The week's pick
Reseach Article
Impact of Packet Size on the Performance Evaluation of Routing Protocols in Cognitive Radio Networks
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 176 - Number 36 |
| Year of Publication: 2020 |
| Authors: Sopan Talekar, Sujatha Terdal |
10.5120/ijca2020920506
|
Sopan Talekar, Sujatha Terdal . Impact of Packet Size on the Performance Evaluation of Routing Protocols in Cognitive Radio Networks. International Journal of Computer Applications. 176, 36 ( Jul 2020), 28-32. DOI=10.5120/ijca2020920506
Abstract
Cognitive Radio Network (CRN) is a next generation of wireless communication. Cognitive Radio (CR) is able to detect the unoccupied spectrum and allow to secondary users (SU) also known as cognitive users (CU) to use it. In order to improve the performance of cognitive radio network, many research has been done in routing protocols. In this paper, the scenario of impact of packet size on two routing protocols are evaluated: i) Routing protocol with Machine Learning (ML) and ii) Routing protocol without Machine Learning (ML). While transmitting packet from sender to receiver, packet size plays an important role in the performance of network. The above two scenario have been implemented in NS2 network simulator. This paper analyses the impact of various packet size on routing protocol in cognitive radio network. Routing protocol with and without ML approach with various packet size is compared and evaluated performance parameters like delay, dropping ratio and packet delivery ratio.
References
- Mitola J, Maguire G.Q. Jr, “Cognitive radio: making software radios more personal”, Personal Communication, IEEE, Vol 6.no 4, pp 13-18 Aug 1999.
- Federal Communications Commission, Spectrum policy Task force Report, ET Docket No.03-222, Notice of Proose Rule making & order 2003.
- B. Wang, KJR Liu, “Advances in Cognitive Radio Networks: A Survey” IEEE Journal of Selected topics in signal processing, Vol.5.pp. 5-23, 2011.
- Moustafa Youssef, Mohamed Ibrahim, Mohamed Abdelatif, Lin Chen, and Athanasios V. Vasilakos, “Routing Metrics of Cognitive Radio Networks: A Survey”, IEEE Communications surveys & tutorials, Vol.16, Issue.1, pp.92-109, 2014.
- Amjad Ali, Muddesar Iqbal, Adeel Baig, Xingheng Wang, “Routing Techniques in Cognitive Radio Networks: A Survey”, International Journal of Wireless & Mobile Networks (IJWMN), Vol.3, No.3, June 2011.
- Shelly Salim and Sangman Moh, “On-demand routing protocols for cognitive radio ad hoc networks”, EURASIP Journal on Wireless Communication and Networking, Article ID: 10499, 2013. doi:doi.org/10.1186/1687-1499-2013-102.
- R.Rai, V.Tiwari and S.Kansal, “Comparison of routing protocol AODV and WCETT in Cognitive Radio Networks”, International Journal of Engineering Research & Technology (IJERT), Vol2, no.10, 2013.
- Sajjad Rahmani, M. Afshin Hemmatyar “Performance Evaluation of Transport Layer in Cognitive Radio Ad-hoc Networks”, International Journal of Computer Applications (0975 – 8887) Volume 103 – No.5, October 2014.
- Yuting Wang, Guoqiang Zheng, Huahong Ma, Yang Li, and Jishun Li, “A Joint Channel Selection and Routing Protocol for Cognitive Radio Network”, Wireless Communication and Mobile Computing, Volume 2018, Article ID 684864, 7 pages. 2018. doi:doi.org/10.1155/2018/6848641
- M. C. Oto and O. B. Akan, "Energy-Efficient Packet Size Optimization for Cognitive Radio Sensor Networks," in IEEE Transactions on M. C. Oto and O. B. Akan, "Energy-Efficient Packet Size Optimization for Cognitive Radio Sensor Networks," in IEEE Transactions on Wireless Communications, vol. 11, no. 4, pp. 1544-1553, April 2012.
- Mohammed Al-Medhwahi, Fazirulhisyam Hashim, Borhanuddin Mohd Ali and A. Sali “Impact of Packet Size in Adaptive Cognitive Radio Sensor Network” Wireless Communications and Mobile Computing Volume 2018, Article ID 3051204, 9 pages doi:doi.org/10.1155/2018/3051204.
- M. Al-Medhwahi, F. Hashim, B. M. Ali, and A. Sali, “Pliable cognitive MAC for heterogeneous adaptive cognitive radio sensor networks,” PLoS ONE, vol. 11, no. 6, p. e0156880, 2016.
- Richard S. Sutton and Andrew G. Barto. 2018. Reinforcement Learning: An Introduction. A Bradford Book, Cambridge, MA, USA.
- S. Talekar and S. Terdal, “Reinforcement Learning Based Channel Selection for Design of Routing Protocol in Cognitive Radio Network”, 2019 4th International Conference on Computational Systems and Information Technology for Sustainable Solutions (CSITSS), Bengaluru, India, 2019, pp. 1-6, doi:10.1109/CSITSS47250.2019.9031024.
- “Cognitive Radio Cognitive Network Simulator”. [Online]. Available: http://stuweb.ee.mtu.edu/~Ijialian/
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