Cognitive Radio Sensor Research Articles

Spectrum sensing performance of wireless cognitive radio sensor network with hard‐decision fusion over generalizedα−μfading channels

SummaryThe analysis of spectrum sensing performance of energy detection (ED)‐based wireless cognitive radio sensor network (ED‐WCRSN) with hard‐decision combining (HDC) is presented in this paper. Particularly, several network parameters are derived to estimate the performance of ED‐WCRSN, considering channel errors, noise, and generalized fading. In the considered network, first, cognitive radio sensor (CRS) senses a primary user (PU), gets sensing data, and then uses an ED to make a local binary decisions about PU's active or inactive status. In both sensing and reporting channels, the channel error probability is also taken into account. Next, HDC technique is used at control center (CC) to combine the locally obtained decisions, and a final decision about the status of the PU is made. To do so, first, the expression for the novel and analytical, which incorporates noise and fading, detection probability in a CRS is derived and validated using Monte Carlo simulations in MATLAB and using an experimental setup. Then, utilizing derived mathematical expressions, closed‐form expressions of an average error rate (AER), optimal numbers of CRSs, and detection thresholds under noisy and fading conditions are developed. The substantial influence of channel and network factors is assessed using receiver operating characteristics (ROC), complementary ROC, and AER. Finally, the impacts of channel and network parameters on ED‐WCRSN performance are explored. For numerous parameters of the considered network, the optimal values detection threshold and number of CRSs are also found.

An integrated antenna for cognitive radio wireless sensor networks and HD video transmission applications

This article introduces an integrated polarization divesity cognitive radio (CR) and Ultrawide Band (UWB) Multiple Inputs and Multiple Output (MIMO) antenna systems for CR wireless sensor networks (CR-WSN) and short-range HD video transmission applications. This proposed integrated antenna system's main purpose is to overcome the impediments of frequency re-configurable UWB/Narrow Band (NB) antenna system. The proposed integrated system consists of two antenna systems: the CR antenna and the UWB MIMO antenna system. The first part of the integrated antenna system is the CR antenna that comprises vertical and horizontal oriented UWB antennas to sense the spectrum and four NB antennas for communication purposes. The second part of the integrated antenna system is a 2 × 2 MIMO antenna that comprises a veritical and horizontal oriented UWB antenna with notch characteristics for MIMO applications. High isolation is achieved without using a decoupling structure by placing radiators in the horizontal and vertical planes. The proposed integrated 3D antenna system is designed with a compact size of 50 × 50 mm2. The antenna's essential parameters, such as the reflection coefficient, mutual coupling, peak gain, the radiation pattern, and radiation efficiency, are analyzed to evaluate the antenna's performance. The proposed integrated antenna system is suitable for off-device applications in industries or Laboratories. The real-time implementation of CR-WSN and short-range wireless HD video transmission applications are demonstrated using National Instruments (NI) Universal Software Radio Peripheral (USRP) and CC2538 kit.

ICBCA – Improved Cluster Based Channel Allocation in Cognitive Radio Sensor Networks

Wireless sensor networks (WSNs) operate in an overcrowded electromagnetic environment, as the spectrum is shared by various wireless communication technologies. This gives rise to various challenges related to optimized and efficient spectrum utilization. Cognitive radio (CR) has emerged as a solution satisfying this requirement, as it is capable of adapting to the dynamic radio spectrum. Thanks to the deployment of cognitive radio in WSNs, the spectrum may be utilized in a more efficient manner. CR may identify the vacant channels dynamically, allowing the sensor nodes to effectively communicate with each other. In this paper a clustering algorithm known as improved cluster-based channel assignment (ICBCA) is implemented, forming clusters of CR sensor nodes and then selecting vacant channels for data transmission purposes. Simulation results show that ICBCA outperforms existing clustering algorithms in CR sensor networks

Review on cross‐layer design for cognitive ad‐hoc and sensor network

Most of the ad-hoc networks including wireless sensor network operates in unlicensed spectrum bands (e.g. 2.4 GHz ISM band), which is shared by other wireless technologies. This leads to coexistent interference and degrades the performance of ad-hoc sensor network (AHSN). Introducing cognitive radio (CR) concept in AHSN, the unutilised licensed spectrum (white spaces) can be used opportunistically without disturbing licensed holders. Conventional TCP/IP layered architecture for CR network lacks in mobility, data transfer performance, energy efficiency, quality of service and so on. Coupling of some of the layers allows coordination, interaction and joint optimisation of protocols to achieve significant performance improvements for CR-AHSN. Of late, many researches are going on different approaches of using CR for reusing unused spectrum and enhancing total system capacity. This study presents a comprehensive review of various cross-layer design approaches in CR sensor network.

