Cognitive Radio Sensor Networks Research Articles

Congestion centric multi‐objective reptile search algorithm‐based clustering and routing in cognitive radio sensor network

AbstractIn recent trends, Cognitive Radio Sensor Networks (CRSNs) are investigated in‐depth and getting momentum in all types of applications. CRSN can make use of the underutilized frequency spectrum in a suitable manner. Due to the above‐mentioned advantage, the scholars have initiated to study of the domain of cognitive radio routing. Network congestion produces transmission delays and packet loss, as well as time and energy wasted on recovery. In order to fulfill the energy efficiency and network lifetime in CRSN, Congestion Centric Multi‐Objective Reptile Search Algorithm (CC‐MORSA)‐based Clustering and Routing are used. The main objective of proposed CC‐MORSA is to improve the lifetime by minimizing the distance among the designated Cluster Head nodes which creates the fitness function by multiple objectives like energy, distance, and load. This technique is appropriate for common sensor nodes in coordinated communications infrastructure and large networks. The simulation results are analyzed through MATALB in terms of remaining energy (999.5 J), average delay (0.36 s), Packet Delivery Ratio (99.8%), Energy Consumption (24.1 J), Throughput (0.98 Mbps), routing overhead (0.54), and Packet Loss Rate (0.2%). From the outcomes, it shows that the presented CC‐MORSA outperformed conventional Stability‐Aware Cluster‐based Routing and Drop Factor‐Based Energy Efficient Routing technique.

A weighted energy consumption minimization-based multi-hop uneven clustering routing protocol for cognitive radio sensor networks

Aiming at solving the effective data delivery and energy hole problem in multi-hop cognitive radio sensor networks (CRSNs), a weighted energy consumption minimization-based uneven clustering (ECMUC) routing protocol is proposed in this paper. For the first time, the impact of control overhead on the network performance is taken into consideration, to be specific, the energy consumption of control overhead is integrated with that of data communication to model the network energy consumption. Through effective transformation and theoretical analysis, cluster radius of each ring is derived by minimizing the network energy consumption and balancing the residual energy among nodes in different rings. Distributed cluster heads (CHs) selection and cluster formation are carried out within this range to control the cluster size and the corresponding energy cost. Expected times for being CHs metric is defined to measure nodes’ energy and spectral potential and help select powerful CHs. Simulation results show that ECMUC protocol is superior to most clustering protocols designed for CRSNs in terms of network surveillance capability and network lifetime, and it is also demonstrated that taking control overhead into consideration is beneficial for improving the network performance.

Design of an adaptive CRSN using OFDM

Cognitive Radio (CR) is a novel concept that enables wireless devices to detect and adapt to their surroundings in order to enhance communication quality. The cognitive radio sensor network (CRSN) has proved to be a cost-effective solution for the spectrum constraints in wireless sensor networks (WSN). Optimizing the optimum packet size is regarded to be an essential energy constrained issue to address the practical implementation of CRSN out of all the difficulties. Small packets generate data traffic in device-to-device communication, while big packets may cause data bit corruption, requiring retransmission at a greater frequency. This will not allow access from the secondary network to the main network, since it may cause further disturbance. To maximise the WSN's energy efficiency, the optimum packet size for CRSN should be maintained while keeping the same degree of interference as the primary licenced users. The purpose of this article is to examine formulations for small, medium, and large packet sizes in order to determine the optimum packet size for adaptive CRSN. To do so, CR requires a flexible physical layer capable of carrying out the necessary tasks. This article examines the performance of CR systems that use the Orthogonal Frequency Division Multiplexing technique, which is a possible transmission technology for CR. Interference and delays are minimised and the channels are ultimately utilised effectively through Scheduling MAC protocol. This article gives the design steps to adjust the network design to get the better performance. For achieving the greatest performance on the Cognitive Radio Sensor Network (CRSN). The Jellyfish Search Optimization algorithm and the hybrid Momentum Search Algorithm are hybridized and results are achieved. This makes it possible to calculate precise packet sizes. The suggested approach decision outperforms existing methods like the Group Sparse Optimization algorithm and the Throughput Maximization Algorithm. The MATLAB/SIMULINK Platform were used to get the results.

