AI-Enabled Reliable Channel Modeling Architecture for Fog Computing Vehicular Networks

Artificial intelligence (AI)-driven fog computing (FC) and its emerging role in vehicular networks is playing a remarkable role in revolutionizing daily human lives. Fog radio access networks are accommodating billions of Internet of Things devices for real-time interactive applications at high reliability. One of the critical challenges in today's vehicular networks is the lack of standard wireless channel models with better quality of service (QoS) for passengers while enjoying pleasurable travel (i.e., highly visualized videos, images, news, phone calls to friends/relatives). To remedy these issues, this article contributes significantly in four ways. First, we develop a novel AI-based reliable and interference-free mobility management algorithm (RIMMA) for fog computing intra-vehicular networks, because traffic monitoring and driver's safety management are important and basic foundations. The proposed RIMMA in association with FC significantly improves computation, communication, cooperation, and storage space. Furthermore, its self-adaptive, reliable, intelligent, and mobility-aware nature, and sporadic contents are monitored effectively in highly mobile vehicles. Second, we propose a reliable and delay-tolerant wireless channel model with better QoS for passengers. Third, we propose a novel reliable and efficient multi-layer fog driven inter-vehicular framework. Fourth, we optimize QoS in terms of mobility, reliability, and packet loss ratio. Also, the proposed RIMMA is compared to an existing competitive conventional method (i.e., baseline). Experimental results reveal that the proposed RIMMA outperforms the traditional technique for intercity vehicular networks.This work is funded by CENIIT project 17.01, and a research grant of PIFI 2020 (2020VBC0002). This work was partially supported by NSERC Discovery and STPG Research funds, andNSERC CREATE-TRANSIT, and Canada Research Chairs Programs. This work is also supported by FCT project UID/EEA/50008/2019 (Este trabalho foi suportadopelo projecto FCT UID/EEA/50008/2019). This article/publication is based on work from COSTAction IC1303-AAPELE-Architectures, Algorithms and Protocols for Enhanced Living Environments and COST Action CA16226-SHELD-ON-Indoor living space improvement: Smart Habitat for the Elderly, supported by COST (European Cooperation in Science and Technology). This work was also partially supported by Operação Centro-010145-FEDER-000019–C4-Centro de Com-petências em Cloud Computing, co-financed by the Programa Operacional Regional do Centro (CENTRO 2020), through the Sistema de Apoio à Investigação Científica e Tecnológica — Pro-gramas Integrados de IC&DT