To enable large-scale and ubiquitous automotive network access, traditional vehicle-to-everything (V2X) technologies are evolving to the Internet of Vehicles (IoV) for increasing demands on emerging ...advanced vehicular applications, such as intelligent transportation systems (ITS) and autonomous vehicles. In recent years, IoV technologies have been developed and achieved significant progress. However, it is still unclear what is the evolution path and what are the challenges and opportunities brought by IoV. For the aforementioned considerations, this article provides a thorough survey on the historical process and status quo of V2X technologies, as well as demonstration of emerging technology developing directions toward IoV. We first review the early stage when the dedicated short-range communications (DSRC) was issued as an important initial beginning and compared the cellular V2X with IEEE 802.11 V2X communications in terms of both the pros and cons. In addition, considering the advent of big data and cloud-edge regime, we highlight the key technical challenges and pinpoint the opportunities toward the big data-driven IoV and cloud-based IoV, respectively. We believe our comprehensive survey on evolutionary V2X technologies toward IoV can provide beneficial insights and inspirations for both academia and the IoV industry.
This article introduces the DAVN, which provides ubiquitous connections for vehicles by efficiently integrating the communication and networking technologies of drones and connected vehicles. ...Specifically, we first propose a comprehensive architecture of the DAVN and outline its potential services. By cooperating with vehicles and infrastructures, drones can improve vehicle-to-vehicle connectivity, infrastructure coverage, network information collection ability, and network interworking efficiency. We then present the challenges and research opportunities of DAVNs. In addition, a case study is provided to demonstrate the effectiveness of DAVNs by leveraging our designed simulation platform. Simulation results demonstrate that the performance of vehicular networks can be significantly enhanced with the proposed DAVN architecture.
•Concrete-filled double-skin stainless steel tube under cyclic loading was tested.•The failure modes and hysteresis curves have been reported.•The effects of the test parameters have been ...discussed.•Comparisons were made between the tests and the predictions.
This paper reports a series of cyclic loading tests on concrete-filled double-skin stainless steel tubular (CFDSST) beam-columns with square hollow section (SHS) outer and circular hollow section (CHS) inner. A total of 24 specimens were tested under constant axial compressive load and cyclically increasing flexural loading to investigate the structural performance of the composite beam-columns. The test parameters included the axial compressive load level (n), the thickness of the outer stainless steel tube (to), the hollow ratio (χ) and the concrete strength (fcu).
The failure modes and hysteresis curves of the concrete-filled double-skin stainless steel tubular (CFDSST) beam-columns have been reported. Based on the test results, the effects of the test parameters on the lateral load (P) versus lateral displacement (Δ) envelope curves, ductility coefficient, dissipated energy and stiffness degradation have been investigated. The results shows that, generally, the axial compressive load level and thickness of outer tubes have a primary influence on the behavior of the test specimens while the hollow ratio and the concrete strength have a little effect when the axial compressive load level is low. Finally, comparisons of initial section flexural stiffness and ultimate bending moments of the beam-columns which were obtained from the tests are made with the predicted ones using the existing design codes and design method.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
Considering the problems of high cost, inefficiency, and time consumption of manual diagnosis of strawberry diseases, G-ResNet50 is proposed based on transfer learning and deep residual network for ...strawberry disease identification and classification. The G-ResNet50 is based on the ResNet50, and the focal loss function is introduced in G-ResNet50 to make the model devote itself to disease images that are difficult to classify. During the training process of the G-ResNet50 model, its convolutional layer and pooling layer inherit the pre-trained weight parameters from the ResNet50 model on the PlantVillage dataset, while adding dropout regularization and batch regularization methods to optimize the network model. The strawberry disease dataset includes four sample images of healthy plants, powdery mildew, strawberry anthracnose, and leaf spot disease. The dataset is enhanced and expanded by operations including angle rotation, adjusting contrast and brightness, and adding Gaussian noise. Compared with existing models such as VGG16, ResNet50, InceptionV3, and MobileNetV2, the results of model training and testing on 7,525 four-category leaf datasets show that the G-ResNet50 model has faster convergence speed and better classification effect, and its average recognition accuracy rate reached 98.67%, which is significantly higher than other models. Through the three evaluation indicators of precision rate, recall rate, and confusion matrix, it is concluded that the G-ResNet50 has good robustness, high stability, and high recognition accuracy and can provide a feasible solution for strawberry disease detection in practical applications.