On the outage performance of decode-and-forward based relay ordering in cognitive wireless sensor networks

In this paper, the issue of secondary network access in cognitive wireless sensor networks is investigated. An efficient scheme (in terms of both power and spectrum) named decode-and-forward based multi-relay scheme with relay ordering (DF-MRRO) is proposed for secondary network access. More important, this scheme is used to enhance the performance of secondary network. In an effort to assess the performance, the received signal-to interference-plus-noise ratio of both the primary and secondary links are derived, from which new exact closed-form expressions for the outage probability of primary and secondary networks are derived. Moreover, the diversity order is calculated for both the primary and secondary network. It is also shown that full diversity order can be achieved if the condition of power requirement is satisfied. Finally, simulation results obtained for the outage performance of the proposed scheme in primary and secondary networks prove the effectiveness of the proposed scheme. It also demonstrates that a maximum of 7 dB SNR gain can be achieved in the proposed DF-MRRO scheme.

Assessment of Dynamic Swarm Heterogeneous Clustering in Cognitive Radio Sensor Networks

Many optimization algorithms have been created to determine the most energy-efficient transmission mode, allowing for lower power consumption during transmission over shorter distances while minimising interference from primary users (PUs). The improved cooperative clustering algorithm (ICCA) performs superior spectrum sensing across groups of multiusers compared to any other method currently available in terms of sensing inaccuracy, power savings, and convergence time than any other method currently available. The proposed ICCA algorithm is employed in this research study to find the optimal numbers of clusters based on its connectivity and the most energy-efficient distributed cluster-based sensing technique available. In this research, many randomly chosen secondary users (SUs) and primary users (PUs) are investigated for potential implementation opportunities. Therefore, as compared to the current optimization strategies, the proposed ICCA algorithm enhanced the convergence speed by integrating the multiuser clustered communication into a single communication channel. Experimental results revealed that the new ICCA algorithm reduced node power by 9.646 percent compared to traditional ways when comparing the novel algorithm to conventional approaches. In a similar vein, as compared to the prior methodologies, the ICCA algorithm reduced the average node power of SUs by 24.23 percent on average. When the SNR is decreased to values below 2 dB, the likelihood of detection improves dramatically, as seen in the figure. ICCA has a low false alarm rate when matched to other optimization algorithms for direct detection, and the proposed method outperforms them all. Following the findings of the simulations, the proposed ICCA technique effectively addresses multimodal optimization difficulties and optimizes network capacity performance in wireless networks. A detailed discussion of SS applications for the IoT and wireless sensor networks, both based on CR, is provided. There is also a thorough discussion of the most recent advancements in spectrum sensing as a facility. IoT or WSN may be essential in feeding the CR networks with spectrum sensing data and the future of spectrum sensing. The use of CR for fifth generation and afar its potential application in frequency allocation are discussed. To stay up with the advancement of communication technology, SS should give additional features to remain competitive, like the capacity to investigate various available channels and accessible places for transmission. Based on present and prospective methods in wireless communications, we highlight the crucial upcoming study paths and difficulty spots in signal processing for cognitive radio and potential solutions (SS-CR).

Energy-Efficient Transmission Range and Duration for Cognitive Radio Sensor Networks

Cognitive Radio (CR) promises an efficient utilization of radio spectrum resources by enabling dynamic spectrum access to overcome the spectrum scarcity problem. Cognitive Radio Sensor Networks (CRSNs) are one type of Wireless Sensor Networks (WSNs) equipped with CR capabilities. CRSN nodes need to operate energy-efficiently to extend network lifetime due to their limited battery capacity. In this paper, for the first time in literature, we formulate the problem of finding a common energy-efficient transmission range and transmission duration for all CRSN nodes and network deployment that would minimize the energy consumed per goodput per meter toward the sink in a greedy forwarding scenario. Results reveal non-trivial relations for energy-efficient CRSN transmission range and duration as a function of nine critical network parameters such as primary user activity levels. These relations provide valuable insights for detailed CRSN designs prior to deployment.

