Artificial intelligence mechanisms for management of QoS-aware connectivity in Internet of vehicles

Today, Internet of Things (IoT) has provided intelligent interactions between sensors, smart devices, actuators, and cloud-based applications to ease human life. Currently, IoT-based connectivity management systems use computer-assisted learning methods to increase learning level and better understanding of the curriculums for students in universities, schools and research centers. On the other hand, virtual connectivity management systems are applied to facilitate teaching and learning methods under taken of pandemic effects. Because, data mining methods have important effect to enhancement and navigate IoT-based connectivity management systems, this paper presents a technical analysis on Artificial Intelligence (AI) approaches for connectivity management systems in IoT environments. This paper provides a comprehensive perspective on vehicular communication systems, Internet of Vehicles (IoV) methods and Vehicular Ad Hoc Network (VANET) environments that have evaluated using machine learning, fuzzy logic and intelligent algorithms. Also, applied evaluation metrics to predict and detect efficient connectivity methods, succeed learning models and enhancement of IoT-based connectivity management systems are discussed and analyzed for existing AI approaches. Finally, new research directions and emerging challenges are outlined to improve the performance of advanced IoT-based connectivity management systems.