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Related Experiment Video

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Design and Optimization Strategies of a High-Performance Vented Box
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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.

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|February 22, 2018
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Summary
This summary is machine-generated.

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.

Keywords:
Quality of ServiceSwarm optimizationVANET Networksnetwork performance.stable routing

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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.