Partially Cooperative Scalable Spectrum Sensing in Cognitive Radio Networks Under SDF Attacks

Massive Internet of Things (IoT) connectivity requires addressing spectrum congestion caused by spectrum scarcity in wireless communications. Over the past decade, cognitive radio has been proposed as a promising solution to utilize the licensed spectrum efficiently. Conventional spectrum sensing approaches are complex and require statistical information about the behavior of licensed users, which is impractical. To overcome this limitation, several reinforcement learning (RL)- based spectrum sensing approaches have been proposed that are also highly adaptable to the dynamics of IoT environments. Additionally, cooperative RL-based spectrum sensing approaches have been widely used because they are more accurate than noncooperative approaches. However, the advantage comes at the cost of scalability due to increased information sharing overhead. Furthermore, cooperative spectrum sensing (CSS) approaches suffer from attacks on the network, such as sensing data falsification (SDF) attacks, which deteriorate sensing accuracy dramatically. In this paper, we present a scalable, partially CSS algorithm that is highly resilient to SDF attacks. The novelty of the proposed algorithm lies in partial cooperation through coalition formation, which reduces sensing and information sharing overhead while improving sensing accuracy. Moreover, the algorithm learns to adapt the sensing participation percentage and selects the most rewarding channel for sensing to maximize rewards while minimizing energy consumption. The proposed algorithm outperforms state-of-the-art CSS algorithms in terms of sensing accuracy and overheard. Contrary to centralized CSS algorithms, the proposed algorithm’s performance is directly proportional to the number of devices; hence, it is suitable for massive connectivity.

Survey on Bio-Inspired Approaches for Allocation of Spectrum in Cognitive Radio Sensor Networks

Survey on Bio-Inspired Approaches for Allocation of Spectrum in Cognitive Radio Sensor Networks

Energy Aware Q-learning AODV (EAQ-AODV) routing for cognitive radio sensor networks

Wireless sensor networks (WSNs) play an important role in various real-time applications such as health monitoring, security application, military applications, etc. However, these networks are deployed in ISM bands where coexistence, interference and frequency overlapping remain the challenging issues to degrade the performance. Current technological advancements facilitate the cognitive radios as a promising technique to mitigate these issues. However, energy consumption degrades the network lifetime and throughput performance which can be addressed by developing the energy aware routing protocol for cognitive radio enabled WSNs. In this work, we introduce Energy aware Q-learning AODV (EAQ-AODV) routing. The proposed EAQ-AODV uses Q-learning based reward mechanism for cluster head selection and AODV enabled routing protocol based on different parameters such as Residual Energy, Common Channel, Number of Hops, Licensed Channel, Communication Range and Trust Factor to establish the routing path. The experimental study shows that the proposed EAQ-AODV routing achieves an improved performance in terms of average end-to-end delay, average energy consumption and network lifetime when compared with the existing techniques.

Spectrum Sensing and Clustering Routing Algorithm in Cognitive Wireless Sensor Networks

Spectrum Sensing and Clustering Routing Algorithm in Cognitive Wireless Sensor Networks

Optimizing Spectrum Sensing by Using Artificial Neural Network in Cognitive Radio Sensor Networks

Optimizing Spectrum Sensing by Using Artificial Neural Network in Cognitive Radio Sensor Networks

Cognitive WSN Control Optimization for Unmanned Farms Under the Two-Layer Game

With the deep integration of modern technology and agricultural production, the development of unmanned precision agriculture has become a breakthrough for agricultural upgrading. Unmanned farms rely on multi-source data provided by dense wireless sensor networks (WSN), which causes greater pressure on the scarce spectrum resources. Combining fuzzy clustering and game theory, this paper studies a joint optimization method for cognitive wireless sensor networks (CWSN) to optimize agricultural data transmission and make full use of network resources. Two objective functions are designed to maximize the network energy efficiency and minimize the transmit power, so as to optimize the transmit power and transmission channel in CWSN. Different optimization objectives are regarded as different players, which turns the multi-objective optimization into a game decision-making problem. By defining the influence model of design variables on different objective functions, fuzzy clustering is adopted to divide the strategy subspace for each player. Then, this paper studies a two-layer cooperative game algorithm. Each node chooses the transmit power and channel in the strategy space based on their price function. The game scheme is obtained by a multi-round two-layer cooperative game. The experimental results show that compared with NSGA-ò, the reinforcement learning (RL) algorithm, and the non-cooperative game method, the joint optimization algorithm can reduce network interference and improve network QoS.

