Hybrid Cooperative Spectrum Structured (HCSS) Approach for Adaptive Routing in Cognitive Radio Ad Hoc Networks
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Cognitive radio networks provide an important function in the efficient use of the radio spectrum. Therefore, dedicated cognitive radio ad hoc networks (CRAHNs) are expected to improve communication performance in a multihop network that requires a dedicated routing protocol that considers the dynamic mobility of secondary user nodes on the basis of the random availability of primary node channels. To enhance routing in the CRAHNs environment, in this paper, a novel Hybrid Cooperative Spectrum Structured (HCSS) approach that combines cooperative spectrum sensing and spectrum-aware routing protocols can be effective in appropriate decision making for routing packets in such a network. This approach integrates the strengths of Spectrum-Aware Semi-Structured Routing (SSR) and Spectrum-Aware Routing Protocol (SARP) to enable adaptive and efficient routing in dynamic CRAHN environments. The results showed that the proposed HCSS routing protocol increased the user demand while ensuring the quality of service (QoS) requirements by achieving an average throughput of 5164.55 Kbps, exceeding the SARP and SSR, which recorded 4585.56 Kbps and 4194.66 Kbps, respectively. The PDR for HCSS was 98.77%, which was significantly higher than that for SARP (95.60%), and the SSR was 90.15%, indicating better reliable connectivity in delivering packets to their final destinations.
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S. Nayak and R. Patgiri, “6G Communication Technology: A Vision on Intelligent Healthcare,” in Health Informatics: A Computational Perspective in Healthcare, 2021, pp. 1–18. doi: 10.1007/978-981-15-9735-0_1.
Q. V. Khanh, N. V. Hoai, L. D. Manh, A. N. Le, and G. Jeon, “Wireless Communication Technologies for IoT in 5G: Vision, Applications, and Challenges,” Wireless Communications and Mobile Computing, vol. 2022, pp. 1–12, Feb. 2022. doi: 10.1155/2022/3229294.
A. Vidal-Balea, Ó. Blanco-Novoa, P. Fraga-Lamas, and T. M. Fernández-Caramés, “Developing the Next Generation of Augmented Reality Games for Pediatric Healthcare: An Open-Source Collaborative Framework Based on ARCore for Implementing Teaching, Training and Monitoring Applications,” Sensors, vol. 21, no. 5, p. 1865, Mar. 2021. doi: 10.3390/s21051865.
A. S. Hamood and S. B. Sadkhan, “Keywords Sensitivity Recognition of Military Applications in Secure CRNs Environments,” in 2017 Second Al-Sadiq International Conference on Multidisciplinary in IT and Communication Science and Applications (AIC-MITCSA), Dec. 2017, pp. 96–101. doi: 10.1109/AIC-MITCSA.2017.8722991.
S. B. A. Khaliq et al., “Defence against PUE attacks in ad hoc cognitive radio networks: a mean field game approach,” Telecommunication Systems, vol. 70, no. 1, pp. 123–140, May 2018. doi: 10.1007/s11235-018-0472-y.
S. M. Elghamrawy, “Security in Cognitive Radio Network: Defense against Primary User Emulation attacks using Genetic Artificial Bee Colony (GABC) algorithm,” Future Generation Computer Systems, Aug. 2018. doi: 10.1016/j.future.2018.08.022.
A. S. Hamood and S. B. Sadkhan, “Cognitive radio network security status and challenges,” in 2017 Annual Conference on New Trends in Information & Communications Technology Applications (NTICT), Mar. 2017, pp. 1–6. doi: 10.1109/NTICT.2017.7976105.
A. R. Uppala, C. V. Narasimhulu, and K. S. Prasad, “A Novel MAC Protocol with Fusion Center and Adaptive Full-Duplex Communication for Cognitive Radio Networks,” IETE Journal of Research, vol. 69, no. 7, pp. 4230–4242, Jul. 2021. doi: 10.1080/03772063.2021.1951368.
T. S. Malik and M. H. Hasan, “Reinforcement Learning-Based Routing Protocol to Minimize Channel Switching and Interference for Cognitive Radio Networks,” Complexity, vol. 2020, pp. 1–24, Aug. 2020. doi: 10.1155/2020/8257169.
H. B. Salameh et al., “Effective peer-to-peer routing in heterogeneous half-duplex and full-duplex multi-hop cognitive radio networks,” Peer-to-Peer Networking and Applications, vol. 14, no. 5, pp. 3225–3234, Jun. 2021. doi: 10.1007/s12083-021-01183-6.
R. Priyadarshi, R. R. Kumar, and Z. Ying, “Techniques employed in distributed cognitive radio networks: a survey on routing intelligence,” Multimedia Tools and Applications, Apr. 2024. doi: 10.1007/s11042-024-19054-6.
R. S. U. Suseela et al., “Cross layer protocol architecture for spectrum-based routing in cognitive radio networks,” IET Networks, Aug. 2023. doi: 10.1049/ntw2.12101.
M. M. Aslam et al., “Sixth Generation (6G) Cognitive Radio Network (CRN) Application, Requirements, Security Issues, and Key Challenges,” Wireless Communications and Mobile Computing, vol. 2021, pp. 1–18, Oct. 2021. doi: 10.1155/2021/1331428.
K. A. Darabkh et al., “Efficient Routing Protocol for Optimal Route Selection in Cognitive Radio Networks Over IoT Environment,” Wireless Personal Communications, vol. 129, no. 1, pp. 209–253, Nov. 2022. doi: 10.1007/s11277-022-10093-6.
