Collaborative Spectrum Research Articles

Mitigating SSDF Attack using Distance-based Outlier approach in Cognitive Radio Networks

Collaborative spectrum sensing is employed in cognitive radio networks for improving the spectrum sensing accuracy. The collaborating cognitive radios send their individual sensing results to the f...

Analog-to-Digital Cognitive Radio: Sampling, Detection, and Hardware

The proliferation of wireless communications has recently created a bottleneck in terms of spectrum availability. Motivated by the observation that the root of the spectrum scarcity is not a lack of resources but an inefficient managing that can be solved, dynamic opportunistic exploitation of spectral bands has been considered, under the name of Cognitive Radio (CR). This technology allows secondary users to access currently idle spectral bands by detecting and tracking the spectrum occupancy. The CR application revisits this traditional task with specific and severe requirements in terms of spectrum sensing and detection performance, real-time processing, robustness to noise and more. Unfortunately, conventional methods do not satisfy these demands for typical signals, that often have very high Nyquist rates. Recently, several sampling methods have been proposed that exploit signals' a priori known structure to sample them below the Nyquist rate. Here, we review some of these techniques and tie them to the task of spectrum sensing in the context of CR. We then show how issues related to spectrum sensing can be tackled in the sub-Nyquist regime. First, to cope with low signal to noise ratios, we propose to recover second-order statistics from the low rate samples, rather than the signal itself. In particular, we consider cyclostationary based detection, and investigate CR networks that perform collaborative spectrum sensing to overcome channel effects. To enhance the efficiency of the available spectral bands detection, we present joint spectrum sensing and direction of arrival estimation methods. Throughout this work, we highlight the relation between theoretical algorithms and their practical implementation. We show hardware simulations performed on a prototype we built, demonstrating the feasibility of sub-Nyquist spectrum sensing in the context of CR.

Electrosense: Open and Big Spectrum Data

While the radio spectrum allocation is well regulated, there is little knowledge about its actual utilization over time and space. This limitation hinders taking effective actions in various applications including cognitive radios, electrosmog monitoring, and law enforcement. We introduce Electrosense, an initiative that seeks a more efficient, safe and reliable monitoring of the electromagnetic space by improving the accessibility of spectrum data for the general public. A collaborative spectrum monitoring network is designed that monitors the spectrum at large scale with low-cost spectrum sensing nodes. The large set of data is stored and processed in a big data architecture and provided back to the community with an open spectrum data as a service model, that allows users to build diverse and novel applications with different requirements. We illustrate useful usage scenarios of the Electrosense data.

Maximum match filtering algorithm to defend spectrum-sensing data falsification attack in CWSN

Cognitive Wireless Sensor Networks (CWSNs) provide better bandwidth utilisation compared to normal Wireless Sensor Networks (WSNs) by using opportunistic spectrum access to transfer data. They transmit data through the primary user's spectrum band when there is heavy traffic in its own network, thereby eliminating collisions and delays in data delivery. During this, CWSNs are subject to several security threats, attacks on secrecy and authentication, attacks on network availability (DOS attacks), stealth attacks on service integrity etc. The Spectrum Sensing Data Falsification (SSDF) attack is a type of DOS attack where the attackers modify the spectrum sensing report to compel the base station to take a wrong collaborative decision regarding the vacant spectrum band in other networks. In this paper, we have proposed the Maximum-Match Filtering algorithm (MMF) for collaborative spectrum sensing and spectrum decision making in CWSNs, which is executed at the base station to counter the SSDF attack.

Cluster-Based Energy Efficient Collaborative Spectrum Sensing for Cognitive Sensor Network

The high energy efficiency for cognitive sensor network (CSN) has been widely studied for supporting various applications in military and civil fields. By identifying time fraction, local detection threshold, and the number of cognitive sensors, we discuss some design considerations for a cluster-based collaborative spectrum sensing scheme. We formulate the optimization problem to maximize energy efficiency of CSN under a collision constraint and false alarm probability constraint. In order to overcome the problem of finding the optimal detection threshold when k-out-of-M fusion rule is used by the sink node (SN), we design a fictitious cognitive sensor (FCS), which has the same sensing performance as SN. The process for finding the optimal local detection threshold can be converted into the process for searching an optimal received SNR of FCS. Theoretical analysis shows that there exists a unique time fraction to maximize the energy efficiency. Finally, our theoretical analysis is verified through numeric simulations.

