iFogSUMO: An Integrated Platform of iFogSim and SUMO to Enhance Simulation Capabilities of Adaptive Traffic Control for Smart City Applications

Authors

DOI:

https://doi.org/10.3991/ijoe.v22i02.58823

Keywords:

SUMO, iFogSim, Co-Simulation, Smart City, iFogSUMO, Adaptive Traffic Control, Ambulance Routing

Abstract


The increasing demand for smart city applications and Internet of Things (IoT) solutions has led to a growing need for simulation tools that can accurately model and analyze complex systems. iFogSim and Simulator of Urban Mobility (SUMO) are two popular simulation tools that cater to different aspects of smart city and IoT applications. iFogSim focuses on fog computing and IoT simulations, while SUMO specializes in traffic and mobility simulations. The integration of iFogSim and SUMO simulators can enable researchers and developers to design and optimize smart city applications more effectively by providing a robust testbed. This paper proposes integrating iFogSim and SUMO to create a comprehensive simulation framework, iFogSUMO, that accurately models and analyzes adaptive traffic control systems. The framework was evaluated for ambulance routing by simulating traffic with various vehicle types and executing the adaptive traffic control algorithm to assess its performance in terms of trip time, waiting time, and time loss during ambulance routing, while maintaining scalability across heterogeneous vehicle fleets.

Author Biographies

Ashwini Matange, COEP Technological University, Pune, Maharashtra, India and Pimpri Chinchwad College of Engineering, Pune, Maharashtra, India

Research Scholar, COEP Technological University, Pune, Maharashtra, India

Assistant Professor at PCET's Pimpri Chinchawad College of Engineering, Pune, Maharashtra, India

Please include both affiliations for the 1st author - Ashwini Matange

Jibi Abraham, COEP Technological University, Pune, Maharashtra, India

Professor, Department of Computer Science and Engineering, School of Engineering and Technology, COEP Technological University, Pune, Maharashtra, India

References

[1] S. Veer, S. Ravichandran, P. Trivedi, J. Abraham, and A. Matange, “A review of traffic-induced stress mitigation through adaptive traffic signaling and fog computing,” in Proc. 5th Int. Conf. Pervasive Comput. Social Netw. (ICPCSN), Salem, India, 2025, pp. 1215–1221. doi: 10.1109/ICPCSN65854.2025.11035795.

[2] X. Ma et al., “Evaluation of accuracy of traffic flow generation in SUMO,” Appl. Sci., vol. 11, no. 6, p. 2584, 2021.

[3] N. Rida, M. Ouadoud, and A. Hasbi, “Coordinated signal control system in urban road network,” Int. J. Online Biomed. Eng. (iJOE), vol. 16, no. 10, pp. 4–22, Sep. 2020. doi: 10.3991/ijoe.v16i10.15473.

[4] T. Azfar, “Traffic co-simulation framework empowered by CARLA and SUMO,” arXiv preprint arXiv:2412.03925, Dec. 2024. [Online]. Available: [https://arxiv.org/abs/2412.03925](https://arxiv.org/abs/2412.03925)

[5] M. Fahimullah et al., “Simulation tools for fog computing: A comparative analysis,” J. Cloud Comput., vol. 12, no. 1, pp. 1–18, 2023.

[6] M. Fahimullah et al., “A review of fog computing and its simulators,” J. Cloud Comput.: Adv. Syst. Appl., vol. 12, no. 1, pp. 1–18, 2023.

[7] K. S. Awaisi and A. Abbas, “iFogSim: A toolkit for modeling and simulation of resource management techniques in Internet of Things, edge and fog computing environments,” Softw.: Pract. Exper., vol. 47, no. 9, pp. 1275–1296, Sep. 2017.

[8] F. Bonomi, R. Milito, J. Zhu, and S. Addepalli, “Fog computing and its role in the Internet of Things,” in Proc. 1st MCC Workshop Mobile Cloud Comput., 2012, pp. 13–16.

[9] M. Chiang and T. Zhang, “Fog and IoT: An overview of research opportunities,” IEEE Internet Things J., vol. 3, no. 6, pp. 854–864, Dec. 2016.

[10] A. Zanella, N. Bui, A. Castellani, L. Vangelista, and M. Zorzi, “Internet of Things for smart cities,” IEEE Internet Things J., vol. 1, no. 1, pp. 22–32, Feb. 2014.

[11] K. Micko, P. Papcun, and I. Zolotova, “Review of IoT sensor systems used for monitoring the road infrastructure,” Sensors, vol. 23, no. 9, p. 4469, May 2023.

