Optimizing the cyber-physical intelligent transportation system network using enhanced models for data routing and task scheduling✩

Srinivasa Gowda G. K; Hayder M.A. Ghanimi; Sudhakar Sengan; Kolla Bhanu Prakash; Meshal Alharbi; Roobaea Alroobaea; Sultan Algarni; Abdullah M. Baqasah

doi:10.1016/j.dcan.2025.01.004

›› 2026, Vol. 12 ›› Issue (1) :210 -222. DOI: 10.1016/j.dcan.2025.01.004

Special issue on cyber-physical systems for intelligent transportation and smart cities

research-article

Optimizing the cyber-physical intelligent transportation system network using enhanced models for data routing and task scheduling^✩

Author information +

^a Department of Computer Science Engineering, ACS College of Engineering, Bangalore, Karnataka 560074, India

^b Department of Information Technology, College of Science, University of Warith Al-Anbiyaa, Karbala, Iraq

^c Department of Computer Science, College of Computer Science and Information Technology, University of Kerbala, Karbala, Iraq

^d Department of Computer Science and Engineering, PSN College of Engineering and Technology, Tirunelveli, 627152, Tamil Nadu, India

^e Department Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Green Fields, Vaddeswaram, Guntur, 522502, Andhra Pradesh, India

^f Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Alkharj, 11942, Saudi Arabia

^g Department of Computer Science, College of Computers and Information Technology, Taif University, P. O. Box 11099, Taif 21944, Saudi Arabia

^h Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia

ⁱ Department of Information Technology, College of Computers and Information Technology, Taif University, P. O. Box 11099, Taif 21944, Saudi Arabia

^* E-mail addresses: seenugowda2008@gmail.com (S.G.G. K), hayder.alghanami@uowa.edu.iq (H.M.A. Ghanimi), sudhasengan@gmail.com (S. Sengan), drkbp@kluniversity.in (K.B. Prakash), mg.alharbi@psau.edu.sa (M. Alharbi), r.robai@tu.edu.sa (R. Alroobaea), saalgarni@kau.edu.sa (S. Algarni), a.baqasah@tu.edu.sa (A.M. Baqasah).

Srinivasa Gowda G. K is a professor with over 20 years of experience in teaching electronics and communication engineering subjects. He is currently working as Professor in the Department of Computer Science, ACS College of Engineering, Bangalore, 560078, India. He has held positions at several colleges in Bangalore, India, including serving as principal at one institution. His areas of expertise include wireless networks, wireless com-munication applications, and conducting research and development. He aims to impart knowledge and skills to students and contribute to the field of science and engineering through research activities.

Hayder M.A. Ghanimi is a distinguished academic and researcher in the field of Computer Science, currently serving as the Dean of the College of Computer Science and Information Technology at the University of Warith Al-Anbiyaa. He holds a PhD in Artifi-cial Intelligence from Universiti Utara Malaysia (UUM), an achievement that underscores his deep expertise in high-level computational theory and algorithmic design. Throughout his career, Dr. Ghanimi has focused on the intersection of human cognition and compu-tational intelligence, becoming a prominent figure in AI research through his extensive publications in cognitive modelling, optimization, and machine learning. As a strategic leader and educator, he is dedicated to advancing the technological landscape by bridg-ing the gap between theoretical AI frameworks and practical, real-world applications.

Sudhakar Sengan is presently working as Professor & Director International Relation in the Department of Computer Science and Engineering at PSN College of Engineering and Technology (Autonomous Institution), Tirunelveli, Tamil Nadu, India. He received Ph.D. degree in Information and Communication Engineering from Anna University, Chen-nai, Tamil Nadu, India. He received his ME degree in Computer Science and Engineering faculty from Anna University, Chennai, Tamil Nadu, India, in 2007. He has 20 years of Experience in Teaching/Research/Industry. He has published papers in 100 International Journals, 20 International Conferences, and 10 National Conferences. His research in-terest includes IoT, Cloud Computing, Machine Learning, Network Security, Information Security, and MANET. He is a member of various professional bodies like MISTE, MIEEE, MIAENG, MIACSIT, MICST, MIE, and MIEDRC. He guided more than 100 Projects for UG & PG students in engineering streams. He is the Recognized Research Supervisor at Anna University under the faculty of Information and Communication Engineering. He received an Honorary Doctorate award (Doctor of Letters-D.LITT.) from International Economics University, SAARC Countries in Education and Students Empowerment, in April 2017. He is currently guiding many research scholars in various Universities. He is a potential re-viewer for many reputed journals. He is Editor and Editor-in-Chief of many International Journals. He has published 20 Indian Patents and 3 International Patents for various do-mains. He has published a Three Textbook for Anna University, Chennai Syllabus.

