International Journal of Computer Networks and Applications (IJCNA)

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International Journal of Computer Networks and Applications (IJCNA)

International Journal of Computer Networks and Applications (IJCNA)

Published By EverScience Publications

ISSN : 2395-0455

Decisiveness PSO-Based Gaussian AOMDV (DPSO-GAOMDV) Routing Protocol: Smart Routing for Dynamic Traffic Conditions 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

Cite this Article

DOI: 10.22247/ijcna/2023/223430

Pages : 850-867

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Abstract

Vehicular Ad Hoc Networks (VANETs) have gained prominence in vehicular communication due to their potential to enhance road safety, traffic efficiency, and infotainment services. However, the evolution of Stochastic VANETs (SVANETs) has introduced a layer of uncertainty, where vehicular interactions are influenced by dynamic factors such as varying traffic conditions, changing communication environments, and unpredictable link qualities. Routing within SVANETs presents distinct challenges stemming from the stochastic nature of the environment. Traditional routing protocols struggle to maintain reliable connections amidst fluctuating link conditions, leading to increased latency, dropped packets, and inefficient route utilization. The novel “Decisiveness PSO-Based Gaussian AOMDV (DPSO-GAOMDV) Routing Protocol” is introduced to address these challenges. This innovative protocol combines the predictive power of Gaussian-Anticipatory On-Demand Distance Vector (GAOMDV) routing with the dynamic adaptability of Particle Swarm Optimization (PSO). GAOMDV’s ability to anticipate link stability using Gaussian distribution is integrated with DPSO’s agility in optimizing routing decisions. The simulation phase of the study evaluates the DPSO-GAOMDV protocol under various stochastic vehicular scenarios. The protocol’s performance is thoroughly analyzed by emulating real-world traffic conditions and communication dynamics. The simulation results underscore the protocol’s efficacy in reducing route maintenance overhead, improved packet delivery ratios, and enhanced network stability. The predictive insights and dynamic optimization mechanisms showcase its potential to drive innovative, resilient and efficient routing strategies in the face of stochastic vehicular conditions.

