Cooperative Fog Computing for Dealing with Big Data in the Internet of Vehicles: Architecture and Hierarchical Resource Management

TLDR

Vehicle applications, mobile devices, and the Internet of Things are rapidly expanding, creating a pressing need for efficient architectures to manage the large volumes of data generated in the Internet of Vehicles. This work proposes a regional cooperative fog‑computing architecture, CFC‑IoV, to handle big data in smart city vehicular networks. CFC‑IoV leverages fog servers to provide services such as mobility control, multi‑source data acquisition, distributed computation and storage, and multi‑path transmission, while considering latency, mobility, localization, and scalability. A hierarchical intra‑fog and inter‑fog resource‑management model is introduced, achieving optimized energy efficiency and reduced packet‑dropping rates for fog servers.

Abstract

As vehicle applications, mobile devices and the Internet of Things are growing fast, and developing an efficient architecture to deal with the big data in the Internet of Vehicles (IoV) has been an important concern for the future smart city. To overcome the inherent defect of centralized data processing in cloud computing, fog computing has been proposed by offloading computation tasks to local fog servers (LFSs). By considering factors like latency, mobility, localization, and scalability, this article proposes a regional cooperative fog-computing-based intelligent vehicular network (CFC-IoV) architecture for dealing with big IoV data in the smart city. Possible services for IoV applications are discussed, including mobility control, multi-source data acquisition, distributed computation and storage, and multi-path data transmission. A hierarchical model with intra-fog and inter-fog resource management is presented, and energy efficiency and packet dropping rates of LFSs in CFC-IoV are optimized.