Securing cognitive radio networks (CRNs): A bibliometric review

Recently, wireless transmission of medical data of the patients in heterogeneous networks evoked keen interest among researchers. However, the challenges such as efficient utilization of the frequency spectrum, increase in the lifetime of the devices are considered to be the most important issues nowadays. In this project, while considering the above mentioned challenges for an efficient use of medical devices among the patients, design of cognitive radio network has been incorporated, along with the Hospital Management System. The proposed paper developed a new algorithm/network called Bio Cog--for the implementation of the cognitive networks for transmitting the medical data, which uses efficient frequency spectrum allocation method. The energy efficiency has been achieved by incorporating new algorithm (D2V algorithm) in cognitive networks. The proposed system uses the principle of novel spectrum sensing techniques in the wireless cognitive radio networks. In this research, heterogeneous network has been taken into account by working with different wireless technologies such as XBee, Wi-Fi and Bluetooth and accordingly, different frequency bands are allocated. When the spectrum is sensed by the designed hardware, it decides on allotting the vacant band to the specified users. All users are connected with temperature sensors whose temperature is measured continuously based on which the users are given priority in allotting the spectrum. Thus the hardware can be used in medical applications and tested for one such application called Hospital Management System which is developed and implemented in this work. The hardware has been designed to implement the Dynamic Distance Vector Algorithm (D2VA) in which the distance plays an important role in deciding the threshold range for sensing.

As the proliferation of wireless handheld devices such as smart phones and tablets grow exponentially, new technologies have to be adopted to efficiently use the radio frequency spectrum. Cognitive Radio(CR) network offers a promising solution to improve spectrum utilization and efficiency by harnessing the available spectrum resources in real-time. However, participants in the CR network can comprise of malicious adversaries who adopt dishonest and non-cooperative strategies to attack the network. In our research, we focus on a critical attack on the CR network: Primary User Emulation (PUE). In a PUE attack, the malicious adversary violates spectrum sharing etiquette by impersonating the primary user at the expense of other unselfish secondary users. To thwart such an attack, we present a strategy to authenticate the primary user, which involves collaborative learning among CR users.

We focus on a specific class of denial-of-service (DoS) attacks that is executed through Channel Eviction Triggering (CET), whereby adversary nodes unduly invoke mechanisms inherent in a cognitive radio (CR) network (CRN) operation to protect the licensed users and thus disrupt secondary access to the otherwise idle licensed bands. Skewing the spectrum sensing decision of CRN through sensing misreports is a manifestation of CET attacks. Whereas most studies in the literature focus on making the cooperative sensing more robust against such sensing misreports, we tackle the problem from the novel perspective of incentive alleviation. We distinguish two classes of such DoS attacks, which we refer to as CET and CET-jamming attacks. In the former case, the incentive of adversary CRs is to remove the competition of truthful CRs in accessing the licensed spectral ranges. The latter class of DoS attack deals with scenarios in which the adversary nodes are mainly interested in denying the chances of communication of CRN over primary bands and as such their incentive cannot be modelled by the same utility maximisation model as truthful CRs. We propose a solution for each class of attacks, and our numerical results verify the effectiveness of the proposed CET defence scheme in both cases. Copyright © 2012 John Wiley & Sons, Ltd.

The Cognitive Radio (CR) is the key technology to deal with spectrum scarcity by allowing unlicensed CR users to coexist with existing users in licensed spectrum bands without interfering with binding communications. Cognitive technology provides the spectrum ability to be shared with licensed networks. This radio system can adjust its transmitter and recipient parameters on the basis of interaction with the current conditions in the environment. Due to this flexibility cognitive radios are exploring different types of threats and security attacks. In this paper, we focus primarily on the Primary User Emulation Attack (PUEA) which is a major attack in the cognitive radio network. PUEA is similar to a Denial-of-service attack that can seriously disrupt the spectrum sensing process and deny other legitimate secondary users to access the available spectrum. We proposed a Neyman-Pearson composite hypothesis test (NPCHT) based analytical model to study the impact of PUEA in a fading wireless communication environment. Simulation results show that the proposed techniques can substantially reduce the impact of the malicious attack on the network

The spectral utilization and energy efficiency are two major factors that are predominantly driving the future of wireless communication. Cognitive Radio Network (CRN) along with energy harvesting is an encouraging solution to overcome spectrum under-utilization and energy paucity. In this paper, performance of prediction based sensing in a cooperative CRN is investigated under OR fusion rule in a binary hypothesis model. The role of distribution function on performance of such model is enormous. In this paper, we introduced Weibull distribution function in characterizing the primary user activity and analyze the performance of various parameters. Spectrum efficiency and quality of service of PUs are improved by using prediction based sensing CRN. Cognitive radio (CR) node have ability to harvest energy either from radio frequency (RF) sources or from non-RF sources. CR node starts transmission and harvests energy based on modify decision of fusion center. Impact of number of cooperative CR nodes, number of frames, prediction error, sensing time, collision constraint and splitting parameter on throughput and energy harvesting are investigated. Analytical expression for detection probability, false alarm probability, betterment in spectrum reuse, normalized throughput, energy harvesting together with energy penalty are derived, and the results are presented in the paper.

