The Experts below are selected from a list of 37806 Experts worldwide ranked by ideXlab platform
Dongsong Zheng - One of the best experts on this subject based on the ideXlab platform.
-
State and Future Development of Cognitive Radio Networks
2011 International Conference on Internet Technology and Applications, 2011Co-Authors: Dongsong ZhengAbstract:Rapid development of wireless communication technology makes our life convenient. However, it causes two issues: one is shortage of spectrum resources leads to the waste of allocated spectrum resources; the other is wireless communication standards are incompatible. Those two issues can be solved by Cognitive Radio Networks. In this paper, current state of Cognitive Radio Networks is discussed detailed and future network architecture is studied.
-
Research on the Key Agreement and Authentication of Cognitive Radio Networks
2011 International Conference on Internet Technology and Applications, 2011Co-Authors: Dongsong ZhengAbstract:Identity authentication is one of the key problems to be solved in Cognitive Radio Networks. Current authentication mechanisms for Cognitive Radio Networks are based on PPP Extensible Authentication Protocols. Future Cognitive Radio network can fusion current wireless standards. However, security requirements of various wireless standards are different which leads to current authentication mechanisms are not suitable for Cognitive Radio Networks. Therefore, an identity authentication mechanism based on Certification Authority (CA) is proposed in this paper. The mechanism adopts SIM authentication for terminal users and secure tunnel +certificates for servers. CA can resolve identity authentication problem of base station, assure security of base station and CA by secure tunnel in core Networks. And current Challenge Handshake Protocol is also improved; challenge handshake exists in whole communication process which resolves connection robbing problem.
-
Security Architecture and Formal Analysis for Cognitive Radio Networks
2011 International Conference on Internet Technology and Applications, 2011Co-Authors: Dongsong ZhengAbstract:Rapid development of Wireless communication leads to more and more wireless standards appear, and security architecture of different standards are not compatible. In this paper, compatibility of security architecture for Cognitive Radio network is discussed and a unified security architecture is proposed. BAN logic analysis verifies the proposed architecture is adaptable, scalable, and suitable for Cognitive Radio Networks.
Yenumula B. Reddy - One of the best experts on this subject based on the ideXlab platform.
-
Nanocomputing in Cognitive Radio Networks to Improve the Performance
Advances in Wireless Technologies and Telecommunication, 2013Co-Authors: Yenumula B. ReddyAbstract:This spectrum sensing application is ideal for nanotechnology implementation because intensive computations are needed. Without nanocomputing it might be infeasible to implement sensing and analysis in real-time for Cognitive Radio Networks with the current available computing power. Therefore, we need complicated distributed processing schemes to achieve our goals and nanocomputing is the best answer. The contribution includes the current state of nanotechnology, the Cognitive Radio Networks, role of nanotechnology in Cognitive Radio Networks, and building the model using nanotechnology for real-time applications.
-
Security Issues and Threats in Cognitive Radio Networks
2013Co-Authors: Yenumula B. ReddyAbstract:Cognitive Radio technology is the vision of pervasive wireless communications that improves the spectrum utilization and offers many social and individual benefits. The objective of the Cognitive Radio network technology is to utilize the unutilized spectrum by primary users and fulfill the secondary users' demands irrespective of time and location (any time and any place). Due to their flexibility, the Cognitive Radio Networks are vulnerable for numerous threats and security problems that will affect the performance of the network. Little attention was given to security aspects in Cognitive Radio Networks that include spectrum sensing (sensing primary user), attacks that threaten the network at various layers and adversary effects on performance due to the security threats. In this survey, we discuss the Cognitive Radio Networks, problems involved in sensing and management, attacks on Cognitive Radio Networks, attacks on various network layers, threats on Cognitive Radio Networks, and the current security and privacy solutions available. Further, we illustrate the need of careful engineering with security checks while designing the Cognitive Radio Networks.
