Cloud Computing Based Cognitive Radio Networking

2013 ◽  
pp. 153-164 ◽  
Author(s):  
Sachin Shetty ◽  
Danda B. Rawat
Keyword(s):  
Cloud Computing ◽  
Cognitive Radio ◽  
Adaptive Learning ◽  
Dynamic Environments ◽  
Spectrum Management ◽  
Computational Capacity ◽  
Fast Processing ◽  
Limited Power ◽  
Adaptation And Learning ◽  
Computing Platforms

This chapter describes state-of-the art techniques to improve performance of spectrum sensing and spectrum management in Cognitive Radio Networks (CRN) by leveraging services available in cloud computing platforms. CRNs are capable of adaptive learning and reconfiguration to provide consistent communications in dynamic environments. However, ensuring adaptation and learning in CRN will require availability of large volume of data and fast processing. However, the performance and security of CRN is considerably constrained by its limited power, memory and computational capacity, it may not be able to achieve its full capability. Fortunately, the advent of cloud computing has the potential to mitigate these constraints due its vast storage and computational capacity.

Cognitive Radio Network-Based Design and Security Challenges in 5G Communication

2020 ◽  
pp. 221-241 ◽  
Author(s):  
Arvind Dhaka ◽  
Amita Nandal ◽  
Rahul Dixit
Keyword(s):  
Cloud Computing ◽  
Cognitive Radio ◽  
Mobile Networks ◽  
Software Defined Radio ◽  
General Purpose ◽  
Security Issues ◽  
Computing Platform ◽  
Security Challenges ◽  
Signal Processing Algorithms ◽  
Computing Platforms

This chapter deals with the main development challenges of 5G network. The 5G terminals can be made as reconfigurable multimode and cognitive radio enabled. Such networks will have software defined radio modulation schemes. The 5G mobile networks will focus on the development of the user terminals where the terminals will have access to different wireless technologies at the same time and will combine different flows from different technologies. It is beneficial to deploy cloud-computing platforms running on general-purpose hardware, leading to a cloud-RAN system. This chapter is focused on the challenges and benefits of implementing reconfigurable signal processing algorithms on a cloud-computing platform and address various security issues with cognitive radio networks.

White Cloud or Black Cloud: Opportunity and Challenge of Spectrum Sharing on Cloud Computing

2012 ◽  
Vol 430-432 ◽  
pp. 1290-1293 ◽  
Author(s):  
Li Xia Liu ◽  
Gang Hu ◽  
Zhen Huang ◽  
Yu Xing Peng
Keyword(s):  
Cloud Computing ◽  
Cognitive Radio ◽  
Spectrum Sharing ◽  
Dynamic Spectrum Access ◽  
Rapid Development ◽  
Radio Networks ◽  
Dynamic Spectrum ◽  
Spectrum Access ◽  
Limited Power ◽  
Computing Capacity

In order to fully utilize the spectrum resource, dynamic spectrum access becomes a promising approach to increase the opportunity of spectrum access with the rapid development of cognitive radio. However the performance of cognitive radio networks (CRN) is considerably constrained by its limited power, memory and computing ability. Fortunately cloud computing, which is the highlight of current research, has the potential to make up for the disadvantages because of its vast storage and computing capacity. In this paper we will discuss the convergence of spectrum sharing and cloud computing from several aspects including model, advantages and challenges. A spectrum sharing model based on cloud computing (SSC) will be introduced.

scholarly journals Constraints Mitigation in Cognitive Radio Networks Using Cloud Computing

2020 ◽  
Vol 2 (1) ◽  
pp. 1-14 ◽  
Author(s):  
Dr. Bindhu V
Keyword(s):  
Cloud Computing ◽  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Performance Enhancement ◽  
Spectrum Management ◽  
Bandwidth Utilization ◽  
Computing Services ◽  
Processing Power ◽  
Generation Network ◽  
Cloud Computing Services

One of the most supportive technologies in enhancing the bandwidth utilization of the next generation network is cognitive radio network (CR-N). However the traditional CR-N is substantially constrained in accessing and the spectrum sensing, due to its limited, processing power and the storage capabilities. To advance the spectrum sensing performance and the spectrum management along with the development in the radio frequency resource allocation in the CR-N the paper clouts the cloud computing services in the proposed method to mitigate the constraints in the cognitive radio networking and also address the intrinsic security threats that are caused by the jamming in the CR-N. The performance of the proposed method is validated and the results are observed to evince the performance enhancement gained in managing the constraint in the CR-N using the cloud.

Cognitive Radio Network-Based Design and Security Challenges in 5G Communication

2021 ◽  
pp. 135-150
Author(s):  
Arvind Dhaka ◽  
Amita Nandal ◽  
Rahul Dixit
Keyword(s):  
Cloud Computing ◽  
Cognitive Radio ◽  
Mobile Networks ◽  
Software Defined Radio ◽  
General Purpose ◽  
Security Issues ◽  
Computing Platform ◽  
Security Challenges ◽  
Signal Processing Algorithms ◽  
Computing Platforms

This chapter deals with the main development challenges of 5G network. The 5G terminals can be made as reconfigurable multimode and cognitive radio enabled. Such networks will have software defined radio modulation schemes. The 5G mobile networks will focus on the development of the user terminals where the terminals will have access to different wireless technologies at the same time and will combine different flows from different technologies. It is beneficial to deploy cloud-computing platforms running on general-purpose hardware, leading to a cloud-RAN system. This chapter is focused on the challenges and benefits of implementing reconfigurable signal processing algorithms on a cloud-computing platform and address various security issues with cognitive radio networks.

