The Internet of vehicles (IoV) is a newly emerged wave that converges Internet of things (IoT) into vehicular networks to benefit from ubiquitous Internet connectivity. Despite various research efforts, vehicular networks are still striving to achieve higher data rate, seamless connectivity, scalability, security, and improved quality of service, which are the key enablers for IoV. It becomes even more critical to investigate novel design architectures to accomplish efficient and reliable data forwarding when it comes to handling the emergency communication infrastructure in the presence of natural epidemics. The article proposes a heterogeneous network architecture incorporating multiple wireless interfaces (e.g., wireless access in vehicular environment (WAVE), long-range wireless fidelity (WiFi), and fourth generation/long-term evolution (4G/LTE)) installed on the on-board units, exploiting the radio over fiber approach to establish a context-aware network connectivity. This heterogeneous network architecture attempts to meet the requirements of pervasive connectivity for vehicular ad hoc networks (VANETs) to make them scalable and adaptable for IoV supporting a range of emergency services. The architecture employs the Best Interface Selection (BIS) algorithm to always ensure reliable communication through the best available wireless interface to support seamless connectivity required for efficient data forwarding in vehicle to infrastructure (V2I) communication successfully avoiding the single point of failure. Moreover, the simulation results clearly argue about the suitability of the proposed architecture in IoV environment coping with different types of applications against individual wireless technologies.

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With the rapid advancement of Internet of Things (IoT) communication technologies, the Internet of Vehicles (IoV) has gained significant attention for providing the real-time exchange of emergency traffic information among vehicles and Road Side Units (RSU) to improve ultimate driving experiences and road safety. Information-Centric Networking (ICN) has emerged as a novel networking architecture that shifts the communication model from Internet protocol (IP) based host-centric to content-centric architecture. ICN provides support to push and pull-based messages for efficient content dissemination and retrieval by aiming at content names rather than IP addresses. The Mobile Edge Computing (MEC) paradigm facilitates proximity-based real-time traffic applications and services, reducing the content retrieval latency from the core network without the excessive broadcast overhead. Deep Learning (DL) techniques have been tremendously successful in detecting the severity of real-time traffic data. The integration of DL based ANN model for edge-based ICN-IoV brings real-time traffic prediction, content caching, and forwarding of push-based messages closer to the target area. Furthermore, the deployment of mobile edge servers at critical network positions enhances the availability and responsiveness of the name-based content in the ICN paradigm. In this paper, we propose Mobile Edge-based Emergency Messages Dissemination Scheme (MEMDS) to deliver push-based messages delivery at the event-reported geographical location. We also propose a hybrid DL-based Artificial Neural Network (ANN) and MEMDS model to detect and predict the severity of the safety application under real traces from different cities based on specific parameters. The simulation results demonstrate that the proposed scheme significantly improves the data delivery ratio, average delay, hop count, content retrieval delay, and network overhead than DCN and flooding techniques. Secondly, the proposed hybrid model successfully detects the severity of the request with the highest accuracy, precision, recall, and f1-scores values of 96% than benchmark models using real-time vehicular datasets.INDEX TERMS Artificial neural network, deep learning, Internet of Vehicles, Internet of Things, information-centric networking, mobile edge computing.The associate editor coordinating the review of this manuscript and approving it for publication was Yiming Tang .

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