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Smart AODV Routing Protocol Strategies Based on Learning Automata to Improve V2V Communication Quality of Services in VANET
Corresponding Author(s) : Ketut Bayu Yogha Bintoro
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control,
Vol. 9, No. 3, August 2024
Abstract
The Adhoc On-Demand Distance Vector (AODV) protocol faces challenges in selecting the best relay nodes, which requires optimization to improve performance in Vehicular ad-hoc networks (VANETs). This study aims to enhance Vehicle-to-Vehicle (V2V) communication in VANETs by implementing the Learning Automata-Driven Ad-hoc On-Demand Distance Vector (LA-AODV) routing protocol. LA-AODV is designed to achieve higher packet delivery ratios and optimize data transfer rates, even under congested network conditions, by dynamically adjusting to changing network scenarios. The performance evaluation includes six key metrics analyzed under varying node densities and time intervals, comparing LA-AODV against the standard AODV protocol. Results indicate that LA-AODV consistently outperforms AODV, demonstrating improved efficiency in flood identifier management, reduced data loss, higher packet delivery ratios, better throughput, and reduced end-to-end delay and jitter. Specifically, under a 20-node scenario, LA-AODV exhibits lower flood ID scores (54 vs. 88), reduced packet loss (11% vs. 12%), higher PDR (88.0% vs. 87.0%), and superior throughput (85.34 Kbps vs. 47.26 Kbps). Additionally, LA-AODV achieves lower end-to-end delay (6.84E+09 ns vs. 3.76E+10 ns) and jitter (2.52E+09 ns vs. 2.15E+10 ns). These findings suggest that LA-AODV significantly enhances Quality of Service (QoS) in vehicular ad-hoc networks, positioning it as a promising solution for optimizing V2V communication performance.
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- J. Liu et al., “Investigation of 5G and 4G V2V Communication Channel Performance Under Severe Weather,” pp. 12–17, 2022. https://doi.org/10.1109/WiSEE49342.2022.9926867
- F. Belamri, S. Boulfekhar, and D. Aissani, “A survey on QoS routing protocols in Vehicular Ad Hoc Network (VANET),” Telecommunication Systems. 2021. https://doi.org/10.1007/s11235-021-00797-8
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- D. Anadu, “in a VANET Environment,” 2018 IEEE Int. Instrum. Meas. Technol. Conf., pp. 1–6, 2018.
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References
J. Liu et al., “Investigation of 5G and 4G V2V Communication Channel Performance Under Severe Weather,” pp. 12–17, 2022. https://doi.org/10.1109/WiSEE49342.2022.9926867
F. Belamri, S. Boulfekhar, and D. Aissani, “A survey on QoS routing protocols in Vehicular Ad Hoc Network (VANET),” Telecommunication Systems. 2021. https://doi.org/10.1007/s11235-021-00797-8
T. Peng et al., “A new safe lane-change trajectory model and collision avoidance control method for automatic driving vehicles,” Expert Syst. Appl., vol. 141, 2020. https://doi.org/10.1016/j.eswa.2019.112953
D. Chen, M. Zhao, D. Sun, L. Zheng, S. Jin, and J. Chen, “Robust H∞ control of cooperative driving system with external disturbances and communication delays in the vicinity of traffic signals,” Phys. A Stat. Mech. its Appl., vol. 542, p. 123385, 2020. https://doi.org/10.1016/j.physa.2019.123385
B. S. Kusuma, D. Risqiwati, and D. R. Akbi, “Analisis Perbandingan Performansi Protokol Ad Hoc On-Demand Distance Vector dan Zone Routing Protocol Pada Mobile Ad Hoc Network,” Kinet. Game Technol. Inf. Syst. Comput. Network, Comput. Electron. Control, 2017. https://doi.org/10.22219/kinetik.v2i3.91
T. K. Bhatia, R. K. Ramachandran, R. Doss, and L. Pan, “A Survey on Controlling the Congestion in Vehicleto-Vehicle Communication,” in ICRITO 2020 - IEEE 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions), 2020. https://doi.org/10.1109/ICRITO48877.2020.9197884
A. H. El Fawal, A. Mansour, and M. Najem, “V2V Influence on M2M and H2H Traffics During Emergency Scenarios,” Global Advancements in Connected and Intelligent Mobility. IGI Global, pp. 93–134, 2020. https://doi.org/10.4018/978-1-5225-9019-4.ch003
K. B. Y. Bintoro and T. K. Priyambodo, “Learning Automata-Based AODV to Improve V2V Communication in A Dynamic Traffic Simulation,” Int. J. Intell. Eng. Syst., vol. 17, no. 1, pp. 666–678, 2024. https://doi.org/10.22266/ijies2024.0229.56
R. S. Bali, N. Kumar, and J. J. P. C. Rodrigues, “An efficient energy-aware predictive clustering approach for vehicular ad hoc networks,” Int. J. Commun. Syst., vol. 30, no. 2, 2017. https://doi.org/10.1002/dac.2924
R. Arief, R. Anggoro, and F. X. Arunanto, “Implementation of Aodv Routing Protocol With Vehicle Movement Prediction in Vanet,” Surabaya Inst. Teknol. Sepuluh Novemb., 2016.
