International Journal of Computer Networks and Applications (IJCNA)

Published By EverScience Publications

ISSN : 2395-0455

International Journal of Computer Networks and Applications (IJCNA)

International Journal of Computer Networks and Applications (IJCNA)

Published By EverScience Publications

ISSN : 2395-0455

Improved Wolf Prey Inspired Protocol for Routing in Cognitive Radio Ad Hoc Networks

Author NameAuthor Details

J. Ramkumar, R. Vadivel

J. Ramkumar[1]

R. Vadivel[2]

[1]Department of Computer Science, VLB Janakiammal College of Arts and Science, Coimbatore, Tamil Nadu, India

[2]Department of Information Technology, Bharathiar University, Coimbatore, Tamil Nadu, India

Abstract

Fifth Generation (5G) technology has started providing the brand new facilities to the mobile communication world. With its enhanced performance and scalability, it has attracted many domains. Routing overhead in 5G networks is increased rapidly because of the complexity present in the route discovery process, where optimization in routing. Poor routing becomes a sophisticated and dynamic challenge in the 5G network. Hence, there exists a need for finding the best route in an optimized manner. This paper proposes an Improved Wolf Prey Inspired Protocol (IWPIP) for finding the ideal route in the dynamic environment like 5G based cognitive radio ad-hoc network. IWPIP focuses on finding the ideal route based on the reliability of route, shorter distance, and shorter hops that minimize the consumption of energy to increase the network lifetime. Before sending the data packets, routes are evaluated using a fitness function. IWPIP's efficiency has been demonstrated through comprehensive simulation, which resulted in promising outcomes in terms of throughput, packet delivery and drop ratio, delay, and energy consumption.

