ICTACT Journal on Communication Technology

Open Access Open Access  Restricted Access Subscription Access

A SURVEY ON OPTIMIZATION OF TRANSMIT POWER IN COGNITIVE RADIO NETWORKS THROUGH NATURE INSPIRED COMPUTATIONAL INTELLIGENCE TECHNIQUES


Affiliations
1 I.K Gujral Punjab Technical University, India
2 Maharaja Ranjit Singh Punjab Technical University, India
 

   Subscribe/Renew Journal


Cognitive radio (CR) is a field which is gaining a lot of interest of researchers in around the whole world. It is also termed as nextgen technology as it aims to solve the problem of unutilized electromagnetic spectrum assigned to licensed users. With increasing wireless devices there is a demand of more spectrum resources and cognitive radio can help in making available the unused or underused spectrum for real time communication. When number of cognitive users communicate together at the same time there exists the problem of interference due to transmit power of all the devices to the licensed users. So there is a need of controlling power of transmission for cognitive users so that they may not interfere with Primary licensed users and maintain QoS with other cognitive users. The power control of a large number of devices together can be done by the application of computational intelligence techniques. In this paper three nature inspired computational intelligence techniques like GA, PSO and ABC for optimization of transmit power in cognitive radio networks are discussed.

Keywords

Cognitive Radio, Secondary User, Primary User, Efficient Utilization of Spectrum, Power Allocation Strategy, Dynamic Spectrum Access.
Subscription Login to verify subscription
User
Notifications
Font Size

  • D. Maresch and J. Gartner, “Make Disruptive Technological Change Happen-The Case of Additive Manufacturing”, Technological Forecasting and Social Change, Vol. 155, pp. 1-15, 2020.

  • M.E. Ahmed and H. Kim, “DDoS Attack Mitigation in Internet of Things Using Software Defined Networking”, Proceedings of International Conference on Big Data Computing Service and Applications, pp. 6-9, 2017.

  • T. Karthikeyan and K. Praghash, “An Improved Task Allocation Scheme in Serverless Computing using Gray Wolf Optimization (GWO) based Reinforcement Learning (RIL) Approach”, Wireless Personal Communications, Vol. 117, No. 3, pp. 1-19, 2020.

  • S. Kannan, G. Dhiman, and M. Gheisari, “Ubiquitous Vehicular Ad-Hoc Network Computing using Deep Neural Network with IoT-Based Bat Agents for Traffic Management”, Electronics, Vol. 10, no. 7, pp. 785-796, 2021.

  • L. Atzori and A. Iera, “The Internet of Things: A Survey”, Computer Networks, Vol. 54, No. 15, pp. 2787-2805, 2010.

  • M. El Hajj, M. Chamoun, A. Fadlallah and A. Serhrouchni, “Analysis of Authentication Techniques in Internet of Things (IoT)”, Proceedings of International Conference on Cyber Security in Networking, pp. 1-3, 2017.

  • L. Celia and Y. Cungang, “Authenticated Key Management Protocols for Internet of Things”, Proceedings of International Conference on Internet of Things, pp. 126-129, 2018.

  • Steve R. Gunn and Basel Halak, “Lightweight PUF-Based Authentication Protocol for IoT Devices”, Proceedings of International Conference on Verification and Security, pp. 38-43, 2018.

  • T. Kothmayr, C. Schmitt, W. Hu, M. Br and G. Carle, “DTLS based Security and Two-Way Authentication for the Internet of Things”, Ad Hoc Networks, Vol. 11, No. 8, pp. 2710-2723, 2013.

  • Amiya Kumar, Suraj Sharma, Deepak Puthal, AbhishekPandey and Rathin Shit, “Secure Authentication Protocol for IoT Architecture”, Proceedings of International Conference on Information Technology, pp. 220-224, 2017.

  • B. Hong Jin, Walter Guttmann and Dog Seong Kim, “A Framework for Automating Security Analysis of the Internet of Things”, Journal of Network and Computer Applications, Vol. 83, pp. 12-27, 2017.

  • J. Jiang and L. Shu, “Authentication protocols for Internet of Things: A Comprehensive Survey”, Security and Communication Networks, Vol. 2017, pp. 1-18, 2017.

  • M.N. Aman, M.H. Basheer and B. Sikdar, “Two-Factor Authentication for IoT with Location Information”, IEEE Internet of Things, Vol. 6, No. 2, pp. 3335-3351, 2018.

  • P. Gope and B. Sikdar, “Lightweight and Privacy-Preserving Two-Factor Authentication Scheme for IoT Devices”, IEEE Internet of Things, Vol. 6, No. 1, pp. 580-589, 2018.

  • N.G. Veerappan Kousik, K. Suresh, R. Patan and A.H. Gandomi, “Improving Power and Resource Management in Heterogeneous Downlink OFDMA Networks”, Information, Vol. 11, No. 4, pp. 203-216, 2020.

