Research on Key Technologies of Adaptive Networking and Reliable Communication for Fog-based IoV

基于雾计算的车联网自适应组网和可靠通信关键技术研究

550000

Fog computing-based Internet of Vehicles(IoV) aims to build an efficient and
reliable vehicular networking system, which improves network connectivity,
stability, flexibility, and intelligence. However, most existing end-edge-cloud
architectures lack the ability to actively adapt to complex traffic and network
environment. As a result, the network stability and communication reliability
cannot meet the requirements of the delay-sensitive and computation-sensitive IoV
applications. This project is dedicated to enabling intelligent networking and
communication technology for IoV under complex environments. we refines the key
scientific problem from three aspects, including dynamic clustering modeling,
adaptive network construction, and reliable data transmission. The innovations lie
in: 1) Considering the multi-dimensionality of sensing information of the high
mobility of sensing node in IoV, we define the theoretical model of vehicle
dynamic aggregation and decomposition, then we propose a dynamic clustering
algorithm based on environmental collaborative perception. 2) Considering the high
heterogeneity and mobility of the network, we investigate the network monitoring
and evaluation mechanism and propose a network construction and maintenance
strategy with adaptability to the environment. 3) Considering complex and diverse
transmission tasks and restricted communication resources in IoV, we build the
correlation model for cross-domain data transmission, and propose reliable
communication and adaptive data transmission based on deep reinforcement learning.
The outcomes of the project strive to complete the theoretical system and overall
architecture of IoV and provide theoretical and technical support for the
development of IoV innovative applications.