Open Access
Subscription Access
Minimizing Delay in Mobile Ad-Hoc Network Using Ingenious Grey Wolf Optimization Based Routing Protocol
One of the most groundbreaking concepts in wireless networking is the mobile ad hoc network (MANET). It is an ever-shifting network of wireless nodes that may be adaptively and indiscriminately positioned, with the interconnections between nodes constantly changing. Defense networks, in particular, are becoming more prominent, and it is the goal and passion of technology to update and improve its components. There is a significant rise in transmission costs due to the high energy usage. Routing protocols have a critical role in reducing energy utilization. Weak routing protocol leads to exhaustive energy consumption, packet delay and packet loss. Ingenious Grey Wolf Optimization-based Routing Protocol (IGWORP) is proposed in this paper to discover the most efficient path to a destination and reduce the amount of delay and energy spent. IGWORP mirrors the natural tendencies of the grey wolf towards foraging for its prey. IGWORP looks for a global route rather than assembling many local routes. Encircling and hunting characteristics of wolves are used in IGWORP to discover and utilize the route for data transmission. Standard network metrics are used in NS3 to evaluate IGWORP's performance. The findings of IGWORP demonstrate that it reduces delays and energy consumption better than the current routing methods.
Keywords
Delay, Routing, Optimization, Wolf, Delay, Energy.
User
Font Size
Information
- L.-L. Wang, J.-S. Gui, X.-H. Deng, F. Zeng, and Z.-F. Kuang, “Routing Algorithm Based on Vehicle Position Analysis for Internet of Vehicles,” IEEE Internet Things J., vol. 7, no. 12, pp. 11701–11712, 2020, doi: 10.1109/JIOT.2020.2999469.
- B. Su, C. Du, and J. Huan, “Trusted Opportunistic Routing Based on Node Trust Model,” IEEE Access, vol. 8, pp. 163077–163090, 2020, doi: 10.1109/ACCESS.2020.3020129.
- S. Amutha and K. Balasubramanian, “Secured energy optimized Ad hoc on-demand distance vector routing protocol,” Comput. Electr. Eng., vol. 72, pp. 766–773, 2018, doi: https://doi.org/10.1016/j.compeleceng.2017.11.031.
- J. Ramkumar and R. Vadivel, “Improved frog leap inspired protocol (IFLIP) – for routing in cognitive radio ad hoc networks (CRAHN),” World J. Eng., vol. 15, no. 2, pp. 306–311, 2018, doi: 10.1108/WJE-08-2017-0260.
- J. Ramkumar and R. Vadivel, “CSIP—cuckoo search inspired protocol for routing in cognitive radio ad hoc networks,” in Advances in Intelligent Systems and Computing, 2017, vol. 556, pp. 145–153, doi: 10.1007/978-981-10-3874-7_14.
- J. Ramkumar and R. Vadivel, “Performance Modeling of Bio-Inspired Routing Protocols in Cognitive Radio Ad Hoc Network to Reduce End-to-End Delay,” Int. J. Intell. Eng. Syst., vol. 12, no. 1, pp. 221–231, 2019, doi: 10.22266/ijies2019.0228.22.
- J. Ramkumar and R. Vadivel, “Multi-Adaptive Routing Protocol for Internet of Things based Ad-hoc Networks,” Wirel. Pers. Commun., pp. 1–23, Apr. 2021, doi: 10.1007/s11277-021-08495-z.
- J. Ramkumar and R. Vadivel, “FLIP: Frog Leap Inspired Protocol for Routing in Cognitive Radio Ad Hoc Networks,” in International Conference on Recent Trends in Engineering and Material Sciences (ICEMS - 2016), 2016, p. 248.
- J. Ramkumar and R. Vadivel, “Intelligent Fish Swarm Inspired Protocol (IFSIP) For Dynamic Ideal Routing in Cognitive Radio Ad-Hoc Networks,” Int. J. Comput. Digit. Syst., vol. 10, no. 1, pp. 1063–1074, 2020, doi: http://dx.doi.org/10.12785/ijcds/100196.
- J. Ramkumar and R. Vadivel, “Bee inspired secured protocol for routing in cognitive radio ad hoc networks,” INDIAN J. Sci. Technol., vol. 13, no. 30, pp. 3059–3069, 2020, doi: 10.17485/IJST/v13i30.1152.
- R. Vadivel and J. Ramkumar, “QoS-Enabled Improved Cuckoo Search-Inspired Protocol (ICSIP) for IoT-Based Healthcare Applications,” pp. 109–121, 2019, doi: 10.4018/978-1-7998-1090-2.ch006.
- J. Ramkumar and R. Vadivel, “Meticulous elephant herding optimization based protocol for detecting intrusions in cognitive radio ad hoc networks,” Int. J. Emerg. Trends Eng. Res., vol. 8, no. 8, pp. 4549–4554, 2020, doi: 10.30534/ijeter/2020/82882020.
- A. Patwardhan, J. Parker, M. Iorga, A. Joshi, T. Karygiannis, and Y. Yesha, “Threshold-based intrusion detection in ad hoc networks and secure AODV,” Ad Hoc Networks, vol. 6, no. 4, pp. 578–599, 2008, doi: https://doi.org/10.1016/j.adhoc.2007.05.001.