A Neuro Fuzzy with Improved GA for Collaborative Spectrum Sensing in CRN

Cognitive Radio Networks (CRN) have recently emerged as an important solution for addressing spectrum constraint and meeting the stringent criteria of future wireless communication. Collaborative spectrum sensing is incorporated in CRNs for proper channel selection since spectrum sensing is a critical capability of CRNs. According to this viewpoint, this study introduces a new Adaptive Neuro Fuzzy logic with Improved Genetic Algorithm based Channel Selection (ANFIGA-CS) technique for collaborative spectrum sensing in CRN. The suggested method’s purpose is to find the best transmission channel. To reduce spectrum sensing error, the suggested ANFIGA-CS model employs a clustering technique. The Adaptive Neuro Fuzzy Logic (ANFL) technique is then used to calculate the channel weight value and the channel with the highest weight is selected for transmission. To compute the channel weight, the proposed ANFIGA-CS model uses three fuzzy input parameters: Primary User (PU) utilization, Cognitive Radio (CR) count and channel capacity. To improve the channel selection process in CRN, the rules in the ANFL scheme are optimized using an updated genetic algorithm to increase overall efficiency. The suggested ANFIGA-CS model is simulated using the NS2 simulator and the results are investigated in terms of average interference ratio, spectrum opportunity utilization, average throughput, Packet Delivery Ratio (PDR) and End to End (ETE) delay in a network with a variable number of CRs.

Cooperative Spectrum Sensing in Cognitive Radio Networks via an Adaptive Gaussian Mixture Model Based on Machine Learning

Spectrum resources are becoming extremely scarce in modern wireless communication. However, the majority of the currently available spectrum resources have not been fully utilized. To mitigate this problem, we suggested Machine learning-based Adaptive Gaussian Mixture Model (AGMM) for cooperative spectrum sensing in cognitive radio networks for pattern classification. We employ the energy level of secondary users to build a feature vector in the proposed method. The training feature vectors for classification are well defined by a combination of Gaussian density functions that are obtained using the proposed method. The proposed method performance is evaluated in terms of accuracy, recall, F1 score, and Receiver Operating Characteristics (ROC) curves. The performance parameters of the proposed method are compared to the existing K-mean clustering approach. As evidenced by the results, the proposed method performs better than an existing method in all comparison parameters, according to the simulation findings in the MATLAB version.

Socially Responsible Technologies in Autonomous Mobility Systems: Self-Driving Car Control Algorithms, Virtual Data Modeling Tools, and Cognitive Wireless Sensor Networks

Socially Responsible Technologies in Autonomous Mobility Systems: Self-Driving Car Control Algorithms, Virtual Data Modeling Tools, and Cognitive Wireless Sensor Networks

Machine and Deep Learning Technologies, Location Tracking and Obstacle Avoidance Algorithms, and Cognitive Wireless Sensor Networks in Intelligent Transportation Planning and Engineering

Machine and Deep Learning Technologies, Location Tracking and Obstacle Avoidance Algorithms, and Cognitive Wireless Sensor Networks in Intelligent Transportation Planning and Engineering

Intelligence-based optimized cognitive radio routing for medical data transmission using IoT

<abstract> <p>The Internet of Things (IoT) is considered an effective wireless communication, where the main challenge is to manage energy efficiency, especially in cognitive networks. The data communication protocol is a broadly used approach in a wireless network based IoT. Cognitive Radio (CR) networks are mainly concentrated on battery-powered devices for highly utilizing the data regarding the spectrum and routing allocation, dynamic spectrum access, and spectrum sharing. Data aggregation and clustering are the best solutions for enhancing the energy efficiency of the network. Most researchers have focused on solving the problems related to Cognitive Radio Sensor Networks (CRSNs) in terms of Spectrum allocation, Quality of Service (QoS) optimization, delay reduction, and so on. However, a very small amount of research work has focused on energy restriction problems by using the switching and channel sensing mechanism. As this energy validation is highly challenging due to dependencies on various factors like scheduling priority to the registered users, the data loss rate of unlicensed channels, and the possibilities of accessing licensed channels. Many IoT-based models involve energy-constrained devices and data aggregation along with certain optimization approaches for improving utilization. In this paper, the cognitive radio framework is developed for medical data transmission over the Internet of Medical Things (IoMT) network. The energy-efficient cluster-based data transmission is done through cluster head selection using the hybrid optimization algorithm named Spreading Rate-based Coronavirus Herding-Grey Wolf Optimization (SR-CHGWO). The network lifetime is improved with a cognitive- routing based on IoT framework to enhance the efficiency of the data transmission through the multi-objective function. This multi-objective function is derived using constraints like energy, throughput, data rate, node power, and outage probability delay of the proposed framework. The simulation experiments show that the developed framework enhances the energy efficiency using the proposed algorithm when compared to the conventional techniques.</p> </abstract>