K. A. Darabkh, M. S. A. Judeh, H. Bany Salameh, and S. Althunibat, “Mobility aware and dual phase AODV protocol with adaptive hello messages over vehicular ad hoc networks,” AEU - Int. J. Electron. Commun., vol. 94, no. July, pp. 277–292, 2018. https://doi.org/10.1016/j.aeue.2018.07.020
M. R. Hasan, Y. Zhao, Y. Luo, G. Wang, and R. M. Winter, “An Effective AODV-based Flooding Detection and Prevention for Smart Meter Network,” Procedia Comput. Sci., vol. 129, pp. 454–460, 2018. https://doi.org/10.1016/j.procs.2018.03.024
M. H. Homaei, S. S. Band, A. Pescape, and A. Mosavi, “DDSLA-RPL: Dynamic Decision System Based on Learning Automata in the RPL Protocol for Achieving QoS,” IEEE Access, 2021. https://ui.adsabs.harvard.edu/link_gateway/2021IEEEA...963131H/doi:10.1109/ACCESS.2021.3075378
D. Zhang, C. Gong, T. Zhang, J. Zhang, and M. Piao, “A new algorithm of clustering AODV based on edge computing strategy in IOV,” Wirel. Networks, 2021. https://doi.org/10.1007/s11276-021-02624-z
A. E. Mezher, A. A. AbdulRazzaq, and R. K. Hassoun, “A comparison of the performance of the ad hoc on-demand distance vector protocol in the urban and highway environment,” Indones. J. Electr. Eng. Comput. Sci., vol. 30, no. 3, pp. 1509–1515, 2023. http://doi.org/10.11591/ijeecs.v30.i3.pp1509-1515
V. Saritha, P. V. Krishna, S. Misra, and M. S. Obaidat, “Learning automata-based channel reservation scheme to enhance QoS in vehicular adhoc networks,” 2016 IEEE Glob. Commun. Conf. GLOBECOM 2016 - Proc., pp. 1–6, 2016. https://doi.org/10.1109/GLOCOM.2016.7841949
A. Asma, “PSO-based dynamic distributed algorithm for automatic task clustering in a robotic swarm,” Procedia Computer Science, vol. 159. pp. 1103–1112, 2019. https://doi.org/10.1016/j.procs.2019.09.279
V. Saritha, P. V. Krishna, S. Misra, and M. S. Obaidat, “Learning automata based optimized multipath routingusing leapfrog algorithm for VANETs,” IEEE Int. Conf. Commun., pp. 1–5, 2017. https://doi.org/10.1109/ICC.2017.7997401
S. Zhao and K. Zhang, “A distributionally robust stochastic optimization-based model predictive control with distributionally robust chance constraints for cooperative adaptive cruise control under uncertain traffic conditions,” Transp. Res. Part B Methodol., vol. 138, pp. 144–178, 2020. https://doi.org/10.1016/j.trb.2020.05.001
H. Ye, “Deep Reinforcement Learning Based Resource Allocation for V2V Communications,” IEEE Trans. Veh. Technol., vol. 68, no. 4, pp. 3163–3173, 2019. https://doi.org/10.1109/TVT.2019.2897134
T. K. Priyambodo, D. Wijayanto, and M. S. Gitakarma, “Performance optimization of MANET networks through routing protocol analysis,” Computers, 2021. https://doi.org/10.3390/computers10010002
G. A. Beletsioti and G. S. Member, “A Learning-Automata-Based Congestion-Aware Scheme for Energy-Efficient Elastic Optical Networks,” IEEE Access, vol. 8, 2020.
J. Naskath, B. Paramasivan, Z. Mustafa, and H. Aldabbas, “Connectivity analysis of V2V communication with discretionary lane changing approach,” J. Supercomput., 2022. https://doi.org/10.1007/s11227-021-04086-8
D. Anadu, “in a VANET Environment,” 2018 IEEE Int. Instrum. Meas. Technol. Conf., pp. 1–6, 2018.
R. S. Bali and N. Kumar, “Learning Automata-assisted Predictive Clustering approach for Vehicular Cyber-Physical System,” Comput. Electr. Eng., vol. 52, pp. 82–97, 2016. https://doi.org/10.1016/j.compeleceng.2015.09.007