Cognitive Radio_Based Backup Protection Scheme for Smart Grid Applications

In this paper, a backup protection scheme is presented. The proposed scheme uses the concept of a cognitive radio-based wireless sensor network to implement a protection scheme without the need for conventional electric wires and/or optical fiber between current and voltage transducers, control panels, and circuit breakers. The proposed scheme uses unlicensed spectrum channels by applying spectrum sensing and frequency allocation algorithms that are used in cognitive radio based systems. The importance of the proposed scheme comes from its cost effectiveness compared to the Ethernet communication both wired and wireless if the data transfer and message exchange is done using cognitive radio_based communication for relatively long distances. The proposed scheme utilizes cognitive radio-based communication network to convey analog and digital signals within a substation. In this way, it saves the cost of the copper wires and/or optical fiber that would be used to transmit digital and analog signals. Further, it does not require the expensive spectrum licenses that would have been purchased if Ethernet long-distance wireless communications are utilized. To assess the benefits/challenges related to the application of cognitive radio_based communication, a detailed and realistic modeling of both power systems and cognitive radio_based communication systems is performed simultaneously. The proposed scheme is simulated using MATLAB/Simulink and performance of the proposed scheme was investigated under different communication scenarios.

Improved Joint Spectrum Sensing and Power Allocation for Cognitive Radio Networks Using Probabilistic Spectrum Access

In this paper, by employing a probabilistic spectrum access (PSA) scheme, we perform optimal disjoint and joint spectrum sensing and downlink power allocation methods in a cognitive radio (CR) network. For the spectrum sensing, we consider a cooperative framework, in which cognitive sensors cooperate via energy-detection-based soft spectrum sensing. The proposed optimization problems related to disjoint and joint spectrum sensing and power allocation are formulated with two main objectives: first, minimizing the false-alarm probability under constant probability of detection; and second, maximizing the average opportunistic CR data rate under limited detection probability and limited CR power budget. These optimization problems are in fact the functional-based optimization problems, in which the optimization parameter is a functional of the measured energy by CR sensors, where the optimal solution is obtained using Euler-Lagrange equation. Spectrum sensing is then performed by using the optimal functional inside a PSA framework. The advantages of the proposed approach are confirmed through several numerical results.

Attack-Aware Defense Strategy: A Robust Cooperative Spectrum Sensing in Cognitive Radio Sensor Networks

Cognitive radio (CR) is a key enabling technology of dynamic spectrum access to achieve better utilization of frequency spectrum. Cooperative spectrum sensing (CSS) exploits the spatial diversity between CR users to improve the accuracy of spectrum sensing. But, in practice, the reliability of the CSS is vulnerable to spectrum sensing data falsification (SSDF) attack. In this type of attack, some malicious CR sensors intentionally report falsified local sensing results to a fusion center and disrupt the global decision-making process. In this study, we present a smart and robust defense scheme called attack-aware CSS (ACSS). The idea is based on the estimation of attack strength where the attack strength corresponds to the attack population. The attack strength is defined as the ratio of the number of malicious sensors to the total number of sensors which is equal to the probability that a specific sensor is malicious. Assuming the malicious sensor behavior or attack strategy, the proposed approach estimates attack strength and uses the Bayesian hypothesis test to improve the collaborative sensing performance. Computer simulation results illustrate the effectiveness of the proposed ACSS approach against SSDF attacks, especially for CR networks located in hostile environments.

Dynamic Threshold Selection Approach in Voting Rule for Detection of Primary User Emulation Attack

DOI: 10.5152/iujeee.2018.1819 Cognitive radio (CR) technology presents a mechanism for efficient spectrum usage. Spectrum sensing is an essential CR function which includes an intelligent signal processing algorithm to identify the vacant frequency bands. Cooperative spectrum sensing (CSS) has been widely adopted to improve the performance of CR networks. Unfortunately, CR networks are vulnerable to security threats. In this study, we propose an optimal threshold selection approach to address one of the most important attacks called primary user emulation attack (PUEA). In PUEA, a malicious attacker mimics some important primary signal features and deceives CR sensors to prevent them from accessing the available channels. In this study, we assume a malicious PUEA which is relatively located near the potential user (PU) transmitter and senses the spectrum and accurately detects the vacant frequency bands to transmit its fake signal. We estimate attack strength and then apply the K-out-of-N rule to obtain an optimum and dynamic threshold K, minimizing the global error probability. Here, the attack strength is defined as the ratio of the average transmission power of the PUEA to the average power of the PU. The achieved simulation results indicate that the performance of the suggested method is satisfactory in detecting the malicious PUEA compared with conventional methods.

