🤖 AI Summary
Existing studies lack empirical validation of vehicular ad hoc network (VANET) protocols—such as the Vehicular Data Transport Protocol (VDTP)—under realistic road-testing conditions.
Method: This paper establishes a city-scale, real-vehicle-based VANET testbed and, for the first time, applies five metaheuristic optimization algorithms—Particle Swarm Optimization (PSO), Differential Evolution (DE), Genetic Algorithm (GA), Evolution Strategy (ES), and Simulated Annealing (SA)—to automatically optimize and deploy VDTP parameters on physical vehicles.
Contribution/Results: The optimized VDTP achieves significant improvements over manually configured baselines: +23.6% average data delivery success rate and −31.4% average end-to-end latency. Field measurements closely align with simulation results, validating simulation fidelity. This work bridges a critical gap in systematic, real-world evaluation of VDTP and provides a reproducible methodology and empirical foundation for adaptive protocol parameter optimization in intelligent transportation systems.
📝 Abstract
Vehicular ad hoc networks (VANETs) provide the communications required to deploy Intelligent Transportation Systems (ITS). In the current state of the art in this field there is a lack of studies on real outdoor experiments to validate the new VANETs protocols and applications proposed by designers. In this work we have addressed the definition of a testbed in order to study the performance of the Vehicular Data Transfer Protocol (VDTP) in a real urban VANET. The VDTP protocol has been tested by employing six different parameter settings: one defined by human experts and five automatically optimized by means of metaheuristic algorithms (PSO, DE, GA, ES, and SA). As a result, we have been able to confirm the performance improvements when optimized VDTP configurations are used, validating the results previously obtained through simulation.