Muktar, B.; Fono, V.; Zongo, M. Predictive Modeling of Signal Degradation in Urban VANETs Using Artificial Neural Networks. Electronics2023, 12, 3928.
Muktar, B.; Fono, V.; Zongo, M. Predictive Modeling of Signal Degradation in Urban VANETs Using Artificial Neural Networks. Electronics 2023, 12, 3928.
Muktar, B.; Fono, V.; Zongo, M. Predictive Modeling of Signal Degradation in Urban VANETs Using Artificial Neural Networks. Electronics2023, 12, 3928.
Muktar, B.; Fono, V.; Zongo, M. Predictive Modeling of Signal Degradation in Urban VANETs Using Artificial Neural Networks. Electronics 2023, 12, 3928.
Abstract
In urban Vehicular Ad Hoc Network (VANET) environments, buildings play a crucial role as they can act as obstacles that attenuate the transmission signal between vehicles. Quantifying the impact of buildings on the transmission quality is essential, especially in critical scenarios involving emergency vehicles, where reliable communication is of utmost importance. In this research, we propose a supervised learning approach based on artificial neural networks (ANNs) to develop a predictive model capable of estimating the level of signal degradation, represented by the bit error rate (BER), based on the obstacles perceived by moving emergency vehicles. By establishing a relationship between the level of signal degradation and the encountered obstacles, our proposed mechanism enables efficient routing decisions to be made prior to the transmission process. Consequently, data packets are routed through paths that exhibit the lowest BER. To gather the necessary training data, we employed SUMO and NS-3 simulations. The simulation results demonstrate that our developed model successfully learns and accurately estimates the BER for new data instances. Overall, our research contributes to enhancing the performance and reliability of communication in urban VANET environments, especially in critical scenarios involving emergency vehicles, by leveraging supervised learning and artificial neural networks to predict signal degradation levels and optimize routing decisions accordingly.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
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