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A Survey on the Edge Computing for the IOT

Muskan Tahura, Madiha Shafoon, Umra ...

Abstract


As IoT devices proliferate, we are inundated with data. These require real-time intelligence and lightning-fast answers, which are Beyond the abilities of traditional cloud-based configurations. With the Hard lifting of computation, storage, and intelligence done right next To the source of the data, edge computing changes the game. This solves Many problems, including system reliability, latency, bandwidth issues, And privacy issues. This survey will look in more depth at IoT edge Computing. Architectures, frameworks, and the latest approaches are All included. We will look at the different levels of these systems And see how it has evolved from cloud, to fog, to edge computing. There are different types, including lightweight machine learning, Network-wide analytics, and combinations of edge and cloud Computing. The areas where this information is relevant include smart cities, Hospitals, industries, self-driving cars, and environmental Monitoring, according to this report. Real-world examples and real-world Performance improvements, not just theory. Then there is the major Challenge and the way ahead: how to set standards, get different Systems to talk to each other, coordinate everything seamlessly, and Finally, make it all highly reliable edge intelligence.


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References


Cui, G., He, Q., Li, B., Xia, X., Chen, F., Jinn,H, Yang, Y.(2021).Efficient Verification of Edge Data Integrity in Edge Computing Environment. IEEE Transactions on Services Computing.

Kumar. U, Varma. P Abbas, S. Q. (2021, January) Bringing Edge Computing into IoT Architecture to Improve Interwork Performance. In 2021 International Conference on Computer Communication and Informatics (ICCCI) (pp. 1-5). IEEE.

Anusuya, R., Renuka, D. K., & Kumar, L. A. (2021, January). Review on Challenges of Secure Data Analytics in Edge Computing. In 2021 International lConference on Computer Communication and Informatics (ICCCI) (pp. 1-5). IEEE.

Mishra, D., Dharminder, D., Yadav, P., Rao, Y. S., Vijayakumar, P., & Kumar, N. (2020). A provably secure dynamic ID-based authenticated key agreement framework for mobile edge computing without a trusted party. Journal of Information Security and Applications, 55,102648.

Sha, K., Yang, T. A., Wei, W., & Davari, S. (2020). A survey of edge computing-based designs for IoT security. Digital Communications and Networks, 6(2), 195-202.

Zhao, X., Shi, Y., & Chen, S. (2020). MAESP: Mobility aware edge service placement in mobile edge networks. Computer Networks,182, 107435.

Goudarzi, M., Wu, H., Palaniswami, M., & Buyya, R. (2020). An application placement technique for concurrent IoT applications in edge and fog computing environments. IEEE Transactions on Mobile Computing, 20(4), 1298-1311.

Cao, K., Liu, Y., Meng, G., & Sun, Q. (2020). An overview on edge computing research. IEEE access, 8, 85714-85728.

Khan, W. Z., Ahmed, E., Hakak, S., Yaqoob, I., & Ahmed, A. (2019).Edge computing: A survey. Future Generation Computer Systems, 97,219-235.

Mondal, S., Das, G., & Wong, E. (2019). Cost-optimal cloudlet Placement frame works over fiber-wireless access networks for lowlatency applications.Journal of Network Computer Applications, 138, 27-38.


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