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

Efficacy Artificial Bee Colony Optimization-Based Gaussian AOMDV (EABCO-GAOMDV) Routing Protocol for Seamless Traffic Rerouting in Stochastic Vehicular Ad Hoc Network

Author NameAuthor Details

M. Kayalvizhi, S. Geetha

M. Kayalvizhi[1]

S. Geetha[2]

[1]Department of Computer Science, Sri Krishna Adithya College of Arts and Science, Coimbatore, Tamil Nadu, India.

[2]Department of Computer Science, Government Arts and Science College for Women, Coimbatore, Tamil Nadu, India.

Abstract

Vehicular Ad Hoc Networks (VANETs) have emerged as a dynamic communication paradigm enabling vehicles to form temporary Ad Hoc networks for seamless information exchange. Stochastic VANETs (SVANETs) introduce complexities due to their stochastic nature, necessitating innovative strategies to handle dynamic traffic conditions and intermittent connectivity. Routing within SVANETs presents unique challenges arising from uncertainties inherent in real-world scenarios. The stochastic environment gives rise to intermittent connectivity, dynamic traffic conditions, and varying network topologies. Traditional routing protocols struggle to provide efficient and reliable solutions under these challenging circumstances. This paper introduces the Efficacy Artificial Bee Colony Optimization-Based Gaussian AOMDV (EABCO-GAOMDV) routing protocol as a promising solution for the routing challenges in SVANETs. The protocol integrates the intelligence of Artificial Bee Colony Optimization (EABCO) with the adaptive characteristics of Gaussian AOMDV, aiming to enhance the efficiency of route discovery and rerouting. Through extensive simulations encompassing diverse SVANET scenarios, EABCO-GAOMDV is rigorously evaluated for performance and effectiveness. The protocol substantially improves route stability, packet delivery ratio, and end-to-end delay. The simulation results unequivocally validate the protocol’s ability to adapt to stochastic conditions, ensuring effective traffic rerouting and heightened network resilience. EABCO-GAOMDV showcases its potential as a robust routing solution for SVANETs, effectively addressing the challenges of stochastic conditions.

