In future multi-hop wireless networks like 5G and B5G, efficient large-scale video sharing and data dissemination are expected to rely heavily on multicast routing and Cognitive Radio (CR) technology. While multicast routing is efficient when the network always has access to the spectrum, the dynamic nature of Primary User (PU) activities, heterogeneous spectrum across the CR Network (CRN), and PU access priority make it challenging to implement efficient multicast routing protocols in CRNs. This paper proposes a hierarchical multicast routing mechanism for multi-hop CRNs that exploits the Shortest Path Tree (SPT) and Minimum Spanning Tree (MST) concepts. The proposed multicast routing mechanism consists of tree construction and channel assignment algorithms. The tree-construction algorithm models the network topology as a multicast tree rooted at the CR source and spanning all the CR nodes. Based on the constructed tree, the channel assignment algorithm employs the Probability Of Success (POS) metric to assign channels to the various layers defined by the constructed SPT or MST, ensuring that the most reliable channel is used for the multi-hop multicast transmissions. Simulation experiments are conducted to evaluate the mechanism’s effectiveness, revealing significant improvements in throughput and Packet Delivery Rate (PDR) compared to state-of-the-art protocols under different network conditions. The simulations also show that the SPT-based mechanism outperforms the MST-based mechanism in terms of throughput but has a higher tree construction complexity.

References

1. Al-Refaei A. and Baidas M., “Network Sum-Rate Maximization for NOMA-Based Multicast Cognitive Radio Networks with SWIPT-Enabled Relays,” in Proceedings of the International Symposium on Networks, Computers and Communications, Dubai, pp. 1-7, 2021. DOI:10.1109/ISNCC52172.2021.9615634
2. Al-rubaye M., “Multi-layer Mechanism for Multicast Routing in Multihop Cognitive Radio Networks,” Thesis-Arabic Digital Library, Yarmouk University, 2016.
3. Alipour-Fanid A. Dabaghchian M., Arora R., Zeng K., “Multiuser Scheduling in Centralized Cognitive Radio Networks: A Multi-Armed Bandit Approach,” IEEE Transactions on Cognitive Communications and Networking, vol. 8, no. 2, pp. 1074-1091, 2022. DOI: 10.1109/TCCN.2022.3149113
4. Almasoud H. and Kamal A., “Energy Efficient Multicasting in Cognitive Radio Networks,” in Proceedings of the IEEE International Conference on Communications, Paris, pp. 1-6, 2017. DOI: 10.1109/ICC.2017.7996474
5. Alwan S. Fajjari I., Aitsaadi N., and Kaddour M., “5G: Optimization of Multicast Routing and Wireless Resource Allocation in D2D Communications,” in Proceedings of the IEEE International Conference on Communications, Montreal, pp. 1-6, 2021. DOI: 10.1109/ICC42927.2021.9500416
6. Baig I., Ul Hasan H., Valsalan P., and Zghaibeh M., “Energy Efficient Multicast Communication in Cognitive Radio Wireless Mesh Network,” Sensors (Basel, Switzerland), vol. 22, vol. 15, 2022. https://doi.org/10.3390/s22155601