-
Nanocomputing in Cognitive Radio Networks to Improve the Performance
Nanotechnology, 1Co-Authors: Yenumula B. ReddyAbstract:Computing and communications are central issues in Cognitive Radio Networks. The Cognitive users are increasing exponentially and the available spectrum resource is constant. Therefore, efficient usage of spectrum through Cognitive Radio Networks is essential. The real-time communications in Cognitive Radio Networks with increasing demand for spectrum is possible through nanocomputing. Nanocomputing is the computing with thousands or millions of computers at the same time to gain more power for less money within minimum possible time. Therefore, the future research requires real-time communications using interconnected nanolevel computer devices. This spectrum sensing application is ideal for nanotechnology implementation because intensive computations are needed. Without nanocomputing it might be infeasible to implement sensing and analysis in real-time for Cognitive Radio Networks with the current available computing power. Therefore, we need complicated distributed processing schemes to achieve our goals and nanocomputing is the best answer. The contribution includes the current state of nanotechnology, the Cognitive Radio Networks, role of nanotechnology in Cognitive Radio Networks, and building the model using nanotechnology for real-time applications.
Koduvayur P. Subbalakshmi - One of the best experts on this subject based on the ideXlab platform.
-
Open research issues in multi-hop Cognitive Radio Networks
IEEE Communications Magazine, 2013Co-Authors: Shamik Sengupta, Koduvayur P. SubbalakshmiAbstract:Cognitive Radio Networks hold the key to achieving better Radio bandwidth utilization and improving the quality of wireless applications. The next step in this fast emerging paradigm is the multi-hop Cognitive Radio network. Well designed multi-hop Cognitive Radio Networks can provide high bandwidth efficiency by using dynamic spectrum access technologies as well as provide extended coverage and ubiquitous connectivity for the wireless end users. However, the special features of multi-hop Cognitive Radio Networks also raise several unique design challenges. In this article, we survey these unique challenges and open research issues in the design of multi-hop Cognitive Radio Networks as well as discuss potential approaches to address these challenges. This article specifically focuses on the medium access control (MAC) and network layers of the multi-hop Cognitive Radio protocol stack. Issues considered include efficient spectrum sharing, optimal relay node selection, interference mitigation, end-to-end delay, etc.
Yasir Saleem - One of the best experts on this subject based on the ideXlab platform.
-
Network Coding in Cognitive Radio Networks: A Comprehensive Survey
IEEE Communications Surveys and Tutorials, 2017Co-Authors: Ayesha Naeem, Mubashir Husain Rehmani, Yasir Saleem, Imran Rashid, Noel CrespiAbstract:Network coding (NC) is a technique used for effective and secure communication by improving the network capacity, throughput, efficiency, and robustness. In NC, data packets are encoded by intermediate nodes and are then decoded at the destination nodes. NC has been successfully applied in a variety of Networks including relay Networks, peer-to-peer Networks, wireless Networks, Cognitive Radio Networks, and wireless sensor Networks. Cognitive Radio network (CRN) is an emerging field which exploits the utilization of unused spectrum or white spaces, effectively and efficiently. In CRNs, NC schemes are also applied to maximize the spectrum utilization, as well as to maintain the effective and secure transmission of data packets over the network. In this paper, we provide a comprehensive survey of NC schemes in Cognitive Radio Networks, highlighting the motivations for and the applications of NC in CRNs. We provide typical case studies of NC schemes in CRNs, as well as the taxonomy of NC schemes in CRNs. Finally, we present open issues, challenges, and future research directions related with NC in Cognitive Radio Networks.
-
Network coding in Cognitive Radio Networks : a comprehensive survey
Communications Surveys and Tutorials IEEE Communications Society, 2017Co-Authors: Ayesha Naeem, Yasir Saleem, Imran Rashid, Rehmani Mubashir Husain, Noel CrespiAbstract:Network Coding (NC) is a technique used for effective and secure communication by improving the network capacity, throughput, efficiency, and robustness. In network coding, data packets are encoded by intermediate nodes and are then decoded at the destination nodes. Network coding has been successfully applied in a variety of Networks including relay Networks, peer-to-peer Networks, wireless Networks, Cognitive Radio Networks, and wireless sensor Networks. Cognitive Radio network (CRN) is an emerging field which exploits the utilization of unused spectrum or white spaces, effectively and efficiently. In CRNs, network coding schemes are also applied to maximize the spectrum utilization, as well as to maintain the effective and secure transmission of data packets over the network. In this paper, we provide a comprehensive survey of network coding schemes in Cognitive Radio Networks, highlighting the motivations for and the applications of network coding in CRNs. We provide typical case studies of network coding schemes in CRNs, as well as the taxonomy of network coding schemes in CRNs. Finally, we present open issues, challenges, and future research directions related with network coding in Cognitive Radio Networks
-
Neighbor discovery in traditional wireless Networks and Cognitive Radio Networks
Journal of Network and Computer Applications, 2015Co-Authors: Athar Ali Khan, Mubashir Husain Rehmani, Yasir SaleemAbstract:Cognitive Radio network is designed to opportunistically exploit the licensed band. To deploy a Cognitive Radio network, nodes need to perform the neighbor discovery process in order to enable communication and connectivity in the network. Neighbor discovery not only helps in successful and efficient communication in Cognitive Radio Networks but also provides solutions to a majority of other traditional wireless network problems, such as gossiping or broadcasting a message, a global common control channel allocation, etc. In this paper, we provide a survey on neighbor discovery for traditional wireless Networks and Cognitive Radio Networks. In this perspective, we first provide basics and features of neighbor discovery, as well as, the challenges when moving from traditional wireless Networks towards Cognitive Radio Networks, in order to pave the way for a better understanding of the neighbor discovery in Cognitive Radio Networks. We provide detailed taxonomy of neighbor discovery protocols in traditional wireless Networks and Cognitive Radio Networks. Finally, open issues, challenges, and future research directions have been highlighted for neighbor discovery in Cognitive Radio Networks.
