Cognitive Radio Sensor Networks Research Articles

Cluster Control and Energy Consumption Minimization for Cooperative Prediction Based Spectrum Sensing in Cognitive Radio Networks

Spectrum sensing is a key technique for dynamically detecting available spectrum in cognitive radio networks (CRNs), which can introduce high resource demands such as energy consumption. In this paper, we propose a novel cluster-based cooperative sensing-after-prediction scheme where a learning cluster and a sensing cluster are jointly considered to perform cooperative prediction and sensing efficiently. This enables us to skip the complex physical sensing to reduce the demands when the spectrum availability can be simply predicted using cooperative prediction. Furthermore, the clustering is flexible, in order to meet different performance requirements. We then formulate two optimization problems to minimize the total number of users in the two clusters or to minimize the total energy consumption, to meet different performance requirements, while in both cases guaranteeing the system accuracy requirement and individual energy constraints. To solve the two challenging integer programming problems, the unconstrained problems are mathematically solved first by relaxing the integer variable and fixing the cluster size. Such analytical solutions serve as a foundation for solving the original optimization problems. Then, two low-complexity search algorithms are proposed to achieve the global optimum, as they can obtain the same performance with exhaustive search. Simulation results validate the accuracy of the derived analytical expressions and demonstrate that the total energy consumption and the number of users contributing to learning and sensing can be greatly reduced by applying our optimized clustered sensing-after-prediction scheme.

On the optimisation of detector threshold for energy efficient CSS over fading channels

ABSTRACT Non-cooperative scenarios in cognitive wireless sensor network (CWSN) are often encountered with shadowing and hidden terminal issues. Cooperative spectrum sensing (CSS) can solve these issues but at the expense of large overhead. It is necessary to optimise the energy of battery-operated unlicensed users, also known as secondary users (SUs). The effect of fading and noise uncertainty is often overlooked when determining CSS performance. Energy efficiency (EE) is a comprehensive parameter that gives a complete picture of the overall performance of the CSS. There are several parameters that affect the EE of CSS. In this paper, the detector threshold is optimised to maximise the EE of the system. A system model is proposed to determine the EE of centralised CSS over different fading channels in noisy reporting conditions. An iterative algorithm is presented which determines the optimum detector threshold for which the EE is maximum. Results show that the optimum value of the detector threshold from the analytical model matches with simulation data.

Optimized Statistical Beamforming for Cooperative Spectrum Sensing in Cognitive Radio Networks

In cognitive radio (CR), cooperative spectrum sensing (CSS) employs a fusion of multiple decisions from various secondary user (SU) nodes at a central fusion center (FC) to detect spectral holes not utilized by the primary user (PU). The energy detector (ED) is a well-established technique of spectrum sensing (SS). However, a major challenge in designing an energy detector-based SS is the requirement of correct knowledge for the distribution of decision statistics. Usually, the Gaussian assumption is employed for the received statistics, which is not true in real practice, particularly with a limited number of samples. Another big challenge in the CSS task is choosing an optimal fusion strategy. To tackle these issues, we have proposed a beamforming-assisted ED with a heuristic-optimized CSS technique that utilizes a more accurate distribution of decision statistics by employing the characterization of the indefinite quadratic form (IQF). Two heuristic algorithms, genetic algorithm with multi-parent crossover (GA-MPC) and constriction factor particle swarm-based optimization (CF-PSO), are developed to design optimum beamforming and optimum fusion weights that can maximize the global probability of detection pd while constraining the global probability of false alarm pf to below a required level. The simulation results are presented to validate the theoretical findings and to asses the performance of the proposed algorithm.

A modified energy efficient relay selection for cognitive radio sensor networks

A modified energy efficient relay selection for cognitive radio sensor networks

SWIPT Enabled Cooperative Cognitive Radio Sensor Network With Non-Linear Power Amplifier

