Routing In Cognitive Radio Networks Research Articles

Secure distance based multi-objective artificial rabbits algorithm for clustering and routing in cognitive radio network

Secure distance based multi-objective artificial rabbits algorithm for clustering and routing in cognitive radio network

Cross layer protocol architecture for spectrum‐based routing in cognitive radio networks

AbstractNew cell phone services and apps consume more spectrum. Wireless spectrum allows services and apps to communicate with one another. Wi‐Fi quality is improved via smart spectrum usage and new CRT services. The use of spectrum is beneficial. Cross‐layer architecture improves the energy efficiency of wireless networks. System performance is improved by connecting protocol layers. Cross‐layer configuration does not introduce layer functionality into a network. By protecting networks, cross‐layer design increases communication. C‐LNRD uses self‐determined time slots to promote communication. Agents that collect information. At each level, the monitoring agent monitors traffic, time, and topology. Each layer of agents has its own database. Data is received by the network, MAC, and physical layers. Based on its measurements, each node grants trust. Routes were altered. PR ATTACK does not have RTS, CTS, or RREQ to reduce false positives. Spectrum allocation is improved via cognitive radio and learning technologies. Adaptive Cognitive Radio Networks are created using AI, GA, Fuzzy Logic, and Game Theory (ACRN). DSA creates high‐bandwidth MCRNs. This research looks at MCRNs in order to optimise spectrum usage, throughput, routing delay, and overhead. Multihop, the proposed approach by CRN takes into account spectrum awareness, quality route establishment, and route maintenance in the event that a connection fails due to spectrum or a node transfer. New strategies improve the cross‐layer network protocols of MCRN. Learners gain from spectrum models. Sensors and routers are linked by layers. The proposed routing improves both performance and spectrum use.

Routing in cognitive radio networks using adaptive full-duplex communications over IoT environment

Routing in cognitive radio networks using adaptive full-duplex communications over IoT environment

Energy Aware Multipath Routing Protocol for Cognitive Radio Ad Hoc Networks

Cognitive radio networks (CRNs) emerged as a paradigm to solve the problem of limited spectrum availability and the spectrum underutilization in wireless networks by opportunistically exploiting portions of the spectrum temporarily vacated by licensed primary users (PUs). Routing in CRNs is a challenging problem due to the PU activities and mobility. On the other hand, energy aware routing is very important in energy-constraint CRNs. In addition, it is crucial that CR users efficiently exchange data with each other before the appearance of PUs. To design a robust routing scheme for mobile CR ad hoc networks (CRANs), the constraints on residual energy of each CR user, reliability, and the protection of PUs must additionally be taken into account. Moreover, multipath routing has great potential for improving the end-to-end performance of ad hoc networks. Considering all these evidences, in this paper, we propose an energy aware on-demand multipath routing (EOMR) protocol for mobile CRANs to ensure the robustness and to improve the throughput. The proposed routing scheme involves energy efficient multipath route selection and spectrum allocation jointly. The simulation results show that our approach improves the overall performance of the network.

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

The article has been withdrawn at the request of the editor of the journal International Journal of Sensors, Wireless Communications and Control due to incoherent content. Bentham Science apologizes to the readers of the journal for any inconvenience this may caused. The Bentham Editorial Policy on Article Withdrawal can be found at https://benthamscience.com/editorial-policies-main.php Bentham Science Disclaimer: It is a condition of publication that manuscripts submitted to this journal have not been published and will not be simultaneously submitted or published elsewhere. Furthermore, any data, illustration, structure or table that has been published elsewhere must be reported, and copyright permission for reproduction must be obtained. Plagiarism is strictly forbidden, and by submitting the article for publication the authors agree that the publishers have the legal right to take appropriate action against the authors, if plagiarism or fabricated information is discovered. By submitting a manuscript, the authors agree that the copyright of their article is transferred to the publishers if and when the article is accepted for publication.

Routing in cognitive radio networks with full-duplex capability under dynamically varying spectrum availability

Abstract This paper proposes a routing scheme for full-duplex-(FD)-based multi-hop CRNs that attempts to maximize the packet-delivery-ratio between any source–destination pair by being aware of the time-varying nature of spectrum-availability of the operating environment. The proposed routing scheme consists of path selection and channel assignment. Two design variants are presented: one for inband- and the other for outband-FD CRNs. Our scheme is performed into three phases: path-discovery (to identify a set of feasible routes), channel-selection (to assign channels along each path) and path-selection (to select the highest effective availability-time path). Compared to a reference scheme, the simulation results indicate that being spectrum-availability-aware can achieve significant performance enhancement.