  • R. Amin, N. Kumar and G.P. Biswas, “A Light Weight Authentication Protocol for IoT-Enabled Devices in Distributed Cloud Computing Environment”, Future Generation Computer Systems, Vol. 78, pp. 1005-1019, 2018.

  • Yong Li and Lijun Liao, “Flexible Certificate Revocation List for Efficient Authentication in IoT”, Proceedings of International Conference on Internet of Things, pp. 1-7, 2018.

  • P. Mahalle, S. Babar, N. Prasad and R. Prasad, “Identity Management Framework Towards Internet of Things (IoT): Roadmap and Key Challenges”, Proceedings of International Conference on Virtual Local Area Network Technology and Applications, pp. 430-439, 2010.

  • K.W. Kim, Y.H. Han and S.G. Min, “An Authentication and Key Management Mechanism for Resource Constrained Devices in IEEE 802.11-based IoT Access Networks”, Sensors, Vol. 17, No. 10, pp. 1-20, 2017.

  • G. Dhiman, K. Somasundaram and K. Sharma, “Nature Inspired-Based Approach for Automated Cyberbullying Classification on Multimedia Social Networking”, Mathematical Problems in Engineering, Vol. 2021, pp. 1- 21, 2021.

  • P.K. Dhillon and S. Kalra, “Multi-Factor User Authentication Scheme for IoT-Based Healthcare Services”, Journal of Reliable Intelligent Environments, Vol. 4, No. 3, pp. 141-160, 2018.

  • M. Wazid, A.K. Das, V. Odelu and N. Kumar, “Design of Secure User Authenticated Key Management Protocol for Generic IoT Networks, IEEE Internet of Things, Vol. 5, No. 1, pp. 269-282, 2017.

  • N.V. Kousik, P. Johri and M.J. Divan, “Analysis on the Prediction of Central Line-Associated Bloodstream Infections (CLABSI) using Deep Neural Network Classification”, Proceedings of International Conference on Computational Intelligence and Its Applications in Healthcare, pp. 229-244, 2020.

  • T. Song, R. Li, B. Mei, J. Yu, X. Xing and X. Cheng, “A Privacy Preserving Communication Protocol for IoT Applications in Smart Homes”, IEEE Internet of Things, Vol. 4, No. 6, pp. 1844-1852, 2017.

  • A.K. Das, P. Sharma, S. Chatterjee and J.K. Sing, “A Dynamic Password-Based User Authentication Scheme for Hierarchical Wireless Sensor Networks”, Journal of Network and Computer Applications, Vol. 35, No. 5, pp. 1646-1656, 2012.

  • P. Johri, “Improved Energy Efficient Wireless Sensor Networks using Multicast Particle Swarm Optimization”, Proceedings of International Conference on Innovative Advancement in Engineering and Technology, pp. 1-6, 2020.

  • O.R. Vincent and O. Folorunso, “A Descriptive Algorithm for Sobel Image Edge Detection”, Proceedings of International Conference on Informing Science and IT Education, pp. 97-107, 2009.

  • J.H. Lin, C.W. Chou and C.H. Yang, “A Chaotic Levy Flight Bat Algorithm for Parameter Estimation in Nonlinear Dynamic Biological Systems”, Computer and Information Technology, Vol. 2, No. 2, pp. 56-63, 2012.

  • M. Abdalla, P. Fouque and D. Pointcheval, “Password Based Authenticated Key Exchange in the Three-Party Setting”, Proceedings of International Conference on Theory and Practice in Public Key Cryptography, pp. 65-84, 2005.


Abstract Views: 216

PDF Views: 128




  • A SURVEY ON OPTIMIZATION OF TRANSMIT POWER IN COGNITIVE RADIO NETWORKS THROUGH NATURE INSPIRED COMPUTATIONAL INTELLIGENCE TECHNIQUES

Abstract Views: 216  |  PDF Views: 128

Authors

Paurav Goel
I.K Gujral Punjab Technical University, India
Avtar Singh
I.K Gujral Punjab Technical University, India
Ashok Goel
Maharaja Ranjit Singh Punjab Technical University, India

Abstract


Cognitive radio (CR) is a field which is gaining a lot of interest of researchers in around the whole world. It is also termed as nextgen technology as it aims to solve the problem of unutilized electromagnetic spectrum assigned to licensed users. With increasing wireless devices there is a demand of more spectrum resources and cognitive radio can help in making available the unused or underused spectrum for real time communication. When number of cognitive users communicate together at the same time there exists the problem of interference due to transmit power of all the devices to the licensed users. So there is a need of controlling power of transmission for cognitive users so that they may not interfere with Primary licensed users and maintain QoS with other cognitive users. The power control of a large number of devices together can be done by the application of computational intelligence techniques. In this paper three nature inspired computational intelligence techniques like GA, PSO and ABC for optimization of transmit power in cognitive radio networks are discussed.

Keywords


Cognitive Radio, Secondary User, Primary User, Efficient Utilization of Spectrum, Power Allocation Strategy, Dynamic Spectrum Access.

References