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FZAB, GIS, IJS, IZUM, KILJ, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBMB, UL, UM, UPUK
Connected vehicles have been considered as an effective solution to enhance driving safety as they can be well aware of nearby environments by exchanging safety beacons periodically. However, under ...dynamic traffic conditions, especially for dense-vehicle scenarios, the naive beaconing scheme where vehicles broadcast beacons at a fixed rate with a fixed transmission power can cause severe channel congestion and thus degrade the beaconing reliability. In this paper, by considering the kinematic status and beaconing rate together, we study the rear-end collision risk and define a danger coefficient <inline-formula> <tex-math notation="LaTeX">\rho </tex-math></inline-formula> to capture the danger threat of each vehicle being in the rear-end collision. In specific, we propose a fully distributed adaptive beacon control scheme, called ABC , which makes each vehicle actively adopt a minimal but sufficient beaconing rate to avoid the rear-end collision in dense scenarios based on individually estimated <inline-formula> <tex-math notation="LaTeX">\rho </tex-math></inline-formula>. With ABC , vehicles can broadcast at the maximum beaconing rate when the channel medium resource is enough and meanwhile keep identifying whether the channel is congested. Once a congestion event is detected, an NP-hard distributed beacon rate adaptation (DBRA) problem is solved with a greedy heuristic algorithm, in which a vehicle with a higher <inline-formula> <tex-math notation="LaTeX">\rho </tex-math></inline-formula> is assigned with a higher beaconing rate while keeping the total required beaconing demand lower than the channel capacity. We prove the heuristic algorithm's close proximity to the optimal result and thoroughly analyze the communication overhead of ABC scheme. By using Simulation of Urban MObility (SUMO)-generated vehicular traces, we conduct extensive simulations to demonstrate the efficacy of our proposed ABC scheme. Simulation results show that vehicles can adapt beaconing rates according to the driving safety demand, and the beaconing reliability can be guaranteed even under high-dense vehicle scenarios.
Internet of vehicles in big data era Xu, Wenchao; Zhou, Haibo; Cheng, Nan ...
IEEE/CAA journal of automatica sinica,
2018-Jan., 2018-1-00, 20180101, Volume:
5, Issue:
1
Journal Article
Peer reviewed
Open access
As the rapid development of automotive telematics, modern vehicles are expected to be connected through heterogeneous radio access technologies and are able to exchange massive information with their ...surrounding environment. By significantly expanding the network scale and conducting both real time and long term information processing, the traditional Vehicular Ad- Hoc Networks ( VANETs ) are evolving to the Internet of Vehicles ( IoV ), which promises efficient and intelligent prospect for the future transportation system. On the other hand, vehicles are not only consuming but also generating a huge amount and enormous types of data, which are referred to as Big Data. In this article, we first investigate the relationship between IoV and big data in vehicular environment, mainly on how IoV supports the transmission, storage, computing of the big data, and in return how IoV benefits from big data in terms of IoV characterization, performance evaluation and big data assisted communication protocol design. We then investigate the application of IoV big data for autonomous vehicles. Finally the emerging issues of the big data enabled IoV are discussed.