In this work, performance of a simultaneous wireless information and power transfer (SWIPT) enabled cooperative cognitive radio sensor network (CRSN) is explored over generalized Nakagami-m faded channels. A time switching (TS) protocol for SWIPT is used and the impact of TS factor on energy harvesting and information transmission phases, and information processing and broadcasting phases are considered. The cooperative CRSN consists of a pair of primary nodes (PNs) and sensor nodes (SNs), where one of the SWIPT enabled SN provides cooperative relaying for PNs communication by utilizing amplify-and-forward protocol. The overlay spectrum sharing protocol is used at the SN, which supports PNs communication along with its own transmission and resolves the spectrum scarcity issue. The impact of a non-linear power amplifier (NLPA) is also considered on the network performance. The analytical expressions of outage probability (OP), asymptotic OP, system throughput, and energy efficiency are obtained over Nakagami-m faded channels for arbitrary m. Further, the analytical expressions of ergodic capacity (EC) and asymptotic EC are obtained. The impact of NLPA and other system parameters on the performance of the considered network are highlighted. Finally, the obtained analytical expressions are validated through Monte-Carlo simulations.

Artificial Neural Network with Firefly Algorithm-Based Collaborative Spectrum Sensing in Cognitive Radio Networks

Artificial Neural Network with Firefly Algorithm-Based Collaborative Spectrum Sensing in Cognitive Radio Networks

Mathematical modeling of resource allocation for cognitive radio sensor health monitoring system using coevolutionary quantum-behaved particle swarm optimization

Independent interactive medical applications cooperate closely with user systems through independent information and data exchange, which realizes digital and intelligent communication for health monitoring system. Due to extensive connectivity and application services, heavy data traffic and limited energy consumption reduce the quality of application services. In this paper, a mathematical model for efficient energy harvesting resource allocation based on cognitive radio sensor network (CRSN) infrastructure is constructed to address energy limitation issues in health monitoring system applications. Because of the highly dynamic nature of available network resources, energy efficiency and communication reliability can be improved. Since the proposed optimization problem is a complex and NP hard problem, coevolutionary theory is adopted, and then a stochastic optimization algorithm based on polynomial approximation and quantum particle swarm optimization is proposed. Simulation results show that the proposed CQPSO algorithm has better performance in terms of re-transmission probability, system throughput, and energy consumption compared to other similar algorithms. Efficient resource allocation improves the performance of the health monitoring system, more effectively and real-time monitoring of human physiological and motion information, providing testers with high-precision quantitative information, and achieving early monitoring and intervention of common diseases.

Energy-efficiency performance of wireless cognitive radio sensor network with hard-decision fusion over generalized $$\alpha -\mu$$ fading channels

Energy-efficiency performance of wireless cognitive radio sensor network with hard-decision fusion over generalized $$\alpha -\mu$$ fading channels

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 imperfect spectrum sensing-based multi-hop clustering routing protocol for cognitive radio sensor networks

Multi-hop clustering routing protocols are potential solutions to achieve effective and energy-efficient data delivery in cognitive radio sensor networks (CRSNs). Current clustering routing protocols for CRSNs generally assume perfect spectrum sensing, i.e., false alarm and missed detection are ignored, which may result in collisions with primary users and transmission failure or constrained spectrum usage. To alleviate the impact of imperfect spectrum sensing on network performance, an imperfect spectrum sensing-based multi-hop clustering routing protocol (ISSMCRP) is proposed for CRSNs in this paper. Cluster head (CH) and relay selection criteria are defined based on detection level function of available channels to help select CHs and relays with high spectrum sensing capability. Idle detection accuracy-based intra-cluster and inter-cluster channel selection criterion is proposed to promote successful intra-cluster and inter-cluster data delivery. In addition, control overhead introduced by CHs selection and cluster formation is strictly controlled, and reasonable cluster radii are set to manage the range of control information exchange, so as to reduce the energy consumption caused by control overhead. Simulation results show that compared with the existing clustering routing protocols for CRSNs, ISSMCRP gains obvious advantages in expanding the network lifespan and enhancing the network surveillance capability.

Extreme Learning Machine Based Identification of Malicious Users for Secure Cooperative Spectrum Sensing in Cognitive Radio Networks

Extreme Learning Machine Based Identification of Malicious Users for Secure Cooperative Spectrum Sensing in Cognitive Radio Networks

A Novel Grasshopper Optimization-based Particle Swarm Algorithm for Effective Spectrum Sensing in Cognitive Radio Networks

A Novel Grasshopper Optimization-based Particle Swarm Algorithm for Effective Spectrum Sensing in Cognitive Radio Networks

Energy-efficient adaptive sensing for Cognitive Radio Sensor Network in the presence of Primary User Emulation Attack

