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Security in cloud computing based cognitive radio networks (Conference Presentation)
Author(s): Yenumula B. Reddy

Paper Abstract

Cloud computing based cognitive radio networks (CCCRN) is an eye-catching research area in recent years to improve the spectrum sensing and spectrum management. Cognitive radio networks (CRN) are capable of adaptive learning and reconfiguration to provide consistent communications in dynamic environments. The adoption and learning in CRN demand fast process of big data. The performance and security in CRN do not meet such requirements due to its low computational power capabilities, particularly in low computational power devices. The advent of cloud capabilities mitigate these constraints. Due to this reason, we suggest the steganography with Advanced Encryption Standard (AES) cryptography technique to protect the cloud data. We identify the critical issues and challenges to implementing CCCRN and provide possible solutions. Even though, both techniques have the same objective, the cloud data in cognitive radio network requires a combination to keep the hackers away from the classified and unclassified data. Integration of cloud computing and cognitive radio increases the performance with added security threats of cloud computing. If the integration overcome these security threats, CCCRN will replace traditional methods of radio operation. The proposed security model incorporated in CCCRN can help the primary user emulation and many other jamming problems. Integrating cognitive radio in cloud arrives secure problems along with real-time processing and energy supply problems. Cloud integration provides resource pooling with additional antennas to meet the real-time performance. Therefore, the cloud is one of the solutions that is facing by CRN. We discuss these problems in the current research paper.

Paper Details

Date Published: 7 June 2017
PDF: 1 pages
Proc. SPIE 10185, Cyber Sensing 2017, 101850A (7 June 2017); doi: 10.1117/12.2266589
Show Author Affiliations
Yenumula B. Reddy, Grambling State Univ. (United States)


Published in SPIE Proceedings Vol. 10185:
Cyber Sensing 2017
Igor V. Ternovskiy; Peter Chin, Editor(s)

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