Optimized Error Probability for Weighted Collaborative Spectrum Sensing in Time- and Energy-Limited Cognitive Radio Networks

In this paper, a collaborative energy-harvesting cognitive radio (CR) network is considered such that the transmitter of the secondary user (SU) is allowed to harvest signal energy of the primary user (PU) when the presence of the PU is detected. The harvested energy is converted to electrical power in order to supply the sensing and transmission energy of SUs. The time frame (time slot) is divided into two phases allocated to sensing (divided into two durations: spectrum sensing and results reporting) and transmission, respectively. The time spanned by the results reporting duration depends on the number of collaborative sensing users, while the time spent on spectrum sensing duration controls the number of sensing samples. A constrained convex optimization problem of the overall probability of error is formulated incorporating constraints on time and energy resources along with PU interference protection presented as a threshold on probability of collision. We use a soft decision rule scheme while considering two energy harvesting scenarios namely, energy surplus and energy deficit. In each scenario, the convexity of the optimization problem is established analytically and the global optimal solution is obtained. Simulation results are provided to demonstrate the impact of the different parameters on the overall system performance as well as to verify the deduced analytical results.

Defeating SSDF Attacks With Trusted Nodes Assistance in Cognitive Radio Networks

Spectrum sensing data falsification (SSDF) is one of the most dangerous attacks that can degrade the reliability of collaborative spectrum sensing in cognitive radio (CR) networks. In this article, we propose a novel defense strategy to thwart SSDF attacks by intelligently verifying sensory data with the assistance of trusted nodes. The proposed scheme employs an efficient and fast reputation-based algorithm to analyze the behavior of each user. Hence, not only will reliable sensory data be accepted by the central entity, but also, malicious users can be easily identified and removed from the network. Both theoretical and simulation results show that the proposed method provides a powerful countermeasure against SSDF attacks in an adversarial CR environment.

Throughput optimization for dual collaborative spectrum sensing with dynamic scheduling

Cognitive radio technology is envisaged to alleviate both spectrum inefficiency and spectrum scarcity problems by exploiting the existing licensed spectrum opportunistically. However, cognitive radio ad hoc networks (CRAHNs) impose unique challenges due to the high dynamic scheduling in the available spectrum, diverse quality of service (QOS) requirements, as well as hidden terminals and shadow fading issues in a harsh radio environment. To solve these problems, this paper proposes a dynamic and variable time-division multiple-access scheduling mechanism (DV-TDMA) incorporated with dual collaborative spectrum sensing scheme for CRAHNs. This study involves the cross-layered cooperation between the Physical (PHY) layer and Medium Access Control (MAC) layer under the consideration of average sensing time, sensing accuracy and the average throughput of cognitive radio users (CRs). Moreover, multiple-objective optimization algorithm is proposed to maximize the average throughput of CRs while still meeting QOS requirements on sensing time and detection error. Finally, performance evaluation is conducted through simulations, and the simulation results reveal that this optimization algorithm can significantly improve throughput and sensing accuracy and reduce average sensing time.

A Multi-factor Trust Management Scheme for Secure Spectrum Sensing in Cognitive Radio Networks

Cognitive radio (CR) is an emerging technology for efficient utilization of the limited and scarce radio spectrum resources. Spectrum sensing is a key task in CR system for detecting the unused radio spectrum or spectrum holes by the primary users. In this regard, collaborative spectrum sensing (CSS) has shown to as an efficient way to improve such spectrum holes detection in cognitive radio network (CRN). However, this cooperative nature makes it vulnerable to many types of security attacks. In this paper, we focus one of these potential CRN specific security attack called spectrum sensing data falsification attack or Byzantine attack. In which the malicious internal member of the network, reports false sensing results with a aim to increase the sensing errors. In order to ensure the security requirements of CRN, we proposed a novel trust management mechanism that evaluates the trustworthiness of each node participating in the CSS scheme called sensing reputation (SR). To reflect the complexity, the SR calculation involves multiple decision factors like history based trust factor, active factor, incentive factor and consistency factor. Based on this SR value, the suspicious users are identified and the malicious users are filtered out from the decision making process of the CSS scheme. We further introduce the concept of sensing reputation chain to record and track the future behavior of the identified suspicious users. Theoretical analysis and simulation results demonstrate the effectiveness of our proposed malicious user detection technique with a higher accuracy and lower false alarm rate.

