A modified social spider algorithm for an efficient data dissemination in VANET.

Area of Science:

• Computer Science
• Electrical Engineering
• Transportation Engineering

Background:

• Advancements in mobile communication enable ubiquitous information sharing, impacting lifestyles and industries.
• The automotive industry is increasingly adopting communication technologies for safety, traffic management, and driver assistance.
• Vehicular networks (VANETs) are gaining significant attention due to their potential to revolutionize road transport.

Purpose of the Study:

• To propose a novel sampling-based estimation scheme (SES) for vehicle communication.
• To enhance probabilistic contacts and optimize routing within vehicular networks.
• To investigate content transmission strategies in VANETs using probabilistic models.

Main Methods:

• Development of a sampling-based estimation scheme (SES) for vehicle communication.
• Analysis of contact duration and probability between vehicles moving in same and opposite directions.
• Application of a flow model with probabilistic capacities for content transmission.
• Experimental validation of the SES performance in VANETs.

Main Results:

• The proposed SES effectively increases probabilistic contacts in vehicle communication.
• The scheme facilitates efficient routing by considering stochastic contact patterns.
• Content transmission is effectively managed using probabilistic capacities.
• Experimental results validate the performance of SES in VANETs.

Conclusions:

• The novel sampling-based estimation scheme (SES) offers a robust solution for enhancing communication in vehicular networks.
• SES improves routing efficiency and content transmission by leveraging probabilistic contact analysis.
• The findings contribute to the advancement of intelligent transportation systems and connected vehicles.