Survey on Artificial Intelligence (AI) techniques for Vehicular Ad-hoc Networks (VANETs)

In view of the rapid development of the Internet of Vehicles (IoV) and wireless communication technology, intelligent transportation systems play an important role in improving urban road safety, promoting the behavioral interaction between users and networks, improving the service quality, and controlling the network cost. Based on the universality and real-time nature of the IoV, data collectors can cooperate with users to sense and collect relevant data within the driving range of vehicles. To achieve this goal, this article proposes a two-tier sensing scheme around the optimization of the sensing mechanism and data processing. In the routing layer, we build a weighted graph model based on vehicle fog, and we propose a new routing strategy to maximize each vehicle’s utilization. We consider that the sensing information collected by multiple vehicles could be repetitive, which would lead to many unnecessary communication flows in the network. Therefore, in the data processing layer, we consider resource consumption in the whole fog, assign different tasks to the sensing vehicles, and filter similar information on the relay nodes of the routing paths to reduce the waste of resources in IoV. Finally, we compare and analyze our two-tier scheme with related approaches, and the results show that our scheme has higher link utilization and lower resource consumption for a high-speed mobile network environment in IoV.

A Two-Tier Collection and Processing Scheme for Fog-Based Mobile Crowdsensing in the Internet of Vehicles

Vehicular networks with Vehicle-to-Vehicle (V2V) or Vehicle-to-Infrastructure (V21) communications can benefit from Mobile Edge Computing (MEC) while sharing the computation abilities amongst vehicles. In this paper, we proposed an effective vehicle scheduling centered on Cumulative Distribution-Satin Bowerbird Optimization (CD-SBO) along with Xavier- Convolutional Neural Network (X-CNN) that leverages the vehicles' cache memory. In the proposed model, initially, regarding the data at-tained from the roadside unit (RSU), the range of vehicles on the road is initialized. After that, the vehicles are clustered using the Kullback-Leibler Divergence-K-Means Algorithm (KLD-KMA) model, which assigns RSU as a cluster of centroids. Then, to avoid data collision and uneven delay, the vehicles' request is balanced by using the CD-SBO algorithm. Next, the optimized requests are transferred to the cloud server through the gateway and Software Defined Network (SDN). Further, in the cloud server, data is decomposed into smaller components to enhance the cache storage. Then, the decomposed data is fed into the X-CNN classifier, which extracts the features from the Decomposed data and schedules the vehicles based on the extracted feature vectors. Finally, the results are forwarded to the targeted vehicles efficiently. The performance of the proposed method is better enhanced compared to the existing approaches. 

Energy-Efficient Resource Scheduling Using X-CNN and CD-SBO for SDN based MEC Enabled IoV

An extension of Software-Defined Networking (SDN) to the vehicular networks can improve the network performance effectively. However, with the explosive growth of vehicles, the architecture of Software-Defined Vehicular Networks (SDVNs) cannot well satisfy the stringent end-to-end latency requirement of vehicles. To cope with the problem, The 5G has been regarded as a promising technology to be integrated into SDVNs. However, there are still some technical challenges in 5G, such as coverage issues, which limit the development of vehicular networks. Beyond 5G (B5G) technology is envisioned as a promising solution to expand the capabilities of vehicular networks in terms of safety, reliability, etc. In this paper, we introduce B5G technology to improve the SDVNs to implement reliable routing. Firstly, we propose a new architecture of SDVNs based on B5G technology, and creat a protocol stack which integrates the 5G protocol into the WAVE protocol. In addition, we design the adaptive link performance monitoring mechanism which balances the control overhead and the accuracy. Finally, we improve the Genetic Algorithm (GA) through cross-generation selection and small-environment strategy and design an reliable QoS routing algorithm. The simulation results demonstrate that the monitoring mechanism can measure the performance of the link more accurately, and the improved GA is superior to standard GA and Dijkstra. In particular, compared with the standard GA, the improved GA can improve bandwidth utilization, delay, and packet loss rate by 34.3%, 30.6%, and 20.9%, respectively.

The reliable routing for software-defined vehicular networks towards beyond 5G

Vehicular networks with Vehicle-to-Vehicle (V2V) or Vehicle-to-Infrastructure (V21) communications can benefit from Mobile Edge Computing (MEC) while sharing the computation abilities amongst vehicles. In this paper, we proposed an effective vehicle scheduling centered on Cumulative Distribution-Satin Bowerbird Optimization (CD-SBO) along with Xavier- Convolutional Neural Network (X-CNN) that leverages the vehicles' cache memory. In the proposed model, initially, regarding the data at-tained from the roadside unit (RSU), the range of vehicles on the road is initialized. After that, the vehicles are clustered using the Kullback-Leibler Divergence-K-Means Algorithm (KLD-KMA) model, which assigns RSU as a cluster of centroids. Then, to avoid data collision and uneven delay, the vehicles' request is balanced by using the CD-SBO algorithm. Next, the optimized requests are transferred to the cloud server through the gateway and Software Defined Network (SDN). Further, in the cloud server, data is decomposed into smaller components to enhance the cache storage. Then, the decomposed data is fed into the X-CNN classifier, which extracts the features from the Decomposed data and schedules the vehicles based on the extracted feature vectors. Finally, the results are forwarded to the targeted vehicles efficiently. The performance of the proposed method is better enhanced compared to the existing approaches.

Vehicular ad hoc network is one of most recent research areas to deploy intelligent Transport System. Due to their highly dynamic topology, energy constrained and no central point coordination, routing with minimal delay, minimal energy and maximize throughput is a big challenge. Software Defined Networking (SDN) is new paradigm to improve overall network lifetime. It incorporates dynamic changes with minimal end-end delay, and enhances network intelligence. Along with this, intelligence secure routing is also a major constraint. This paper proposes a novel approach to Energy efficient secured routing protocol for Software Defined Internet of vehicles using Restricted Boltzmann Algorithm. This algorithm is to detect hostile routes with minimum delay, minimum energy and maximum throughput compared with traditional routing protocols.

An Efficient Secured Routing Protocol for Software Defined Internet of Vehicles

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An Efficient Secured Routing Protocol for Software Defined Internet of Vehicles