A New Game for Future Wi-Fi Spectrum Sharing

This article offers a vision for moving on from the paradigm of “constrained politeness” that has long governed Wi-Fi access to a shared spectrum – as embodied by the CSMA/CA mechanism and restricted EIRP limits. Instead, it is proposed to embrace a paradigm of a “cooperative game” where quasi-cognitive devices establish by themselves a mutually collaborative spectrum coexistence community, based on a unified set of simple rules, which operates on the basis of balancing individual transmits powers as a function of overall noise environment. It is shown that the proposed new mode of spectrum access, results in a significantly increased overall spectrum use efficiency, defined in terms of aggregate throughput carried in given bandwidth by a populace of devices over unit area. Most importantly, the new spectrum access would be still implicitly suitable for the Wi-Fi ethos of self-management and uncoordinated deployment. The proposed method, supported by both theoretical and practical simulations, represents one of the first practical implementations that prove feasibility and usefulness of Game Theory to the practice of wireless communications.DOI: http://dx.doi.org/10.5755/j01.eie.23.3.18339

Towards Privacy-Preserving Aggregation for Collaborative Spectrum Sensing

Collaborative spectrum sensing has become increasingly popular in cognitive radio networks to enable unlicensed secondary users to coexist with the licensed primary users and share spectrum without interference. Despite its promise in performance enhancement, collaborative sensing is still facing a lot of security challenges. The problem of revealing secondary users’ location information through sensing reports has been reported recently. Unlike any existing work, in this paper we not only address the location privacy issue in the collaborative sensing to be against semi-honest adversaries, but also take malicious adversaries into consideration. We propose efficient schemes to protect secondary users’ reports from being revealed in the aggregation process at the fusion center. We rigorously prove that our privacy-preserving collaborative sensing schemes are secure against attacks from both the fusion center and secondary users. We also evaluate our schemes extensively and verify its efficiency and feasibility.

Robust Collaborative Spectrum Sensing Using PHY-Layer Fingerprints in Mobile Cognitive Radio Networks

Collaborative spectrum sensing has been proposed to significantly improve the performance of spectrum sensing in cognitive radio networks (CRNs). However, a serious attack, called a primary user emulation attack (PUEA), could decrease the performance of collaborative spectrum sensing. In this letter, according to mobile CRNs, we propose a novel robust collaborative spectrum sensing method using physical-layer fingerprints power in a multipath Rayleigh fading channel. Moreover, a fingerprints-power-belief-based noncentral detection algorithm is designed to defend against PUEAs. Simulation results show that our proposed method can rapidly detect the presence of a primary user under PUEAs with good performance.

Faithworthy Collaborative Spectrum Sensing Based on Credibility and Evidence Theory for Cognitive Radio Networks

Cognitive radio (CR) has become a tempting technology that achieves significant improvement in spectrum utilization. To resolve the hidden terminal problem, collaborative spectrum sensing (CSS), which profits from spatial diversity, has been studied intensively in recent years. As CSS is vulnerable to the attacks launched by malicious secondary users (SUs), certain CSS security schemes based on the Dempster–Shafer theory of evidence have been proposed. Nevertheless, the available works only focus on the real-time difference of SUs, like the difference in similarity degree or SNR, to evaluate the credibility of each SU. Since the real-time difference is unilateral and sometimes inexact, the statistical information comprised in SUs’ historical behaviors should not be ignored. In this paper, we propose a robust CSS method based on evidence theory and credibility calculation. It is executed in four consecutive procedures, which are basic probability assignment (BPA), holistic credibility calculation, option and amelioration of BPA and evidence combination via the Dempster–Shafer rule, respectively. Our scheme evaluates the holistic credibility of SUs from both the real-time difference and statistical sensing behavior of SUs. Moreover, considering that the transmitted data increase with the number of SUs increasing, we introduce the projection approximation approach to adjust the evidence theory to the binary hypothesis test in CSS; on this account, both the data volume to be transmitted and the workload at the data fusion center have been reduced. Malicious SUs can be distinguished from genuine ones based on their historical sensing behaviors, and SUs’ real-time difference can be reserved to acquire a superior current performance. Abounding simulation results have proven that the proposed method outperforms the existing ones under the effect of different attack modes and different numbers of malicious SUs.

