The Role of Computational Intelligence in Real-Time Traffic Management in IoT-enabled Autonomous Vehicle Networks

Abstract

The Internet of Things (IoT) and the Internet of Vehicles (IoV) have revolutionized the multi-purpose operational systems of vehicular transportation mechanisms in today’s era. IoT is a network of devices, which can communicate with each other through the internet [1]. The key technologies of IoT are cloud computing and big data analytics helpful in monitoring, tracking, and controlling vehicular pollution, traffic, and mobility. Cloud computing provides resources for services, management, and application support. Machine learning and big data analytics analyze the available data for extracting the meaningful insights, trends, and predictions, therefore, assisting in managing, controlling traffic, and pollutants [2]. The future of vehicular mobility is envisioned with autonomous, intelligent, and connected vehicles such as self-driven cars. Autonomous vehicles are fully developed to operate independently of human interaction. The most critical problem in intelligent autonomous vehicle networks is to analyze, monitor, and control traffic intelligently, ensuring minimal fuel consumption along with achieving reduced CO2 emissions which also enables the sustainable growth of the economy. In the developed world, the usage of automobiles has significantly increased during the twenty-first century. This gradually increased the demand for fossil fuels which ultimately produced air pollutants due to vehicle emissions. The literature survey regarding autonomous and connected vehicle networks represents the real-time traffic management, the navigation of vehicles in vehicular communication systems, real-time traffic signals with vehicular ad hoc networks, and lastly, energy-efficient resource management in vehicular networks.