AI and Machine Learning in Cloud-Based Internet of Things (IoT) Solutions: A Comprehensive Review and Analysis

Uday Krishna Padyana; Hitesh Premshankar Rai; Pavan Ogeti; Narendra Sharad Fadnavis; Gireesh Bhaulal Patil

doi:10.55544/ijrah.3.3.20

Authors

Uday Krishna Padyana Independent Researcher, USA.
Hitesh Premshankar Rai Independent Researcher, USA.
Pavan Ogeti Independent Researcher, USA.
Narendra Sharad Fadnavis Independent Researcher, USA.
Gireesh Bhaulal Patil Independent Researcher, USA.

DOI:

https://doi.org/10.55544/ijrah.3.3.20

Keywords:

IoT, AI and Machine Learning, Cloud and the related concepts of Edge and Fog Computing, Security for internet connected devices, Real-time data analytics, the applications of IoT in Cities and Industries

Abstract

This paper undertakes a detailed review of a novel topic that revolves around integrating AI and ML in cloud-based IoT systems. This paper focuses on those technologies and discusses their potential interaction and influence on IoT systems and frameworks for IoT data and devices, analytics, management, security, and decision-making methods. Mentioning the findings in the current literature, and after analysing a number of successful and failing implementations of Gamification, this paper reveals the specific problems, presents new ideas and makes suggestions for the future studies. The prospects of affinity unveil the futuristic potential of AI & ML in optimising the performance, capacity, and wisdom of cloud-based IoT systems in the fields of smart cities, industrial IoT, and healthcare, etc. The study shows the following increase in efficiency: the energy consumption decreases by 30-40%; the accuracy of prediction increases by 30-50%; the decrease in the network latency is 25-35%. However, the following challenges persist with the current implementations; the disclosure of users’ data privacy, compatibility, and continuing debate on standards.

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References

Aazam, M., Khan, I., Alsaffar, A. A., & Huh, E. N. (2014). Cloud of Things: Integrating Internet of Things and cloud computing and the issues involved. In Proceedings of 2014 11th International Bhuban Conference on Applied Sciences & Technology (IBCAST) (pp. 414-419). IEEE.

Atzori, L., Iera, A., & Morabito, G. (2020). AI in the 6G era: Challenges, opportunities, and future research directions. IEEE Internet of Things Journal, 7(8), 6606-6610.

Bonomi, F., Milito, R., Zhu, J., & Addepalli, S. (2012). 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).

Carbone, P., Katsifodimos, A., Ewen, S., Markl, V., Haridi, S., & Tzoumas, K. (2015). Apache flink: Stream and batch processing in a single engine. Bulletin of the IEEE Computer Society Technical Committee on Data Engineering, 36(4).

Carvalho, T. P., Soares, F. A., Vita, R., Francisco, R. D. P., Basto, J. P., & Alcalá, S. G. (2019). A systematic literature review of machine learning methods applied to predictive maintenance. Computers & Industrial Engineering, 137, 106024.

Chalapathy, R., & Chawla, S. (2019). Deep learning for anomaly detection: A survey. arXiv preprint arXiv:1901.03407.

Cisco. (2020). Cisco Annual Internet Report (2018–2022) White Paper. Cisco.

Deng, S., Zhao, H., Fang, W., Yin, J., Dustdar, S., & Zomaya, A. Y. (2020). Edge intelligence: The confluence of edge computing and artificial intelligence. IEEE Internet of Things Journal, 7(8), 7457-7469.

Diro, A. A., & Chilamkurti, N. (2018). Distributed attack detection scheme using deep learning approach for Internet of Things. Future Generation Computer Systems, 82, 761-768.

Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013). Internet of Things (IoT): A vision, architectural elements, and future directions. Future Generation Computer Systems, 29(7), 1645-1660.

Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., & Adam, H. (2017). MobileNets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861.

Huh, S., Cho, S., & Kim, S. (2017). Managing IoT devices using blockchain platform. In 2017 19th International Conference on Advanced Communication Technology (ICACT) (pp. 464-467). IEEE.

Islam, S. R., Kwak, D., Kabir, M. H., Hossain, M., & Kwak, K. S. (2015). The Internet of Things for health care: A comprehensive survey. IEEE Access, 3, 678-708.

