Cognitive Radio Sensor Networks Research Articles

A Metaheuristic Based Approach for Threshold Optimization for Spectrum Sensing in Cognitive Radio Networks

Background: The mounting growth of wireless technology is attracting a high demand for the frequency spectrum. The measurements of spectrum usage depict that a significant portion of the spectrum lays unoccupied or overcrowded. The main cause of the glitch is the existing inefficient and fixed scheme of spectral allocation. Cognitive radio is one such technology that permits wireless devices to detect the unused frequency band and reconfigure its operating parameters to attain the required quality of service. Objective: To permit the dynamic allocation of the frequency band, spectrum sensing is performed which is an essential function of Cognitive radio and involves detection of an unused spectrum space to set up a communication link. Method: This paper presents a meta-heuristic approach for selection of a decision threshold for energy detection based spectrum sensing. At low SNR and in the presence of noise uncertainty, the performance of energy detection method fails. A novel adaptive double threshold based spectrumsensing method is proposed to avoid such a sensing failure. Further, the metaheuristic approach employs Particle Swarm Optimization (PSO) algorithm to compute an optimal value of the threshold to attain robustness against noise uncertainty at low SNR. Results: The simulation results of the proposed metaheuristic double threshold based spectrum sensing method demonstrate enhanced performance in comparison to the existing methods in terms of reduced error rate and increased detection probability. Some of the existing methods have been analyzed and compared from a survey of recent patents on spectrum sensing methods to support the new findings The concept of adaptive thresholding improves the detection probability by 39 % and 27 % at noise uncertainty of 1.02 and 1.04, respectively at a signal to noise ratio of -10 dB. Furthermore, the error probability reduces to 58% at the optimal threshold using Particle Swarm Optimization (PSO) algorithm for the signal to noise ratio of -9 dB. Conclusions: The main outcome of this work is the reduction in the probability of sensing failure and improvement in the detection probability using adaptive double thresholds at low SNR. Further, particle swarm optimization helps in obtaining the minimum probability of error under noise uncertainty with an optimal threshold.

Cooperative Communication Enabled Cognitive Radio in a Home-Care Application

In this paper, cooperative communication (CC) assisted cognitive wireless sensor network (CWSN) is presented for monitoring health and activity of an end-user in a smart indoor environment. The cooperative communication ensures better data delivery by utilizing minimum resources and also helps to track the location of the user, non-intrusively. The increase in demand for wireless sensor network insists various factors, such as spectrum sensing, sharing and optimal utilization of resources in various applications. CWSN is considered as a promising technology that improves spectrum utilization and communication quality. The study proposes a payload-centric adaptive multi-channel hopping (PC-AMCH) algorithm in CC enabled CWSN to further improve energy efficiency of conventional multi-channel wireless sensor networks, especially in a smart home-care system. All participated nodes, such as wearable cognitive node (C), relay nodes (Rn) and a destination node (D) are indigenously developed by involving CC2530 microcontroller that consists of an in-built transceiver at 2.4 GHz. Further, an adaptive relay selection scheme is used to establish a dynamic link between wearable cognitive node (C) and relay node (Rn) by analyzing factors such as, link quality indicator (LQI) and received signal strength indicator (RSSI). The performance of developed nodes is evaluated in terms of throughput, packet delivery ratio (PDR), RSSI and transmission offset. Similarly, the performance of proposed PC-AMCH is validated by measuring PDR, number of transmission, energy efficiency.

Parallel cuckoo search for cognitive wireless sensor networks

In cognitive wireless sensor networks (CWSNs), the limited energy of the sensor node is the core defect that restricts its comprehensive network performance. This paper proposes a parallel cuckoo search medoids (PCS-medoids) algorithm to manage the energy consumption in CWSNs efficiently. Firstly, a parallel cuckoo search algorithm (PCS) with communication is proposed to speed up the convergence of CS. Then, the PCS is applied to k-medoids to get cluster heads quickly. Finally, the PCS-medoids is presented to manage the consumption of sensor nodes. First experimental results illustrate that PCS tends to get optimal solutions quickly and accurately compared to CS and PSO. The other experimental results demonstrate that PCS-medoids has advantages over energy management in CWSNs compared to low-energy adaptive clustering hierarchy, LEACH-centralised, and hybrid energy-efficient distributed clustering. Besides, the ad-vantages are more obvious with the increase of sensor nodes in CWSNs.