Kolla Bhanu Prakash is a Professor and Associate Dean [R&D] at K.L. Deemed to be University. He is an Adjunct Professor at Taylors University, Malaysia. He has published 150+ research papers in international and national journals and conferences. He pub-lished Scopus and SCI publications 100 with an H-index of 23 and a total citation of 2100. He is a reviewer for several international journals, including IEEE, Elsevier, Springer, Wiley, Taylor, and Francis, and he has served as a reviewer, keynote speaker, and TPC member for several International conferences. His research interests include deep learning, data science, and quantum computing. He received the Best Researcher Award 4 times. He authored three and edited 15 books with internationally reputed publishers like IEEE, Elsevier, Springer, River, CRC, Degryuter, Nova and Wiley. He also published 15 patents, two patent grants, and 1 Copyright. He is guiding 3 Ph.D scholars. Under his guidance, 3 Ph.D’s were awarded. Dr. K. Bhanu Prakash is an IEEE senior member, fellow ISRD, and treasurer of the ACM Amaravathi Chapter in India, LMISTE, MIAENG, and SMIRED. He is the Series Editor of “Next Generation Computing & Communication Engineering” at Wi-ley Publishers. He is a series editor-“Industry 5.0: Artificial Intelligence, Cyber-Physical Systems, Mechatronics, and Smart Grids”-CRC publishers. He is a NAAC Accessor. He is listed in the top 2% of cited researchers worldwide by Stanford University and Elsevier for 2021. He received a grant of 27 lakhs from Meity for establishing a Quantum Computing Research lab [QCAL] at KLEF and a grant from DST SERB worth five lakhs for organizing a workshop and conference in Quantum Computing. He delivered many keynote speeches and expert talks worldwide on quantum computing, quantum communication, machine learning, and deep learning.

Meshal Alharbi received the M.Sc. degree in computer science from Wayne State University, USA, in 2014, and the Ph.D. degree in computer science from Durham Uni-versity, U.K., in 2020. He has ten years of experience in teaching/research/industry. He is currently an Assistant Professor of artificial intelligence with the Department of Computer Science, Prince Sattam Bin Abdulaziz University, Saudi Arabia. His research in-terests include artificial intelligence applications and algorithms, agent-based modeling and simulation applications, disaster/emergency management and resilience, optimiza-tion applications, and machine learning.

Roobaea Alroobaea received the bachelor’s degree (Hons.) in computer science from King Abdulaziz University (KAU), Saudi Arabia, in 2008, and the master’s degree in in-formation system and the Ph.D. degree in computer science from the University of East Anglia, U.K., in 2012 and 2016, respectively. He is currently a Professor with the College of Computers and Information Technology, Taif University, Saudi Arabia. His research interests include human computer interaction, cloud computing, and machine learning.

Sultan Algarni received the B.S. degree in Computer Science from King Abdulaziz University in 2008, the M.Sc. degree in Information Technology from UNSW, Australia, in 2014, and the PhD degree from King Abdulaziz University in Computer Science in 2022. He is currently an Assistant Professor in the department of Information systems, King Abdulaziz University, Jeddah, Saudi Arabia. His research interest includes Networking, Cyber security, Artificial intelligence.

