Research Article
Load Aware Traffic Congestion Control Mechanism Using Fuzzy Logic
Ermias Melku Tadesse*
,
Libsework Alemu Tsegaye,
Ayene Zinabie Mengesha
Issue:
Volume 10, Issue 2, April 2025
Pages:
26-37
Received:
21 March 2025
Accepted:
2 April 2025
Published:
29 April 2025
DOI:
10.11648/j.mcs.20251002.11
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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.
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 comm...
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Research Article
Moving Block Bootstrap Method for Determining Confidence Intervals for a Change Point in Time Series in the Presence of Conditional Heteroscedasticity
Josephine Njeri Ngure*,
Anthony Gichuhi Waititu,
Simon Maina Mundia
Issue:
Volume 10, Issue 2, April 2025
Pages:
38-42
Received:
3 February 2025
Accepted:
19 March 2025
Published:
6 May 2025
DOI:
10.11648/j.mcs.20251002.12
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Abstract: This paper seeks to use the Moving Block Bootstrap method to determine the confidence intervals for a change point in conditional variance function of data exhibiting conditional heteroscedasticity and heterogeneity. Confidence intervals for a change point normally provide or give a range within which the true change point location is likely to lie. This is usually based on a specified confidence level. This helps to in turn determine whether the change point is statistically significant especially after determining the critical values for the distribution of the change point test statistic. Confidence intervals are also called interval estimates as opposed to point estimates which provide a single estimate for a parameter.
Abstract: This paper seeks to use the Moving Block Bootstrap method to determine the confidence intervals for a change point in conditional variance function of data exhibiting conditional heteroscedasticity and heterogeneity. Confidence intervals for a change point normally provide or give a range within which the true change point location is likely to lie...
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