Parallel cuckoo search for cognitive wireless sensor networks

In cognitive wireless sensor networks (CWSNs), the limited energy of the sensor node is the core defect that restricts its comprehensive network performance. This paper proposes a parallel cuckoo search medoids (PCS-medoids) algorithm to manage the energy consumption in CWSNs efficiently. Firstly, a parallel cuckoo search algorithm (PCS) with communication is proposed to speed up the convergence of CS. Then, the PCS is applied to k-medoids to get cluster heads quickly. Finally, the PCS-medoids is presented to manage the consumption of sensor nodes. First experimental results illustrate that PCS tends to get optimal solutions quickly and accurately compared to CS and PSO. The other experimental results demonstrate that PCS-medoids has advantages over energy management in CWSNs compared to low-energy adaptive clustering hierarchy, LEACH-centralised, and hybrid energy-efficient distributed clustering. Besides, the ad-vantages are more obvious with the increase of sensor nodes in CWSNs.

Performance Evaluation of Spectrum Aware Clustering Protocols – A Comparative Study

The recent applications of WSN have started integrating Cognitive Radio (CR) technology to make sure unlicensed users in the communication also gain access over the vacant spectrum portions. This sensor network designed embedding CR technology is termed as Cognitive Radio Sensor Networks (CRSN) which overcomes the limitations of conventional WSNs by providing dynamic access mechanisms to accomplish efficient spectrum utilization. Since the CRSN is resource constrained, spectrum access mechanisms should incorporate overhead minimizing mechanisms. Clustering method has been explored in recent works of CRSN to enhance the network performance and minimize the transmission overhead. Since clustering allows communication in a more restricted manner, network resources such as node energy, link bandwidth can be utilized efficiently. Data transmission rate can be further enhanced as lesser number of nodes involve in communication. In this paper recent clustering protocols in CRSN are discussed briefly and the comparative performance analysis of some of these protocols is made. The experimental results are derived by simulation and the results are plotted for performance parameters such as Packet Delivery Ratio (PDR), packets dropped, packet delay, average energy consumption and throughput. The paper concludes by highlighting the performance variations of the benchmark protocols taken under consideration.

Recent Paradigms for Efficient Spectrum Sensing in Cognitive Radio Networks: Issues and Challenges

Rapidly advancing technological advancements in the recent past has made the life of consumers at ease through innovation of sophisticated and state of the art gadgets. The consumers are able to access data at high speeds at their own free will irrespective of time and location. With increasing benefits of these wireless gadgets and technologies working on well-known and efficient radio frequency spectrum, an increasing scarcity in availability of radio frequency spectrum is found to be a rising consequence in recent times. With ever increasing number of wireless gadgets, increasing burden on spectrum allocation is emerging to be a prevalent research topic in recent times. Cognitive radio networks (CRNs) have been found to be effective and intelligent solutions, which by a sequence of intelligent sensing, aggregation of sensed information and decision making, provide an optimal method of allocation of spectrum to demanding users. This paper provides a detailed insight into various methods used in cognitive spectrum sensing, their classifications and methodologies. A vast survey of literature has been systematically provided in this paper with the issues and challenges forming the concluding parts of this survey. Knowledge of existing methods described in the literature with their merits and limitation help in developing and improving the performance of existing spectrum sensing methods to a great extent.