7.  Balakrishnan V., Schaum’s Outline of Theory and Problems of Graph Theory, McGraw Hill, 1997.
8. Bhattacharjee S., Acharya T., and Bhattacharya U., “Energy-Efficient Multicasting in Hybrid Cognitive Small Cell Networks: A Cross-Layer Approach,” IEEE/ACM Transactions on Networking, vol. 28, no. 1, pp. 262-274, 2020. DOI: 10.1109/TNET.2019.2962309
9. Cacciapuoti A., C Calcagno., M Caleffi., L Paura “CAODV: Routing in Mobile Ad-Hoc Cognitive Radio Networks,” in Proceedings of the IFIP Wireless Days, Venice, pp. 1-5, 2010. DOI: 10.1109/WD.2010.5657754
10. Chen B., Gao Z., Yang M.,
    Ning Q., Yu C., Pan W.,
    Nian M., and Xie D., “Packet Multicast in Cognitive Radio Ad Hoc Networks: A Method Based on Random Network Coding,” IEEE Access, vol. 6, pp. 8768-8781, 2018. DOI: 10.1109/ACCESS.2018.2805762
11. Chen P., Cheng S., Ao W., and Chen K., “Multi-path Routing with End-To-End Statistical Qos Provisioning in Underlay Cognitive Radio Networks, in Proceedings of the IEEE Conference on Computer Communications Workshops, Shanghai, pp. 7-12, 2011. DOI: 10.1109/INFCOMW.2011.5928921
12. Chinnathampy S., Thangavelu A., and Muthukumaran N., “Performance Analysis of Efficient Spectrum Utilization in Cognitive Radio Networks by Dynamic Spectrum Access and Artificial Neuron Network Algorithms,” The International Arab Journal of Information Technology, vol. 19, no. 2, pp. 224-229, 2022. https://doi.org/10.34028/iajit/19/2/9
13. Ding L., Melodia T., Batalama S., Matyjas S., and Medley M., “Cross-Layer Routing and Dynamic Spectrum Allocation in Cognitive Radio Ad Hoc Networks,” IEEE Transactions on Vehicular Technology, vol. 59, no. 4, pp. 1969-1979, 2010. DOI: 10.1109/TVT.2010.2045403
14. Geng L., Liang Y., and Chin F., “Network Coding for Wireless Ad Hoc Cognitive Radio Networks,” in Proceedings of the IEEE 18^th International Symposium on Personal, Indoor and Mobile Radio Communications, Athens, pp. 1-5, 2007. DOI: 10.1109/PIMRC.2007.4394568
15. Halloush R., Salaimeh R., and Al-Dalqamoni R., “Availability-Aware Channel Allocation for Multi-Cell Cognitive Radio 5G Networks,” IEEE Transactions on Vehicular Technology, vol. 71, no. 4, pp. 3931-3947, 2022. DOI: 10.1109/TVT.2022.3148063
16. Hu D., Mao S., and Reed J., “On Video Multicast in Cognitive Radio Networks,” in Proceedings of the IEEE INFOCOM, Rio de Janeiro, pp. 2222-2230, 2009. DOI: 10.1109/INFCOM.2009.5062147
17. Huang X., Tang X., and Hu F., “Dynamic Spectrum Access for Multimedia Transmission Over Multi-User, Multi-Channel Cognitive Radio Networks,” IEEE Transactions on Multimedia, vol. 22, no.1, pp. 201-214, 2020. DOI: 10.1109/TMM.2019.2925960
18. Huang Y. Li Q., Ma M., and Zhang S., “Robust Multicast Beamforming for Spectrum Sharing-Based Cognitive Radios,” IEEE Transactions on Signal Processing, vol. 60, no.1, pp. 527-533, 2012. DOI: 10.1109/TSP.2011.2169061
19. Jayaram M. and Anjaneyulu B., “Opportunistic Cooperative Cognitive Multicast Routing Protocol for Ad Hoc Networks,” in Proceedings of the International Conference on Wireless Communications Signal Processing and Networking (WiSPNET), Chennai, pp. 236-243, 2022. DOI: 10.1109/WiSPNET54241.2022.9767116
20. Jin J., Xu H., and Li B., “Multicast Scheduling with Cooperation and Network Coding in Cognitive Radio Networks,” in Proceedings of the IEEE INFOCOM, San Diego, pp. 1-9, 2010. DOI: 10.1109/INFCOM.2010.5461917
21. Kamal S. and Nazar B., “Performance Evaluation of Combining SMT-ETX Metric with POS Scheme for Implementing Multilayer Multicast Mobile Ad Hoc Network (MANET) CRN,” IET Communications, vol. 14, no. 4, pp. 610-618, 2020. https://doi.org/10.1049/iet-com.2019.0595
