Data Dissemination in VANETs Using Particle Swarm Optimization.

Area of Science:

• Vehicular Ad-Hoc Networks (VANETs)
• Mobile Ad-Hoc Networks (MANETs)
• Network Optimization

Background:

• Increasing vehicle connectivity necessitates efficient urban traffic management and data dissemination solutions.
• VANETs face challenges like congestion and broadcast storms, requiring robust data delivery strategies.
• Multi-objective optimization is crucial for balancing competing metrics in VANET communication.

Purpose of the Study:

• To develop an efficient data dissemination approach for VANETs, prioritizing emergency message delivery.
• To enhance network performance by optimizing routing paths for both emergency and normal traffic.
• To investigate the effectiveness of meta-heuristic algorithms in solving complex VANET routing problems.

Main Methods:

• A VANET simulation environment was created with vehicles as network nodes.
• Time delay-based Multipath Routing (TMR) was employed to identify multiple communication routes.
• Particle Swarm Optimization (PSO) was utilized to select the optimal and secure path for data transmission.

Main Results:

• The proposed PSO-based TMR method demonstrated significant improvements in throughput and packet loss ratio.
• Experimental results showed reduced end-to-end delay, overhead ratio, and energy consumption compared to other methods.
• The approach effectively prioritized emergency messages for immediate delivery to stationary nodes.

Conclusions:

• The PSO-enhanced TMR method offers a superior solution for data dissemination in VANETs.
• This optimization strategy effectively addresses challenges in urban traffic communication and network congestion.
• The findings highlight the potential of meta-heuristic algorithms for enhancing VANET performance and reliability.