Abdullah M. Baqasah received the bachelor’s degree in computer science from King Abdulaziz University (KAU), Saudi Arabia, in 2003, and the master’s degree in information technology and the Ph.D. degree in computer science from La Trobe University, Australia, in 2009 and 2015, respectively. He is currently an Assistant Professor with the College of Computers and Information Technology, Taif University, Saudi Arabia. His research inter-ests include semantic web, software engineering, the Internet of Things, cloud computing, artificial intelligence, and machine learning.

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Abstract

Advanced technologies like Cyber-Physical Systems (CPS) and the Internet of Things (IoT) have supported modernizing and automating the transportation region through the introduction of Intelligent Transportation Systems (ITS). Integrating CPS-ITS and IoT provides real-time Vehicle-to-Infrastructure (V2I) communication, supporting better traﬃc management, safety, and eﬃciency. These technological innovations generate complex problems that need to be addressed, uniquely about data routing and Task Scheduling (TS) in ITS. Attempts to solve those problems were primarily based on traditional and experimental methods, and the solutions were not so successful due to the dynamic nature of ITS. This is where the scope of Machine learning (ML) and Swarm Intelligence (SI) has significantly impacted dealing with these challenges; in this line, this research paper presents a novel method for TS and data routing in the CPS-ITS. This paper proposes using a cutting-edge ML algorithm for data transmission from CPS-ITS. This ML has Gated Linear Unit-approximated Reinforcement Learning (GLRL). Greedy Iterative-Particle Swarm Optimization (GI-PSO) has been recommended to develop the Particle Swarm Optimization (PSO) for TS. The primary objective of this study is to enhance the security and eﬀectiveness of ITS systems that utilize CPS-ITS. This study trained and validated the models using a network simulation dataset of 50 nodes from numerous ITS environments. The experiments demonstrate that the proposed GLRL reduces End-to-End Delay (EED) by 12%, enhances data size use from 83.6% to 88.6%, and achieves higher bandwidth allocation, particularly in high-demand scenarios such as multimedia data streams where adherence improved to 98.15%. Furthermore, the GLRL reduced Network Congestion (NC) by 5.5%, demonstrating its eﬃciency in managing complex traﬃc conditions across several environments. The model passed simulation tests in three diﬀerent environments: urban (UE), suburban (SE), and rural (RE). It met the high bandwidth requirements, made task scheduling more eﬃcient, and increased network throughput (NT). This proved that it was robust and flexible enough for scalable ITS applications. These innovations provide robust, scalable solutions for real-time traﬃc management, ultimately improving safety, reducing NC, and increasing overall NT. This study can aﬀect ITS by developing it to be more responsive, safe, and eﬀective and by creating a perfect method to set up UE, SE, and RE.

Keywords

Cyber-physical systems / Internet of things / Task scheduling optimization / Gated linear unit / Machine learning

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Srinivasa Gowda G. K, Hayder M.A. Ghanimi, Sudhakar Sengan, Kolla Bhanu Prakash, Meshal Alharbi, Roobaea Alroobaea, Sultan Algarni, Abdullah M. Baqasah. Optimizing the cyber-physical intelligent transportation system network using enhanced models for data routing and task scheduling^✩. , 2026, 12(1): 210-222 DOI:10.1016/j.dcan.2025.01.004

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CRediT authorship contribution statement

Srinivasa Gowda G. K:Supervision, Data curation.Hayder M.A. Ghanimi:Software, Resources.Sudhakar Sengan:Writing-original draft, Methodology, Investigation, Formal analysis, Conceptualization.Kolla Bhanu Prakash:Writing-review & editing, Resources.Meshal Alharbi:Project administration, Formal analysis.Roobaea Alroobaea:Validation, Supervision, Software.Sultan Algarni:Supervision, Project administration.Abdullah M. Baqasah:Resources, Data curation.

Consent for publication

All the authors reviewed the results, approved the final version of the manuscript, and agreed to publish it.

Publication statement

This work described has not been published previously and is not under consideration for publication elsewhere.

Funding statement

This research was funded by Taif University, Taif, Saudi Arabia, project number (TU-DSPP-2024-17).

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to ifluence the work reported in this paper.

Acknowledgement

The authors extend their appreciation to Taif University Taif Uni-versity, Saudi Arabia, for supporting this work through project number (TU-DSPP-2024-17).