A Cognitive Radio Sensor Network (CRSN) is a promising technology incorporating cognitive radio in traditional wireless sensor networks. In general, Primary User Emulation Attack (PUEA) is becoming the major bottleneck for implementing CRSN in reality. In CRSN-PUEA, malicious users are also equipped with a Spectrum Sensing capability, the so-called smart PEUA, which further complicates the spectrum access of Cognitive Users (CU). In this paper, we propose a novel Energy-Efficient Adaptive Sensing (EEAS) technique for CRSN to overcome the spectrum access problem in the presence of PUEA. The proposed EEAS enhances the sensing accuracy by differentiating the PUEA and Primary User signals that, reduce the sensing error probability and enhance the underlying system model’s throughput and energy efficiency. The numerical results show that the proposed EEAS technique has improved the throughput and energy efficiency by about 12% and 44%, respectively, compared to the state-of-the-art works in the field of PUEA-CRSN. In addition, it is observed that the sensing error probability is reduced by 22%. Furthermore, a Software Defined Radio (SDR) based hardware system setup has been developed to verify the functionality of the proposed system model.

Graph Learning-Based Cooperative Spectrum Sensing in Cognitive Radio Networks

In cooperative spectrum sensing (CSS), received signal strengths (RSSs) of multiple secondary users (SUs) are combined to improve sensing performance. In existing CSS schemes, RSS levels are often assumed in the same order of magnitude, which, however, is not the case due to, e.g., shadowing in some scenarios, leading to performance degradation. To address this issue, in this letter, we first reveal that the expectation of a RSS matrix from SUs has the property of low rank. Then, using this property, we propose a graph learning method to select SUs of the same order of RSS levels. Specifically, a probability matrix of graph edges is used to represent the correlations of SUs, and a graph learning problem is formulated for learning the probability matrix, which is further solved by using alternating direction method of multipliers (ADMM). Then SUs with high correlations are collected for implementing CSS. Moreover, false-alarm and detection probabilities of the proposed detector are analyzed. Numerical results demonstrate the superiority of the proposed detector compared to state-of-the-art detectors.

RF energy harvesting schemes for intelligent reflecting surface-aided cognitive radio sensor networks

Energy harvesting (EH) is a potential solution to enhance the node sustainability and prolong the network lifetime of cognitive radio sensor networks (CRSNs). However, CRSNs nodes can only harvest energy from the direct link with energy sources, and severe path loss results in low energy utilization ratio. To solve the above problem, intelligent reflecting surface (IRS) is introduced, and a shared reflection coefficient matrix-based EH scheme is proposed for IRS-aided CRSNs in this paper. An optimization problem with the objective of maximizing the total amount of energy harvested by all CRSNs nodes is formulated, and by optimally adjusting the IRS reflection coefficient, CRSNs nodes can harvest energy from both the direct link and the cascaded reflection link via IRS, which increases the amount of harvested energy. In addition, a subsurface partition-based EH scheme is proposed to reduce the additional computational complexity brought by increasing IRS elements or CRSNs nodes. Simulation results show that the proposed schemes can both dramatically improve energy utilization ratio, and the subsurface partition-based EH scheme will bring in less than 1 percent performance loss when compared with the other scheme, i.e., reasonable subsurface partition can achieve a balance between harvested energy and computational complexity.

Network Lifetime Optimization in Multi-hop Industrial Cognitive Radio Sensor Networks

Industrial cognitive radio sensor networks (ICRSNs) extend channel resources by occupying the vacant licensed channels in the absence of licensed users. In ICRSNs, industrial devices should switch to a common available channel to set up a communication link. However, channel switching leads to severe energy consumption. As the energy resources of battery-powered industrial devices are limited, it is crucial to carefully allocate channels to prolong the network lifetime of multi-hop ICRSNs. This paper is the first work that studies the channel allocation problem to optimize the network lifetime by considering the channel-switching (CS) energy consumption and the time-critical requirements of industrial applications. The problem is formulated to maximize the minimum residual energy at each round of data transmission, which is linearized as integer linear programming. As the channel allocation results will affect the residual energy at subsequent rounds, we propose a switching distance-optimized channel allocation (SDOCA) scheme that shortens the CS distances to improve the residual energy of each device. Moreover, we analyze the characteristics of SDOCA, i.e., convergent CS distance and guaranteed end-to-end delay. Extensive simulation results show that SDOCA can adaptively allocate channels according to the end-to-end delay requirement and significantly prolong the network lifetime.