Index Terms

Ad Hoc Network

Bio-inspired Optimization

Routing

Stochastic

VANET

AOMDV

SVANET

EABCO-GAOMDV

Vehicle

Reference

  1. 1.
    S. Gupta, R. C. Poonia, and X. Z. Gao, “Performance evaluation of flooding based routing protocol for delay tolerant networks,” Int. J. Recent Technol. Eng., vol. 7, no. 6, pp. 18–22, 2019.
  2. 2.
    E. Khoza, C. Tu, and P. A. Owolawi, “Decreasing traffic congestion in vanets using an improved hybrid ant colony optimization algorithm,” J. Commun., vol. 15, no. 9, pp. 676–686, 2020, doi: 10.12720/jcm.15.9.676-686.
  3. 3.
    N. Ganeshkumar and S. Kumar, “Qos Aware Modified Harmony Search Optimization For Route Selection In Vanets,” Indian J. Comput. Sci. Eng., vol. 13, no. 2, pp. 288–299, 2022, doi: 10.21817/indjcse/2022/v13i2/221302014.
  4. 4.
    J. Ramkumar and R. Vadivel, “Multi-Adaptive Routing Protocol for Internet of Things based Ad-hoc Networks,” Wirel. Pers. Commun., vol. 120, no. 2, pp. 887–909, Apr. 2021, doi: 10.1007/s11277-021-08495-z.
  5. 5.
    D. Tian et al., “A microbial inspired routing protocol for VANETs,” IEEE Internet Things J., vol. 5, no. 4, pp. 2293–2303, 2018, doi: 10.1109/JIOT.2017.2737466.
  6. 6.
    L. Mani, S. Arumugam, and R. Jaganathan, “Performance Enhancement of Wireless Sensor Network Using Feisty Particle Swarm Optimization Protocol,” ACM Int. Conf. Proceeding Ser., pp. 1–5, Dec. 2022, doi: 10.1145/3590837.3590907.
  7. 7.
    M. Laanaoui and S. Raghay, “Enhancing OLSR Protocol by an Advanced Greedy Forwarding Mechanism for VANET in Smart Cities,” Smart Cities, vol. 5, no. 2, pp. 650–667, 2022, doi: 10.3390/smartcities5020034.
  8. 8.
    D. Raychaudhuri and N. B. Mandayam, “Frontiers of wireless and mobile communications,” Proc. IEEE, vol. 100, no. 4, pp. 824–840, 2012, doi: 10.1109/JPROC.2011.2182095.
  9. 9.
    R. Hou et al., “Cluster Routing-Based Data Packet Backhaul Prediction Method in Vehicular Named Data Networking,” IEEE Trans. Netw. Sci. Eng., vol. 8, no. 3, pp. 2639–2650, 2021, doi: 10.1109/TNSE.2021.3102969.
  10. 10.
    R. Chakroun, S. Abdellatif, and T. Villemur, “Q-Learning Relay Placement for Alert Message Dissemination in Vehicular Networks,” Procedia Comput. Sci., vol. 203, pp. 222–230, 2022, doi: https://doi.org/10.1016/j.procs.2022.07.029.
  11. 11.
    C. Liu, G. Zhang, W. Guo, and R. He, “Kalman Prediction-Based Neighbor Discovery and Its Effect on Routing Protocol in Vehicular Ad Hoc Networks,” IEEE Trans. Intell. Transp. Syst., vol. 21, no. 1, pp. 159–169, 2020, doi: 10.1109/TITS.2018.2889923.
  12. 12.
    M. A. de Pastre and Y. Quinsat, “Virtual volume correlation of lattice structures: From volumetric data to geometrical and dimensional defects identification,” Addit. Manuf., vol. 61, p. 103347, 2023, doi: 10.1016/j.addma.2022.103347.
  13. 13.
    A. Sarkar, K. Daripa, M. Z. Khan, and A. Noorwali, “Cloud enabled Blockchain-based secured communication in mutual intelligent transportation using neural synchronization,” Veh. Commun., vol. 38, p. 100533, 2022, doi: https://doi.org/10.1016/j.vehcom.2022.100533.
  14. 14.
    H. Ayaz, M. Waqas, G. Abbas, Z. H. Abbas, and M. Bilal, “Multiple re-configurable intelligent surfaces based physical layer eavesdropper detection for V2I communications,” Phys. Commun., vol. 58, p. 102074, 2023, doi: https://doi.org/10.1016/j.phycom.2023.102074.
  15. 15.
    J. Wu, H. Lu, Y. Xiang, R. Wu, and F. Wang, “MBR: A Map-Based Relaying Algorithm for Reliable Data Transmission through Intersection in VANETs,” IEEE Trans. Intell. Transp. Syst., vol. 20, no. 10, pp. 3661–3674, 2019, doi: 10.1109/TITS.2018.2877993.
  16. 16.
    P. Senthilraja and B. G. Geetha, “Avoiding fuel theft in multifleet vehicles using vehicular adhoc network,” Cluster Comput., vol. 22, pp. 11175–11181, 2019, doi: 10.1007/s10586-017-1347-9.
  17. 17.
    I. Almomani, M. Ahmed, D. Kosmanos, A. Alkhayer, and L. Maglaras, “An Efficient Localization and Avoidance Method of Jammers in Vehicular Ad Hoc Networks,” IEEE Access, vol. 10, pp. 131640–131655, 2022, doi: 10.1109/ACCESS.2022.3229623.
  18. 18.
    K. L. K. Sudheera, M. Ma, and P. H. J. Chong, “Real-time cooperative data routing and scheduling in software defined vehicular networks,” Comput. Commun., vol. 181, pp. 203–214, 2022, doi: 10.1016/j.comcom.2021.10.003.
  19. 19.
    A. Rahim et al., “Social acquaintance based routing in Vehicular Social Networks,” Futur. Gener. Comput. Syst., vol. 93, pp. 751–760, 2019, doi: 10.1016/j.future.2017.07.059.
  20. 20.
    M. M. Hamdi, L. Audah, and S. A. Rashid, “Data Dissemination in VANETs Using Clustering and Probabilistic Forwarding Based on Adaptive Jumping Multi-Objective Firefly Optimization,” IEEE Access, vol. 10, pp. 14624–14642, 2022, doi: 10.1109/ACCESS.2022.3147498.