An Improved Adaptive Genetic Algorithm Based on Multi - user Cooperative Spectrum Sensing

In this paper, the collaborative spectrum sensing technique of multiuser is studied, and the global detection probability is obtained by allocating the weight of each user through the control center under the given false alarm probability. The maximum global detection probability is deduced, and an improved adaptive genetic algorithm is used to solve the optimal combination of weight distribution to reduce the detection time of spectrum sensing. In addition, the non-cooperative and cooperative, optimal linear, genetic algorithm and improved adaptive genetic algorithm are simulated and compared. The results show that the detection reliability of spectrum sensing system can be effectively improved by multi-user cooperative, and the convergence rate of the optimal solution is improved by the improved adaptive genetic algorithm, which reduces the spectrum detection time.

Collaborative Spectrum Sharing Through Non-Collaborative Gaming for Next-Generation Small Cells

Existing spectrum-sharing schemes either allow the secondary-network users (SUs) to utilize the spectrum when primary-network users (PUs) remain idle, or require the SUs to coordinate with the PUs, causing signaling overhead. In this paper, we propose a game-theoretic spectrum-sharing scheme, which enables the SUs and PUs to utilize the spectrum simultaneously, without compromising the quality of service (QoS) of the PUs and ensuring reduced signaling overhead. We formulate a multi-priority non-cooperative power-control game by considering a scenario where multiple small cell base stations belonging to either the primary network or secondary network utilize the available spectrum resources at the same time. The base stations are empowered to adjust their transmit powers in an automated manner based on measured interference, until their transmit powers are stabilized. As a key idea, a game parameter, dynamic price coefficient, is designed to give the primary network priority over the secondary network for accessing the spectrum. We determine appropriate bounds for the game parameters to ensure the existence and uniqueness of the Nash equilibrium of the proposed game. Furthermore, we propose a novel dual-mode solution to reduce the real-time signaling overhead between the networks, by minimizing the information exchange during the game required to reach an equilibrium point. Extensive simulation results are presented to prove the convergence of the game to a Nash equilibrium, along with a throughput performance analysis.

Cluster-Based Collaborative Spectrum Sensing for Energy Harvesting Cognitive Wireless Communication Network

We consider a cluster-based collaborative spectrum sensing (CSS) scheme in energy harvesting cognitive wireless communication network (EH-CWCN), where cognitive nodes (CNs) are clustered based on their received power levels for enhancing sensing performance. In CSS scheme, time resource is limited and shared by energy harvesting, spectrum sensing, and data transmission. The purpose of this paper is to maximize the average throughput of EH-CWCN by identifying the optimal parameter set, including the durations of energy harvesting and spectrum sensing, local detection threshold, and the number of CNs. Moreover, it is hard to confirm the optimal local detection threshold at CNs with different received power levels. By constructing a fictitious cognitive node (FCN), which is assumed to have the same sensing performance as the sink node, the process for finding the optimal local detection threshold can be converted into the process for searching the optimal received signal-to-noise ratio of the FCN. Then, we formulate the general optimization problem under the collision constraint and energy constraint. Then, the existence and uniqueness of time fractions for energy harvesting and spectrum sensing are proved in this paper. The optimal parameter set is achieved by using the bisection method and a simplified linear search method. Finally, the theoretical analysis and the impact of the optimized parameters on the system performance are verified and shown through numerical simulations.