Secure Distance Based Improved Leach Routing to Prevent Puea in Cognitive Radio Network

Routing in cognitive radio networks (CRNs) faces numerous limitations in misbehaving activities and secure the routing requests and reply messages. In this research work, Secure Distance based Improved LEACH Routing (SDILR) protocol is presented to avoid the primary user emulation attack (PUEA) in CRN. Initially, the nodes in the cognitive radio network are clustered by using distance based improved Low- energy adaptive clustering hierarchy (ILEACH). After the formation of clusters, secure routing is presented using support value based signature authentication to avoid PUEA. The proposed secure ILEACH routing results the secure data sharing through the primary user nodes PUEA.

On Platform to Enable the Cognitive Radio Over 5G Networks

With the increase in the number of communication devices, the requirement for higher bandwidth is essential. To achieve this goal, research and industrial communities have both suggested that future wireless systems will take advantage of the numerous emerging technologies. Utilization of Cognitive Radio (CR) for the next-generation Fifth Generation (5G) communication technology is the major advancement for getting a higher bandwidth in a cellular communication network. In this paper, we present a comprehensive study of CR from the perspectives of spectrum allocation schemes, impact and role of MAC layer in spectrum sensing and sharing, CR application in multi-hop wireless networks, and challenges associated with channel selection and packet routing in multi-hop heterogeneous CR networks. This paper also presents the analysis, in literature, of a range of intelligent routing protocols that are considered viable for packets routing in CR networks. The need to address the issue of spectrum depletion and the apparent underutilization of available scarce spectrum resources in existing wireless networks is the primary motivation behind this study. Considering the fact that CR technology can potentially maximize the utilization of bulk of the unused communication spectrum bands for the future 5G of wireless network and beyond.

Towards Securing Routing Based on Nodes Behavior During Spectrum Sensing in Cognitive Radio Networks

Routing is a challenge in cognitive radio networks (CRNs) due to the properties of cognitive radio (CR) technology, as well as other limitations. Firstly, the CR’s frequency band is considered a dynamic spectrum. Therefore, since the routing algorithms used in other types of networks rely on a fixed frequency band, they cannot be directly used in CRNs. Secondly, the dynamic spectrum access, which is enabled by CR technology, negatively affects the network performance. Thirdly, having an effective routing in CRNs needs a local and continual knowledge of its changeable environment. Lastly, the presence of adversary nodes and their malicious activities affect the route establishment process, thereby reducing the network performance. This paper addresses these limitations by combining the spectrum sensing and the spectrum management phases by proposing a novel and secure routing algorithm. Security in the proposed algorithm combines two aspects. The first aspect is measuring the nodes’ behavior during the spectrum sensing phase through a parameter called belief level (BL), which refers to the nodes’ reliability to correctly find and use the white spectrum channels. The second aspect is securing the routing request and reply messages by encoding them with the existing cryptography techniques. The main goal of the proposed approach is to make the available paths between any two communicating nodes secure, reduce the negative effects to the licensed users over the spectrum channels, and moderate the total cost of the used channels over the best path(s). The performance evaluation in terms of end-to-end delay, packet delivery ratio, packet loss ratio, and routing overhead show that the proposed approach outperforms multiple existing routing algorithms. Moreover, the proposed algorithm is validated and verified in terms of security functionality against any attacks.

A Survey on Routing Protocols for Delay and Energy-Constrained Cognitive Radio Networks

The ever-growing demand for higher network data rates, lower delay, and conservative energy consumption at reduced costs, to support Internet-of-things (IoT) communications, has pushed wireless technologies into a new frontier. The growing demand for such technologies can be attributed to several factors, such as the massive number of the upcoming bandwidth-hungry IoT applications and the enormous number of - often battery-powered - devices expected to connect to the network. Cognitive Radio Networks (CRNs) can play a significant role in future generations of mobile-communication technologies by providing dynamic access to underutilized licensed bands. However, battery-operated devices, which communicate delay-sensitive data over multihop links, impose serious energy-delay limits. In such CRN-based IoT communications, as one device depletes its energy, network disconnectivity may arise, which can degrade the network efficiency. Therefore, building a reactive routing protocol to recover from any sudden link breakage is necessary to maintain prolonged network connectivity. This paper provides a comprehensive survey of CRN routing protocols that are based on two essential metrics, namely, packet delay, energy consumption, or both, while excluding all other CRN routing protocols. The survey is meant to support the designers of future CRN-based IoT communication frameworks with a detailed comparative survey, which targets the most relevant proposed routing protocols, including the specifics of the routing metrics, implemented spectrum awareness strategy, and employed medium-access control standard along with the simulator tool used for performance evaluation. In addition, this survey finds that the majority of cognitive radio routing protocols address either delay or energy consumption, but only a few consider a joint delay-energy metric, which suits delay-sensitive IoT applications running on energy-constrained devices.

