Nature-Inspired Algorithms in Internet of Vehicles: A Survey and Analysis

Abstract

As vehicles are becoming increasingly smart and connected to the Internet of Things (IoT), vehicular networks are evolving into the Internet of Vehicles (IoV). IoV technology has a great potential to support intelligent and large-scale safety and nonsafety applications. However, its dynamic nature poses great challenges for efficient routing and network security. To address these challenges, nature-inspired algorithms (NIAs), which mimic strategies from nature, have been employed with notable success across various domains. In this article, we expand on previous categorization of the application domains of NIAs in IoV by introducing clustering as an important domain alongside routing and security domains. We develop an exhaustive taxonomy of NIAs based on their search mechanisms. We then survey, analyze, and classify representative NIAs in IoV within the taxonomy and highlight important areas where NIAs could potentially improve IoV environment. A mapping between swarm intelligence (SI) algorithms and IoV application domains based on common characteristics of SI algorithms has been established to facilitate the selection of suitable algorithms for specific application domains. To enhance the practicality of NIAs models, we identify key performance metrics that are essential to evaluate models fitness in real-world scenarios. Future research can utilize the results of this study to develop NIAs that are suited for specific real-life applications in IoV.