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Optimizing Connected Vehicle Routing Protocol for Smart Transportation Systems
Corresponding Author(s) : Ketut Bayu Yogha Bintoro
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control,
Vol. 10, No. 2, May 2025
Abstract
The significant growth in integrating connected vehicles into intelligent transportation networks has underscored the importance of Vehicle-to-Vehicle (V2V) communication in optimizing route efficiency, reducing traffic congestion, and enhancing road safety. However, routing protocols such as AODV face substantial challenges in dynamic automotive environments characterized by high mobility and rapid topology changes, leading to issues like packet loss, delays, and network congestion. Reactive protocols like AODV often suffer from route discovery delays, while proactive protocols like DSDV, although reducing latency, increase bandwidth consumption, making them less effective in highly dynamic contexts. This study introduces the Learning Automata Ad Hoc On-Demand (LA-AODV) routing protocol, designed to improve relay node selection and V2V communication efficiency. The proposed method leverages real-time vehicle data to predict and select optimal relay nodes under dynamic traffic conditions, thereby enhancing packet delivery ratio, throughput, and reducing latency and routing overhead. The results demonstrate that LA-AODV significantly outperforms AODV and DSDV across various traffic scenarios, with an increase in packet delivery ratio up to 4% in high traffic conditions, throughput reaching 125 units, and a reduction in end-to-end delay within the range of 2E+10 to 6E+14. These improvements highlight LA-AODV's superior efficiency in handling packet loss and latency, making it a suitable protocol for data-intensive and safety-critical applications that demand reliable and efficient data transmission. This study contributes by developing the LA-AODV protocol, which significantly enhances V2V communication performance in dynamic traffic scenarios and provides a robust simulation model replicating real-world conditions, potentially reducing traffic accidents.
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- N. Lu, N. Cheng, N. Zhang, X. Shen, and J. W. Mark, “Connected vehicles: Solutions and challenges,” IEEE Internet Things J., vol. 1, no. 4, pp. 289–299, 2014, doi: 10.1109/JIOT.2014.2327587.
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References
N. Lu, N. Cheng, N. Zhang, X. Shen, and J. W. Mark, “Connected vehicles: Solutions and challenges,” IEEE Internet Things J., vol. 1, no. 4, pp. 289–299, 2014, doi: 10.1109/JIOT.2014.2327587.
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, doi: 10.11591/ijeecs.v30.i3.pp1509-1515.
S. Boussoufa-Lahlah, F. Semchedine, and L. Bouallouche-Medjkoune, “Geographic routing protocols for Vehicular Ad hoc NETworks (VANETs): A survey,” Veh. Commun., vol. 11, pp. 20–31, 2018, doi: 10.1016/j.vehcom.2018.01.006.
C. E. Perkins and P. Bhagwat, “Highly dynamic Destination-Sequenced Distance-Vector routing (DSDV) for mobile computers,” ACM SIGCOMM Comput. Commun. Rev., vol. 24, no. 4, pp. 234–244, 1994, doi: 10.1145/190809.190336.
A. A. Abdullhaj Saif and K. Kumar, “Enhance the performance of AODV routing protocol in mobile ad-hoc networks,” J. Phys. Conf. Ser., vol. 2327, no. 1, 2022, doi: 10.1088/1742-6596/2327/1/012057.
P. M. Kumar, U. Devi G, G. Manogaran, R. Sundarasekar, N. Chilamkurti, and R. Varatharajan, “Ant colony optimization algorithm with Internet of Vehicles for intelligent traffic control system,” Comput. Networks, vol. 144, pp. 154–162, 2018, doi: 10.1016/j.comnet.2018.07.001.
L. Hota, B. P. Nayak, A. Kumar, B. Sahoo, and G. G. M. N. Ali, “A Performance Analysis of VANETs Propagation Models and Routing Protocols,” Sustain., vol. 14, no. 3, pp. 1–20, 2022, doi: 10.3390/su14031379.
W. Xiong and Q.-Q. Li, “Performance evaluation of data disseminations for vehicular ad hoc networks in highway scenarios,” Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. - ISPRS Arch., vol. 37, pp. 1015–1020, 2008.