Performance Evaluation of Relay Selection Schemes in Beacon-Assisted Dual-Hop Cognitive Radio Wireless Sensor Networks under Impact of Hardware Noises.

To solve the problem of energy constraints and spectrum scarcity for cognitive radio wireless sensor networks (CR-WSNs), an underlay decode-and-forward relaying scheme is considered, where the energy constrained secondary source and relay nodes are capable of harvesting energy from a multi-antenna power beacon (PB) and using that harvested energy to forward the source information to the destination. Based on the time switching receiver architecture, three relaying protocols, namely, hybrid partial relay selection (H-PRS), conventional opportunistic relay selection (C-ORS), and best opportunistic relay selection (B-ORS) protocols are considered to enhance the end-to-end performance under the joint impact of maximal interference constraint and transceiver hardware impairments. For performance evaluation and comparison, we derive the exact and asymptotic closed-form expressions of outage probability (OP) and throughput (TP) to provide significant insights into the impact of our proposed protocols on the system performance over Rayleigh fading channel. Finally, simulation results validate the theoretical results.

Remaining idle time aware intelligent channel bonding schemes for cognitive radio sensor networks

Channel bonding (CB) is a technique used to provide larger bandwidth to users. It has been applied to various networks such as wireless local area networks, wireless sensor networks, cognitive radio networks, and cognitive radio sensor networks (CRSNs). The implementation of CB in CRSNs needs special attention as primary radio (PR) nodes traffic must be protected from any harmful interference by cognitive radio (CR) sensor nodes. On the other hand, CR sensor nodes need to communicate without interruption to meet their data rate requirements and conserve energy. If CR nodes perform frequent channel switching due to PR traffic then it will be difficult to meet their quality of service and data rate requirements. So, CR nodes need to select those channels which are stable. By stable, we mean those channels which having less PR activity or long remaining idle time and cause less harmful interference to PR nodes. In this paper, we propose two approaches remaining idle time aware intelligent channel bonding (RITCB) and remaining idle time aware intelligent channel bonding with interference prevention (RITCB-IP) for cognitive radio sensor networks which select stable channels for CB which have longest remaining idle time. We compare our approaches with four schemes such as primary radio user activity aware channel bonding scheme, sample width algorithm, cognitive radio network over white spaces and AGILE. Simulation results show that our proposed approaches RITCB and RITCB-IP decrease harmful interference and increases the life time of cognitive radio sensor nodes.

OLSR protocol for vehicular sensor network in cognitive radio

In our proposed system, the vehicular sensor network (VSN) is formulated based on the cognitive radio with the aid of optimal link state routing (OLSR) protocol. Here the cognitive radio related vehicular sensor networks are anticipated for the data communication. The optimal link state routing protocol is modified by utilising the hybrid group search and genetic algorithm (HGSO-GA) algorithm. The OLSR protocol is used to pick the adjacent nodes which disseminate the power package to diminish the quantity of control transparency in the network. The data packages are conveyed from source to destination with the help of multi point relay (MPR) vehicles, which is the subset of the chosen neighbour vehicles by each source to access all the two-hop neighbours act as forwarders. OLSR routing protocol to efficiently manage spectrum allocation that helps reduce the routing delay. The proposed technique is implemented in the working platform of NS2.

A Strategic Bargaining Game for a Spectrum Sharing Scheme in Cognitive Radio-Based Heterogeneous Wireless Sensor Networks.