As a cognitive radio (CR) modulation technique, transform domain communication system (TDCS) has been proposed by utilizing white space spectrum for the network access and achieving low probability of interception (LPI). In the previously reported TDCS- based cognitive radio networks (CRN), however, the non-zero periodic correlation function between any pair of users results in multiuser interference (MUI). In this paper, a novel framework under the CR constraints is presented by employing a two- dimension spreading scheme (i.e., time and frequency domains). We first obtain a class of spreading sequences with almost perfect periodic correlation function for arbitrary spectrum utilization pattern. Then, an universal criteria is presented for synchronous MUI-free CRNs when employing TDCS. Simulation results demonstrate that the proposed TDCS-based CRN architecture is a preferable candidate for distribution wireless networks, such as CR Ad-hoc wireless sensor networks.

In recent years, channel-hopping-based medium access control protocols have been proposed to improve the capacity in a decentralized multichannel cognitive radio (CR) network without using extra control channels. Each CR user has to stochastically follow a default channel-hopping sequence in order to locate a channel and conduct its frame transmission. In this paper, theoretical analysis is conducted on the probability of channel availability and the average frame delay for primary users (PUs) by considering the impact caused by imperfect sensing of CR users and imperfect synchronization between the primary and CR networks. According to the proposed analytical model with realistic considerations, an optimal channel-hopping sequence (OCS) approach is designed for the CR users based on a dynamic programming technique. It is designed by exploiting the optimal load balance between channel availability and channel utilization within the delay constraints of PUs. By adopting the OCS approach, maximum aggregate throughput of CR users can be achieved while considering PU's quality-of-service (QoS) requirements. Moreover, in addition to the paired CR networks, the logical partition problem that occurs in generalized CR networks will also be addressed. This problem can severely degrade the aggregate throughput due to the decreased probability of connectivity between CR users, especially in a CR network with heavy traffic. Therefore, both wake-up successive contention (WSC) and wake-up counter-reset successive contention (WCSC) algorithms are proposed to increase the number of negotiations by both exploring the blind spot of imperfect sensing and amending the contention mechanisms between CR users. Compared to conventional channel-hopping sequences, numerical results illustrate that the proposed approaches can effectively maximize aggregate throughput for CR users under the QoS requirements of PUs.

The ever increasing demand for communication bandwidth has led to spectrum scarcity. We need to manage spectrum as a scare resource. Better utilization of spectrum is the key requirement for the radio communication systems. The inefficient usage of the existing spectrum can be improved through opportunistic access to the licensed bands without interfering with the primary users. This introduces the concept of dynamic spectrum access. Cognitive radio is must for providing quality of service in dynamic spectrum access. To deal with the cognition at physical layer, upper layers are also modified to support cognition and adaptation which make a network cognitive network. This book chapter describes the technical challenges and approaches for cognitive radio networks. Spectrum access models from regulatory point of view are presented. It also explains the challenges and solutions from literature for physical, MAC and network layer of cognitive networks. In the end of the chapter, real hope for designing protocols for such networks, i.e. cross layer protocol design is discussed.

In the recent years Cognitive Radio (CR) technology imposed itself like a very good solution to the increasing spectrum utilization. This technology proposes the development of a new radio type - a cognitive radio - endowed with intelligence that shall sense, share, and use the spectrum opportunities (SOP) of the preexisting wireless networks, that is the channels that are not used by the licensed users. A new routing algorithm for CR networks (CRN): IPSAG - IP Spectrum Aware Geographic based Routing Protocol is presented in the paper. As suggested by the name, the IP hop-by-hop, the geographic, and the spectrum aware routing concepts were used together in the protocol's design. The dynamism and flexibility, characteristic to both CR and IP networks, made us compare the CR concept with the IP concept (a wireless versus wired flexible environment comparison); thus we assumed the IP routing approach for CRN. The proposed strategy is based on hop-by-hop routing, local decisions being taken by nodes according to both local information (CR node's neighborhood) and global information (the entire topology of CRN), channels availability, and destination location.