-
Primary Radio user activity models for Cognitive Radio Networks: A survey
Journal of Network and Computer Applications, 2014Co-Authors: Yasir Saleem, Mubashir Husain RehmaniAbstract:Abstract Cognitive Radio Networks have been emerged as a promising solution for solving the problem of spectrum scarcity and improving spectrum utilization by opportunistic use of spectrum. Cognitive Radio Networks utilize the spectrum which is licensed to primary Radio users when they are not utilizing it, i.e., when the spectrum is idle. Thus, the performance of Cognitive Radio Networks is highly dependent upon the activity of primary Radio users. Hence, it is very important to model primary Radio users activity in Cognitive Radio Networks. By keeping this in mind, several models in the literature have been proposed for modeling primary Radio users activity. But there is not any source which consolidate all these models into single platform. Therefore, this paper combines all the primary Radio user activity models for Cognitive Radio Networks at a single place. The goal of this paper is to provide a single source in the form of survey paper by which a reader can get an idea about which primary Radio user activity models have been used in the literature for Cognitive Radio Networks and how the modeling is performed. Furthermore, we also discuss issues, challenges and future directions for primary Radio activity models. In fact, in this paper, different primary Radio user activity models have been presented along with their classification. This paper also discusses those approaches which performed real implementation for spectrum occupancy along with spectrum bands on which the implementation is performed and location where implementation is carried out. In summary, this paper provides up-to-date survey of primary Radio user activity models for Cognitive Radio Networks.
Mubashir Husain Rehmani - One of the best experts on this subject based on the ideXlab platform.
-
Network Coding in Cognitive Radio Networks: A Comprehensive Survey
IEEE Communications Surveys and Tutorials, 2017Co-Authors: Ayesha Naeem, Mubashir Husain Rehmani, Yasir Saleem, Imran Rashid, Noel CrespiAbstract:Network coding (NC) is a technique used for effective and secure communication by improving the network capacity, throughput, efficiency, and robustness. In NC, data packets are encoded by intermediate nodes and are then decoded at the destination nodes. NC has been successfully applied in a variety of Networks including relay Networks, peer-to-peer Networks, wireless Networks, Cognitive Radio Networks, and wireless sensor Networks. Cognitive Radio network (CRN) is an emerging field which exploits the utilization of unused spectrum or white spaces, effectively and efficiently. In CRNs, NC schemes are also applied to maximize the spectrum utilization, as well as to maintain the effective and secure transmission of data packets over the network. In this paper, we provide a comprehensive survey of NC schemes in Cognitive Radio Networks, highlighting the motivations for and the applications of NC in CRNs. We provide typical case studies of NC schemes in CRNs, as well as the taxonomy of NC schemes in CRNs. Finally, we present open issues, challenges, and future research directions related with NC in Cognitive Radio Networks.
