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  • Open Access


    Design of Clustering Techniques in Cognitive Radio Sensor Networks

    R. Ganesh Babu1,*, D. Hemanand2, V. Amudha3, S. Sugumaran4

    Computer Systems Science and Engineering, Vol.44, No.1, pp. 441-456, 2023, DOI:10.32604/csse.2023.024049

    Abstract In recent decades, several optimization algorithms have been developed for selecting the most energy efficient clusters in order to save power during transmission to a shorter distance while restricting the Primary Users (PUs) interference. The Cognitive Radio (CR) system is based on the Adaptive Swarm Distributed Intelligent based Clustering algorithm (ASDIC) that shows better spectrum sensing among group of multiusers in terms of sensing error, power saving, and convergence time. In this research paper, the proposed ASDIC algorithm develops better energy efficient distributed cluster based sensing with the optimal number of clusters on their connectivity. In this research, multiple random… More >

  • Open Access


    Enhanced Primary User Emulation Attack Inference in Cognitive Radio Networks Using Machine Learning Algorithm

    N. Sureka*, K. Gunaseelan

    Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 1893-1906, 2022, DOI:10.32604/iasc.2022.026098

    Abstract Cognitive Radio (CR) is a competent technique devised to smart sense its surroundings and address the spectrum scarcity issues in wireless communication networks. The Primary User Emulation Attack (PUEA) is one of the most serious security threats affecting the performance of CR networks. In this paper, machine learning (ML) principles have been applied to detect PUEA with superior decision-making ability. To distinguish the attacking nodes, Reinforced Learning (RL) and Extreme Machine Learning (EML-RL) algorithms are proposed to be based on Reinforced Learning (EML). Various dynamic parameters like estimation error, attack detection efficiency, attack estimation rate, and learning rate have been… More >

  • Open Access


    Analysis of Cognitive Radio for LTE and 5G Waveforms

    Ramesh Ramamoorthy1, Himanshu Sharma2, A. Akilandeswari3, Nidhi Gour2, Arun Kumar4,*, Mehedi Masud5

    Computer Systems Science and Engineering, Vol.43, No.3, pp. 1207-1217, 2022, DOI:10.32604/csse.2022.024749

    Abstract Spectrum sensing is one of the major concerns in reaching an efficient Quality of service (QOS) in the advanced mobile communication system. The advanced engineering sciences such as 5G, device 2 device communications (D2D), Internet of things (IoT), MIMO require a large spectrum for better service. Orthogonal frequency division multiplexing (OFDM) is not a choice in advanced radio due to the Cyclic Prefix (CP), wastage of the spectrum, and so on. Hence, it is important to explore the spectral efficient advanced waveform techniques and combine a cognitive radio (CR) with the 5G waveform to sense the idle spectrum, which overcomes… More >

  • Open Access


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

    S. Velmurugan1,*, P. Ezhumalai2, E. A. Mary Anita3

    Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1093-1108, 2022, DOI:10.32604/iasc.2022.026308

    Abstract 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… More >

  • Open Access


    Cognitive Radio Networks Using Intelligent Reflecting Surfaces

    Raed Alhamad*

    Computer Systems Science and Engineering, Vol.43, No.2, pp. 751-765, 2022, DOI:10.32604/csse.2022.021932

    Abstract In this article, we optimize harvesting and sensing duration for Cognitive Radio Networks (CRN) using Intelligent Reflecting Surfaces (IRS). The secondary source harvests energy using the received signal from node A. Then, it performs spectrum sensing to detect Primary Source PS activity. When PS activity is not detected, The Secondary Source SS transmits data to Secondary Destination SD where all reflected signals on IRS are in phase at SD. We show that IRS offers 14, 20, 26, 32, 38, 44, 50 dB enhancement in throughput using M = 8, 16, 32, 64, 128, 256, 512 reflectors with respect to CRN without IRS. We… More >

  • Open Access


    Detection of Attackers in Cognitive Radio Network Using Optimized Neural Networks

    V. P. Ajay1,*, M. Nesasudha2

    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 193-204, 2022, DOI:10.32604/iasc.2022.024839