Index Terms

Ad Hoc Network

Bio-Inspired Optimization

Routing

Stochastic

VANET

Vehicle

Reference

  1. 1.
    H. Ghafoor and I. Koo, “CR-SDVN: A Cognitive Routing Protocol for Software-Defined Vehicular Networks,” IEEE Sens. J., vol. 18, no. 4, pp. 1761–1772, 2018, doi: 10.1109/JSEN.2017.2788014.
  2. 2.
    N. Lin, D. Zhao, L. Zhao, A. Hawbani, M. Guizani, and N. Kumar, “ALPS: An Adaptive Link-State Perception Scheme for Software-Defined Vehicular Networks,” IEEE Trans. Veh. Technol., vol. 72, no. 2, pp. 2564–2575, 2023, doi: 10.1109/TVT.2022.3214660.
  3. 3.
    A. V. Kumar and S. K. Mohideen, “Security aware routing protocol for hybrid wireless network (SARP-HWNs) via trust enhanced mechanism,” Int. J. Bus. Data Commun. Netw., vol. 15, no. 1, pp. 34–57, 2019, doi: 10.4018/IJBDCN.2019010103.
  4. 4.
    A. M. Mezher and M. A. Igartua, “G-3MRP: A game-theoretical multimedia multimetric map-aware routing protocol for vehicular ad hoc networks,” Comput. Networks, vol. 213, p. 109086, 2022, doi: 10.1016/j.comnet.2022.109086.
  5. 5.
    J. Arshad, M. A. Azad, K. Salah, R. Iqbal, M. I. Tariq, and T. Umer, “Performance analysis of content discovery for ad-hoc tactile networks,” Futur. Gener. Comput. Syst., vol. 94, pp. 726–739, 2019, doi: 10.1016/j.future.2018.11.037.
  6. 6.
    F. H. Kumbhar and S. Y. Shin, “Innovating Multi-Objective Optimal Message Routing for Unified High Mobility Networks,” IEEE Trans. Veh. Technol., vol. 72, no. 5, pp. 6571–6583, 2023, doi: 10.1109/TVT.2022.3232567.
  7. 7.
    A. Ali, F. Aadil, M. F. Khan, M. Maqsood, and S. Lim, “Harris Hawks Optimization-Based Clustering Algorithm for Vehicular Ad-Hoc Networks,” IEEE Trans. Intell. Transp. Syst., vol. 24, no. 6, pp. 5822–5841, 2023, doi: 10.1109/TITS.2023.3257484.
  8. 8.
    S. Mokhtari, N. Nouri, J. Abouei, A. Avokh, and K. N. Plataniotis, “Relaying Data With Joint Optimization of Energy and Delay in Cluster-Based UAV-Assisted VANETs,” IEEE Internet Things J., vol. 9, no. 23, pp. 24541–24559, 2022, doi: 10.1109/JIOT.2022.3188563.
  9. 9.
    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.
  10. 10.
    B. Zhang, X. Wang, R. Xie, C. Li, H. Zhang, and F. Jiang, “A reputation mechanism based Deep Reinforcement Learning and blockchain to suppress selfish node attack motivation in Vehicular Ad-Hoc Network,” Futur. Gener. Comput. Syst., vol. 139, pp. 17–28, 2023, doi: 10.1016/j.future.2022.09.010.
  11. 11.
    G. D. Singh, S. Kumar, H. Alshazly, S. A. Idris, M. Verma, and S. M. Mostafa, “A Novel Routing Protocol for Realistic Traffic Network Scenarios in VANET,” Wirel. Commun. Mob. Comput., vol. 2021, 2021, doi: 10.1155/2021/7817249.
  12. 12.
    C. R. Komala and N. K. Srinath, “Routing solutions of GJIBR for unica sting and multicasting over AODV in VANET,” Int. J. Innov. Technol. Explor. Eng., vol. 8, no. 7, pp. 2581–2586, 2019.
  13. 13.
    P. Upadhyay, V. Marriboina, S. Kumar, S. Kumar, and M. A. Shah, “An Enhanced Hybrid Glowworm Swarm Optimization Algorithm for Traffic-Aware Vehicular Networks,” IEEE Access, vol. 10, pp. 110136–110148, 2022, doi: 10.1109/ACCESS.2022.3211653.
  14. 14.
    J. Ramkumar, S. S. Dinakaran, M. Lingaraj, S. Boopalan, and B. Narasimhan, “IoT-Based Kalman Filtering and Particle Swarm Optimization for Detecting Skin Lesion,” in Lecture Notes in Electrical Engineering, 2023, vol. 975, pp. 17–27. doi: 10.1007/978-981-19-8353-5_2.
  15. 15.
    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.
  16. 16.
    R. Jaganathan and V. Ramasamy, “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.
  17. 17.
    P. Cirne, A. Zúquete, and S. Sargento, “TROPHY: Trustworthy VANET routing with group authentication keys,” Ad Hoc Networks, vol. 71, pp. 45–67, 2018, doi: 10.1016/j.adhoc.2017.12.005.
  18. 18.
    S. Shokrollahi and M. Dehghan, “TGRV: A trust-based geographic routing protocol for VANETs,” Ad Hoc Networks, vol. 140, p. 103062, 2023, doi: 10.1016/j.adhoc.2022.103062.
  19. 19.
    A. S. Al-Obaidi et al., “Cauchy Density-Based Algorithm for VANETs Clustering in 3D Road Environments,” IEEE Access, vol. 10, pp. 76376–76385, 2022, doi: 10.1109/ACCESS.2022.3187698.
  20. 20.
    K. Giridhar, C. Anbuananth, and N. Krishnaraj, “Energy efficient clustering with Heuristic optimization based Ro/uting protocol for VANETs,” Meas. Sensors, vol. 27, p. 100745, 2023, doi: 10.1016/j.measen.2023.100745.
  21. 21.
    H. Yang, C. Pu, J. Wu, Y. Wu, and Y. Xia, “Enhancing OLSR protocol in VANETs with multi-objective particle swarm optimization,” Phys. A Stat. Mech. its Appl., vol. 614, p. 128570, 2023, doi: 10.1016/j.physa.2023.128570.
  22. 22.
    M. Naderi and M. Ghanbari, “Adaptively prioritizing candidate forwarding set in opportunistic routing in VANETs,” Ad Hoc Networks, vol. 140, p. 103048, 2023, doi: 10.1016/j.adhoc.2022.103048.
  23. 23.
    Parveen, S. Kumar, R. P. Singh, A. Kumar, R. Yaduwanshi, and D. P. Dora, “TS-CAGR:Traffic sensitive connectivity-aware geocast routing protocol in internet of vehicles,” Ad Hoc Networks, vol. 147, p. 103210, 2023, doi: 10.1016/j.adhoc.2023.103210.
  24. 24.
    M. Khalid Diaa, I. Samer Mohamed, and M. Ayman Hassan, “OPBRP - obstacle prediction based routing protocol in VANETs,” Ain Shams Eng. J., vol. 14, no. 7, p. 101989, 2023, doi: 10.1016/j.asej.2022.101989.
  25. 25.
    H. Ning et al., “Modeling and analysis of traffic warning message dissemination system in VANETs,” Veh. Commun., vol. 39, p. 100566, 2023, doi: 10.1016/j.vehcom.2022.100566.
  26. 26.
    U. Arul, R. Gnanajeyaraman, A. Selvakumar, S. Ramesh, T. Manikandan, and G. Michael, “Integration of IoT and edge cloud computing for smart microgrid energy management in VANET using machine learning,” Comput. Electr. Eng., vol. 110, p. 108905, 2023, doi: 10.1016/j.compeleceng.2023.108905.
  27. 27.
    R. K. Satyanarayana and K. Selvakumar, “Bi-linear mapping integrated machine learning based authentication routing protocol for improving quality of service in vehicular Ad-Hoc network,” e-Prime - Adv. Electr. Eng. Electron. Energy, vol. 4, p. 100145, 2023, doi: 10.1016/j.prime.2023.100145.
  28. 28.
    R. Shahin, S. M. Saif, A. A. El-Moursy, H. M. Abbas, and S. M. Nassar, “Fog-ROCL: A Fog based RSU Optimum Configuration and Localization in VANETs,” Pervasive Mob. Comput., vol. 94, p. 101807, 2023, doi: 10.1016/j.pmcj.2023.101807.
  29. 29.
    K. Lakshmi Narayanan and R. Naresh, “An efficient key validation mechanism with VANET in real-time cloud monitoring metrics to enhance cloud storage and security,” Sustain. Energy Technol. Assessments, vol. 56, p. 102970, 2023, doi: 10.1016/j.seta.2022.102970.
  30. 30.
    S. K and C. chinnasamy, “Efficient VANET handover scheme using SSDN by incorporating media independent handover framework,” Meas. Sensors, vol. 26, p. 100684, 2023, doi: 10.1016/j.measen.2023.100684.
  31. 31.
    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.
  32. 32.
    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.
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