Power Aware Spectrum sensing and Sleep Scheduling Technique for Cognitive Radio Networks
Keywords:
Cognitive, SU, CRSN, IoT, SpectrumAbstract
In a cognitive radio networks (CRN), the secondary users (SUs) opportunistically access the channels where the primary users (PUs) are not present. In existing spectrum sensing techniques, the sensing interval is mostly static and the final spectrum sensing decision becomes inaccurate due to incorrect sleep schedules of SUs. To resolve these issues, this paper proposes a power aware spectrum sensing and sleep scheduling (PASS) technique for CRN. In this technique, the spectrum sensing intervals (SIs) of SUs are adaptively determined based on the required transmission power and battery capacity of SUs. The correctness of cooperative spectrum sensing decisions is validated at the fusion center, based on which the sleeping schedules of the SUs are determined. The proposed technique is implemented in NS2 and simulation outcomes show that PASS attains higher probability of successful detection and throughput with reduced energy consumption.
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ZilongJin ,Yu Qiao ,Alex Liu, and Lejun Zhang, "EESS: An Energy-Efficient Spectrum Sensing Method by Optimizing Spectrum Sensing Node in Cognitive Radio Sensor Networks", Hindawi, Wireless Communications and Mobile Computing ,Volume 2018, Article ID 9469106, 11 pages, 2018.
Ji Wang, Ing-Ray Chen, Jeffrey J.P. Tsai, Ding-Chau Wang, "Trust-based Mechanism Design for Cooperative Spectrum Sensing in Cognitive Radio Networks", Computer Communications, 2017.
Zhiguo Sun, Zhenyu Xu, Zengmao Chen, Xiaoyan Ning and Lili Guo, "Reputation-Based Spectrum Sensing Strategy Selection in Cognitive Radio Ad Hoc Networks", Sensors, 2018.
Adele Khalunezhad, Neda Moghim, Behrouz ShahgholiGhahfarokhi, "Trust-based multi-hop cooperative spectrum sensing in cognitive radio networks", Elsevier,Journal of Information Security and Applications 42 (2018) 29–35, 2018.
Yuan Gao, Zhixiang Deng, Dongmin Choi, Chang Choi, "Combined pre-detection and sleeping for energy-efficient spectrum sensing in cognitive radio networks", J. Parallel Distrib. Comput.,2017.
Zan Li,Boyang Liu, Jiangbo Si and Fuhui Zhou, "Optimal Spectrum Sensing Interval in Energy-Harvesting Cognitive Radio Networks", IEEE, 2017.
XianghuiCao,XiangweiZhou,Lu Liu and Yu Cheng, "Energy-Efficient Spectrum Sensing for Cognitive Radio Enabled Remote State Estimation over Wireless Channels", IEEE, 2014.
M Ramchandran and E N Ganesh,”Energy Efficient and Interference-aware Spectrum Sensing Technique for Improving the Throughput in Cognitive Radio Networks”, IOP Conference Series: Materials Science and Engineering 993 (2020) 012092
Abbass Nasser, Hussein Al Haj Hassan, JadAbouChaaya, Ali Mansour and Koffi-Clément Yao,”Spectrum Sensing for Cognitive Radio: Recent Advances and Future Challenge”, Sensors, March 2021
Fabrício B. S. de Carvalho, Waslon T. A. Lopes, Marcelo S. Alencar,”Performance of Cognitive Spectrum Sensing Based on Energy Detector in Fading Channels”, International Conference on Communication, Management and Information Technology (ICCMIT),Procedia Computer Science, Elsevier,2015
Yasmin Hassan,Mohamed El-Tarhuni, and Khaled Assaleh,”Learning-Based Spectrum Sensing for Cognitive Radio Systems”, Journal of Computer Networks and Communications,Hindawi, 2012
M Ramchandran and E N Ganesh,”MBSO Algorithm For Handling Energy-Throughput Trade-Off In Cognitive Radio Networks”, The Computer Journal, Oxford, May 2021
Daniela Mercedes Martínez Plataa, Ángel Gabriel Andrade Reátiga,” Evaluation of energy detection for spectrum sensing based on the dynamic selection of detection-threshold”, International Meeting of Electrical Engineering Research (ENIINVIE), Elsevier, 2012
Ranga, K. K. ., Nagpal, C. K. ., & Vedpal, V. (2023). Trip Planner: A Big Data Analytics Based Recommendation System for Tourism Planning. International Journal on Recent and Innovation Trends in Computing and Communication, 11(3s), 159–174. https://doi.org/10.17762/ijritcc.v11i3s.6176
Kamau, J., Goldberg, R., Oliveira, A., Seo-joon, C., & Nakamura, E. Improving Recommendation Systems with Collaborative Filtering Algorithms. Kuwait Journal of Machine Learning, 1(3). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/134
Juneja, V., Singh, S., Jain, V., Pandey, K. K., Dhabliya, D., Gupta, A., & Pandey, D. (2023). Optimization-Based Data Science for an IoT Service Applicable in Smart Cities. In Handbook of Research on Data-Driven Mathematical Modeling in Smart Cities (pp. 300–321). IGI Global.
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