In this study, radio channel measurements were conducted in an urban inland river environment at 5.9 GHz. The measurements consisted of both the line of sight (LOS) and non-line of sight (NLOS) cases. We estimate and model channel characteristics based on the measured data. For small-scale fading properties, best fit small-scale fading distribution is determined. Statistical models of the Ricean K-factors and Weibull shape parameters are provided. In addition, a distance-dependent Ricean K-factor model for the urban inland river environment is proposed. For the power delay profile (PDP), we perform cluster identification and obtain the cumulative distribution function (CDF) of the inter-cluster interval. A statistical model of the intracluster decay time constant is also presented, and an exponential decay model is applied for modeling the inter-cluster decay of the PDP. For delay spread and Doppler spread, the root mean square (RMS) delay spreads in the LOS case are compared with those in the NLOS case. Moreover, we estimate and analyze RMS Doppler spreads in both the LOS and NLOS cases. Finally, a bimodal Gaussian mixture distribution (BGMD) is employed to characterize the RMS delay spreads and RMS Doppler spreads. The BGMDs exhibit good matching levels.

This paper focuses on vehicle-to-vehicle (V2V) radio channel properties under ramp scenarios with different structures. Ramps are categorized according to different construction structures into: 1) viaduct ramp with soundproof walls in an urban area and 2) a general ramp without soundproof walls in a suburban region. Furthermore, considering whether the line of sight is available, the entire propagation process of the radio signal is divided into various propagation zones. Propagation characteristics, including the distribution of fading, fading depth (FD), level crossing rate, average fade duration, Root-Mean-Square (rms) delay spread, propagation path loss, and shadow fading, have been estimated and extracted. In particular, the radio channel properties in different types of ramp scenarios are compared and some interesting findings are obtained: 1) an abrupt fluctuation of the received signal level (RSL) in the urban viaduct ramp scenario indicates the nonignorable impact of soundproof walls on V2V radio channel and 2) continuous changes of RSL and different FD values in various propagation zones can be observed in suburban ramp scenarios. Furthermore, the statistical characteristics of RMS delay spread are fitted using a generalized extreme value model with a good fit. Furthermore, propagation path loss is modeled, demonstrating the difference of path loss values in the transition region owing to the impact of soundproof walls. Overall, the research results emphasize the significance of the V2V radio channel modeling under ramp scenarios.

The Li-ion battery is becoming the optimal choice for the Electric Vehicle’s (EV) power supply. In order to protect the Li-ion battery from charging damage and to prolong the battery’s life, a special control strategy based on the dynamic balanced point along with a non-dissipative equalizer is presented. The main focus of the proposed control strategy is to insure that the individual cell of a battery pack will be rapidly, efficiently and simultaneously balanced, by adjusting equalizing current of each cell dynamically. In this paper, a model of a four series connected Li-ion batteries pack has been established to evaluate the proposed control strategy. Superior performance is demonstrated by the simulation and experiment.

The application based on big data is an important development trend of underwater acoustic sensor networks. However, traditional underwater acoustic sensor networks rely on the hardware infrastructure. The flexibility and scalability cannot be satisfied greatly. Due to the low performance of underwater acoustic sensor networks, it creates significant barriers to the implementation of big data. Software-defined network is regarded as a new infrastructure of next-generation network. It offers a novel solution for designing underwater acoustic sensor networks of high performance. In this article, a software-defined network–based solution is proposed to build the architecture of underwater acoustic sensor networks in big data. The design procedures of the data plane and control plane are described in detail. In the data plane, the works include the hardware design of OpenFlow-based virtual switch and the design of the physical layer based on software-defined radio. The hierarchical clustering technology and the node addressing techniques for designing media access control layer are well introduced. In the control plane, exploiting the hardware of the controller and designing the core module of controllers are presented as well. Through the study, it is supposed to maximize the capacity of underwater acoustic sensor networks, reduce the management complexity, and provide critical technical support for the high-performance underwater acoustic sensor networks.

The prediction of lane-change behavior is a challenging issue in intelligent and connected vehicles (ICVs), which can help vehicles predict in advance and change lanes safely. In this paper, a novel intelligent approach, which considering both the driving style-based lane-change environment and the driving trajectory-related parameters of the ICV and surrounding vehicles, is proposed to predict the lane-change behaviors for ICVs. By analyzing the characteristics of the lane-change behavior of the vehicle, a modified dataset for the prediction of lane-change behavior was established based on the Next-Generation Simulation (NGSIM) dataset, which is made up of real vehicle trajectories collected by camera. In the proposed approach, the hidden Markov model (HMM)-based model is designed to judge whether the current environment is suitable for lane change according to the driving environment parameters around the vehicle; then according to the driving state of the vehicle, a learning-based prediction-then-judgment model is proposed and designed to realize the prediction of the ICV’s lane-change behavior. Experiments are implemented by using the modified dataset. From the experimental results, the lane-change probability value on the target lane in the truth of the lane-change behavior calculated by the designed HMM-based model is basically above 0.5, indicating that the model can make a more accurate judgment on the surrounding lane-change environment. The proposed learning-based prediction-then-judgment model has an accuracy of 99.32% for the prediction of lane-change behavior, and the accuracy of the lane-change detection algorithm in the model is 99.56%. The experimental results show that the proposed approach has a good performance in the prediction of lane-change behavior, which could effectively assist ICVs to change lanes safely.

Cellular Vehicle-to-Everything (C-V2X) Mode 4, developed by 3GPP, supports vehicle-to-vehicle communication without a cellular base station via LTE sidelink. The messages transmitted in the Internet of Vehicles (IoV) include periodic single-hop broadcast Beacon messages and multi-hop broadcast Emergency messages triggered by events. In this paper, we analyze the impact of multi-hop broadcast of Emergency messages in C-V2X Mode 4 on the performance of Beacon messages and propose collision probability models for Beacon messages and Emergency messages. The non-periodicity nature and lower broadcast latency requirement of the multi-hop Emergency messages may cause resource allocation conflicts in the resource selection process of C-V2X Mode 4, thus result in forwarding collision of Emergency messages and collision of subsequent Beacon messages. Therefore, we propose several multi-hop broadcast schemes for Emergency messages to reduce the collision probability by assigning independent resource grants for Emergency messages and adjusting the number of forwarding nodes. The performance of Beacon messages and Emergency messages of several different forwarding schemes proposed are simulated. The simulation results show that the proposed broadcasting schemes significantly improve the performance of Beacon messages and Emergency messages.