Index Terms

Optimization

Routing

Bio-Inspired

Energy

Delay

Cognitive Radio Ad Hoc Networks

Wolf Prey Inspired Protocol

Reference

  1. 1.
    F. Tang, H. Zhang, L. Fu and X. Li, "Distributed Stable Routing with Adaptive Power Control for Multi-Flow and Multi-Hop Mobile Cognitive Networks," IEEE Transactions on Mobile Computing, vol. 18, no. 12, pp. 2829-2841, 2019. https://doi.org/10.1109/TMC.2018.2885762
  2. 2.
    J. Singh and M. Rai, "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
  3. 3.
    H. Salameh, S. Otoum, M. Aloqaily, R. Derbas, I. Ridhawi and Y. Jararweh, "Intelligent jamming-aware routing in multi-hop IoT-based opportunistic cognitive radio networks", Ad Hoc Networks, vol. 98, p. 102035, 2020. https://doi.org/10.1016/j.adhoc.2019.102035
  4. 4.
    R. Yadav, R. Misra and D. Saini, "Energy aware cluster based routing protocol over distributed cognitive radio sensor network", Computer Communications, vol. 129, pp. 54-66, 2018. https://doi.org/10.1016/j.comcom.2018.07.020
  5. 5.
    I. Akyildiz, W. Lee and K. Chowdhury, "CRAHNs: Cognitive radio ad hoc networks", Ad Hoc Networks, vol. 7, no. 5, pp. 810-836, 2009. https://doi.org/10.1016/j.adhoc.2009.01.001
  6. 6.
    J.Ramkumar and R.Vadivel, "Improved frog leap inspired protocol (IFLIP) – for routing in cognitive radio ad hoc networks (CRAHN)", World Journal of Engineering, vol. 15, no. 2, pp. 306-311, 2018. https://doi.org/10.1108/WJE-08-2017-0260
  7. 7.
    J.Ramkumar and R.Vadivel, "CSIP—Cuckoo Search Inspired Protocol for Routing in Cognitive Radio Ad Hoc Networks", Advances in Intelligent Systems and Computing, Vol. 556, pp. 145-153, 2017. https://doi.org/10.1007/978-981-10-3874-7_14
  8. 8.
    J.Ramkumar and R.Vadivel, "Intelligent Fish Swarm Inspired Protocol (IFSIP) For Dynamic Ideal Routing in Cognitive Radio Ad-Hoc Networks", International Journal of Computing and Digital Systems, Vol. 10, pp. 2-11. 2020. https://journal.uob.edu.bh:443/handle/123456789/3961
  9. 9.
    X. Tang, J. Zhou, S. Xiong, J. Wang and K. Zhou, "Geographic Segmented Opportunistic Routing in Cognitive Radio Ad Hoc Networks Using Network Coding," IEEE Access, vol. 6, pp. 62766-62783, 2018. https://doi.org/10.1109/ACCESS.2018.2875566
  10. 10.
    R. Sahu, S. Sharma, M.A. Rizvi, "ZBLE: Zone Based Leader Election Energy Constrained AOMDV Routing Protocol", International Journal of Computer Networks and Applications, Vol. 6, no. 3, pp. 39-46, 2019. https://doi.org/10.22247/ijcna/2019/49643
  11. 11.
    J.Ramkumar and R.Vadivel, "Performance Modeling of Bio-Inspired Routing Protocols in Cognitive Radio Ad Hoc Network to Reduce End-to-End Delay", International Journal of Intelligent Engineering and Systems, Vol.12, No.1, pp. 221-231, 2019. https://doi.org/10.22266/ijies2019.0228.22
  12. 12.
    X. Jin, R. Zhang, J. Sun and Y. Zhang, "TIGHT: A Geographic Routing Protocol for Cognitive Radio Mobile Ad Hoc Networks", IEEE Transactions on Wireless Communications, vol. 13, no. 8, pp. 4670-4681, 2014. https://doi.org/10.1109/TWC.2014.2320950
  13. 13.
    H. Riasudheen, K. Selvamani, S. Mukherjee and I. Divyasree, "An efficient energy-aware routing scheme for cloud-assisted MANETs in 5G", Ad Hoc Networks, vol. 97, p. 102021, 2020. https://doi.org/10.1016/j.adhoc.2019.102021
  14. 14.
    A. Mesodiakaki, E. Zola, R. Santos and A. Kassler, "Optimal user association, backhaul routing and switching off in 5G heterogeneous networks with mesh millimeter wave backhaul links", Ad Hoc Networks, vol. 78, pp. 99-114, 2018. https://doi.org/10.1016/j.adhoc.2018.05.008
  15. 15.
    L. Martin, Dooley and K. Wong, "5G multi-layer routing strategies for TV white space secondary user access", IET Communications, vol. 13, no. 12, pp. 1801-1807, 2019. https://doi.org/10.1049/iet-com.2018.5848
  16. 16.
    Z. Li, Y. Hu, T. Hu and R. Ma, "PARS-SR: A scalable flow forwarding scheme based on Segment Routing for massive giant connections in 5G networks", Computer Communications, vol. 159, pp. 206-214, 2020. https://doi.org/10.1016/j.comcom.2020.05.014
  17. 17.
    F. Palmieri, "A Reliability and latency-aware routing framework for 5G transport infrastructures", Computer Networks, vol. 179, p. 107365, 2020. https://doi.org/10.1016/j.comnet.2020.107365
  18. 18.
    J. Mu, "An improved AODV routing for the zigbee heterogeneous networks in 5G environment", Ad Hoc Networks, vol. 58, pp. 13-24, 2017. https://doi.org/10.1016/j.adhoc.2016.12.002
  19. 19.
    M. Abolhasan, M. Abdollahi, W. Ni, A. Jamalipour, N. Shariati and J. Lipman, "A Routing Framework for Offloading Traffic From Cellular Networks to SDN-Based Multi-Hop Device-to-Device Networks", IEEE Transactions on Network and Service Management, vol. 15, no. 4, pp. 1516-1531, 2018. https://doi.org/10.1109/TNSM.2018.2875696
  20. 20.
    H. Rastegarfar, T. Svensson and N. Peyghambarian, "Optical Layer Routing Influence on Software-Defined C-RAN Survivability", Journal of Optical Communications and Networking, vol. 10, no. 11, p. 866, 2018. https://doi.org/10.1364/JOCN.10.000866
  21. 21.
    P. Yan, S. Choudhury, F. Al-Turjman and I. Al-Oqily, "An energy-efficient topology control algorithm for optimizing the lifetime of wireless ad-hoc IoT networks in 5G and B5G", Computer Communications, vol. 159, pp. 83-96, 2020. https://doi.org/10.1016/j.comcom.2020.05.010
  22. 22.
    Z. Ma, B. Li, Z. Yan and M. Yang, "Remaining bandwidth based multipath routing in 5G millimeter wave self-backhauling network", Wireless Networks, vol. 25, no. 7, pp. 3839-3855, 2019. https://doi.org/10.1007/s11276-018-01919-y
  23. 23.
    Z. Khan, P. Fan, F. Abbas, H. Chen and S. Fang, "Two-Level Cluster Based Routing Scheme for 5G V2X Communication", IEEE Access, vol. 7, pp. 16194-16205, 2019. https://doi.org/10.1109/ACCESS.2019.2892180
  24. 24.
    R. Rahim, S. Murugan, S. Priya, S. Magesh and R. Manikandan, "Taylor Based Grey Wolf Optimization Algorithm (TGWOA) For Energy Aware Secure Routing Protocol", International Journal of Computer Networks and Applications, vol. 7, no. 4, p. 93, 2020. https://doi.org/10.22247/ijcna/2020/196041
SCOPUS
SCImago Journal & Country Rank