Nauman Ali, Iqbal Adeel, Khurshaid Tahir, Kim Sung Won
School of Computer Science and Engineering, Yeungnam University, Gyeongsan-si 38541, Republic of Korea.
Department of Electrical Engineering, Yeungnam University, Gyeongsan-si 38541, Republic of Korea.
Sensors (Basel). 2024 Oct 11;24(20):6548. doi: 10.3390/s24206548.
Every year, about 1.19 million people are killed in traffic accidents; hence, the United Nations has a goal of halving the number of road traffic deaths and injuries by 2030. In line with this objective, technological innovations in telecommunication, particularly brought about by the rise of 5G networks, have contributed to the development of modern Vehicle-to-Everything (V2X) systems for communication. A New Radio V2X (NR-V2X) was introduced in the latest Third Generation Partnership Project (3GPP) releases which allows user devices to exchange information without relying on roadside infrastructures. This, together with Massive Machine Type Communication (mMTC) and Ultra-Reliable Low Latency Communication (URLLC), has led to the significantly increased reliability, coverage, and efficiency of vehicular communication networks. The use of artificial intelligence (AI), especially K-means clustering, has been very promising in terms of supporting efficient data exchange in vehicular ad hoc networks (VANETs). K-means is an unsupervised machine learning (ML) technique that groups vehicles located near each other geographically so that they can communicate with one another directly within these clusters while also allowing for inter-cluster communication via cluster heads. This paper proposes a multi-layered VANET-enabled Intelligent Transportation System (ITS) framework powered by unsupervised learning to optimize communication efficiency, scalability, and reliability. By leveraging AI in VANET solutions, the proposed framework aims to address road safety challenges and contribute to global efforts to meet the United Nations' 2030 target. Additionally, this framework's robust communication and data processing capabilities can be extended to eHealth monitoring systems, enabling real-time health data transmission and processing for continuous patient monitoring and timely medical interventions. This paper's contributions include exploring AI-driven approaches for enhanced data interaction, improved safety in VANET-based ITS environments, and potential applications in eHealth monitoring.
每年约有119万人死于交通事故；因此，联合国设定了到2030年将道路交通死亡和受伤人数减半的目标。为实现这一目标，电信领域的技术创新，尤其是5G网络的兴起带来的创新，推动了用于通信的现代车联网（V2X）系统的发展。最新的第三代合作伙伴计划（3GPP）版本中引入了新无线电V2X（NR-V2X），它允许用户设备在不依赖路边基础设施的情况下交换信息。这与大规模机器类型通信（mMTC）和超可靠低延迟通信（URLLC）一起，显著提高了车辆通信网络的可靠性、覆盖范围和效率。人工智能（AI）的应用，尤其是K均值聚类，在支持车载自组织网络（VANET）中的高效数据交换方面前景十分广阔。K均值是一种无监督机器学习（ML）技术，它将地理位置相近的车辆分组，使它们能够在这些集群内直接相互通信，同时也允许通过集群头进行集群间通信。本文提出了一个由无监督学习驱动的多层VANET智能交通系统（ITS）框架，以优化通信效率、可扩展性和可靠性。通过在VANET解决方案中利用人工智能，该框架旨在应对道路安全挑战，并为实现联合国2030年目标的全球努力做出贡献。此外，该框架强大的通信和数据处理能力可扩展到电子健康监测系统，实现实时健康数据传输和处理，以便持续监测患者并及时进行医疗干预。本文的贡献包括探索人工智能驱动的方法以增强数据交互、提高基于VANET的ITS环境中的安全性以及在电子健康监测中的潜在应用。