-
Broadcasting strategies for Cognitive Radio Networks
Computers & Electrical Engineering, 2016Co-Authors: Bushra Rashid, Mubashir Husain Rehmani, Ayaz AhmadAbstract:We give an overview of various broadcasting strategies proposed so far for Cognitive Radio Networks (CRNs).We identify required key characteristics of broadcasting strategies in CRNs.We propose a comprehensive and detailed classification of broadcasting strategies in CRNs.We provide a detailed study of broadcast storm problem in CRNs.We discuss the possible scenarios for the generation of broadcast storm problem and its related challenges in CRNs. Display Omitted Broadcasting is the simplest form of communication in which nodes disseminate the same information simultaneously to all of their neighbors. Broadcasting has been widely used in many types of Networks including wireless Networks, wireless sensor Networks, and mobile ad-hoc Networks. Likewise these Networks, broadcasting is also used in Cognitive Radio Networks (CRNs) to accomplish various tasks such as spectrum sensing, spectrum sharing, spectrum management, and spectrum mobility. This article investigates and provides a comprehensive overview of various broadcasting strategies that have been proposed so far for Cognitive Radio Networks. Moreover, it provides a detailed study of broadcast storm problem in CRNs. Finally, it discusses issues, challenges and future research directions for broadcasting strategies in CRNs.
-
Neighbor discovery in traditional wireless Networks and Cognitive Radio Networks
Journal of Network and Computer Applications, 2015Co-Authors: Athar Ali Khan, Mubashir Husain Rehmani, Yasir SaleemAbstract:Cognitive Radio network is designed to opportunistically exploit the licensed band. To deploy a Cognitive Radio network, nodes need to perform the neighbor discovery process in order to enable communication and connectivity in the network. Neighbor discovery not only helps in successful and efficient communication in Cognitive Radio Networks but also provides solutions to a majority of other traditional wireless network problems, such as gossiping or broadcasting a message, a global common control channel allocation, etc. In this paper, we provide a survey on neighbor discovery for traditional wireless Networks and Cognitive Radio Networks. In this perspective, we first provide basics and features of neighbor discovery, as well as, the challenges when moving from traditional wireless Networks towards Cognitive Radio Networks, in order to pave the way for a better understanding of the neighbor discovery in Cognitive Radio Networks. We provide detailed taxonomy of neighbor discovery protocols in traditional wireless Networks and Cognitive Radio Networks. Finally, open issues, challenges, and future research directions have been highlighted for neighbor discovery in Cognitive Radio Networks.
-
Primary Radio user activity models for Cognitive Radio Networks: A survey
Journal of Network and Computer Applications, 2014Co-Authors: Yasir Saleem, Mubashir Husain RehmaniAbstract:Abstract Cognitive Radio Networks have been emerged as a promising solution for solving the problem of spectrum scarcity and improving spectrum utilization by opportunistic use of spectrum. Cognitive Radio Networks utilize the spectrum which is licensed to primary Radio users when they are not utilizing it, i.e., when the spectrum is idle. Thus, the performance of Cognitive Radio Networks is highly dependent upon the activity of primary Radio users. Hence, it is very important to model primary Radio users activity in Cognitive Radio Networks. By keeping this in mind, several models in the literature have been proposed for modeling primary Radio users activity. But there is not any source which consolidate all these models into single platform. Therefore, this paper combines all the primary Radio user activity models for Cognitive Radio Networks at a single place. The goal of this paper is to provide a single source in the form of survey paper by which a reader can get an idea about which primary Radio user activity models have been used in the literature for Cognitive Radio Networks and how the modeling is performed. Furthermore, we also discuss issues, challenges and future directions for primary Radio activity models. In fact, in this paper, different primary Radio user activity models have been presented along with their classification. This paper also discusses those approaches which performed real implementation for spectrum occupancy along with spectrum bands on which the implementation is performed and location where implementation is carried out. In summary, this paper provides up-to-date survey of primary Radio user activity models for Cognitive Radio Networks.
-
Data Dissemination and Channel Selection in Cognitive Radio Networks
Advances in Wireless Technologies and Telecommunication, 2013Co-Authors: Mubashir Husain Rehmani, Yasir FaheemAbstract:In this chapter, the authors provide a comprehensive review of broadcasting and channel selection strategies for wireless Cognitive Radio Networks. In the beginning, some applications of the data dissemination in wireless Cognitive Radio Networks are discussed to highlight their importance and utility. Next, the authors provide a detailed classification of broadcasting protocols in light of the existing literature, and the pros and cons of each classified category are discussed. Afterwards, the data dissemination is briefly discussed in the context of multi-channel environments and the related issues are highlighted. Then, the authors discuss the challenges of data dissemination in Cognitive Radio Networks, followed by the classification of channel selection strategies along with their advantages and disadvantages for various classes of applications. In the last part, the authors conclude this chapter with open research issues that need to be addressed to provide efficient channel selection and data dissemination strategies in Cognitive Radio Networks.
