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 .
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.