Pragmatic Evaluation of IoV based Cluster Formation Models for Efficient Routing Scenarios

Abstract

Internet of Vehicles (IoVs) based networks are highly ad-hoc in nature and require dynamic routing models in order to communicate packets between source-destination pairs. To perform efficient routing, destination-aware or source-aware clustering must be applied, which assists in filtering in-path nodes, thereby reducing complexity and delay needed for routing operations. A wide variety of such models are proposed by researchers, and each of them vary in terms of their internal operating characteristics and efficiency levels. Due to these variations, it is difficult for researchers to identify optimal models for their performance-specific & function-specific deployments. To overcome these issues, this text performs a detailed discussion of recently proposed IoV clustering models in terms of their deployment-specific nuances, performance-specific advantages, application-specific limitations, and context-specific future scopes. To perform this task, Dynamic network topology, Heterogeneity, Interference, Security, privacy, and Scalability challenges were considered and evaluated in this text. Based on this discussion, researchers & IoV designers will be able to identify optimum bioinspired, and deep learning models for their functionality-specific routing use cases. This text further compares these models in terms of their qualitative metrics that include routing delay, computational complexity, energy efficiency, scalability and throughput levels, which will assist readers to identify optimal routing performance as per their performance-specific scenarios. To further assist in model selection, this text proposes evaluation of a novel IoV Route Clustering Rank Metric (IRCRM), which combines these metrics, in order to assist identification of routing & clustering models that showcase low delay, high energy efficiency, low complexity, high scalability, and throughput levels under real-time IoV network scenarios.