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(Universitas Buddhi Dharma, Indonesia)(2) Setyawan Widyarto
(Universiti Selangor, Malaysia)*corresponding author
AbstractThis study proposes a Deep Reinforcement-Driven Clustering and Routing Protocol (DRCRP) to enhance energy efficiency and routing stability in smart vehicular networks. The protocol integrates an Actor–Critic deep reinforcement learning framework with Proximal Policy Optimization (PPO) to enable adaptive decision-making in dynamic Internet of Vehicles (IoV) environments. Through continuous learning, DRCRP adjusts cluster head selection and routing paths according to real-time vehicular mobility, residual energy, and link quality. Simulation experiments conducted using NS-2 and VanetMobiSim show that DRCRP achieves superior performance compared to benchmark algorithms such as AI-EECR, GWO-CH, and DMCNF. Quantitatively, the proposed model improved the Packet Delivery Ratio (PDR) by up to 4.3%, reduced End-to-End Delay by 18–22%, and lowered Energy Consumption by 12–16%. Moreover, DRCRP effectively minimized communication overhead and extended cluster head and member lifetimes, confirming its ability to balance reliability and energy efficiency. These results demonstrate the capability of reinforcement learning-based architectures to support intelligent, sustainable, and scalable vehicular communication systems under complex mobility conditions
KeywordsInternet of Vehicles (IoV), Deep Reinforcement Learning, Clustering, Routing Protocol, Energy Efficiency
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DOIhttps://doi.org/10.29099/ijair.v9i2.1576 |
Article metrics10.29099/ijair.v9i2.1576 Abstract views : 3 |
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References
A. Nikitas, K. Michalakopoulou, E. T. Njoya, and D. Karampatzakis, “Artificial intelligence, transport and the smart city: Definitions and dimensions of a new mobility era,” Sustainability, vol. 12, no. 7, p. 2789, 2020.
R. D. Knowles, F. Ferbrache, and A. Nikitas, “Transport’s historical, contemporary and future role in shaping urban development: Re-evaluating transit oriented development,” Cities, vol. 99, p. 102607, 2020.
R. Gasmi, M. Aliouat, and H. Seba, “Geographical Information Based Clustering Algorithm for Internet of Vehicles,” in Machine Learning for Networking, vol. 12629, É. Renault, S. Boumerdassi, and P. Mühlethaler, Eds., in Lecture Notes in Computer Science, vol. 12629. , Cham: Springer International Publishing, 2021, pp. 107–121. doi: 10.1007/978-3-030-70866-5_7.
V. Albino, U. Berardi, and R. M. Dangelico, “Smart Cities: Definitions, Dimensions, Performance, and Initiatives,” J. Urban Technol., vol. 22, no. 1, pp. 3–21, Jan. 2015, doi: 10.1080/10630732.2014.942092.
F. Creutzig, P. Jochem, O. Y. Edelenbosch, L. Mattauch, and D. P. V. Vuuren, “Transport: A roadblock to climate change mitigation?,” Science, vol. 350, no. 6263, pp. 911-912, 2015.
S. K. Lakshmanaprabu et al., “An effect of big data technology with ant colony optimization based routing in vehicular ad hoc networks: Towards smart cities,” J. Clean. Prod., vol. 217, pp. 584–593, Apr. 2019, doi: 10.1016/j.jclepro.2019.01.115.
J. Cheng, J. Cheng, M. Zhou, F. Liu, and S. Gao, “Routing in internet of vehicles: A review,” IEEE Trans. Intell. Transp. Syst., vol. 16, no. 5, pp. 2339-2352, 2015.
P. C. Srinivasa Rao, A. J. Sravan Kumar, Q. Niyaz, P. Sidike, and V. K. Devabhaktuni, “Binary chemical reaction optimization based feature selection techniques for machine learning classification problems,” Expert Syst. Appl., vol. 167, p. 114169, Apr. 2021, doi: 10.1016/j.eswa.2020.114169.
X. Cheng and B. Huang, “A Center-Based Secure and Stable Clustering Algorithm for VANETs on Highways,” Wirel. Commun. Mob. Comput., vol. 2019, pp. 1–10, Jan. 2019, doi: 10.1155/2019/8415234.
Y. Wu, H.-N. Dai, and H. Tang, “Graph Neural Networks for Anomaly Detection in Industrial Internet of Things,” IEEE Internet Things J., vol. 9, no. 12, pp. 9214–9231, June 2022, doi: 10.1109/JIOT.2021.3094295.
O. Senouci, Z. Aliouat, and S. Harous, “A review of routing protocols in internet of vehicles and their challenges,” Sens. Rev., vol. 39, no. 1, pp. 58-70, 2019.