Energy-Efficient Cooperative Spectrum Sensing Based on Stochastic Programming in Dynamic Cognitive Radio Sensor Networks

Nowadays, Cognitive Radio Sensor Networks (CRSN) arise as an emergent technology to deal with the spectrum scarcity issue and the focus is on devising novel energy-efficient solutions. In static CRSN, where nodes have spatial fixed positions, several reported solutions are implemented via sensor selection strategies to reduce consumed energy during cooperative spectrum sensing. However, energy-efficient solutions for dynamic CRSN, where nodes are able to change their spatial positions due to their movement, are nearly reported despite today's growing applications of mobile networks. This paper investigates a novel framework to optimally predict energy consumption in cooperative spectrum sensing tasks, considering node mobility patterns suitable to model dynamic CRSN. A solution based on the Kataoka criterion is presented, that allows to minimize the consumed energy. It accurately estimates -with a given probability-the spent energy on the network, then to derive an optimal energy-efficient solution. An algorithm of reduced-complexity is also implemented to determine the total number of active nodes improving the exhaustive search method. Proper performance of the proposed strategy is illustrated by extensive simulation results for pico-cells and femto-cells in dynamic scenarios.

A corner truncated fractal slot ultrawide spectrum sensing antenna for wireless cognitive radio sensor network

SummaryDue to the scarcity of the radio frequency spectrum, the emergent network sharing technique elicits the efficient use of licensed unutilized spectrum. Here, an ultra‐wideband (UWB) microstrip antenna is proposed for wireless cognitive radio sensor network (WCRSN). The antenna is uniquely designed for large spectrum scanning of channel vacancies in licensed frequency bands. Software‐defined radios (SDR) govern the algorithmic controls on spectrum sensing and channel interface using an appropriate antenna. The antenna comprises the effects of fractal slot patch and electromagnetic bandgap (EBG) ground structures on 12 × 18 × 1.6 mm3 FR4 (relative dielectric constant of 4.4) substrate. Circular polarization is achieved through corner truncation to make antenna orientation free in wireless sensing networks. The measured |S11| parameter offers 0.75‐ to 12‐GHz wide spectrum sensing with 2.51‐dBi average gain. The antenna is unique in compactness and includes lower edge bands of frequencies (0.75–3.1 GHz) along with UWB (3.1–12.0 GHz) for a very wide range scanning to utilize vacant channel opportunity in WCRSN through SDR. The proposed structure is experimented and validated for real‐time wideband scanning of the frequency spectrum within the operating limit as per the allotted FCC standard.

Link‐delay and spectrum‐availability aware routing in cognitive sensor networks

This study designs and presents routing protocols multi-channel, multi-hop cognitive radio sensor network (CRSN), which are based on spectrum sensing, spectrum-availability analysis and per-hop delay analysis. In order to cope with the spectrum dynamics in the CRSNs, a framework is proposed to estimate the remaining duration of spectrum availability which is based on the expected idle length of channels, sensing period and communication history on the channel. Such methodology allows cognitive users to mitigate communication interruptions caused by arrivals of primary users, thus to improve the transmission performance over each hop. In particular, in combination with the per-hop delay analysis, the routing paths from sources to the sink can be established such as they provide the shortest delay opportunistically or shorter delay but reliable communication along the selected paths. Extensive simulation results show that the proposed routing algorithms potentially improve the network performances in terms of throughput, delay and energy consumption.

Quality of service provisioning through resource optimisation in heterogeneous cognitive radio sensor networks

Recently, cognitive radio sensor networks (CRSN) have evolved as a result of the introduction of cognitive capabilities to conventional wireless sensor networks. In most CRSN designs, secondary users and/or sensor nodes are permitted, under certain constraints, to use the limited resources of a primary network. One major challenge with CRSN is how to optimally appropriate and use the limited resources available in driving their communication demands. To overcome this challenge, in this paper, we develop a resource allocation (RA) model that is capable of achieving a target quality of service (QoS) demand for the heterogeneous CRSN, despite the huge resource constraints imposed on the network. The RA problem developed is a complex optimisation problem. We analyse and solve the complex RA problem using the optimisation approaches of integer linear programming, Lagrangian duality and by a heuristic. We then study the performance of the RA model for the different solution approaches investigated. The results obtained are used to establish the optimality-complexity trade-off, which is a critical criterion for QoS decision-making in practical CRSN applications.