S. Gudihatti, S. H. Manjula, and V. K. R, “Samcar: Spectrum Aware Multi-coefficient Based Shortest Anypath Routing In Cognitive Radio Networks,” International Journal of Electronics & Communication Engineering & Technology, vol. 10, no. 3, Jun. 2019. doi: 10.34218/IJECET.10.3.2019.004.
H. Al-Mahdi and Y. Fouad, “Design and analysis of routing protocol for cognitive radio ad hoc networks in Heterogeneous Environment,” International Journal of Electrical and Computer Engineering (IJECE), vol. 9, no. 1, pp. 341–351, Feb. 2019. doi: 10.11591/ijece.v9i1.pp341-351.
H. Tran-Dang and D. Kim, “Link-delay and spectrum-availability aware routing in cognitive sensor networks,” IET Communications, vol. 14, no. 20, pp. 3639–3651, Nov. 2020. doi: 10.1049/iet-com.2019.0994.
S. Akter and N. Mansoor, “A Spectrum Aware Mobility Pattern Based Routing Protocol for CR-VANETs,” in 2020 IEEE Wireless Communications and Networking Conference (WCNC), May 2020. doi: 10.1109/WCNC45663.2020.9120760.
M. Khasawneh, A. Azab, and A. Agarwal, “Towards Securing Routing Based on Nodes Behavior During Spectrum Sensing in Cognitive Radio Networks,” IEEE Access, vol. 8, pp. 171512–171527, 2020. doi: 10.1109/ACCESS.2020.3024662.
S. Akter et al., “An Efficient Routing Protocol for Secured Communication in Cognitive Radio Sensor Networks,” in 2020 IEEE Region 10 Symposium (TENSYMP), Jan. 2020. doi: 10.1109/TENSYMP50017.2020.9230638.
A. V. Dang et al., “Performance Analysis of Typical Routing Protocols for Cognitive Radio Ad Hoc Networks,” Journal of Communications, pp. 844–850, 2022. doi: 10.12720/jcm.17.10.844-850.
M. Sheela et al., “Secure Routing and Reliable Packets Transmission In MANET Using Fast Recursive Transfer Algorithm,” BJN, vol. 2024, pp. 78–87, Jun. 2024.
P. Phaswana and M. Velempini, “Spectrum-Aware Transitive On-Demand Routing Protocol for Military Cognitive Radio Ad Hoc Networks,” South African Computer Journal, vol. 35, no. 2, Dec. 2023. doi: 10.18489/sacj.v35i2.17389.
Q. M. Salih et al., “Dynamic channel estimation-aware routing protocol in mobile cognitive radio networks for smart IIoT applications,” Digital Communications and Networks, vol. 9, no. 2, pp. 367–382, Apr. 2023. doi: 10.1016/j.dcan.2023.01.019.
S. M. Kamruzzaman, A. Alghamdi, and M. Rahman, “Spectrum and energy aware multipath routing for cognitive radio ad hoc networks,” in 2014 International Conference on Information and Communication Technology Convergence (ICTC), Oct. 2014. doi: 10.1109/ICTC.2014.6983150.
J. Wang and C. Liu, “An imperfect spectrum sensing-based multi-hop clustering routing protocol for cognitive radio sensor networks,” Scientific Reports, vol. 13, no. 1, Mar. 2023. doi: 10.1038/s41598-023-31865-5.
M. Y. Darus and M. M. Hata, “Enhancing Cognitive Radio Ad Hoc Networks: Integration of Q-Routing into Clustering Protocols,” in Advances in Computer Science Research, pp. 488–497, Jan. 2024. doi: 10.2991/978-94-6463-589-8_45.
H. Mostafaei, “Energy-Efficient Algorithm for Reliable Routing of Wireless Sensor Networks,” IEEE Transactions on Industrial Electronics, vol. 66, no. 7, pp. 5567–5575, Jul. 2019. doi: 10.1109/TIE.2018.2869345.
T. Chin, M. Rahouti, and K. Xiong, “Applying software-defined networking to minimize the end-to-end delay of network services,” Applied Computing Review, vol. 18, no. 1, pp. 30–40, Apr. 2018. doi: 10.1145/3212069.3212072.
H. Chen et al., “Ultra-Reliable Low Latency Cellular Networks: Use Cases, Challenges and Approaches,” IEEE Communications Magazine, vol. 56, no. 12, pp. 119–125, Dec. 2018. doi: 10.1109/MCOM.2018.1701178.
J.-D. M. M. Biomo, “MBA-DRR: A Delay-Reducing Routing Protocol for Multi-Beam Directional Antennas in Multi-Hop Ad Hoc Networks,” Nov. 2019. doi: 10.22215/etd/2019-13707.
R. Ghosh et al., “Performance analysis based on probability of false alarm and miss detection in cognitive radio network,” International Journal of Wireless and Mobile Computing, vol. 20, no. 4, pp. 390–390, Jan. 2021. doi: 10.1504/IJWMC.2021.117530.
V. O. Nyangaresi, “AI-Driven Energy Forecasting Enhancing Smart Grid Efficiency with LSTM Networks,” EDRAAK Journal, vol. 2024, pp. 32–38, Mar. 2024. doi: 10.70470/EDRAAK/2024/005.
S. M. Khaniabadi et al., “An intelligent sustainable efficient transmission internet protocol to switch between User Datagram Protocol and Transmission Control Protocol in IoT computing,” Expert Systems, Sep. 2022. doi: 10.1111/exsy.13129.
K. Guleria and A. K. Verma, “Comprehensive review for energy efficient hierarchical routing protocols on wireless sensor networks,” Wireless Networks, vol. 25, no. 3, pp. 1159–1183, Mar. 2018. doi: 10.1007/s11276-018-1696-1.