In Wireless Sensor Networks (WSNs), unlicensed users, that is, sensor nodes, have excessively exploited the unlicensed radio spectrum. Through Cognitive Radio (CR), licensed radio spectra, which are owned by licensed users, can be partly or entirely shared with unlicensed users. This paper proposes a strategic bargaining spectrum-sharing scheme, considering a CR-based heterogeneous WSN (HWSN). The sensors of HWSNs are discrepant and exist in different wireless environments, which leads to various signal-to-noise ratios (SNRs) for the same or different licensed users. Unlicensed users bargain with licensed users regarding the spectrum price. In each round of bargaining, licensed users are allowed to adaptively adjust their spectrum price to the best for maximizing their profits. . Then, each unlicensed user makes their best response and informs licensed users of “bargaining” and “warning”. Through finite rounds of bargaining, this scheme can obtain a Nash bargaining solution (NBS), which makes all licensed and unlicensed users reach an agreement. The simulation results demonstrate that the proposed scheme can quickly find a NBS and all players in the game prefer to be honest. The proposed scheme outperforms existing schemes, within a certain range, in terms of fairness and trade success probability.

Modeling of non-Gaussian colored noise and application in CR multi-sensor networks

Motivated by the practical and accurate demand of intelligent cognitive radio (CR) sensor networks, a new modeling method of practical background noise and a novel sensing scheme are presented, where the noise model is the non-Gaussian colored noise based on α stable process and the sensing method is improved fractional low-order moment (FLOM) detection algorithm with balance parameter. First, we establish the non-Gaussian colored noise model through combining α-distribution with a linear system represented by a matrix. And a fitting curve of practical noise data is given to verify the validity of the proposed model. Then we present a parameter estimation method with low complexity to obtain the balance parameter, which is an important part of the detection algorithm. The balance parameter-based FLOM (BP-FLOM) detector does not require any a priori knowledge about the primary user signal and channels. Monte Carlo simulations clearly demonstrate the performance of the proposed method versus the generalized signal-to-noise ratio, the characteristic exponent α, and the number of detectors in sensing networks.

Optimizing Spectrum Sensing Time With Adaptive Sensing Interval for Energy-Efficient CRSNs

The cognitive radio (CR) technology allows secondary users (SUs) to occupy the licensed bands opportunistically without causing interferences to primary users (PUs). SUs perform spectrum sensing to detect whether PUs are busy or idle. Therefore, spectrum sensing directly affects the performance of the PU protection and the secondary throughput. The sensing time is a critical parameter for spectrum sensing performance, and the optimum sensing time is a tradeoff between the spectrum sensing performance and the secondary throughput. In this paper, a novel spectrum sensing scheme is proposed to maximize both sensing accuracy and network energy efficiency. In order to provide a better protection for the PU, another spectrum sensing is adaptively performed according to the first sensing result. In other words, SU will perform spectrum sensing again to confirm that the PU is indeed idle when the first sensing result indicates the PU is idle. Due to the energy constraint in CR sensor networks, this adaptive sensing interval can also be adjusted according to the varying activity of the PU to maximize the network energy efficiency. Finally, our simulation study validates that the proposed scheme improves both the spectrum sensing performance and the energy efficiency compared with other existing methods.

Joint Channel Assignment and Routing Protocol for Cognitive Radio Wireless Sensor Networks

Multi-hop transmissions over a single channel that is shared by all the nodes in a network limit the throughput of conventional multi-hop wireless sensor networks. To improve the performance of these networks, the use of multiple channels has been proposed. Channel assignment is an important problem in multi-channel wireless sensor networks; it asks the question of how channels should be assigned so that the network performance in maximized. Many existing channel assignment scheme seek to improve network performance by trying to reduce interference. However, it is not easy to measure the actual amount of interference in a network. We tackled the channel assignment problem from a novel approach. Instead of distributing the nodes, links or network interfaces to different channels to reduce interference, we try to assign as many channels as possible to the links. A new distributed scheme is proposed and we show that is outperforms over comparable schemes.

Outage Probability Minimization for Energy Harvesting Cognitive Radio Sensor Networks.

The incorporation of cognitive radio (CR) capability in wireless sensor networks yields a promising network paradigm known as CR sensor networks (CRSNs), which is able to provide spectrum efficient data communication. However, due to the high energy consumption results from spectrum sensing, as well as subsequent data transmission, the energy supply for the conventional sensor nodes powered by batteries is regarded as a severe bottleneck for sustainable operation. The energy harvesting technique, which gathers energy from the ambient environment, is regarded as a promising solution to perpetually power-up energy-limited devices with a continual source of energy. Therefore, applying the energy harvesting (EH) technique in CRSNs is able to facilitate the self-sustainability of the energy-limited sensors. The primary concern of this study is to design sensing-transmission policies to minimize the long-term outage probability of EH-powered CR sensor nodes. We formulate this problem as an infinite-horizon discounted Markov decision process and propose an ϵ-optimal sensing-transmission (ST) policy through using the value iteration algorithm. ϵ is the error bound between the ST policy and the optimal policy, which can be pre-defined according to the actual need. Moreover, for a special case that the signal-to-noise (SNR) power ratio is sufficiently high, we present an efficient transmission (ET) policy and prove that the ET policy achieves the same performance with the ST policy. Finally, extensive simulations are conducted to evaluate the performance of the proposed policies and the impaction of various network parameters.