Reputation-based cooperative spectrum sensing algorithm for mobile cognitive radio networks

Identifying malicious users accurately in cognitive radio networks (CRNs) is the guarantee for excellent detection performance. However, existing algorithms fail to take the mobility of secondary users into consideration. If applied directly in mobile CRNs, those conventional algorithms would overly punish reliable users at extremely bad or good locations, leading to an obvious decrease in detection performance. To overcome this problem, we divide the whole area of interest into several cells to consider the location diversity of the network. Each user's reputation score is updated after each sensing slot and is used for identifying whether it is malicious or not. If so, it would be removed away. And then our algorithm assigns users in cells with better channel conditions, i.e. larger signal-to-noise ratios (SNRs), with larger weighting coefficients, without requiring the prior information of SNR. Detailed analysis about the validity of our algorithm is presented. The simulation results show that in a CRN with 60 mobile secondary users, among which, 18 are malicious, our solution has an improvement of detection probability by 0.97-dB and 3.57-dB when false alarm probability is 0.1, compared with a conventional trust-value-based algorithm and a trusted collaborative spectrum sensing for mobile CRNs, respectively.

Multibit-Quantization-Based Collaborative Spectrum Sensing Scheme for Cognitive Sensor Networks

In order to overcome the bandwidth limitations of common control channels, we introduce a multibit quantization scheme to preprocess raw measurements in a collaborative spectrum sensing (CoSS) scheme. After quantifying the raw measurement, the cognitive sensors (CSs) send the multibit sensing information to a sink node (SN), which makes a final decision by using a soft decision fusion rule. In order to better understand the effects of quantization parameters on collaborative spectrum sensing in a cognitive sensor network (CSN), we discuss the following two problems: the average error probability minimum problem (AEPMP) and throughput maximum problem. By identifying the number of quantization bits, number of CSs, and global decision threshold, we can discuss some design considerations for the multibit-quantization-based CoSS scheme. A closed-form expression for the optimal global decision threshold is derived in the process of analyzing the AEPMP. Furthermore, an iterative algorithm with low complexity is proposed to find the optimal parameters for maximizing the throughput of a CSN under a target detection probability constraint. Finally, we investigate the impact of optimized parameters on the system performance of a CSN. Our theoretical analysis is verified through numerical simulations.

Byzantine Defense in Collaborative Spectrum Sensing via Bayesian Learning

Collaborative spectrum sensing (CSS) enables secondary users in cognitive radio networks to collaboratively explore spectrum holes as well as protecting the primary user from being interfered. Unfortunately, the emergence of spectrum sensing data falsification (SSDF) attack, also known as the Byzantine attack, brings significant threat to the reliability of the CSS. Majority of the existing studies on Byzantine defense can be divided into two categories: one is directly to make the judgment based on the current spectrum sensing data, while the other uses the historical spectrum sensing data to update sensors’ reputation. The first category of studies does not take the historical spectrum sensing data into account, while most of the second category of studies are heuristic in nature. In this paper, we invoke Bayesian learning to design Byzantine defense schemes. First, we develop a Bayesian offline learning algorithm by considering one practical challenge that the ground-truth spectrum state is unavailable for training. Then, we develop a Bayesian online learning algorithm by considering the case that the sensors’ attribute may be time-varying. In addition, we present simulations to show the performance of the proposed defence algorithms.

A Secure Collaborative Spectrum Sensing Strategy in Cyber-Physical Systems

Cyber-physical systems (CPS) have the great potential to transform people’s lives. Smart cities, smart homes, robot assisted living, and intelligent transportation systems are examples of popular CPS systems and applications. It is an essential but challenging requirement to offer secure and trustworthy real-time feedback to CPS users using spectrum sharing wireless networks. This requirement can be satisfied using collaborative spectrum sensing technology of cognitive radio networks. Despite its promising benefits, collaborative spectrum sensing introduces new security threats especially internal attacks (i.e., attacks launched by internal nodes) that can degrade the efficiency of spectrum sensing. To tackle this challenge, we propose a new transferring reputation mechanism and dynamic game model-based secure collaborative spectrum sensing strategy (TRDG). More specifically, a location-aware transferring reputation mechanism is proposed to resolve the reputation loss problem caused by user mobility. Furthermore, a dynamic game-based recommendation incentive strategy is built to incentivize secondary users to provide honest information. The simulation experiments show that the TRDG enhances the accuracy of spectrum sensing and defends against the internal attacks effectively without relying on a central authority.