To enable ever-increasing vehicular applications, heterogeneous vehicular networks (HetVNets) are recently emerged to provide enhanced and cost-effective wireless network access. Meanwhile, edge ...caching is imperative to future vehicular content delivery to reduce the delivery delay and alleviate the unprecedented backhaul pressure. This work investigates content caching in HetVNets where Wi-Fi roadside units (RSUs), TV white space (TVWS) stations, and cellular base stations are considered to cache contents and provide content delivery. Particularly, to characterize the intermittent network connection provided by Wi-Fi RSUs and TVWS stations, we establish an on-off model with service interruptions to describe the content delivery process. Content coding then is leveraged to resist the impact of unstable network connections with optimized coding parameters. By jointly considering file characteristics and network conditions, we minimize the average delivery delay by optimizing the content placement, which is formulated as an integer linear programming (ILP) problem. Adopting the idea of student admission model, the ILP problem is then transformed into a many-to-one matching problem and solved by our proposed stable-matching-based caching scheme. Simulation results demonstrate that the proposed scheme can achieve near-optimal performances in terms of delivery delay and offloading ratio with low complexity.
In this paper, we propose a drone assisted radio access networks architecture in which drone-cells are leveraged to relay data between base stations and users. Based on the state-of-the-art ...drone-to-user and drone-to-base station (D2B) channel models, we first analyze the user coverage and the D2B backhaul connection features of drone-cells. We then formulate the 3-D drone-cell deployment problem with the objective of maximizing the user coverage while maintaining D2B link qualities, for a given number of drone cells being deployed. To solve the problem, the particle swarm optimization (PSO) algorithm is leveraged for its low computational cost and unique features suiting the spatial deployment of drone-cells. We propose a per-drone iterated PSO (DI-PSO) algorithm that optimizes drone-cell deployments for different drone-cell numbers, and prevents the drawbacks of the pure PSO-based algorithm derived from related works. Simulations show that the DI-PSO algorithm can achieve higher coverage ratio with less complexity comparing to the pure PSO-based algorithm.
The 6G wireless network is promising to build bridges toward smart society in the digital world, which calls for innovative architectures and new solutions. The future 6G network should be ...sensing-based and data-driven for near-instant and massive connectivity with distributed intelligence. With a majority of intelligent applications being deployed at the edge, artificial intelligence (AI) is envisioned to play a key role in satisfying key requirements of 6G networks. Edge intelligence, as the marriage of AI and edge computing, is envisioned to fully meet the potential requirements of edge big data with energy, bandwidth, storage, and privacy concerns. However, it is an attractive issue to deal with distributed edge intelligence for the complexities and heterogeneous requirements, especially considering the time-varying channels and network dynamics. Furthermore, the ever increasing number of smart devices present great challenges for intelligent network management and newly modular network design in 6G networks, which needs to enable liquid self-management with comprehensive network intelligence. Hence, in this article, we first comprehensively give an overview on AI toward 6G networks, and characterize the requirements of a 6G network for AI applications. In particular, we investigate distributed edge intelligence challenges, requirements, and trends in future 6G networks. Then a liquid-specific and flexible software-defined network architecture for AI applications is inspired and discussed by 6G networks, which will play a crucial role in both academia and industry.
Internet of vehicles (IoV) is an emerging paradigm for accommodating the requirements of future intelligent transportation systems (ITSs) with the overwhelming trend of equipping vehicles with ...versatile sensors and communications modules, and facilitating drivers and passengers with a variety of innovative ITS applications. However, the implementation of IoV still faces many challenges, such as flexible and efficient connections, quality of service guarantee, and multiple concurrent support requests. To this end, in this paper we introduce the software-defined IoV (SD-IoV), which is able to tackle the above-mentioned issues by adopting the software-defined networking framework. We first present the architecture of SD-IoV and develop a centralized vehicular connection management approach. Then, we aim to allocate dedicated communications resources and underlying vehicular nodes to satisfy each service. We formulate the dynamic vehicular connection as an overlay vehicular network creation (OVNC) problem. A comprehensive utility function is also designed to serve as the optimization objective of OVNC. Finally, we solve the OVNC problem by developing a graph-based genetic algorithm and a heuristic algorithm, respectively. Extensive simulation results are provided to demonstrate the effectiveness of our proposed solution of dynamic vehicular connection management.
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