Correction to: Joint Optimization of Fusion Rule Threshold and Transmission Power for Energy Efficient CSS in Cognitive Wireless Sensor Networks

Correction to: Joint Optimization of Fusion Rule Threshold and Transmission Power for Energy Efficient CSS in Cognitive Wireless Sensor Networks

Distributed Artificial Intelligence Empowered Sustainable Cognitive Radio Sensor Networks: A Smart City on-demand Perspective

Smart cities are claimed to be smart if the new technologies are capable of providing desired sustainable outcome. The sustainable properties of smart city applications require less energy consumption and efficient resource allocation. The Internet-of-Things (IoT), 5G, and fog networks have emerged as the most crucial researched areas due to their numerous applications for smart cities to provide the desired sustainable outcome. The sustainable properties of Wireless Sensor Networks (WSNs) play a vital role in the deployment of these technologies into the physical world and efficient utilization of the available spectrum is a major problem faced here. As a potential solution of this, Cognitive Radio (CR) merged with WSN as Cognitive Radio Sensor Networks (CRSNs) make the smart perspective with high resource management through cooperative communication. The proposed work establishes a dynamic correlation between Secondary Users/nodes (SUs) in a single cluster according to their statistical behavior at the time of performing smart cooperative communication in CRSNs to improve sustainability of smart world IoT applications. Distributed Artificial Intelligence (DAI) is used to calculate the real-time resource allocation to these clusters using their respective Coordinator Agent (CoA) based on the dynamic behaviors. To improve sustainability in the smart city applications, the time delay in the prediction of vacant channels is reduced which results in making these applications become more energy efficient. The effectiveness of the proposed work is illustrated with mathematical analysis and simulation results confirm its better sustainable performance compared to the existing techniques.

Dynamic Compilation of Pattern based clustering and Volumetric Probabilistic Mining for Network Routing in Cognitive Radio Sensor Networks

Objectives: The key objective is to investigate the current state of the art in web service prediction systems and also to improve the retrieving process with improved accuracy and to reduce the searching time. As well as to enhance the performance of data validation, quality of services and speed of the process. Method: In this study, an advanced model of the Dynamic Compilation of Pattern (DCP) method with a Lexical Subgroup (LS) system was used to estimate the similarity between the request data and the entire network. These are all indexed and grouped as a cluster to form a paged format of network structure which can reduce the computation time during the searching period. Also, with the help of prediction, the relevancy of feature attributes in the network is predicted, and the matching index is sorted to provide the recommended data for given request data. This was achieved by using Volumetric Probabilistic Mining (VPM). Findings: The performance of the proposed DCP-VPM is proved through extensive simulations and compared to those of the state-of-the-art methods. On the average, it is realized that the DCP-VPM always outperforms EACRP, ERP, ESUCR and ESAC related to minimizing average energy consumption, packet delivery ratio, end-to-end delay at different number of clusters by 10.2%, 18.6%, 11.3%, 12.5% compared to EACRP, ERP, ESUCR and ESAC respectively. Proposed cluster-based routing technique out performs all other routing techniques. Novelty: Route based request prediction system was focused to predict and analyse data from the network. That is why enhanced clustering, distance-based similarity and retrieving mechanism are used. Irrelevant parameters removal and ordering are performed using DCP with LS system. Then nodes are processed for learning model using VPM prediction model. Finally, as the recommended result for the routing application, the matched data that is related to the request input is listed. Keywords: Dynamic Compilation of Pattern; Lexical Subgroup system; Volumetric Probabilistic Mining; Request prediction; Cognitive Radio