Load Aware Traffic Congestion Control Mechanism Using Fuzzy Logic

Ermias Melku Tadesse; Libsework Alemu Tsegaye; Ayene Zinabie Mengesha

doi:doi:10.11648/j.mcs.20251002.11

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Load Aware Traffic Congestion Control Mechanism Using Fuzzy Logic

Ermias Melku Tadesse^*

, Libsework Alemu Tsegaye, Ayene Zinabie Mengesha

Published in Mathematics and Computer Science (Volume 10, Issue 2)

Received: 21 March 2025 Accepted: 2 April 2025 Published: 29 April 2025

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Abstract

Recently, vehicular networks (VANETs) have emerged as promising technology for enabling communication between vehicles and infrastructures to improve road safety and driving experience. However, the dynamic nature of VANETs, characterized by rapidly changing traffic conditions and varying network load, poses significant challenges for reliable communication. Congestion control is a critical aspect of VANETs to prevent network saturation, reduce packet loss, and enhance overall system performance. In this context, the application of fuzzy-logic-based approaches offers a flexible and adaptive solution to dynamically adjust the network performance. This research introduced a fuzzy-logic-based congestion control mechanism for VANEts. The approach focused on dynamically adjusting the beacon busy ratio, road segment, and vehicle speed to address the fluctuating traffic condition, thereby mitigating congestion and enhancing vehicular network efficiency. Leveraging fuzzy logic, the proposed system can make route suggestions through the communication between roadside units based on input variables such as beacon busy ratio, road segment, and vehicle speed. On the result and analysis, the performance analysis of the system-based implemented Network Simulator-3 (NS3) and Simulation for Urban Mobility (SUMO) network simulation tool is used. Through simulation, the efficacy of the approach is demonstrated, showing its ability to adapt to evolving traffic dynamics and alleviate congestion on VANETs for enhancing network performance and reliability. The simulation result shows that our proposed system achieves a packet delivery ratio of 95%, throughput of 110 Kbps, and end-to-end delay of 1.93 seconds. This result shows that our scheme is feasible and effective.

Published in	Mathematics and Computer Science (Volume 10, Issue 2)
DOI	10.11648/j.mcs.20251002.11
Page(s)	26-37
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2025. Published by Science Publishing Group