Data availability

The experimental data used to support the findings of this study are available from the corresponding author on request.

References

Publishing order | Descend order by publishing year | Descend order by cited within

[1]	Giannaros A. Karras L. Theodorakopoulos D. Tsolis, Autonomous vehicles: so-phisticated attacks, safety issues, challenges, open topics, blockchain, and future directions, J. Cybersecur. Priv. 3 (3) (2023) 493-543.

[2]	Ding L. Zhu L. Shen Z. Li Y. Li Q. Liang, The intelligent traﬃc flow control system based on 6G and optimized genetic algorithm, IEEE Trans. Intell. Transp. Syst. (2024) 1-14, https://doi.org/10.1109/TITS.2024.3467269.

[3]	Zhang J. Qiao S. Wang R. Chen H. Dui Y. Zhang Y. Zhou, Importance measures based on system performance loss for multi-state phased-mission systems, Reliab. Eng. Syst. Saf. 256 (110776) (2025) 1-25.

[4]	Zhu, Intelligent robot path planning and navigation based on reinforcement learning and adaptive control, J. Logist. Inf. Service Sci. 10 (3) (2023) 235-248.

[5]	L. Chen, T. Chen, T. Lan, C. Chen, J. Pan, The contributions of population distribu-tion, healthcare resourcing, and transportation infrastructure to spatial accessibility of health care, Inquiry (2023).

[6]	Song J. Zhao B. Zhu J. Han S. Jia, Subjective driving risk prediction based on spatiotemporal distribution features of human driver’s cognitive risk, IEEE Trans. Intell. Transp. Syst. 25 (11) (2024) 16687-16703.

[7]	Wang, et al., Transformer-based spatio-temporal traﬃc prediction for access and metro networks, J. Lightwave Technol. 42 (15) (2024) 5204-5213.

[8]	Sun L. Song H. Yu V. Chang X. Du M. Guizani, V2V routing in a VANET based on the autoregressive integrated moving average model, IEEE Trans. Veh. Technol. 68 (1) (2019) 908-922.

[9]	Sun Y. Zhang D. Liao H. Yu X. Du M. Guizani, Bus-trajectory-based street-centric routing for message delivery in urban vehicular ad hoc networks, IEEE Trans. Veh. Technol. 67 (8) (2018) 7550-7563.

[10]	Sun Y. Zhang H. Yu X. Du M. Guizani, Intersection fog-based distributed rout-ing for V2V communication in urban vehicular ad hoc networks, IEEE Trans. Intell. Transp. Syst. 21 (6) (2020) 2409-2426.

[11]	Sun Z. Wang H. Su H. Yu B. Lei M. Guizani, Profit maximization of independent task oﬄoading in MEC-enabled 5G Internet of vehicles, IEEE Trans. Intell. Transp. Syst. 25 (11) (2024) 16449-16461.

[12]	Y. Rong, et al., Du-bus: a realtime bus waiting time estimation system based on multi-source data, IEEE Trans. Intell. Transp. Syst. 23 (12) (2022) 24524-24539.

[13]	Chen Y. Bei W. Huang S. Chen F. Huang X. Huang, Graph cross-correlated network for recommendation, IEEE Trans. Knowl. Data Eng. 37 (2) (2025) 710-723.

[14]	Sheng, et al., Discriminative feature learning with co-occurrence attention network for vehicle ReID, IEEE Trans. Circuits Syst. Video Technol. 34 (5) (2024) 3510-3522.

[15]	Chen C. Yu Y. Wang Z. Zhou Z. Liu, Hybrid modeling for vehicle lateral dynamics via AGRU with a dual-attention mechanism under limited data, Control Eng. Pract. 151 (2024) 106015, 1-25.

[16]	Chen M. Xu W. Xu D. Li W. Peng H. Xu, A flow feedback traﬃc prediction based on visual quantified features, IEEE Trans. Intell. Transp. Syst. 24 (9) (2023) 10067-10075.