[12] S. Yi, Z. Hao, Z. Qin, and Q. Li, “Fog computing: Platform and applications,” in Proc. 3rd HotWeb Workshop, 2015.

[13] C. Puliafito et al., “Fog computing for the Internet of Things: A survey,” ACM Comput. Surveys, vol. 52, no. 3, pp. 1–36, Jul. 2019.

[14] S. Sarkar and S. Misra, “Theoretical modelling of fog computing: A green computing paradigm to support IoT applications,” IET Netw., vol. 5, no. 2, pp. 23–29, Mar. 2016.

[15] P. Lopez et al., “Microscopic traffic simulation using SUMO,” in Proc. IEEE Intell. Transp. Syst. Conf. (ITSC), 2018, pp. 2575–2582.

[16] M. Behrisch, L. Bieker, J. Erdmann, and D. Krajzewicz, “SUMO—simulation of urban mobility: An overview,” in Proc. 3rd Int. Conf. Adv. Syst. Simulation (SIMUL), 2011, pp. 55–60.

[17] R. M. Rodriguez et al., “Smart traffic light control with reinforcement learning,” Appl. Sci., vol. 10, no. 1, p. 233, Jan. 2020.

[18] D. Seo, S. Shahhosseini, M. A. Mehrabadi, B. Donyanavard, S.-S. Lim, A. M. Rahmani, and N. Dutt, “Dynamic iFogSim: A framework for full-stack simulation of dynamic resource management in IoT systems,” in Proc. Int. Conf. Omni-layer Intell. Syst. (COINS), 2020, pp. 1–7. doi: 10.1109/COINS49042.2020.9191663.

[19] Z. Wei, C. Huang, B. Li, Y. Zhao, X. Cheng, L. Yang, and R. Zhang, “AirFogSim: A light-weight and modular simulator for UAV-integrated vehicular fog computing,” arXiv preprint arXiv:2409.02518, Sep. 2024.

[20] F. Dressler and C. Sommer, “Using the right simulator for the job: A case study on vehicular network evaluation,” IEEE Commun. Mag., vol. 52, no. 11, pp. 132–139, Nov. 2014.

[21] S. Bajpai, G. K. Sahoo, S. K. Das, and P. Singh, “An efficient inter-vehicle communication framework on road traffic accident detection using OMNET++ and SUMO,” in Proc. IEEE Int. Symp. Sustain. Energy, Signal Process. Cyber Secur. (iSSSC), Gunupur, India, 2022, pp. 1–6. doi: 10.1109/iSSSC56467.2022.10051410.

[22] R. A. Pratama et al., “Performance evaluation on VANET routing protocols in the way of central Jakarta using ns-3 and SUMO,” in Proc. Int. Seminar Appl. Technol. Inf. Commun. (iSemantic), 2020.

[23] K. H. M. Gularte et al., “Integrating cybersecurity in V2X: A review of simulation environments,” IEEE Access, 2024.

[24] M. Klischat, O. Dragoi, M. Eissa, and M. Althoff, “Coupling SUMO with a motion planning framework for automated vehicles,” in Proc. SUMO User Conf. (Simulating Connected Urban Mobility), EPiC Ser. Comput., vol. 62, 2019, pp. 1–9. doi: 10.29007/1P2D.

[25] M. Franchi, R. Kahn, L. B. Ngo, S. M. Khan, M. Chowdhury, K. Kennedy, and A. Apon, “Webots.HPC: A parallel simulation pipeline for autonomous vehicles on high performance computing,” in Proc. ACM/IEEE Conf., 2022. doi: 10.1145/3491418.3535133.

[26] X. Liang et al., “CityFlow: A multi-agent reinforcement learning environment for large scale city traffic scenario,” arXiv preprint, 2019.

[27] A. “Optimal Routing in Urban Road Networks: A Graph-Based Approach Using Dijkstra’s Algorithm,” Applied Sciences, vol. 15, no. 8, article 4162, Apr. 2025. doi:10.3390/app15084162.

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Published

2026-02-09

How to Cite

Matange, A., & Abraham, J. (2026). iFogSUMO: An Integrated Platform of iFogSim and SUMO to Enhance Simulation Capabilities of Adaptive Traffic Control for Smart City Applications. International Journal of Online and Biomedical Engineering (iJOE), 22(02), pp. 38–54. https://doi.org/10.3991/ijoe.v22i02.58823

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Papers