H. A. Ameen, A. K. Mahamad, S. Saon, D. M. Nor, and K. Ghazi, “A review on vehicle to vehicle communication system applications,” Indones. J. Electr. Eng. Comput. Sci., vol. 18, no. 1, pp. 188–198, 2019, doi: 10.11591/ijeecs.v18.i1.pp188-198.
K. Bayu and Y. Bintoro, “Learning Automata-Based AODV to Improve V2V Communication in A Learning Automata-Based AODV to Improve V2V Communication in A Dynamic Traffic Simulation,” no. January, 2024, doi: 10.22266/ijies2024.0229.56.
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, vol. 9, pp. 63131–63148, 2021, doi: 10.1109/ACCESS.2021.3075378.
M. Hasanzadeh-Mofrad and A. Rezvanian, “Learning Automata Clustering,” J. Comput. Sci., vol. 24, pp. 379–388, 2018, doi: 10.1016/j.jocs.2017.09.008.
G. Koulinas, L. Kotsikas, and K. Anagnostopoulos, “A particle swarm optimization based hyper-heuristic algorithm for the classic resource constrained project scheduling problem,” Inf. Sci. (Ny)., vol. 277, pp. 680–693, 2014, doi: 10.1016/j.ins.2014.02.155.
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, doi: 10.1109/ICC.2017.7997401.
Z. Shariat, A. Movaghar, and M. Hoseinzadeh, “A learning automata and clustering-based routing protocol for named data networking,” Telecommun. Syst., vol. 65, no. 1, pp. 9–29, 2017, doi: 10.1007/s11235-016-0209-8.
A. M. Bamhdi, “Efficient dynamic-power AODV routing protocol based on node density,” Comput. Stand. Interfaces, vol. 70, no. November 2019, p. 103406, 2020, doi: 10.1016/j.csi.2019.103406.
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, doi: 10.1016/j.procs.2018.03.024.
I. Wahid, A. A. Ikram, M. Ahmad, S. Ali, and A. Ali, “State of the Art Routing Protocols in VANETs: A Review,” Procedia Comput. Sci., vol. 130, pp. 689–694, 2018, doi: 10.1016/j.procs.2018.04.121.
M. A. Al-absi, A. A. Al-absi, and H. J. Lee, “Channel Throughput and Radio Propagation,” pp. 507–512, 2017.
B. B. Maaroof et al., “Current Studies and Applications of Shuffled Frog Leaping Algorithm: A Review,” Arch. Comput. Methods Eng., vol. 29, no. 5, pp. 3459–3474, 2022, doi: 10.1007/s11831-021-09707-2.
F. A. Kafilahudin and M. Akbar, “Pengembangan Media Pembelajaran Interaktif Sistem Pernafasan Hewan Berbasis 3D Augmented Reality,” sudo J. Tek. Inform., vol. 3, no. 1, pp. 31–40, 2024, doi: 10.56211/sudo.v3i1.469.
A. K. Bhoopalam, N. Agatz, and R. Zuidwijk, “Planning of truck platoons: A literature review and directions for future research,” Transp. Res. Part B Methodol., vol. 107, pp. 212–228, 2018, doi: 10.1016/j.trb.2017.10.016.
A. Al-Ahwal and R. A. Mahmoud, “Performance Evaluation and Discrimination of AODV and AOMDV VANET Routing Protocols Based on RRSE Technique,” Wirel. Pers. Commun., vol. 128, no. 1, pp. 321–344, 2023, doi: 10.1007/s11277-022-09957-8.
S. Ali, “Vehicle to Vehicle communication,” 2019, doi: 10.13140/RG.2.2.24951.88487.
J. Derks et al., Increasing the throughput of sensitive proteomics by plexDIA, vol. 41, no. 1. 2023. doi: 10.1038/s41587-022-01389-w.
K. Afzal, R. Tariq, F. Aadil, Z. Iqbal, N. Ali, and M. Sajid, “An Optimized and Efficient Routing Protocol Application for IoV,” Math. Probl. Eng., vol. 2021, 2021, doi: 10.1155/2021/9977252.