An Energy Efficient Spectrum Aware Routing for Cognitive Radio Networks (CRNs)

2020 ◽  
Vol 10 ◽  
Author(s):  
Dileep Reddy Bolla ◽  
Jijesh J J ◽  
Mahaveer Penna ◽  
Shiva Shankar
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Energy Efficient ◽  
Research Work ◽  
Spectrum Management ◽  
Residual Energy ◽  
Radio Networks ◽  
Traffic Load ◽  
The Impact

Back Ground/ Aims:: Now-a-days in the Wireless Communications some of the spectrum bands are underutilized or unutilized; the spectrum can be utilized properly by using the Cognitive Radio Techniques using the Spectrum Sensing mechanisms. Objectives:: The prime objective of the research work carried out is to achieve the energy efficiency and to use the spectrum effectively by using the spectrum management concept and achieve better throughput, end to end delay etc., Methods:: The detection of the spectrum hole plays a vital role in the routing of Cognitive Radio Networks (CRNs). While detecting the spectrum holes and the routing, sensing is impacted by the hidden node issues and exposed node issues. The impact of sensing is improved by incorporating the Cooperative Spectrum Sensing (CSS) techniques. Along with these issues the spectrum resources changes time to time in the routing. Results:: All the issues are addressed with An Energy Efficient Spectrum aware Routing (EESR) protocol which improves the timeslot and the routing schemes. The overall network life time is improved with the aid of residual energy concepts and the overall network performance is improved. Conclusion:: The proposed protocol (EESR) is an integrated system with spectrum management and the routing is successfully established to communication in the network and further traffic load is observed to be balanced in the protocol based on the residual energy in a node and further it improves the Network Lifetime of the Overall Network and the Individual CR user, along with this the performance of the proposed protocol outperforms the conventional state of art routing protocols.

scholarly journals RAP Framework for Spectrum Management in Cognitive Radio

2014 ◽  
Vol 7 (10) ◽  
pp. 1925-1931
Author(s):  
Kishore V. Krishnan ◽  
Sibaram Khara ◽  
J. Christopher Clement ◽  
A. Bagubali
Keyword(s):  
Cognitive Radio ◽  
Spectrum Management

Econometrics Pedagogy and Cloud Computing: Training the Next Generation of Economists and Data Scientists

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Danielle V. Handel ◽  
Anson T. Y. Ho ◽  
Kim P. Huynh ◽  
David T. Jacho-Chávez ◽  
Carson H. Rea
Keyword(s):  
Cloud Computing ◽  
Data Science ◽  
Internet Access ◽  
Next Generation ◽  
Web Browser ◽  
Computer Laboratory ◽  
Economics Students ◽  
Computing Platforms

AbstractThis paper describes how cloud computing tools widely used in the instruction of data scientists can be introduced and taught to economics students as part of their curriculum. The demonstration centers around a workflow where the instructor creates a virtual server and the students only need Internet access and a web browser to complete in-class tutorials, assignments, or exams. Given how prevalent cloud computing platforms are becoming for data science, introducing these techniques into students’ econometrics training would prepare them to be more competitive when job hunting, while making instructors and administrators re-think what a computer laboratory means on campus.

scholarly journals A Parallel Unmixing-Based Content Retrieval System for Distributed Hyperspectral Imagery Repository on Cloud Computing Platforms

2021 ◽  
Vol 13 (2) ◽  
pp. 176
Author(s):  
Peng Zheng ◽  
Zebin Wu ◽  
Jin Sun ◽  
Yi Zhang ◽  
Yaoqin Zhu ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Large Scale ◽  
Retrieval System ◽  
Hyperspectral Image ◽  
Parallel Implementation ◽  
Remotely Sensed Data ◽  
Web Interfaces ◽  
Content Retrieval ◽  
Service Mode ◽  
Computing Platforms

As the volume of remotely sensed data grows significantly, content-based image retrieval (CBIR) becomes increasingly important, especially for cloud computing platforms that facilitate processing and storing big data in a parallel and distributed way. This paper proposes a novel parallel CBIR system for hyperspectral image (HSI) repository on cloud computing platforms under the guide of unmixed spectral information, i.e., endmembers and their associated fractional abundances, to retrieve hyperspectral scenes. However, existing unmixing methods would suffer extremely high computational burden when extracting meta-data from large-scale HSI data. To address this limitation, we implement a distributed and parallel unmixing method that operates on cloud computing platforms in parallel for accelerating the unmixing processing flow. In addition, we implement a global standard distributed HSI repository equipped with a large spectral library in a software-as-a-service mode, providing users with HSI storage, management, and retrieval services through web interfaces. Furthermore, the parallel implementation of unmixing processing is incorporated into the CBIR system to establish the parallel unmixing-based content retrieval system. The performance of our proposed parallel CBIR system was verified in terms of both unmixing efficiency and accuracy.

scholarly journals Predictability of IP Address Allocations for Cloud Computing Platforms

2020 ◽  
Vol 15 ◽  
pp. 500-511 ◽  
Author(s):  
Hussain M. J. Almohri ◽  
Layne T. Watson ◽  
David Evans
Keyword(s):  
Cloud Computing ◽  
Ip Address ◽  
Computing Platforms

scholarly journals Efficient Spectrum Management Techniques for Cognitive Radio Networks for Proximity Service

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 43795-43805 ◽  
Author(s):  
S. Nandakumar ◽  
T. Velmurugan ◽  
Utthara Thiagarajan ◽  
Marimuthu Karuppiah ◽  
Mohammad Mehedi Hassan ◽  
...  
Keyword(s):  
Cognitive Radio ◽  
Cognitive Radio Networks ◽  
Spectrum Management ◽  
Radio Networks ◽  
Management Techniques ◽  
Proximity Service
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