WITHDRAWN: Grasshopper algorithm based channel assignment for cognitive radio networks

With growing concern about environmental issues and an emerging green communications paradigm, cognitive radio (CR) networks (CRNs) have to be considered from the energy efficiency perspective. In this paper, we focus on scheduling in CRNs, in which a cognitive base station (CBS) makes frequency allocations to the CRs at the beginning of each frame. A cognitive scheduler must consider the diversity among CRs' queues and channel capacities in terms of number of bits and the channel switching cost from one frequency to another. Taking all these into account, we formulate the scheduling problem as an energy efficiency maximization problem, which is a nonlinear integer programming (NLP) problem and is thereby hard to solve. We seek alternate computationally easier solutions. To this aim, we propose a polynomial-time heuristic algorithm, i.e., the energy-efficient heuristic scheduler (EEHS), which allocates each idle frequency to the CR that attains the highest energy efficiency at this frequency. Next, we reformulate the original problem first as a throughput maximization problem subject to energy consumption restrictions and then as an energy consumption minimization problem subject to minimum throughput guarantees. These two schedulers also have the power to provide fairness in resource allocation. We analyze the energy efficiency and successful transmission probability of the proposed schedulers under both contiguous and fragmented spectrum scenarios. Performance studies show that, compared with a pure opportunistic scheduler with a throughput maximization objective, proposed schedulers can attain almost the same throughput performance with better energy efficiency.

In cognitive radio networks, secondary users (SUs) are considered as selfish spectrum users, thus how to maximize the spectrum efficiency by these selfish users becomes an endless research topic. Recent studies mostly focus on the competition analysis between SUs using economic mechanism, such as game theory and auction, but the spectrum owner can hardly increase the spectrum efficiency directly when SUs apply the distributed manner. In this paper, we consider the slotted uplink scenario where several SUs have data transmitted to secondary access point (AP) under distributed random access manner. The AP decides how to divide its spectrum which maximizes the whole throughput, then SUs select the channels which would maximize their own profit. Our results show that SUs' channel selection process leads to a Nash Equilibrium, and the AP derives the proper number of channels based on the properties of NE. Moreover, we derive a rule for AP to decide which SUs should access the spectrum and lead to the increment in the whole throughput.

Empowered by the cognitive radio technology, and motivated by the sporadic channel utilization, both spatially and temporally, dynamic spectrum access networks (also referred to as cognitive radio networks and next generation wireless networks) have emerged as a solution to improve spectrum utilization and provide more flexibility to wireless communication. A cognitive radio network is composed of wireless users, referred to as secondary users, which are allowed to use a given licensed spectrum band as long as there are no primary, licensed, users occupying this band in their vicinity. This restricted spectrum access strategy leads to heterogeneity in channel availability among secondary users. Heterogeneity in channel availability forms a significant source of performance degradation for cognitive radio networks, and poses a great challenge on protocol design. In this dissertation, we propose spectrum allocation algorithms that take this heterogeneity property and its effect on the network performance into consideration. The spectrum allocation solutions proposed in this dissertation address three major objectives in cognitive radio mesh networks. The first objective is maximizing the network coverage, in terms of the total number of served clients, and at the same time simplifying the communication coordination function. To address this objective, we proposed a received based channel allocation strategy that alleviates the need for a common control channel, thus simplifying the coordination function, and at the same time maximizes the number of clients served with link reliability guarantees. We show the superiority of the proposed allocation strategy over other existing strategies. The second objective is improving the multicast throughput to compensate for the performance degradation caused by the heterogeneity in channel availability. We proposed a

The Software-defined radio (SDR) technology is considered as a promising solution to address the issue of spectrum scarcity by providing a high level of flexibility and configurability to wireless communication systems. This technology, widely used in cognitive radio (CR) networks, aims at optimizing the spectrum efficiency. However, to reach this objective, a new conception of spectrum sensing methods needs to be adopted. Indeed, in the emerging wireless communications systems, telecom researchers face the challenge of detecting the presence of energy with a very low signal-to-noise ratio (SNR) in a very complex environment. For this purpose, for complex environments such as CR networks, machine-learning (ML) algorithms seem to be a suitable solution allowing a smart spectrum-sensing scheme. This scheme enables the increase of the signal detection probability and the decrease the false detection probability of signal detection. In this paper, four supervised ML models are compared: Naive Bayes classifier, Support Vector Machine, Gradient Boosting Machine and Distributed Random Forest. Furthermore, a principal component analysis (PCA) is performed to reduce the dimensionality of the complex dataset while preserving the useful variability. Their performance is subsequently evaluated by computing their receiver operating characteristic (ROC) curves. The entire algorithms are then implemented and demonstrated using the GNU (GNU’s Not Unix) Radio platform and an SDR-based electronic card, HackRF One.

The prime objective of spectrum sensing in Cognitive Radio (CR) networks is to increase the efficiency of spectrum utilization and improve the network performance. Both of false probability and misdetection probability can reduce the network performance. We use the mean error detection probabilities to denote the network performance. As the value of the mean detection probabilities decreases, the network performance will be improved. In order to achieve this aim, we use the Markov model of the channel occupancy to estimate the prior probabilities for the hypothesis test in dynamic traffic environment. Because of the uncertainty under local detection, the spectrum detection scheme is extended to cooperative sensing among multiple second users. Moreover, we also propose a dynamic decision threshold to optimize the cooperative spectrum detection. The numerical results show that our proposed scheme can further reduce the mean error detection probabilities.