[17]	Chen Q. Wang H.H. Cheng W. Peng W. Xu, A review of vision-based traﬃc semantic understanding in ITSs, IEEE Trans. Intell. Transp. Syst. 23 (11) (2022) 19954-19979.

[18]	Gong Y. Liu T. Li H. Chai Y. Li,Empowering spatial knowledge graph for mo-bile traﬃc prediction, in:Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems, vol. 24, Association for Computing Machinery, New York, NY, USA, 2023, pp. 1-11.

[19]	Liang K. Yang C. Tan J. Wang G. Yin, Enhancing high-speed cruising performance of autonomous vehicles through integrated deep reinforcement learning framework, IEEE Trans. Intell. Transp. Syst. 26 (1) (2025) 835-848.

[20]	Lu C. Osorio, A probabilistic traﬃc-theoretic network loading model suitable for large-scale network analysis, Transp. Sci. 52 (6) (2018) 1509-1530.

[21]	G.Luo, G. Wang, G. Li, et al., Transport infrastructure connectivity and conflict res-olution: a machine learning analysis, Neural Comput. Appl. 34 (2022) 6585-6601.

[22]	Shen H. Sheng S. Wang R. Cong D. Yang Y. Zhang, Blockchain-based distributed multiagent reinforcement learning for collaborative multiobject tracking framework, IEEE Trans. Comput. 73 (3) (2024) 778-788.

[23]	Xiao, et al., CALRA: practical conditional anonymous and leakage-resilient au-thentication scheme for vehicular crowdsensing communication, IEEE Trans. Intell. Transp. Syst. 26 (1) (2025) 1273-1285.

[24]	J.Y. Xia, S. Li, J.J. Huang, Z. Yang, I.M. Jaimoukha, D. Gündüz, Metalearning-based alternating minimization algorithm for nonconvex optimization, IEEE Trans. Neural Netw. Learn. Syst. 34 (9) (2023) 5366-5380.

[25]	J. Yang, K. Yang, Z. Xiao, H. Jiang, S. Xu, S. Dustdar, Improving commute experience for private car users via blockchain-enabled multitask learning, IEEE Internet Things J. 10 (24) (2023) 21656-21669.

[26]	Hamzah M.M. Islam S. Hassan M.N. Akhtar M.J. Ferdous M.B. Jasser A.W. Mo-hamed, Distributed control of cyber-physical systems on various domains: a critical review, Systems 11 (4) (2023) 208.

[27]	Li R.Y. Zhong T. Qu, Spatial-temporal out-of-order execution for advanced planning and scheduling in cyber-physical factories, J. Intell. Manuf. 33 (2022) 1355-1372.

[28]	Q. Cheng, W. Chen, R. Sun, J. Wang, D. Weng, RANSAC-based instantaneous real-time kinematic positioning with GNSS triple-frequency signals in urban areas, J. Geod. 98 (4) (2024) 1-24.

[29]	R. Shang, Y. Zhang, Z.-J. Max Shen, Y. Zhang, Analyzing the eﬀects of road type and rainy weather on fuel consumption and emissions: a mesoscopic model based on big traﬃc data, IEEE Access 9 (2021) 62298-62315.

[30]	S. Wang, H. Sheng, D. Yang, Y. Zhang, Y. Wu, S. Wang, Extendable multiple nodes recurrent tracking framework with RTU++, IEEE Trans. Image Process. 31 (2022) 5257-5271.

[31]	T. Li, et al., Driving Big Data: A First Look at Driving Behavior via a Large-Scale Private Car Dataset, IEEE 35th International Conference on Data Engineering Work-shops (ICDEW), Macao, China, 2019, pp. 61-68.

[32]	T. Wang, et al., Synchronous spatiotemporal graph transformer: a new framework for traﬃc data prediction, IEEE Trans. Neural Netw. Learn. Syst. 34 (12) (2023) 10589-10599.

[33]	W. Liu, et al., Towards explainable traﬃc signal control for urban networks through genetic programming, Swarm Evol. Comput. 88 (101588) (2024) 1-25.

