Empirical Analysis of QoS & Security Aware IoV Routing Models from a Statistical Perspective

Abstract

Exponential advancements in Internet of Things (IoT) technology have given boost & enhanced viability to Internet of Vehicles (IoV) networks. Due to which a large variation is observed in network deployments that use IoV based nodes. These include variant Quality of Service (QoS), variant security levels, highly variant deployment interfaces, etc. This makes it ambiguous for researchers to identify optimum models for their performance-specific & application-specific use cases. Thus, researchers & network designers usually validate multiple models on their test scenarios, and then select optimum models based on their simulative performance levels. This increases delay, and deployment cost for real-time network use cases. To overcome these issues, and assist network designers & researchers in identification of optimum IoV deployment models, this text discusses a detailed review of recently proposed IoV model deployments. This discussion reviews the models in terms of their applicative nuances, functional advantages, contextual limitations, and deployment specific future research scopes. After referring this detailed discussion, it was observed that bioinspired models when combined with low-complexity routing techniques showcase better performance levels. Thus, researchers will be able to identify such optimal models & their combinations for the functional use cases. This text also evaluates these models in terms of their qualitative response metrics, that include, communication delay, security level, cost of deployment, computational complexity, and scalability levels. The reviewed models are also compared in terms of these metrics, which will assist readers to identify optimal models for their performance-specific use cases. To further simplify the process of IoV model selection, this text proposes evaluation of a novel IoV Rank Metric (IVRM) that combines these metrics in order to identify optimal models in terms of comprehensive performance levels.