Lee, J., Bagheri, B., & Kao, H. A. (2015). A cyber-physical systems architecture for industry 4.0-based manufacturing systems. Manufacturing Letters, 3, 18-23.

Li, H., Ota, K., & Dong, M. (2018). Learning IoT in edge: Deep learning for the Internet of Things with edge computing. IEEE Network, 32(1), 96-101.

Liakos, K. G., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018). Machine learning in agriculture: A review. Sensors, 18(8), 2674.

Liang, Y., Ke, S., Zhang, J., Yi, X., & Zheng, Y. (2019). GeoMAN: Multi-level attention networks for geo-sensory time series prediction. In Proceedings of the 27th International Joint Conference on Artificial Intelligence (pp. 3428-3434).

Lim, C., Kim, K. J., & Maglio, P. P. (2018). Smart cities with big data: Reference models, challenges, and considerations. Cities, 82, 86-99.

Mahdavinejad, M. S., Rezvan, M., Barekatain, M., Adibi, P., Barnaghi, P., & Sheth, A. P. (2018). Machine learning for Internet of Things data analysis: A survey. Digital Communications and Networks, 4(3), 161-175.

Malhotra, P., Ramakrishnan, A., Anand, G., Vig, L., Agarwal, P., & Shroff, G. (2016). LSTM-based encoder-decoder for multi-sensor anomaly detection. arXiv preprint arXiv:1607.00148.

Mao, Y., You, C., Zhang, J., Huang, K., & Letaief, K. B. (2017). A survey on mobile edge computing: The communication perspective. IEEE Communications Surveys & Tutorials, 19(4), 2322-2358.

Mohammadi, M., Al-Fuqaha, A., Sorour, S., & Guizani, M. (2018). Deep learning for IoT big data and streaming analytics: A survey. IEEE Communications Surveys & Tutorials, 20(4), 2923-2960.

Park, J., Kim, K. J., & Lee, S. (2018). An adaptive learning framework for efficient service provisioning in mobile edge computing. IEEE Access, 6, 67623-67635.

Raza, U., Kulkarni, P., & Sooriyabandara, M. (2019). Low power wide area networks: An overview. IEEE Communications Surveys & Tutorials, 19(2), 855-873.

Reddy, R. K., Ramkumar, K. R., Verma, A., & Churi, P. P. (2020). Real-time prediction of blood glucose level for type 2 diabetic patients using LSTM network. In 2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA) (pp. 198-203). IEEE.

Shi, W., Cao, J., Zhang, Q., Li, Y., & Xu, L. (2016). Edge computing: Vision and challenges. IEEE Internet of Things Journal, 3(5), 637-646.

Shokri, R., & Shmatikov, V. (2015). Privacy-preserving deep learning. In Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security (pp. 1310-1321).

Truong, N. B., Sun, K., Lee, G. M., & Guo, Y. (2019). GDPR-compliant personal data management: A blockchain-based solution. IEEE Transactions on Information Forensics and Security, 15, 1746-1761.

Wu, Q., Ding, G., Xu, Y., Feng, S., Du, Z., Wang, J., & Long, K. (2014). Cognitive Internet of Things: A new paradigm beyond connection. IEEE Internet of Things Journal, 1(2), 129-143.

Chenchala, P. K., Choppadandi, A., Kaur, J., Nakra, V., & Pandian, P. K. G. (2020). Predictive Maintenance and Resource Optimization in Inventory Identification Tool Using ML. International Journal of Open Publication and Exploration, 8(2), 43-50. https://ijope.com/index.php/home/article/view/127

Kaur, J., Choppadandi, A., Chenchala, P. K., Nakra, V., & Pandian, P. K. G. (2019). AI Applications in Smart Cities: Experiences from Deploying ML Algorithms for Urban Planning and Resource Optimization. Tuijin Jishu/Journal of Propulsion Technology, 40(4), 50-56.

Case Studies on Improving User Interaction and Satisfaction using AI-Enabled Chatbots for Customer Service . (2019). International Journal of Transcontinental Discoveries, ISSN: 3006-628X, 6(1), 29-34. https://internatioHappy Guru Purnima sir charan sparsh