E 2CL, HC: Energy Efficient Cooperative Localized & Hierarchical Cluster Routing in CRN

With the advancement of Cognitive Radio Network (CRN), localization of primary users progress the improvement of network performance with respect to power adoption and reliability. Moreover, CRN localization is essential because storage energy of CR devices is limited and causes increased network lifetime. By considering these issues, we propose an Energy Efficient Cooperative Localization approach (EE-CL) in CRN, which is used for placing Primary User (PU) position with the help of mobile a aided CR. Moreover, communication among CRs cooperation which leads to demand of more energy, a mobile CR is allowed to manage the overall positional accuracy and wake up minimum number of CR to collaborate CR manager. In order to investigate influence of accurate location knowledge, a location-aware CR (LaCR) routing protocol employed and to improve the network life time a Hierarchical clustering approach is applied. We evaluated the simulation results for the proposed Energy Efficient Cooperative Localization (EE-CL) Routing approach which achieves better performance compared to existing Cooperative Localization (CL) with respect to energy efficiency. Furthermore, remarkable performance is accomplished by LaCR protocol in terms of PUs collision risk with the help of localization knowledge obtained from EE-CL approach. Also Hierarchical Cluster Routing protocol acheives significant performance in enhancing the network life time.

Primary User-Aware Optimal Discovery Routing for Cognitive Radio Networks

Routing protocols in multi-hop cognitive radio networks (CRNs) can be classified into two main categories: local and global routing. Local routing protocols aim at decreasing the overhead of the routing process while exploring the route by choosing, in a greedy manner, one of the direct neighbors. On the contrary, global routing protocols choose the optimal route by exploring the whole network to the destination paying the flooding overhead cost. In this paper, we propose a primary user-aware $k$ -hop routing scheme where $k$ is the discovery radius. This scheme can be plugged into any CRN routing protocol to adapt, in real time, to network dynamics like the number and activity of primary users. The aim of this scheme is to cover the gap between local and global routing protocols for CRNs. It is based on balancing the routing overhead and the route optimality, in terms of primary users avoidance, according to a user-defined utility function. We analytically derive the optimal discovery radius ( $k$ ) that achieves this target. Evaluations on NS2 with a side-by-side comparison with traditional CRNs protocols show that our scheme can achieve the user-defined balance between the route optimality, which in turn reflected on throughput and packet delivery ratio, and the routing overhead in real time.

Design of optimum criterion for opportunistic multi-hop routing in cognitive radio networks

Design of optimum criterion for opportunistic multi-hop routing in cognitive radio networks

Exploring the impact of social properties on routing in cognitive radio networks

SummaryIn this paper, we study the social properties, including community, friendship, and individual selfishness of cognitive radio networks and analyze the effect of these social properties on the performance of routing protocols. We first introduce the concept of cooperation willingness considering social relations and individual selfishness. Then, we model the impact of cooperation willingness on the routing performance in cognitive radio networks. After that, we perform extensive simulations to evaluate the accuracy of our model. The simulation results show that our model matched the simulation results well. Finally, our results suggest that community members, friends, and nodes with lower individual selfishness should be selected in the forwarding routes.

A Reinforcement Learning Based Routing in Cognitive Radio Networks for Primary Users with Multi-stage Periodicity

Designing an efficient routing protocol for cognitive radio networks is critical due to the dynamic behavior of the primary users. Based on empirical studies, the primary users activity on the licensed channels has periodicity comprised of several stages, and that the model of primary users activity may change during different stages. This paper has identified two main challenges facing designers: how to transmit packets via a stable route, and how to ensure imposing of minimal interference on the primary users. To address these, we propose a routing protocol which is based on a generalized version of Q-learning and which exploits the said model of primary users behavior. We divide the infinite time horizon into cycles (corresponding to periods), then break each cycle into several sub-cycles (corresponding to stages), making an assumption that the statistical model parameters of the primary users activity will not change during a sub-cycle. The extensive simulations confirm that our proposed routing approach outperforms existing schemes in terms of throughput and minimal interference. The paper also verifies that imposing of significant interference on the primary users and degradation of QoS of secondary users stem from lack of attention to the multi-stage periodic behavior of primary users.