    Abstract Cognitive radio network (CRN) is a growing technology targeting more resourcefully exploiting the available spectrum for opportunistic network usage. By the concept of cognitive radio, the wastage of available spectrum reduced about 30% worldwide. The key operation of CRN is spectrum sensing. The sensing results about the spectrum are directly proportional to the performance of the network. In CRN, the final result about the available spectrum is decided by combing the local sensing results. The presence or participation of attackers in the network leads to false decisions and the performance of the network will be degraded. In this work, an… More >

  • Open Access


    Spectral Vacancy Prediction Using Time Series Forecasting for Cognitive Radio Applications

    Vineetha Mathai*, P. Indumathi

    Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1729-1746, 2022, DOI:10.32604/iasc.2022.024234

    Abstract An identification of unfilled primary user spectrum using a novel method is presented in this paper. Cooperation among users with the utilization of machine learning methods is analyzed. Learning methods are applied to construct the classifier, which selects the suitable fusion algorithm for the considered environment so that the out of band sensing is performed efficiently. Sensing performance is looked into with the existence of fading and it is observed that sensing performance degrades with fading which coincides with earlier findings. From the simulation, it can be inferred that Weibull fading outperforms all the other fading models considered. To accomplish… More >

  • Open Access


    Non-Cooperative Learning Based Routing for 6G-IoT Cognitive Radio Network

    Tauqeer Safdar Malik1,*, Kaleem Razzaq Malik1, Muhammad Sanaullah2, Mohd Hilmi Hasan3, Norshakirah Aziz3

    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 809-824, 2022, DOI:10.32604/iasc.2022.021128

    Abstract Cognitive Radio Network (CRN) has turn up to solve the issue of spectrum congestion occurred due to the wide spread usage of wireless applications for 6G based Internet of Things (IoT) network. The Secondary Users (SUs) are allowed to access dynamically the frequency channels owned by the Primary Users (PUs). In this paper, we focus the matter of contention of routing in multi hops setup by the SUs for a known destination in the presence of PUs. The traffic model for routing is generated on the basis of Poison Process of Markov Model. Every SU requires to reduce the end-to-end… More >

  • Open Access


    Cross Layer QoS Aware Scheduling based on Loss-Based Proportional Fairness with Multihop CRN

    K. Saravanan1,*, G. M. Tamilselvan2, A. Rajendran3

    Computer Systems Science and Engineering, Vol.42, No.3, pp. 1063-1077, 2022, DOI:10.32604/csse.2022.020789

    Abstract As huge users are involved, there is a difficulty in spectrum allocation and scheduling in Cognitive Radio Networks (CRNs). Collision increases when there is no allocation of spectrum and these results in huge drop rate and network performance degradation. To solve these problems and allocate appropriate spectrum, a novel method is introduced termed as Quality of Service (QoS) Improvement Proper Scheduling (QIPS). The major contribution of the work is to design a new cross layer QoS Aware Scheduling based on Loss-based Proportional Fairness with Multihop (QoSAS-LBPFM). In Medium Access Control (MAC) multi-channel network environment mobile nodes practice concurrent broadcast between… More >

  • Open Access


    Energy-Efficient Scheduling for a Cognitive IoT-Based Early Warning System

    Saeed Ahmed1,2, Noor Gul1,3, Jahangir Khan4, Junsu Kim1, Su Min Kim1,*

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5061-5082, 2022, DOI:10.32604/cmc.2022.023639

    Abstract Flash floods are deemed the most fatal and disastrous natural hazards globally due to their prompt onset that requires a short prime time for emergency response. Cognitive Internet of things (CIoT) technologies including inherent characteristics of cognitive radio (CR) are potential candidates to develop a monitoring and early warning system (MEWS) that helps in efficiently utilizing the short response time to save lives during flash floods. However, most CIoT devices are battery-limited and thus, it reduces the lifetime of the MEWS. To tackle these problems, we propose a CIoT-based MEWS to slash the fatalities of flash floods. To extend the… More >

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