Keywords

Load, Fuzzy-logics, Roadside Unit, Vehicle, Road Segment

References

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[2]	A. R. Ragab, “A new classification for ad-hoc network,” Int. J. Interact. Mob. Technol., vol. 14, no. 14, pp. 214–223, 2020, https://doi.org/10.3991/IJIM.V14I14.14871
[3]	R. Barskar, “Vehicular Ad hoc Networks and its Applications in Diversified Fields,” vol. 123, no. 10, pp. 7–11, 2015.
[4]	T. Lingala, A. Galipelli, and M. Thanneru, “Traffic Congestion Control through Vehicle-to-Vehicle and Vehicle to Infrastructure Communication,” vol. 5, no. 4, pp. 5081–5084, 2014.
[5]	J. Stalin and R. S. Rajesh, “Fuzzy Logic Based Secured Routing In Vanets Fuzzy Logic Based Secured Routing In Vanets,” vol. 12, no. 11, pp. 335–348, 2021.
[6]	B. Singh and A. K. Mishra, “Fuzzy Logic Control System and its Applications,” no. December, 2023.
[7]	C. Series, “Fuzzy logic controller based priority model for VANET scheduling Fuzzy logic controller based priority model for VANET scheduling,” 2020, https://doi.org/10.1088/1742-6596/1706/1/012054
[8]	T. S. Balaji, S. Srinivasan, S. P. Bharathi, and B. Ramesh, “Fuzzy-Based Secure Clustering with Routing Technique for VANETs,” 2022, https://doi.org/10.32604/csse.2022.023269
[9]	W. L. Zhang, X. Y. Yang, Q. X. Song, and L. Zhao, “V2V Routing in VANET Based on Fuzzy Logic and Reinforcement Learning,” 2021.
[10]	K. Edve, N. T. Rabe, E. R. Arboleda, A. A. Andilab, and R. M. Dellosa, “Fuzzy Logic Based Vehicular Congestion Estimation Monitoring System Using Image Processing and KNN Classifier,” no. August, 2019.
[11]	B. S. Siddhartha, S. K. B. R, K. Arpitha, and S. N. Shwetha, “Routing Protocol using Fuzzy Logic for Vehicular Ad-Hoc Networks,” no. 2, pp. 4789–4794, 2019, https://doi.org/10.35940/ijrte.B2127.078219
[12]	B. Mohamed, “An Efficient Greedy Traffic Aware Routing Scheme for Internet of Vehicles An Efficient Greedy Traffic Aware Routing Scheme for Internet of Vehicles,” no. January, 2019, https://doi.org/10.32604/cmc.2019.07580
[13]	M. Subramaniam, C. Rambabu, G. Chandrasekaran, and N. S. Kumar, “A Traffic Density-Based Congestion Control Method for VANETs,” Wirel. Commun. Mob. Comput., vol. 2022, 2022, https://doi.org/10.1155/2022/7551535
[14]	M. J. Ahmed, S. Iqbal, K. M. Awan, K. Sattar, Z. A. Khan, and H. H. R. Sherazi, “A Congestion Aware Route Suggestion Protocol for Traffic Management in Internet of Vehicles,” Arab. J. Sci. Eng., vol. 45, no. 4, pp. 2501–2511, 2020, https://doi.org/10.1007/s13369-019-04099-9
[15]	W. Ahmad, G. Husnain, S. Ahmed, F. Aadil, and S. Lim, “Received Signal Strength-Based Localization for Vehicle Distance Estimation in Vehicular Ad Hoc Networks (VANETs),” J. Sensors, vol. 2023, 2023, https://doi.org/10.1155/2023/7826992
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[18]	C. Science and S. Engineering, “Simulation of VANET Using NS-3 and SUMO,” vol. 4, no. 4, pp. 563–569, 2014.

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APA Style

Tadesse, E. M., Tsegaye, L. A., Mengesha, A. Z. (2025). Load Aware Traffic Congestion Control Mechanism Using Fuzzy Logic. Mathematics and Computer Science, 10(2), 26-37. https://doi.org/10.11648/j.mcs.20251002.11

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ACS Style

Tadesse, E. M.; Tsegaye, L. A.; Mengesha, A. Z. Load Aware Traffic Congestion Control Mechanism Using Fuzzy Logic. Math. Comput. Sci. 2025, 10(2), 26-37. doi: 10.11648/j.mcs.20251002.11

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AMA Style

Tadesse EM, Tsegaye LA, Mengesha AZ. Load Aware Traffic Congestion Control Mechanism Using Fuzzy Logic. Math Comput Sci. 2025;10(2):26-37. doi: 10.11648/j.mcs.20251002.11

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@article{10.11648/j.mcs.20251002.11,
  author = {Ermias Melku Tadesse and Libsework Alemu Tsegaye and Ayene Zinabie Mengesha},
  title = {Load Aware Traffic Congestion Control Mechanism Using Fuzzy Logic
},
  journal = {Mathematics and Computer Science},
  volume = {10},
  number = {2},
  pages = {26-37},
  doi = {10.11648/j.mcs.20251002.11},
  url = {https://doi.org/10.11648/j.mcs.20251002.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mcs.20251002.11},
  abstract = {Recently, vehicular networks (VANETs) have emerged as promising technology for enabling communication between vehicles and infrastructures to improve road safety and driving experience. However, the dynamic nature of VANETs, characterized by rapidly changing traffic conditions and varying network load, poses significant challenges for reliable communication. Congestion control is a critical aspect of VANETs to prevent network saturation, reduce packet loss, and enhance overall system performance. In this context, the application of fuzzy-logic-based approaches offers a flexible and adaptive solution to dynamically adjust the network performance. This research introduced a fuzzy-logic-based congestion control mechanism for VANEts. The approach focused on dynamically adjusting the beacon busy ratio, road segment, and vehicle speed to address the fluctuating traffic condition, thereby mitigating congestion and enhancing vehicular network efficiency. Leveraging fuzzy logic, the proposed system can make route suggestions through the communication between roadside units based on input variables such as beacon busy ratio, road segment, and vehicle speed. On the result and analysis, the performance analysis of the system-based implemented Network Simulator-3 (NS3) and Simulation for Urban Mobility (SUMO) network simulation tool is used. Through simulation, the efficacy of the approach is demonstrated, showing its ability to adapt to evolving traffic dynamics and alleviate congestion on VANETs for enhancing network performance and reliability. The simulation result shows that our proposed system achieves a packet delivery ratio of 95%, throughput of 110 Kbps, and end-to-end delay of 1.93 seconds. This result shows that our scheme is feasible and effective.
},
 year = {2025}
}

