanathan, S., Thumbs up?: sentiment classification using machine learning techniques. Proceedings of the ACL-02 conference on Empirical methods in natural language processing, vol. 10, Association for Computational Linguistics, 2002.
19. Horvitz, E.J., Apacible, J., Sarin, R., Liao, L., Prediction, Expectation, and Surprise: Methods, Designs, and Study of a Deployed Traffic Forecasting Service. Microsoft Research, 2012. Retrieve: https://www.microsoft.com/en-us/research/wp-content/uploads/2014/06/horvitz_traffic_uai2005.pdf
20. Clarke, B., Fokoue, E., Zhang, H.H., Principles and theory for data mining and machine learning, Springer Series in Statistics, Springer Verlag New York, 2009.
21. Mowry, M., A Survey of RFID in the medical industry with emphasis on applications to surgery and surgical devices. MAE188, Introduction to RFID, Dr. Rajit Gadh, UCLA, p. 22, Jun. 9, 2008. Retrieve: https://silo.tips/download/a-survey-of-rfid-in-the-medical-industry-contents#
22. Namboodiri, V. and Gao, L., Energy-aware tag anti-collision protocols for RFID systems. IEEE Trans. Mob. Comput., 9, 1, 44–59, 2010.
23. Xu, X., Gu, L., Wang, J., Xing, G., Cheung, S., Read more with less: An adaptive approach to energy-efficient RFID systems. IEEE J. Sel. Areas Commun., 29, 8, 1684–1697, 2011.
24. Li, T., Wu, S., Chen, S., Yang, M., Generalized energy-efficient algorithms for the RFID estimation problem. IEEE ACM Trans. Netw., 20, 6, 1978–1990, 2012.
25. Amin, Y., Printable green RFID antennas for embedded sensors. PhD dissertation, KTH School of Information and Communication Technology, Kista, Sweden, 2013.
26. Lee, C., Kim, D., Kim, J., An energy efficient active RFID protocol to avoid over heading problem. IEEE Sens. J., 14, 1, 15–24, 2014.
27. Shaikh, F., Zeadally, S., Exposito, E., Enabling Technologies for GreenInternet of Things. IEEE Syst. J., 11, 2, 983–994, 2017.
28.