Distributed Egocentric Betweenness Measure as a Vehicle Selection Mechanism in VANETs: A Performance Evaluation Study

Area of Science:

• Computer Science
• Network Engineering
• Distributed Systems

Background:

• Traditional centrality measures (sociocentric) require global network knowledge, limiting their use in large or dynamic networks.
• Egocentric centrality measures utilize local network information, making them suitable for distributed environments.
• The vehicle selection mechanism (VSM) is a distributed approach for ranking vehicles in a network.

Purpose of the Study:

• To validate the effectiveness of egocentric measures within a VSM for distributed information management systems.
• To assess the viability of egocentric centrality for vehicular ad hoc networks (VANETs).

Main Methods:

• Implemented and evaluated the egocentric betweenness measure for node importance in VANETs.
• Compared the performance of egocentric betweenness against sociocentric measures in dynamic network conditions.
• Utilized a case study to analyze the VSM's effectiveness in information aggregation and knowledge generation.

Main Results:

• Egocentric measures provide sufficient accuracy for VSM implementation in VANETs.
• The VSM using egocentric betweenness significantly reduces bandwidth consumption.
• The VSM effectively overcomes challenges posed by highly dynamic network topologies.
• Egocentric betweenness demonstrated high similarity to sociocentric measures in dynamic VANETs.

Conclusions:

• Egocentric centrality measures are a practical and effective alternative to sociocentric measures in VANETs.
• The VSM, powered by egocentric betweenness, enhances distributed information management in dynamic vehicular environments.
• This study confirms the utility of egocentric measures for improving VANET performance and scalability.