A. K. Dutta, M. Elhoseny, V. Dahiya, and K. Shankar, “An efficient hierarchical clustering protocol for multihop internet of vehicles communication,” Trans. Emerg. Telecommun. Technol., vol. 31, no. 5, pp. 1-13, 2020.
H. Wu, H. Tang, and L. Dong, “A Novel Routing Protocol Based on Mobile Social Networks and Internet of Vehicles,” in Internet of Vehicles – Technologies and Services, vol. 8662, R. C.-H. Hsu and S. Wang, Eds., in Lecture Notes in Computer Science, vol. 8662. , Cham: Springer International Publishing, 2014, pp. 1–10. doi: 10.1007/978-3-319-11167-4_1.
C.-J. Huang et al., “An adaptive multimedia streaming dissemination system for vehicular networks,” Appl. Soft Comput., vol. 13, no. 12, pp. 4508–4518, Dec. 2013, doi: 10.1016/j.asoc.2013.07.025.
T. Zaheer, A. W. Malik, A. U. Rahman, A. Zahir, and M. M. Fraz, “A vehicular network–based intelligent transport system for smart cities,” Int. J. Distrib. Sens. Netw., vol. 15, no. 11, p. 155014771988884, 2019.
N. Omar, N. Yaakob, Z. Husin, and M. Elshaikh, “Design and development of greedlea routing protocol for internet of vehicle (iov,” in IOP Conference Series: Materials Science and Engineering, 2020, p. 012034,.
S. Ebadinezhad, Z. Dereboylu, and E. Ever, “Clustering-Based Modified Ant Colony Optimizer for Internet of Vehicles (CACOIOV),” Sustainability, vol. 11, no. 9, p. 2624, May 2019, doi: 10.3390/su11092624.
K. Lin, F. Xia, and G. Fortino, “Data-driven clustering for multimedia communication in Internet of vehicles,” Future Gener. Comput. Syst., vol. 94, pp. 610–619, May 2019, doi: 10.1016/j.future.2018.12.045.
S. Arjunan, S. Pothula, and D. Ponnurangam, “F5N?based unequal clustering protocol (F5NUCP) for wireless sensor networks,” Int. J. Commun. Syst., vol. 31, no. 17, p. e3811, Nov. 2018, doi: 10.1002/dac.3811.
N. Omar, N. Yaakob, Z. Husin, and M. Elshaikh, “Design and Development of GreedLea Routing Protocol for Internet of Vehicle (IoV),” IOP Conf. Ser. Mater. Sci. Eng., vol. 767, no. 1, p. 012034, Feb. 2020, doi: 10.1088/1757-899X/767/1/012034.
R. K. Yadav and H. Banka, “An improved chemical reaction-based approach for multiple sequence alignment,” Curr. Sci., vol. 112, no. 3, p. 527, 2017.
J. Zhang, Y. Wang, S. Li, and S. Shi, “An Architecture for IoT-Enabled Smart Transportation Security System: A Geospatial Approach,” IEEE Internet Things J., vol. 8, no. 8, pp. 6205–6213, Apr. 2021, doi: 10.1109/JIOT.2020.3041386.
F. Aadil, W. Ahsan, Z. U. Rehman, P. A. Shah, and S. Rho, “Clustering algorithm for internet of vehicles (IoV) based on dragonfly optimizer (CAVDO,” J. Supercomput., vol. 74, no. 9, pp. 4542-4567, 2018.
M. Ahmed Hamza, H. Mesfer Alshahrani, F. N. Al-Wesabi, M. Al Duhayyim, A. Mustafa Hilal, and H. Mahgoub, “Artificial Intelligence Based Clustering with Routing Protocol for Internet of Vehicles,” Comput. Mater. Contin., vol. 70, no. 3, pp. 5835–5853, 2022, doi: 10.32604/cmc.2022.021059.
M. Buvanesvari, J. Uthayakumar, and J. Amudhavel, “Fuzzy based clustering to maximize network lifetime in wireless mobile sensor networks,” J. Adv. Res. Dyn. Control Syst., vol. 9, no. 12, pp. 2156-2167, 2017.
H. Fatemidokht and M. Kuchaki Rafsanjani, “QMM-VANET: An efficient clustering algorithm based on QoS and monitoring of malicious vehicles in vehicular ad hoc networks,” J. Syst. Softw., vol. 165, p. 110561, July 2020, doi: 10.1016/j.jss.2020.110561.
F. Wang, M. Zhang, X. Wang, X. Ma, and J. Liu, “Deep Learning for Edge Computing Applications: A State-of-the-Art Survey,” IEEE Access, vol. 8, pp. 58322–58336, 2020, doi: 10.1109/ACCESS.2020.2982411.
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