To improve the urban traffic condition, alleviate pollution, reduce congestion and accidents, we propose a platoon-driving model for autonomous vehicles in a free-flow traffic state. This model allows vehicles with successful path matching to be grouped in a platoon and led by the platoon head (PH). In addition, a PH selection scheme is introduced to provide an incentive for vehicles to be PHs and maintain the dynamic update of platoons. Next, a smart contract is employed to enable the payment based on a blockchain between PH and platoon members (PMs), avoiding the malicious and false payments. The numerical results show that the platoon model is superior to the individual driving model in terms of fuel consumption. The comparison between carpooling and non-carpooling modes within the platoon shows that our model has a better performance in terms of PH revenue and PM's service charge.

Recent advancements in Intelligent Transportation Systems suggest that the roads will gradually be filled with autonomous vehicles that are able to drive themselves while communicating with each other and the infrastructure. As a representative driving pattern of autonomous vehicles, the platooning technology has great potential for reducing transport costs by lowering fuel consumption and increasing traffic efficiency. In this paper, to improve the driving efficiency of autonomous vehicular platoon in terms of fuel consumption, a path planning scheme is envisioned using deep reinforcement learning on the network edge node. At first, the system model of autonomous vehicles platooning is given on the common highway. Next, a joint optimization problem is developed considering the task deadline and fuel consumption of each vehicle in the platoon. After that, a path determination strategy employing deep reinforcement learning is designed for the platoon. To make the readers readily follow, a case study is also presented with instantiated parameters. Numerical results shows that our proposed model could significantly reduce the fuel consumption of vehicle platoons while ensuring their task deadlines.

The lack of remote sensing images and poor quality limit the performance improvement of follow-up research such as remote sensing interpretation. In this article, a generative adversarial network (GAN) is proposed for data augmentation of remote sensing images abstracted from Jiangxi and Anhui Provinces in China, i.e., deeply supervised GAN (D-sGAN). D-sGAN can generate high-quality images that are rich in changes, greatly shorten the generation time, and provide data support for applications such as semantic interpretation of remote sensing images. First, to modulate the layer activations, a downsampling scheme is designed based on the segmentation map. Then, the architecture of the generator is Unet++ with the proposed downsampling module. Next, the generator of this net is deeply supervised by the discriminator using deep convolutional neural network. This article further proved that the proposed downsampling module and the dense connection characteristics of UNet++ are significantly beneficial to the retention of semantic information of remote sensing images. Numerical results demonstrated that the images generated by D-sGAN could be used to improve accuracy of the segmentation network, with the faster generation speed compared to the CoGAN, SimGAN, and CycleGAN models. Furthermore, the remote sensing data generated by the model helped the interpretation network to increase the accuracy by 9%, meeting actual generation requirements.

Vehicular named data networking (VNDN) has recently emerged as a novel paradigm to facilitate content-centric data sharing for Internet of Vehicles. However, an information holder can spread fake data to clients for malicious purposes, which may affect the driving decision of the recipient, or even worse, cause traffic congestion and accidents. In this article, we build a data-sharing system that consists of a double-layer blockchain. The nodes at the bottom layer request for service by announcing their requirements in the NDN paradigm. For the upper layer, the nodes submit their demands and supplies to the nearest roadside unit for further matching. We model the balance between the demand and supply as a matching game. To encourage nodes to provide positive services, a reputation management mechanism that combines negative and positive transaction records is proposed. Simulation results verify the validity of our system, and the data-sharing mechanism fosters a secure information interaction in the VNDN.

In recent years, vehicular named data networking (VNDN) has quickly ascended to the spotlight and gained enormous popularity, which has emerged as a candidate to support various applications of vehicular communications. VNDN has the potential improve the data dissemination efficiency by mitigating the performance degradation from Internet Protocol (IP) addressing, unstable connectivity and diversified service requirements. With the number of connected vehicles increasing rapidly, the traffic burden of the base station (BS) also grows. As an effective edge computing paradigm, in-vehicle caching can significantly relieve the pressure of the BS. However, the design of a fair caching strategy is still challenging due to the selfish nature of individuals. In this paper, to address the above issues, a popularity-incentive caching scheme (PICS) is proposed in VNDN, where the BS will reward vehicles who execute cache offloading and content sharing with others. To balance the conflict of interest between the BS and vehicles, a Stackelberg game is modeled with rational utilities envisioned. Next, we propose the solution of this game model and evaluate the influence of different weight parameters. Finally, simulation results validate the effectiveness of PICS.

Since remote sensing images are difficult to obtain and need to go through a complicated administrative procedure for use in China, it cannot meet the requirement of huge training samples for Waterside Change Detection based on deep learning. Recently, data augmentation has become an effective method to address the issue of an absence of training samples. Therefore, an improved Generative Adversarial Network (GAN), i.e., BTD-sGAN (Text-based Deeply-supervised GAN), is proposed to generate training samples for remote sensing images of Anhui Province, China. The principal structure of our model is based on Deeply-supervised GAN(D-sGAN), and D-sGAN is improved from the point of the diversity of the generated samples. First, the network takes Perlin Noise, image segmentation graph, and encoded text vector as input, in which the size of image segmentation graph is adjusted to 128 × 128 to facilitate fusion with the text vector. Then, to improve the diversity of the generated images, the text vector is used to modify the semantic loss of the downsampled text. Finally, to balance the time and quality of image generation, only a two-layer Unet++ structure is used to generate the image. Herein, “Inception Score”, “Human Rank”, and “Inference Time” are used to evaluate the performance of BTD-sGAN, StackGAN++, and GAN-INT-CLS. At the same time, to verify the diversity of the remote sensing images generated by BTD-sGAN, this paper compares the results when the generated images are sent to the remote sensing interpretation network and when the generated images are not added; the results show that the generated image can improve the precision of soil-moving detection by 5%, which proves the effectiveness of the proposed model.