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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.

Sensors (Basel, Switzerland)

|February 22, 2018

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 Service Swarm optimization VANET Networks network 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.