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TY  - JOUR
T1  - Load Aware Traffic Congestion Control Mechanism Using Fuzzy Logic

AU  - Ermias Melku Tadesse
AU  - Libsework Alemu Tsegaye
AU  - Ayene Zinabie Mengesha
Y1  - 2025/04/29
PY  - 2025
N1  - https://doi.org/10.11648/j.mcs.20251002.11
DO  - 10.11648/j.mcs.20251002.11
T2  - Mathematics and Computer Science
JF  - Mathematics and Computer Science
JO  - Mathematics and Computer Science
SP  - 26
EP  - 37
PB  - Science Publishing Group
SN  - 2575-6028
UR  - https://doi.org/10.11648/j.mcs.20251002.11
AB  - Recently, vehicular networks (VANETs) have emerged as promising technology for enabling communication between vehicles and infrastructures to improve road safety and driving experience. However, the dynamic nature of VANETs, characterized by rapidly changing traffic conditions and varying network load, poses significant challenges for reliable communication. Congestion control is a critical aspect of VANETs to prevent network saturation, reduce packet loss, and enhance overall system performance. In this context, the application of fuzzy-logic-based approaches offers a flexible and adaptive solution to dynamically adjust the network performance. This research introduced a fuzzy-logic-based congestion control mechanism for VANEts. The approach focused on dynamically adjusting the beacon busy ratio, road segment, and vehicle speed to address the fluctuating traffic condition, thereby mitigating congestion and enhancing vehicular network efficiency. Leveraging fuzzy logic, the proposed system can make route suggestions through the communication between roadside units based on input variables such as beacon busy ratio, road segment, and vehicle speed. On the result and analysis, the performance analysis of the system-based implemented Network Simulator-3 (NS3) and Simulation for Urban Mobility (SUMO) network simulation tool is used. Through simulation, the efficacy of the approach is demonstrated, showing its ability to adapt to evolving traffic dynamics and alleviate congestion on VANETs for enhancing network performance and reliability. The simulation result shows that our proposed system achieves a packet delivery ratio of 95%, throughput of 110 Kbps, and end-to-end delay of 1.93 seconds. This result shows that our scheme is feasible and effective.

VL  - 10
IS  - 2
ER  -

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Author Information

Ermias Melku Tadesse

Information Technology Department, Kombolcha Institute of Technology, Wollo University, Kombolcha, Ethiopia

Contact Email

http://orcid.org/0009-0005-1187-9190
Libsework Alemu Tsegaye

Computer Science Department, Ethiopian Telecommunication, Woldia, Ethiopia
Ayene Zinabie Mengesha

Computer Science Department, College of Computing, Woldia University, Woldia, Ethiopia

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Plain Text BibTeX RIS

APA Style

Tadesse, E. M., Tsegaye, L. A., Mengesha, A. Z. (2025). Load Aware Traffic Congestion Control Mechanism Using Fuzzy Logic. Mathematics and Computer Science, 10(2), 26-37. https://doi.org/10.11648/j.mcs.20251002.11

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ACS Style

Tadesse, E. M.; Tsegaye, L. A.; Mengesha, A. Z. Load Aware Traffic Congestion Control Mechanism Using Fuzzy Logic. Math. Comput. Sci. 2025, 10(2), 26-37. doi: 10.11648/j.mcs.20251002.11

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AMA Style

Tadesse EM, Tsegaye LA, Mengesha AZ. Load Aware Traffic Congestion Control Mechanism Using Fuzzy Logic. Math Comput Sci. 2025;10(2):26-37. doi: 10.11648/j.mcs.20251002.11