[34]	W. Yue, J. Li, C. Li, N. Cheng, J. Wu, A channel knowledge map-aided personalized resource allocation strategy in air-ground integrated mobility, IEEE Trans. Intell. Transp. Syst. 25 (11) (2024) 18734-18747.

[35]	W. Zou, et al., Limited sensing and deep data mining: a new exploration of develop-ing city-wide parking guidance systems, IEEE Intell. Transp. Syst. Mag. 14 (1) (2022) 198-215.

[36]	X. Peng, S. Song, X. Zhang, M. Dong, K. Ota, Task oﬄoading for IoAV under extreme weather conditions using dynamic price driven double broad reinforcement learning, IEEE Internet Things J. 11 (10) (2024) 17021-17033.

[37]	X. Xu, Z. Wei, Dynamic pickup and delivery problem with transshipments and LIFO constraints, Comput. Ind. Eng. 175 (2023) 108835, 1-25.

[38]	Y. Fu, M. Dong, L. Zhou, C. Li, F.R. Yu, N. Cheng, A distributed incentive mechanism to balance demand and communication overhead for multiple federated learning tasks in IoV, IEEE Internet Things J. (2024).

[39]	Y. Gong, H. Yao, A. Nallanathan, Intelligent sensing, communication, computation, and caching for satellite-ground integrated networks, IEEE Netw. 38 (4) (2024) 9-16.

[40]	Y. Gong, H. Yao, X. Liu, M. Bennis, A. Nallanathan, Z. Han, Computation and privacy protection for satellite-ground digital twin networks, IEEE Trans. Commun. 72 (9) (2024) 5532-5546.

[41]	Y. Ren, Z. Lan, L. Liu, H. Yu, EMSIN: enhanced multistream interaction network for vehicle trajectory prediction, IEEE Trans. Fuzzy Syst. 33 (1) (2025) 54-68.

[42]	Y. Wang, R. Sun, Q. Cheng, W.Y. Ochieng, Measurement quality control aided mul-tisensor system for improved vehicle navigation in urban areas, IEEE Trans. Ind. Electron. 71 (6) (2024) 6407-6417.

[43]	Z. Li, J. Hu, B. Leng, L. Xiong, Z. Fu, An integrated of decision making and mo-tion planning framework for enhanced oscillation-free capability, IEEE Trans. Intell. Transp. Syst. 25 (6) (2024) 5718-5732.

[44]	Z. Li, Y. Wang, R. Zhang, F. Ding, C. Wei, J.-G. Lu, A LiDAR-OpenStreetMap matching method for vehicle global position initialization based on boundary directional fea-ture extraction, IEEE Trans. Intell. Veh. (2025), https://doi.org/10.1109/TIV.2024.3393229.

[45]	Z. Wang, M. Chen, Y. Guo, Z. Li, Q. Yu, Bridging the domain gap in satellite pose estimation: a self-training approach based on geometrical constraints, IEEE Trans. Aerosp. Electron. Syst. 60 (3) (2024) 2500-2514.

[46]	Z. Xiao, J. Shu, H. Jiang, G. Min, H. Chen, Z. Han, Overcoming occlusions: percep-tion task-oriented information sharing in connected and autonomous vehicles, IEEE Netw. 37 (4) (2023) 224-229.

[47]	Z. Zhou, Y. Wang, G. Zhou, X. Liu, M. Wu, K. Dai, Vehicle lateral dynamics-inspired hybrid model using neural network for parameter identification and error charac-terization, IEEE Trans. Veh. Technol. 73 (11) (2024) 16173-16186.

[48]	Z. Sai, W. Junrui, Y. Haisheng, W. Wenyong, H. Huang, N. Wei, L. Yan, Eﬃciency-optimized 6G: a virtual network resource orchestration strategy by enhanced particle swarm optimization, Digital Commun. Netw. 10 (5) (2024) 1221-1233.

[49]	W. Yue, J. Tao, G. Qinghe, W. Yingzhen, H. Yan, Game-theoretic physical layer au-thentication for spoofing detection in Internet of things, Digital Commun. Netw. 10 (5) (2024) 1394-1404.