Energy-efficient sensor selection for multi-channel cooperative spectrum sensing based on game theory

This paper considers a wireless cognitive sensor network (WCSN) composed of distributed sensors that cooperatively sense multiple channels. Since the energy consumption of WCSNs is an important issue besides sensing quality requirements, the paper proposes a sensor selection method for multi-channel cooperative spectrum sensing that reduces energy consumption under detection quality constraints, for scenarios that the recharge of sensors is difficult or impossible. It is assumed that the sensors cannot sense more than one channel in a sensing duration because of hardware limitations. This problem is modeled as a coalition game in which sensors act as game players and decide to make disjoint coalitions. Each coalition senses one of the channels while guaranteeing that the necessary detection and false alarm probability thresholds are satisfied. Other nodes, that decide to sense none of the channels, turn off their sensing module at the sensing period, to extend the network lifetime. A novel proper utility function is proposed based on the energy consumption, false alarm probability, and detection probability of sensors. This paper proposes an efficient algorithm to reach a Nash-stable coalition structure. The proposed algorithm is compared with the optimal algorithm and two other existing algorithms through simulations. In addition, the computational complexity of the proposed algorithm is analyzed.

Opportunistic transmission using hybrid sensing for Cognitive Radio Sensor Network in the presence of smart Primary User Emulation Attack

ABSTRACT The ever-increasing spectrum demand in wireless communication networks like Sensor Network has routed to the development of Cognitive Radio Sensor Networks (CRSN). Securing the CRSN against the Primary User Emulation Attack (PUEA) plays a crucial role in the current research. In this paper, we propose an Opportunistic Transmission using the Hybrid Sensing method for the CRSN in the presence of smart PUEA. In Hybrid sensing method, both proactive sensing and reactive sensing techniques are effectively combined to reduce the error probability in detecting the Primary User signal. Furthermore, we have overcome the effect of PUEA by using the Opportunistic Transmission to improve the throughput and energy efficiency of the SU network. Both simulation and Software Defined Radio (SDR) based hardware test bed results show that the throughput is increased by 18%, the energy efficiency is increased by 11% and the error probability is reduced by 20% compared to the existing method.

DEDC: Sustainable data communication for cognitive radio sensors in the Internet of Things

The distributed tree-based data communication protocol is a widely accepted efficient method in wireless sensor network-enabled Internet of Things (IoT). Due to increasing demand for IoT applications, cognitive radio sensors play a vital role in spectrum utilization. This short communication proposes a delay aware, energy-efficient data communication protocol (DEDC) for cognitive radio sensor networks (CRSN). This research proposes a first communication protocol that integrates the solution for delay and energy issues in CRSNs specific to IoT application. The respective simulation results and comparative studies with the existing models on other parameters prove the novel contribution of this research in terms of delay and energy utilization.

Compact Corner Truncated Fractal Slot Antenna for Cognitive Radio Sensor Network

The cognitive radio (CR) opens new paradigms of opportunistic utilization of unused frequency spectrum through spectrum sharing. Dynamic spectrum access (DSA) provides an effective solution to channel congestion with the help of software-defined radio in cognitive radio networks (CRN). Software defined radio (SDR) offers manipulative wide spectrum sensing and reconfigurable transmission properties between licensed primary user (PU) and unlicensed secondary user (SU). The opportunistic spectrum sharing elicits the need for hardware compatibility from a common radiating antenna for extra-large band spectrum scanning. This paper proposes a novel compact antenna for wide spectrum sensing to utilize vacant channel opportunity in wireless cognitive radio sensor network (WCRSN). The antenna design comprises the combinational effects of fractal slots and electromagnetic bandgap structure on compact FR4 substrate 12 × 18 × 1.6 mm2. The circular polarization improves the feasibility of antenna for orientation-free scanning of spectrum through corner truncation. The novel antenna design exhibits large measured impedance bandwidth from 0.79 to 12.0 GHz compatible with Zigbee, Bluetooth, GPS, GSM, LoRa, WiFi and other UWB applications. The antenna offers average gain 2.59 dBi over entire operating frequency range and validates compatibility for spectrum sensing in WCRSN as per FCC standards.

