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Updated: Feb 14, 2026

Design and Optimization Strategies of a High-Performance Vented Box
Published on: June 9, 2023
An Efficient Framework Model for Optimizing Routing Performance in VANETs.
Nori M Al-Kharasani1, Zuriati Ahmad Zulkarnain2, Shamala Subramaniam3
1Department of Wireless and Communication Technology, Faculty of Computer Science and Information Technolog, University Putra Malaysia, Serdang 43400, Malaysia. nori_kh@yahoo.com.
This study presents a statistical framework to optimize routing parameters in Vehicular Ad hoc Networks (VANET). The proposed model enhances Vehicle-to-Vehicle (V2V) communication by balancing network dynamics and Quality of Service (QoS) requirements.
Area of Science:
- Computer Science
- Network Engineering
- Telecommunications
Background:
- Vehicular Ad hoc Networks (VANET) face routing challenges due to high mobility and dynamic topology.
- Network factors like density, bandwidth, traffic, and mobility patterns impact routing efficiency.
- Quality of Service (QoS) is crucial for enhancing routing protocols and network performance in VANETs.
Purpose of the Study:
- To introduce a statistical framework for optimizing routing configuration parameters in Vehicle-to-Vehicle (V2V) communication.
- To address the trade-off between network topology changes and QoS requirements in VANETs.
- To effectively utilize network resources reflecting the current network state.
Main Methods:
- A three-stage framework: simulation, function aggregation, and optimization.
- Simulation stage executes various urban scenarios to gather network data.
- Function stage competitively aggregates weighted factors into a single value.
- Optimization stage evaluates communication cost and determines optimal configurations.
Main Results:
- Significant improvements in Packet Delivery Ratio (PDR).
- Reduction in Normalized Routing Load (NRL).
- Decreased Packet Loss (PL) and End-to-End Delay (E2ED).
Conclusions:
- The proposed statistical framework effectively optimizes routing parameters in V2V communication.
- The model successfully balances network dynamics with QoS demands.
- Demonstrated performance enhancements in key network metrics validate the framework's efficacy.