Copy | Download

@article{10.11648/j.mcs.20251002.11,
  author = {Ermias Melku Tadesse and Libsework Alemu Tsegaye and Ayene Zinabie Mengesha},
  title = {Load Aware Traffic Congestion Control Mechanism Using Fuzzy Logic
},
  journal = {Mathematics and Computer Science},
  volume = {10},
  number = {2},
  pages = {26-37},
  doi = {10.11648/j.mcs.20251002.11},
  url = {https://doi.org/10.11648/j.mcs.20251002.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mcs.20251002.11},
  abstract = {Recently, vehicular networks (VANETs) have emerged as promising technology for enabling communication between vehicles and infrastructures to improve road safety and driving experience. However, the dynamic nature of VANETs, characterized by rapidly changing traffic conditions and varying network load, poses significant challenges for reliable communication. Congestion control is a critical aspect of VANETs to prevent network saturation, reduce packet loss, and enhance overall system performance. In this context, the application of fuzzy-logic-based approaches offers a flexible and adaptive solution to dynamically adjust the network performance. This research introduced a fuzzy-logic-based congestion control mechanism for VANEts. The approach focused on dynamically adjusting the beacon busy ratio, road segment, and vehicle speed to address the fluctuating traffic condition, thereby mitigating congestion and enhancing vehicular network efficiency. Leveraging fuzzy logic, the proposed system can make route suggestions through the communication between roadside units based on input variables such as beacon busy ratio, road segment, and vehicle speed. On the result and analysis, the performance analysis of the system-based implemented Network Simulator-3 (NS3) and Simulation for Urban Mobility (SUMO) network simulation tool is used. Through simulation, the efficacy of the approach is demonstrated, showing its ability to adapt to evolving traffic dynamics and alleviate congestion on VANETs for enhancing network performance and reliability. The simulation result shows that our proposed system achieves a packet delivery ratio of 95%, throughput of 110 Kbps, and end-to-end delay of 1.93 seconds. This result shows that our scheme is feasible and effective.
},
 year = {2025}
}

Copy | Download

TY  - JOUR
T1  - Load Aware Traffic Congestion Control Mechanism Using Fuzzy Logic

AU  - Ermias Melku Tadesse
AU  - Libsework Alemu Tsegaye
AU  - Ayene Zinabie Mengesha
Y1  - 2025/04/29
PY  - 2025
N1  - https://doi.org/10.11648/j.mcs.20251002.11
DO  - 10.11648/j.mcs.20251002.11
T2  - Mathematics and Computer Science
JF  - Mathematics and Computer Science
JO  - Mathematics and Computer Science
SP  - 26
EP  - 37
PB  - Science Publishing Group
SN  - 2575-6028
UR  - https://doi.org/10.11648/j.mcs.20251002.11
AB  - Recently, vehicular networks (VANETs) have emerged as promising technology for enabling communication between vehicles and infrastructures to improve road safety and driving experience. However, the dynamic nature of VANETs, characterized by rapidly changing traffic conditions and varying network load, poses significant challenges for reliable communication. Congestion control is a critical aspect of VANETs to prevent network saturation, reduce packet loss, and enhance overall system performance. In this context, the application of fuzzy-logic-based approaches offers a flexible and adaptive solution to dynamically adjust the network performance. This research introduced a fuzzy-logic-based congestion control mechanism for VANEts. The approach focused on dynamically adjusting the beacon busy ratio, road segment, and vehicle speed to address the fluctuating traffic condition, thereby mitigating congestion and enhancing vehicular network efficiency. Leveraging fuzzy logic, the proposed system can make route suggestions through the communication between roadside units based on input variables such as beacon busy ratio, road segment, and vehicle speed. On the result and analysis, the performance analysis of the system-based implemented Network Simulator-3 (NS3) and Simulation for Urban Mobility (SUMO) network simulation tool is used. Through simulation, the efficacy of the approach is demonstrated, showing its ability to adapt to evolving traffic dynamics and alleviate congestion on VANETs for enhancing network performance and reliability. The simulation result shows that our proposed system achieves a packet delivery ratio of 95%, throughput of 110 Kbps, and end-to-end delay of 1.93 seconds. This result shows that our scheme is feasible and effective.

VL  - 10
IS  - 2
ER  -

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