Centralized Monitored Spectrum Management using Multi-resource Parallel Sensing in Cognitive Radio Networks

Spectrum sensing has a very important role in the operation of cognitive radio network. A proper sensing and utilization improve the performance of cognitive radio network (CRN). Different methodologies were developed in past to improve the sensing and utilization performance in CRN. Parallel sensing is used as an optimal approach in spectrum sensing with reduced delay of operation but restraint with multiple requests for spectrum sensing. A random generation of sensing requests results in large time delay and interference in the network. To minimize the interference effect and requesting overhead, this paper presents a new approach of spectrum sensing by centralized monitoring of spectrum engagement of Primary User (PU). The proposed approach operates on a repository monitoring of channel engagement to control the request granting for spectrum sensing in PUs. In addition, the sharing of spectrum usage is controlled by monitoring the spectral efficiency and interference in the network. Simulation results obtained illustrates an enhancement in network throughput, spectrum sensing and spectrum utilization performance where an average network throughput is observed to improve by 80% for varying number of primary channels and varying load conditions. The spectrum sensing is improved by 70% in average for varying primary channels and load conditions and Ideal channel utilization for the proposed approach is observed to improve by 90%.

An Energy Balance-Oriented Clustering Routing Protocol for Cognitive Radio Sensor Networks

Uneven clustering is an effective solution to solve the energy hole problem in multihop cognitive radio sensor networks (CRSNs). However, current literature leaves control overhead and multihop intercluster routing out of consideration and adopts constant weight coefficients for cluster radius calculation instead of reasonably setting their values according to specific network configurations. To solve the above problems, a distributed energy balance-oriented uneven clustering routing protocol (EBUCRP) is proposed in this article. First, by fully considering the energy consumption of control information exchange and data transmission, EBUCRP theoretically derives the optimal cluster radius of each unit within which distributed clusters are formed. Second, communication capability-based selection criteria are defined from joint energy and spectral efficiency perspectives to help select high-quality cluster heads and next-hop relays. In this case, the monitoring information can be transferred to the sink through efficient intracluster data aggregation and intercluster multihop forwarding. Third, EBUCRP strictly restricts the energy consumption of control information exchange during network operation. Simulation results show that EBUCRP gains obvious advantages over competing protocols in enhancing the lifetime and supervision capability of both single-hop and multihop CRSNs.

An energy efficient fuzzy clustering-based congestion control algorithm for cognitive radio sensor networks

An energy efficient fuzzy clustering-based congestion control algorithm for cognitive radio sensor networks

Traffic-driven ions motion optimization-based clustering routing protocol for cognitive radio sensor networks.

In cognitive radio sensor networks, single clustering protocol cannot simultaneously satisfy the various requirements of time-triggered and event-driven traffic, as a result, different kinds of clustering protocols are designed to serve them separately. In addition, for event-driven traffic, the long delay incurred by clustering and searching for available routes after events results in poor timeliness of information transmission. In order to solve above problems, a traffic-driven ions motion optimization-based clustering routing protocol (TD-IMOCRP) is proposed in this paper. For the first time, time-triggered and event-driven traffic can be served by a single clustering protocol. To be specific, ions motion optimization algorithm is leveraged to automatically determine the optimal number of clusters and form basic clustering structure. In this case, time-triggered traffic can be periodically served. Priority-based schedule and corresponding frame structure are designed to ensure priority delivery of event-driven information. The clustering architecture built for time-triggered traffic is leveraged, and there is no cluster construction and route selection after emergent events. Only the CRSNs nodes which discover emergent events and corresponding CHs participate in data transmission, which means that TD-IMOCRP covers fewer nodes, especially when the sink is located at the corner. Therefore, it can help reduce node energy consumption and delay. Simulation results demonstrate that compared with representative event-driven clustering protocols, TD-IMOCRP can decrease the average number of covered nodes and the total energy consumption by more than 66.3% and 25%, respectively. In addition, when serving time-triggered traffic, TD-IMOCRP can achieve almost the same performance as its basic version IMOCRP which is better than majority of current time-triggered clustering protocols. In a word, TD-IMOCRP can guarantee in-time delivery of event-driven information while guaranteeing its performance of serving time-triggered traffic.