  21. 21.
    S. Samreen, “Resistance to malicious packet droppers through enhanced AODV in a MANET,” Comput. Mater. Contin., vol. 72, no. 2, pp. 4087–4106, 2022, doi: 10.32604/cmc.2022.026141.
  22. 22.
    J. Sathiamoorthy, B. Ramakrishnan, and M. Usha, “Design of a proficient hybrid protocol for efficient route discovery and secure data transmission in CEAACK MANETs,” J. Inf. Secur. Appl., vol. 36, pp. 43–58, 2017, doi: 10.1016/j.jisa.2017.08.001.
  23. 23.
    Z. H. Ali, N. A. Sakr, N. El-Rashidy, and H. A. Ali, “A reliable position-based routing scheme for controlling excessive data dissemination in vehicular ad-hoc networks,” Comput. Networks, vol. 229, p. 109785, 2023, doi: 10.1016/j.comnet.2023.109785.
  24. 24.
    A. Nahar and D. Das, “MetaLearn: Optimizing routing heuristics with a hybrid meta-learning approach in vehicular ad-hoc networks,” Ad Hoc Networks, vol. 138, p. 102996, 2023, doi: 10.1016/j.adhoc.2022.102996.
  25. 25.
    Y. Wang, X. Li, X. Zhang, X. Liu, and J. Weng, “ARPLR: An All-Round and Highly Privacy-Preserving Location-Based Routing Scheme for VANETs,” IEEE Trans. Intell. Transp. Syst., vol. 23, no. 9, pp. 16558–16575, 2022, doi: 10.1109/TITS.2021.3134686.
  26. 26.
    Y. A. Debalki, J. Hou, H. Ullah, and B. Y. Adane, “Multi-hop data dissemination using a multi-metric contention-based broadcast suppression strategy in VANETs,” Ad Hoc Networks, vol. 140, p. 103070, 2023, doi: 10.1016/j.adhoc.2022.103070.
  27. 27.
    Y. Song, K. Jiang, Y. Cao, R. Zhou, C. Suthaputchakun, and Y. Zhuang, “STALB: A Spatio-Temporal Domain Autonomous Load Balancing Routing Protocol,” IEEE Trans. Netw. Serv. Manag., vol. 20, no. 1, pp. 73–87, 2023, doi: 10.1109/TNSM.2022.3208025.
  28. 28.
    M. Yao, Q. Gan, X. Wang, and Y. Yang, “A key-insulated secure multi-server authenticated key agreement protocol for edge computing-based VANETs,” Internet of Things (Netherlands), vol. 21, p. 100679, 2023, doi: 10.1016/j.iot.2023.100679.
  29. 29.
    B. Chen, Z. Wang, T. Xiang, J. Yang, D. He, and K. K. R. Choo, “BCGS: Blockchain-assisted privacy-preserving cross-domain authentication for VANETs,” Veh. Commun., vol. 41, p. 100602, 2023, doi: 10.1016/j.vehcom.2023.100602.
  30. 30.
    M. kumar Pulligilla and C. Vanmathi, “An authentication approach in SDN-VANET architecture with Rider-Sea Lion optimized neural network for intrusion detection,” Internet of Things (Netherlands), vol. 22, p. 100723, 2023, doi: 10.1016/j.iot.2023.100723.
  31. 31.
    Y. Huang and M. Ma, “ILL-IDS: An incremental lifetime learning IDS for VANETs,” Comput. Secur., vol. 124, p. 102992, 2023, doi: 10.1016/j.cose.2022.102992.
  32. 32.
    R. Tirumalasetti and S. K. Singh, “Automatic Dynamic User Allocation with opportunistic routing over vehicles network for Intelligent Transport System,” Sustain. Energy Technol. Assessments, vol. 57, p. 103195, 2023, doi: 10.1016/j.seta.2023.103195.
  33. 33.
    Z. Han, C. Xu, S. Ma, Y. Hu, G. Zhao, and S. Yu, “DTE-RR: Dynamic Topology Evolution-Based Reliable Routing in VANET,” IEEE Wirel. Commun. Lett., vol. 12, no. 6, pp. 1061–1065, 2023, doi: 10.1109/LWC.2023.3260142.
  34. 34.
    L. Luo, L. Sheng, H. Yu, and G. Sun, “Intersection-Based V2X Routing via Reinforcement Learning in Vehicular Ad Hoc Networks,” IEEE Trans. Intell. Transp. Syst., vol. 23, no. 6, pp. 5446–5459, 2022, doi: 10.1109/TITS.2021.3053958.
  35. 35.
    N. Bachir, H. Harb, C. Zaki, M. Nabaa, G. A. Nys, and R. Billen, “PEMAP: An intelligence-based framework for post-event management of transportation systems,” Comput. Electr. Eng., vol. 110, p. 108856, 2023, doi: 10.1016/j.compeleceng.2023.108856.
  36. 36.
    R. Han, J. Shi, Q. Guan, F. Banoori, and W. Shen, “Speed and Position Aware Dynamic Routing for Emergency Message Dissemination in VANETs,” IEEE Access, vol. 10, pp. 1376–1385, 2022, doi: 10.1109/ACCESS.2021.3138960.
  37. 37.
    J. Liu, H. Weng, Y. Ge, S. Li, and X. Cui, “A Self-Healing Routing Strategy Based on Ant Colony Optimization for Vehicular Ad Hoc Networks,” IEEE Internet Things J., vol. 9, no. 22, pp. 22695–22708, 2022, doi: 10.1109/JIOT.2022.3181857.
  38. 38.
    G. D. Singh, M. Prateek, S. Kumar, M. Verma, D. Singh, and H. N. Lee, “Hybrid Genetic Firefly Algorithm-Based Routing Protocol for VANETs,” IEEE Access, vol. 10, pp. 9142–9151, 2022, doi: 10.1109/ACCESS.2022.3142811.
  39. 39.
    J. Ramkumar, “Bee inspired secured protocol for routing in cognitive radio ad hoc networks,” Indian J. Sci. Technol., vol. 13, no. 30, pp. 2159–2169, 2020, doi: 10.17485/ijst/v13i30.1152.
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