An Accurate Data Integrity in Edge Computing.

Open Access

Year : 2021 | Volume : | Issue : 1 | Page : 25-28
By

    Rakesh C Bhavsar

Abstract

Edge computing is the evolution and more-efficient form of Cloud computing and is the ability to process and store data faster, enabling for more efficient real-time applications. Since Cloud based storage is dependent on having an internet connection. If you want to access your data in your require time and place it will not possible without internet. It costs extra expenses for transferring and downloading records from the cloud. Also provide the storage and manage it. In edge computing, data may travel between different distributed nodes connected through the internet, that data centers process or store critical data locally and push all received data to a central data center. Edge servers are utilized as a go between an organization and a cloud server farm, retaining a segment of an IoT gadget’s information preparing exercises. Edge registering can reduce the cost, navigation of data or information and provide high capability

Keywords: Edge Computing, Cloud computing, IOT Devices, Edge Server, Accurate

[This article belongs to International Journal of Embedded Systems and Emerging Technologies(ijeset)]

How to cite this article: Rakesh C Bhavsar An Accurate Data Integrity in Edge Computing. ijeset 2021; 7:25-28
How to cite this URL: Rakesh C Bhavsar An Accurate Data Integrity in Edge Computing. ijeset 2021 {cited 2021 Oct 23};7:25-28. Available from: https://journals.stmjournals.com/ijeset/article=2021/view=90376

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References

1. Md. Rubaiyat Hasan. Data Compression using Huffman based LZW Encoding Technique. International Journal of Scientific & Engineering Research [Online]. Available from https://www.ijser.org/paper/Data-Compression-using-Huffman-based-LZW-Encoding-Technique. html
2. A. Alarabeyyat, S. Al-Hashemi, et al. Lossless Image Compression Technique Using Combination Methods. Journal of Software Engineering and Applications. 2012; 5: Pp752–763.
3. S. Renugadevi and P.S Nithya Darsini. Huffman and Lempel-Ziv Based Data Compression Algorithm for Wireless Sensor Networks. 2013 International Conference on Pattern Recognition, Informatics and Mobile Engineering. New York, 21–22 Feb.: Salem, India, New York: IEEE; 2013.
4. I Turkoglu. Hybrid Lossless Compression Method for Binary Images. Istanbul University-Journal of Electrical and Electronics Engineering. 2011; 11 (2): Pp 1399–1405.
5. A.Yazdanpanah and M.R. Hashemi. A New Compression Ratio Prediction Algorithm for Hardware Implementation of LZW Data Compression. 2010 15th CSI International Symposium on Computer Architecture and Digital Systems.; 23–24 Sept. 2010; Tehran, Iran, New York: IEEE; 2010.
6. Weisong Shi, Jie Cao, et al. Edge Computing: Vision and Challenges. IEEE Internet of Things Journal. 2016; 3 (5): Pp 637–646.
7. Bo Li, Qiang He, et al. Cooperative Assurance of Cache Data Integrity for Mobile Edge Computing. IEEE Transactions on Information Forensics and Security (Early Access). 2021.
8. Long Ye, Qianhan Liu, et al. A Novel Image Compression Framework at Edges. 2017 IEEE Visual Communications and Image Processing (VCIP). 10–13 Dec. 2017; St. Petersburg, FL, USA, New York: IEEE; 2018.
9. B. Nivedha, M. Priyadharshini, et al. Lossless Image Compression in Cloud Computing. 2017 International Conference on Technical Advancements in Computers and Communications (ICTACC).
10–11 April 2017; Melmaurvathur, India, New York: IEEE; 2017. 10. N. Hassan, S. Gillani, et al. The Role of Edge Computing in Internet of Things. IEEE Communications Magazine. 2018; 56 (11) Pp: 110–115.


Regular Issue Open Access Article
Volume 7
Issue 1
Received October 7, 2021
Accepted October 17, 2021
Published October 23, 2021