Improved Whale Optimization Algorithm and Convolutional neural network based Cooperative Spectrum Sensing in Cognitive Radio Networks

ABSTRACT In this manuscript, we establish a Cognitive Radio Network (CRN) based on Cooperative Spectrum Sensors (CSS) with Improved Whale Optimization Algorithm (IWOA). In our work, the main users are provided for the support of multiple cooperating secondary users. To obtain, the computed particular exposure samples spatial and spectral correlation is utilized. To instruct the particular exposure samples of the secondary user, the convolutional neural network (CNN)-based Cooperative Spectrum Sensors (CSS) framework is used. An Improved Whale Optimization Algorithm (IWOA) is comprised to calculate the weight function of CNN optimally raises the accuracy. A weight of inertia for Whale Optimization Algorithm (WOA) used to perform the WOA (IWOA). Finally, the primary user of the CNN structure can accurately perceive. Assessment metrics are performed based on delay, delivery ratio, energy consumption, impartiality index, and performance parameters. According to the proposed method, accuracy is 95.36%.

Energy preservation and network critic based channel scheduling (EPNCS) in cognitive radio sensor networks

Cognitive Radio Sensor Network (CRSN) is a distributed network of Cognitive Radio (CR) enabled wireless sensor nodes where there is no guarantee of the availability of vacant channels for data transfer. In CRSN nodes spend energy in radio spectrum sensing to sense the availability of vacant channels and hence energy preservation becomes a major concern. In this proposed work, a novel algorithm—energy preservation and network critics based channel scheduling (EPNCS) algorithm in CRSNs is designed which regulates the slot time for sensor nodes. Depending on environmental data traffic, the sleep time of sensor nodes is varied which reduces power consumption. Scalable dynamic slots are computed for each sensor node based on the average buffer occupancy which leads to optimal channel utilization. Simulation results show that EPNCS improves the network lifetime and throughput of CRSN significantly in comparison with existing protocols.

Cooperative Spectrum Sensing in Cognitive Radio Networks: A Survey on Machine Learning-based Methods

The continuous growth of demand experienced by wireless networks creates a spectrum availability challenge. Cognitive radio (CR) is a promising solution capable of overcoming spectrum scarcity. It is an intelligent radio technology that may be programmed and dynamically configured to avoid interference and congestion in cognitive radio networks (CRN). Spectrum sensing (SS) is a cognitive radio life cycle task aiming to detect spectrum holes. A number of innovative approaches are devised to monitor the spectrum and to determine when these holes are present. The purpose of this survey is to investigate some of these schemes which are constructed based on machine learning concepts and principles. In addition, this review aims to present a general classification of these machine learningbased schemes

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

Fuzzy genetic based dynamic spectrum allocation approach for cognitive radio sensor networks

Fuzzy genetic based dynamic spectrum allocation approach for cognitive radio sensor networks

A Chaotic Hybrid Immune Genetic Algorithm for Spectrum Allocation Optimization in ICRSN

The growing usage of the industrial cognitive radio sensor network (ICRSN) brings profound changes to the Internet of Things. The ICRSN is an emerging technique to transfer industrial data, which has strict and accurate communication requirements in a large number of areas such as environmental surveillance, building monitoring, control, and many other areas. The problem of maximizing the sum bandwidth by using a spectrum allocation algorithm has been extensively studied in this paper. Inspired by chaos theory and quantum computing, this work presents a new chaotic hybrid immune genetic algorithm (CHIGA). We then introduce a spectrum allocation model that considers both network reward, throughput, and convergence time. The improvement of CHIGA performance through experimental simulations is evaluated in terms of the sum network reward compared to methods based on simulated annealing (SA), ant colony optimization (ACO), and particle swarm optimization (PSO). Simulation results show that the CHIGA has a higher network reward and throughput existing optimized algorithms while maintaining total system throughput.