- O. S. Younes and U. A. Albalawi, “Analysis of Route Stability in Mobile Multihop Networks Under Random Waypoint Mobility,” IEEE Access, vol. 8, pp. 168121–168136, 2020, doi: 10.1109/ACCESS.2020.3023142.
- M. A. K. Akhtar and G. Sahoo, “Enhancing cooperation in MANET using neighborhood compressive sensing model,” Egypt. Informatics J., vol. 22, no. 3, pp. 373–387, 2021, doi: https://doi.org/10.1016/j.eij.2016.06.007.
- B. Yang, Z. Wu, Y. Shen, X. Jiang, and S. Shen, “On delay performance study for cooperative multicast MANETs,” Ad Hoc Networks, vol. 102, p. 102117, 2020, doi: https://doi.org/10.1016/j.adhoc.2020.102117.
- M. A. Gawas and S. S. Govekar, “A novel selective cross layer based routing scheme using ACO method for vehicular networks,” J. Netw. Comput. Appl., vol. 143, pp. 34–46, 2019, doi: https://doi.org/10.1016/j.jnca.2019.05.010.
- L. Zhang, L. Hu, F. Hu, Z. Ye, X. Li, and S. Kumar, “Enhanced OLSR routing for airborne networks with multi-beam directional antennas,” Ad Hoc Networks, vol. 102, p. 102116, 2020, doi: https://doi.org/10.1016/j.adhoc.2020.102116.
- I. Manolopoulos, K. Kontovasilis, I. Stavrakakis, and S. C. A. Thomopoulos, “Methodologies for calculating decision-related event occurrence times, with applications to effective routing in diverse MANET environments,” Ad Hoc Networks, vol. 99, p. 102068, 2020, doi: https://doi.org/10.1016/j.adhoc.2019.102068.
- D. Sarkar, S. Choudhury, and A. Majumder, “Enhanced-Ant-AODV for optimal route selection in mobile ad-hoc network,” J. King Saud Univ. - Comput. Inf. Sci., vol. 33, no. 10, pp. 1186–1201, 2021, doi: https://doi.org/10.1016/j.jksuci.2018.08.013.
- D. S. K. Tiruvakadu and V. Pallapa, “Confirmation of wormhole attack in MANETs using honeypot,” Comput. Secur., vol. 76, pp. 32–49, 2018, doi: https://doi.org/10.1016/j.cose.2018.02.004.
- C. R. da C. Bento and E. C. G. Wille, “Bio-inspired routing algorithm for MANETs based on fungi networks,” Ad Hoc Networks, vol. 107, p. 102248, 2020, doi: https://doi.org/10.1016/j.adhoc.2020.102248.
- A. M. Bamhdi, “Efficient dynamic-power AODV routing protocol based on node density,” Comput. Stand. Interfaces, vol. 70, p. 103406, 2020, doi: https://doi.org/10.1016/j.csi.2019.103406.
- L. A. L. F. da Costa, R. Kunst, and E. Pignaton de Freitas, “Q-FANET: Improved Q-learning based routing protocol for FANETs,” Comput. Networks, vol. 198, p. 108379, 2021, doi: https://doi.org/10.1016/j.comnet.2021.108379.
- V. K. Sharma, L. P. Verma, and M. Kumar, “CL-ADSP: Cross-Layer Adaptive Data Scheduling Policy in Mobile Ad-hoc Networks,” Futur. Gener. Comput. Syst., vol. 97, pp. 530–563, 2019, doi: https://doi.org/10.1016/j.future.2019.03.013.
- B. Deokate, C. Lal, D. Trček, and M. Conti, “Mobility-aware cross-layer routing for peer-to-peer networks,” Comput. Electr. Eng., vol. 73, pp. 209–226, 2019, doi: https://doi.org/10.1016/j.compeleceng.2018.11.014.
- D. Zhang, T. Zhang, Y. Dong, X. Liu, Y. Cui, and D. Zhao, “Novel optimized link state routing protocol based on quantum genetic strategy for mobile learning,” J. Netw. Comput. Appl., vol. 122, pp. 37–49, 2018, doi: https://doi.org/10.1016/j.jnca.2018.07.018.
- K. Poularakis, Q. Qin, E. M. Nahum, M. Rio, and L. Tassiulas, “Flexible SDN control in tactical ad hoc networks,” Ad Hoc Networks, vol. 85, pp. 71–80, 2019, doi: https://doi.org/10.1016/j.adhoc.2018.10.012.
- A. Chriki, H. Touati, H. Snoussi, and F. Kamoun, “FANET: Communication, mobility models and security issues,” Comput. Networks, vol. 163, p. 106877, 2019, doi: https://doi.org/10.1016/j.comnet.2019.106877.
- K. Nabar and G. Kadambi, “Affinity Propagation-driven Distributed clustering approach to tackle greedy heuristics in Mobile Ad-hoc Networks,” Comput. Electr. Eng., vol. 71, pp. 988–1011, 2018, doi: https://doi.org/10.1016/j.compeleceng.2017.10.014.
- S. Dalal et al., “An adaptive traffic routing approach toward load balancing and congestion control in Cloud–MANET ad hoc networks,” Soft Comput. 2022, pp. 1–12, Apr. 2022, doi: 10.1007/S00500-022-07099-4.
Abstract Views: 275
PDF Views: 2