22. Lavanya P., Reddy V., and Prasad A., “Research and Survey on Multicast Routing Protocols for MANETs,” in Proceedings of the 2^nd International Conference on Electrical, Computer and Communication Technologies (ICECCT), Coimbatore, pp. 1-4, 2017. DOI: 10.1109/ICECCT.2017.8117929
23. Tashman D., Hamouda W., “Physical-Layer Security for Cognitive Radio Networks over Cascaded Rayleigh Fading Channels,” in Proceedings of GLOBECOM IEEE Global Communications Conference, Taipei, pp. 1-6, 2020. DOI: 10.1109/GLOBECOM42002.2020.9348134
24. Olagbegi B., “A Review of the Energy Efficient and Secure Multicast Routing Protocols for Mobile Ad Hoc Networks,” in Proceedings of International Journal on Applications of Graph Theory in Wireless ad hoc Networks and Sensor Networks, vol. 2, no. 2, pp. 1-15, 2010. https://doi.org/10.48550/arXiv.1006.3366
25. Phaswana P. and Velempini M., “Paper Spectrum-Aware Transitive Multicast on Demand Distance Vector Routing for Military Cognitive Radio Ad Hoc Networks,” in Proceedings of IEEE AFRICON, Accra, pp. 1-4, 2019. DOI: 10.1109/AFRICON46755.2019.9133934
26. Qadir J., Baig A., Ali A., and Shafi Q., “Multicasting in Cognitive Radio Networks: Algorithms, Techniques and Protocols,” Journal of Network and Computer Applications, vol. 45, pp. 44-61, 2014. https://doi.org/10.1016/j.jnca.2014.07.024
27. Ramakrishnan S., Sevalaiappan L., and Chandran S., “Traffic-Aware Clustering Scheme for MANET using Modified Elephant Herding Optimization Algorithm,” The International Arab Journal of Information Technology, vol. 18, no. 5, pp. 694-703, 2021. https://doi.org/10.34028/iajit/18/5/9
28. Ren W., Xiao X., and Zhao Q., “Minimum-Energy Multicast Tree in Cognitive Radio Networks,” in Proceedings of the 43^rd Asilomar Conference on Signals, Systems and Computers, Pacific Grove, pp. 312-316, 2009. DOI: 10.1109/ACSSC.2009.5470092
29. Samra R., “Intelligent Multicast Routing for Multimedia Over Cognitive Radio Networks: A Probabilistic Approach,” Master Thesis-Arabic Digital Library-Yarmouk University, 2018.
30. Singaravelan M. and Mariappan B., “Reinforcement Energy Efficient Ant Colony Optimization of Mobile Ad Hoc Multipath Routing Performance Enhancement,” The International Arab Journal of Information Technology, vol. 19, no. 2, pp. 224-229, 2022. https://doi.org/10.34028/iajit/19/2/6
31. Singh J., and Rai M., “CROP: Cognitive Radio Routing Protocol for Link Quality Channel Diverse Cognitive Networks,” Journal of Network and Computer Applications, vol. 104, pp. 48-60, 2018. https://doi.org/10.1016/j.jnca.2017.12.014
32. Wu B. and Chao K., Spanning Trees and Optimization Problems, CRC PRESS, 2004.
33. Yim Y., Lee J., Ko N., Park H., “Localized Optimal Real-time Multicast Routing in Geolocation-based Wireless Sensor Networks,” in Proceedings of the International Conference on Information and Communication Technology Convergence, Jeju, pp. 1777-1779, 2020. DOI: 10.1109/ICTC49870.2020.9289617
34. Yun L., Fengxie Q., Zhanjun L., Hongcheng Z., “Cognitive Radio Routing Algorithm Based on The Smallest Transmission Delay,” in Proceedings of the 2^nd International Conference on Future Computer and Communication, Wuhan, pp. V2-306-V2-310, 2010. DOI: 10.1109/ICFCC.2010.5497417
35. Zakariya A., Tayel A., Rabia S., and Mansour A., “Modeling and Analysis of Cognitive Radio Networks with Different Channel Access Capabilities of Secondary Users,” Simulation Modelling Practice and Theory, vol. 103, pp. 102096, 2020. https://doi.org/10.1016/j.simpat.2020.102096