User social activity-based routing for cognitive radio networks

The social activities of Primary Users (PUs) and Secondary Users (SUs) affect actual accessible whitespace in Cognitive Radio Networks (CRNs). However, the impacts of primary activities on available whitespace have been extensively investigated due to the dominating priority of PUs, while the impacts of secondary activities on actual accessible whitespace have been ignored. Therefore, we propose to incorporate the primary and secondary activities in the analysis and decision of the accessible whitespace, namely, both the dominance of PUs over SUs and the competitions among SUs are simultaneously taken into account. Specifically, we first approximate primary activity probability based on the real datasets of mobile phone usage records, then the spectrum opportunity between a pair of communication SUs is deduced based on primary activities. Next, we infer the access probability limit of SUs successfully accessing the whitespace according to the primary activity probability, and depict the secondary activity probability from the views of social activity patterns and social networks respectively. Furthermore, the actual accessible probability of whitespace is given by introducing the competitions among SUs. Finally, a greedy routing algorithm, considering the accessible whitespace and the distance to the destination, is proposed to verify our idea. The experiment results based on the real datasets demonstrate the correctness of our analysis and the advantages of the proposed algorithm.

Clustering and Reinforcement-Learning-Based Routing for Cognitive Radio Networks

The CRN is a future generation wireless communication system that allows SUs to use the underutilized or unused spectrum, known as white spaces, in licensed spectrum with minimum interference to PUs. However, the dynamic conditions of CRNs (e.g., PUs' activities and channel availability) make routing more challenging compared to traditional wireless networks. In this tutorial, we focus on solving the routing problem in CRNs with the help of a clustering mechanism. Cluster-based routing in CRNs enhances network scalability by reducing the flooding of routing overheads, as well as network stability by reducing the effects of dynamicity of channel availability. Additionally, RL, an artificial intelligence approach, is applied as a tool to further enhance network performance. We present SMART, which is a cluster-based routing scheme designed for the CRN, and evaluate its performance via simulations in order to show the effectiveness of cluster- based routing in CRNs using RL.

Non-cooperative reinforcement learning based routing in cognitive radio networks

Cognitive Radio Networks (CRNs) have emerged to overcome the problem of spectrum scarcity caused by spreading of wireless applications. In CRNs, Secondary Users (SUs) are permitted to access opportunistically the authorized frequency bands owned by Primary Users (PUs). In this paper, we address the problem of routing several flows generated by SUs to a given destination considering the presence of PUs traffic modeled by a more realistic model based on Markov Modulated Poisson Process (MMPP). Each source SU wants to selfishly minimize the end-to-end delay of its flow meanwhile the Quality of Service (QoS) requirements of the PUs would be met. To consider quick adaptation of SUs routing decision to environment changes and non-cooperative interaction of them, we formulate the routing problem as stochastic learning processes featured by non-cooperative games. Then, we propose a distributed reinforcement learning-based scheme for solving the routing problem that can avoid information exchange between the competing SUs. The proposed scheme provably converges and simulation results demonstrate effectiveness of the proposed scheme in decreasing the delay while meeting the QoS requirements of the PUs.

Energy Aware Multipath Routing Protocol for Cognitive Radio Ad Hoc Networks

Cognitive radio networks (CRNs) emerged as a paradigm to solve the problem of limited spectrum availability and the spectrum underutilization in wireless networks by opportunistically exploiting portions of the spectrum temporarily vacated by licensed primary users (PUs). Routing in CRNs is a challenging problem due to the PU activities and mobility. On the other hand, energy aware routing is very important in energy-constraint CRNs. In addition, it is crucial that CR users efficiently exchange data with each other before the appearance of PUs. To design a robust routing scheme for mobile CR ad hoc networks (CRANs), the constraints on residual energy of each CR user, reliability, and the protection of PUs must additionally be taken into account. Moreover, multipath routing has great potential for improving the end-to-end performance of ad hoc networks. Considering all these evidences, in this paper, we propose an energy aware on-demand multipath routing (EOMR) protocol for mobile CRANs to ensure the robustness and to improve the throughput. The proposed routing scheme involves energy efficient multipath route selection and spectrum allocation jointly. The simulation results show that our approach improves the overall performance of the network.

Spectrum aware on-demand routing in cognitive radio networks

Spectrum aware on-demand routing in cognitive radio networks