A self-organised routing algorithm for cognitive radio-based wireless sensor networks using biologically-inspired method

Wireless sensor networks area collection of a large number of unattended sensor nodes. The sensor nodes are dynamically deployed in the region. In wireless sensor networks, the unlicensed sensor nodes use their temporarily available vacant licensed channels or spectrum band in a telecommunication network. The many open challenges with issues such as adaptive in self-organisation, fault-tolerance and network scalability with cooperative spectrum sensing and sharing are identifying new challenges in wireless sensor networks. In this paper, we proposed the bio-inspired adaptive routing algorithm for cognitive radio wireless sensor networks. The utilisation of the foraging approach of ANTs and BEEs for making the decision of using the available spectrum sensing and sharing of channels over multi-hops communication, provides the method to utilise idle channel and decrease the delay for communication in wireless sensor network. In this paper, we design the algorithm to solve the issue of wireless sensor network and performance of classical routing protocol LEACH and cognitive radio sensor networks (CRSN) evaluates the parameters which proof the self-organisation routing algorithm.

Energy-Harvesting-Aided Spectrum Sensing and Data Transmission in Heterogeneous Cognitive Radio Sensor Network

The incorporation of cognitive radio (CR) and energy harvesting (EH) capabilities in wireless sensor networks enables spectrum and energy-efficient heterogeneous CR sensor networks (HCRSNs). The new networking paradigm of HCRSNs consists of EH-enabled spectrum sensors and battery-powered data sensors. Spectrum sensors can cooperatively scan the licensed spectrum for available channels, whereas data sensors monitor an area of interest and transmit sensed data to the sink over those channels. In this paper, we propose a resource-allocation solution for the HCRSN to achieve the sustainability of spectrum sensors and conserve the energy of data sensors. The proposed solution is achieved by two algorithms that operate in tandem: a spectrum sensor scheduling (SSS) algorithm and a data sensor resource allocation (DSRA) algorithm. The SSS algorithm allocates channels to spectrum sensors such that the average detected available time for the channels is maximized, while the EH dynamics are considered and primary user (PU) transmissions are protected. The DSRA algorithm allocates the transmission time, power, and channels such that the energy consumption of the data sensors is minimized. Extensive simulation results demonstrate that the energy consumption of the data sensors can be significantly reduced, while maintaining the sustainability of the spectrum sensors.

Mitigating RF Front-End Nonlinearity of Sensor Nodes to Enhance Spectrum Sensing.

The cognitive radio wireless sensor network (CR-WSN) has gained worldwide attention in recent years for its potential applications. Reliable spectrum sensing is the premise for opportunistic access to sensor nodes. However, as a result of the radio frequency (RF) front-end nonlinearity of sensor nodes, distortion products can easily degrade the spectrum sensing performance by causing false alarms and degrading the detection probability. Given the limitations of the widely-used adaptive interference cancellation (AIC) algorithm, this paper develops several details to avoid these limitations and form a new mitigation architecture to alleviate nonlinear distortions. To demonstrate the efficiency of the proposed algorithm, verification tests for both simulations and actual RF front-end measurements are presented and discussed. The obtained results show that distortions can be suppressed significantly, thus improving the reliability of spectrum sensing. Moreover, compared to AIC, the proposed algorithm clearly shows better performance, especially at the band edges of the interferer signal.

Adaptive Window Size-Based Medium Access Control Protocol for Cognitive Radio Wireless Sensor Networks

Many existing medium access control protocols for cognitive radio wireless sensor networks waste bandwidth and suffer from delay because of the fixed channel negotiation period in the common control channel. In this paper, we propose a medium access control protocol for cognitive radio wireless sensor networks that adjusts the channel negotiation period based on network density. We simulate the proposed approach and compare it to the performance of the fixed channel negotiation period based approaches. The results show that the proposed approach efficiently utilizes white spaces and increases the aggregated goodput.