Abstract

An approach has been introduced for vehicular ad hoc networks (VANETs) that employs a machine learning (ML)-integrated modified wireless access in vehicular environments (WAVE) protocol for efficient emergency message dissemination. The protocol leverages self-attention bidirectional long short-term memory (BiLSTM) layers to predict communication delays and effectively prioritize emergency messages. Evaluation results demonstrate substantial improvements over the conventional WAVE protocol, including reduced message dissemination delay (MDD), improved communication range utilization, higher packet delivery ratio, and increased throughput. To further optimize network efficiency, dynamic density-based clustering for VANETs (DDBC-VANET) is employed, enabling clusters to adapt to varying vehicular densities. In addition, a hybrid optimization algorithm that combines gradient descent with genetic algorithms is used to fine-tune the protocol’s prioritization module, thereby enhancing overall performance. Collectively, these innovations establish the ML-integrated modified WAVE protocol as a promising solution for adaptive and reliable emergency communication in dynamic urban VANET environments.

Keywords

Vehicular ad hoc networks (VANETs), WAVE protocol, Machine learning (ML), Bidirectional LSTM (BiLSTM), Dynamic density-based clustering.

Cite this article

Mishra DK, Sharma K, Sharma S. ML-integrated modified WAVE protocol for adaptive emergency communication in VANETs . International Journal of Advanced Technology and Engineering Exploration. 2025; 12(130):1396-1413

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