IoT Device Cost Effective Storage Architecture and Real-Time Data Analysis/Data Privacy Framework
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32926
IoT Device Cost Effective Storage Architecture and Real-Time Data Analysis/Data Privacy Framework

Authors: Femi Elegbeleye, Seani Rananga

Abstract:

This paper focused on cost effective storage architecture using fog and cloud data storage gateway, and presented the design of the framework for the data privacy model and data analytics framework on a real-time analysis when using machine learning method. The paper began with the system analysis, system architecture and its component design, as well as the overall system operations. Several results obtained from this study on data privacy models show that when two or more data privacy models are integrated via a fog storage gateway, we often have more secure data. Our main focus in the study is to design a framework for the data privacy model, data storage, and real-time analytics. This paper also shows the major system components and their framework specification. And lastly, the overall research system architecture was shown, including its structure, and its interrelationships.

Keywords: IoT, fog storage, cloud storage, data analysis, data privacy.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 190

References:


[1] Botta, A., et al., Integration of cloud computing and internet of things: a survey. Future generation computer systems, 2016. 56: p. 684-700.
[2] Jiang, C., et al., An edge computing platform for intelligent operational monitoring in internet data centers. IEEE Access, 2019. 7: p. 133375-133387.
[3] Okay, F.Y. and S. Ozdemir. A fog computing based smart grid model. in 2016 international symposium on networks, computers and communications (ISNCC). 2016. IEEE.
[4] Dastjerdi, A.V., et al., Fog computing: Principles, architectures, and applications, in Internet of things. 2016, Elsevier. p. 61-75.
[5] Gupta, H., et al., iFogSim: A toolkit for modeling and simulation of resource management techniques in the Internet of Things, Edge and Fog computing environments. Software: Practice and Experience, 2017. 47(9): p. 1275-1296.
[6] Abdallah, M., et al., Delay-sensitive video computing in the cloud: A survey. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 2018. 14(3s): p. 1-29.
[7] Popli, S., R.K. Jha, and S. Jain, A survey on energy efficient narrowband internet of things (NBIoT): Architecture, application and challenges. IEEE Access, 2018. 7: p. 16739-16776.
[8] Alexandru, A., D. Coardos, and E. Tudora. IoT-Based Healthcare Remote Monitoring Platform for Elderly with Fog and Cloud Computing. in 2019 22nd International Conference on Control Systems and Computer Science (CSCS). 2019. IEEE.
[9] NICOLAU, D.N., A. Alexandru, and M. Ianculescu, An IoT, Virtual Machines and Cloud Computing-based Framework for an Optimal Management of Healthcare Data Collected from a Smart Environment. A Case Study: RO-Smart Ageing Project. Informatica Economica, 2019. 23(3).
[10] Jain, P., M. Gyanchandani, and N. Khare, Big data privacy: a technological perspective and review. Journal of Big Data, 2016. 3(1): p. 1-25.
[11] Yu, S., Big privacy: Challenges and opportunities of privacy study in the age of big data. IEEE access, 2016. 4: p. 2751-2763.
[12] Salas, J. and V. Torra, A general algorithm for k-anonymity on dynamic databases, in Data privacy management, cryptocurrencies and blockchain technology. 2018, Springer. p. 407-414.
[13] Dwork, C. and A. Roth, The algorithmic foundations of differential privacy. Foundations and Trends in Theoretical Computer Science, 2014. 9(3-4): p. 211-407.
[14] Holohan, N., et al., ($ k $, $\epsilon $)-Anonymity: $ k $-Anonymity with $\epsilon $-Differential Privacy. arXiv preprint arXiv:1710.01615, 2017.
[15] Mahmud, R. and R. Buyya, Modelling and simulation of fog and edge computing environments using iFogSim toolkit. Fog and edge computing: Principles and paradigms, 2019: p. 1-35.
[16] Bala, M.I. and M.A. Chishti. Offloading in cloud and fog hybrid infrastructure using iFogSim. in 2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence). 2020. IEEE.
[17] Dhingra, S., et al., Internet of things-based fog and cloud computing technology for smart traffic monitoring. Internet of Things, 2020: p. 100175.
[18] Baucas, M.J. and P. Spachos, Using cloud and fog computing for large scale iot-based urban sound classification. Simulation Modelling Practice and Theory, 2020. 101: p. 102013.
[19] Sunyaev, A., Fog and edge computing, in Internet Computing. 2020, Springer. p. 237-264.
[20] Wang, T., et al., Data collection from WSNs to the cloud based on mobile Fog elements. Future Generation Computer Systems, 2020. 105: p. 864-872.
[21] Chaurasia, N., et al., Comprehensive survey on energy-aware server consolidation techniques in cloud computing. The Journal of Supercomputing, 2021: p. 1-56.
[22] Linthicum, D.S., Connecting fog and cloud computing. IEEE Cloud Computing, 2017. 4(2): p. 18-20.
[23] Bonomi, F., et al. Fog computing and its role in the internet of things. in Proceedings of the first edition of the MCC workshop on Mobile cloud computing. 2012.