Parametric Analysis of the Nature-Inspired Algorithms based on VANETs

Because of their ability to efficiently communicate in fast-changing conditions, Vehicular Ad-Hoc Networks (VANETs) have become a focus for research. Unlike MANETs, VANETs are subject to disconnects, high-speed movement and quick changes in their network structure which creates many challenges when...

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Veröffentlicht in:2025 International Conference on Modern Sustainable Systems (CMSS) S. 726 - 733
Hauptverfasser: Muniraj, Ashokkumar, Kiruthika, S., Balasupramani, A, Siva Kumar, T., Vijayakumar, P., Sharmiladevi, D.
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 12.08.2025
Schlagworte:
Ad hoc networks
Bio-Inspired Algorithms
CS-BFOA
Data privacy
Medical diagnostic imaging
Network Performance Metrics
Optimization
Performance metrics
Reliability
Routing
Routing Optimization
Routing protocols
Throughput
Transformers
VANETs
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Zusammenfassung:Because of their ability to efficiently communicate in fast-changing conditions, Vehicular Ad-Hoc Networks (VANETs) have become a focus for research. Unlike MANETs, VANETs are subject to disconnects, high-speed movement and quick changes in their network structure which creates many challenges when designing and optimizing protocols and resources. Emergency alerts, collision avoidance, managing traffic jams and cooperative driving all depend on highly trustworthy communication routes. To deal with these issues, Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) and the Bacterial Foraging Optimization Algorithm (BFOA) have been used, helping find the best way to go. Bringing together Cuckoo Search Algorithm (CSA) and Differential Evolution (DE) further improves how efficiently the search is performed. These algorithms are analyzed using NS2, considering packet delivery ratio, throughput, the amount of overhead in routing, delay and packet loss rate. Results from experiments demonstrate that bio-inspired methods work more efficiently and reliably than traditional methods.
DOI:10.1109/CMSS66566.2025.11182572