Narrow Results

Although the technology of Internet of Vehicles (IoV) is developing rapidly, it will still take a long time to realize its overall popularization. Aimed at this transition phase, this paper proposes to set up an IoV lane on the urban road, which is specially designed for the connected vehicles, to provide a better driving environment for the connected vehicles. Considering the operation characteristics of traffic flow under traditional and IoV environment, this paper establishes a three-lane cellular automata model for urban road traffic flow considering IoV lane, modified on the basis of the Modified Comfortable Driving (MCD) model and symmetric two-lane cellular automata (STCA) model, and then takes simulation by MATLAB and makes analysis. The result shows that the setting of IoV lane can improve the velocity of networked vehicles to a great extent with no or just a bit decline in the ordinary vehicles’ speed, and it has a great effect on the mixture traffic flow, including the increase in both traffic volume and the average speed. What’s more, when the networking proportion is between 0.2 and 0.76, and the space occupation ranges from 0.18 to 0.56, the traffic benefit of IoV special lane can reach the best.

In order to improve the comprehensive effect of Urban Traffic Control System (UTCS) and Urban Traffic Flow Guidance System (UTFGS), this paper puts forward a collaboration optimization model of dynamic traffic control and guidance based on Internet of Vehicles (IOV). With consideration of dynamic constraints of UTCS and UTFGS, UTCS is taken as the fast variable, and UTFGS is taken as the slow variable in the collaboration optimization modeling. The conception of Variable Cycle Management (VCM) is presented to solve the mathematical modeling problem under the background of the two variables. A unified framework for VCM is proposed based on IOV. The delay and travel time are calculated based on lane-group-based cell transmission model (LGCTM). The collaboration optimization problem is abstracted into a tri-level programming model. The upper level model is a cycle length optimization model based on multi-objective programming. The middle level model is a dynamic signal control decision model based on fairness analysis. The lower level model is a user equilibrium model based on average travel time. A Heuristic Iterative Optimization Algorithm (HIOA) is set up to solve the tri-level programming model. The upper level model is solved by Non-dominated Sorting Genetic Algorithm II (NSGA II), the middle level model and the lower level model are solved by Method of Successive Averages (MSA). A case study shows the efficiency and applicability of the proposed model and algorithm.

Communication technology has achieved unprecedented development in recent years, and its applications in transportation systems and automobiles are also increasing. During driving, the driver obtains not only traffic information through observation but also more traffic information beyond the visual range through the Internet of Vehicles (IoV). Therefore, to better describe the evolution law of traffic flow in the IoV environment and to provide some theoretical and strategic support for the coming of the autonomous driving era in the future, an extended car-following model accounting for average optimal velocity difference and backward-looking effect based on IoV environment is proposed. The linear analysis and nonlinear analysis of the model are calculated separately, and the neutral stability curve and the coexistence curve together prove the validity of the model. Subsequently, the numerical simulation verified the accuracy of the theoretical analysis and also proved that the extended model can enhance the stability of traffic flow.

Restaurant recommendation is one of the most recommendation problems because the result of recommendation varies in different environments. Many methods have been proposed to recommend restaurants in a mobile environment by considering user preference, restaurant attributes, and location. However, there are few restaurant recommender systems according to the internet of vehicles environment. This paper presents a recommender system based on the prediction of traffic conditions in the internet of vehicles environment. This recommender system uses a phased selection method to recommend restaurants. The first stage is to screen restaurants that are on the user’s driving route; the second stage is to recommend restaurants from the user attributes, restaurant attributes (with traffic conditions), and vehicle context, using a deep learning model. The experimental evaluation shows that the proposed recommender system is both efficient and effective.

Due to uneven space–time distribution of vehicles, Internet of Vehicles (IoV) has problems with load imbalance and low resource utilization of Base Stations (BSs) in the Coordinated Multi-Point (CoMP) communication scenario. This paper proposes a dynamic load balancing algorithm based on vehicle prediction. It is assumed that the number of vehicles arriving at the BSs obeys the segmented Poisson distribution to determine the current and predicted load statuses of BSs. First, analyze the load status of each BS and the location of users (vehicles). Then, screen out BSs whose load below the full load threshold as a switchable low-load cooperative cluster, which can convert interference signals into useful signals and reduce the interference between adjacent BSs. Finally, complete load balancing by redistributing the communication service of edge users through sharing channel information and user date among coordinated BSs. Because IoV is a dynamic network, the proposed algorithm runs dynamically in cycles. Simulation results show that the algorithm can perform balance the load of BSs well, the overload rates of BSs during the traffic off-peak period and peak period are reduced significantly, and the average information rate of users is greatly improved.

In the large-scale and ultra-dense Internet of Vehicles (IoVs), constructing the simplest backbone network is an urgent problem to be solved. In fact, constructing the simplest backbone network is an NP-hard problem, and at present, there is no effective solution. In this paper, we propose a graph-based clustering algorithm to solve this problem and construct the simplest backbone network in the large-scale and ultra-dense IoV. We establish a backbone network model for the large-scale and ultra-dense IoV and optimize the backbone network by employing a novel local search iterative algorithm. Simulation results show that with the increase in node density, the number of clusters selected by the proposed algorithm tends to be stable, while the number of optimized clusters decreases by 28.87% on an average. Thus, the proposed algorithm can effectively simplify the backbone network.

Denial of Service (DoS)-Oriented cyber attack has been a major threat for physical security in many kinds of network media, including the Internet of Vehicles (IoV). This paper focuses on the scenario of IoV, and proposes a machine learning-based fast detection method for adversarial neural network-based fast detection method for DoS-oriented cyber attacks. First, by analyzing the implementation principles and attack characteristics of three attack types, three aspects of statistical features are extracted: maximum matching packet growth rate, source address entropy value, and flow table similarity. Then, they are used as the input features to establish an adversarial machine learning-based DoS cyber attack detection method. On this basis, the field features of six stream rules are extracted, and two DoS cyber attack detection methods via machine learning are formulated. The proposals are able to detect the low-rate DoS-based cyber attacks against the data layer. The experimental results show that the proposed DoS attack detection method based on machine learning can effectively detect three DoS attacks under IoV, and these two algorithms have higher detection rates when compared with other algorithms.

Internet of vehicles (IoV) has become an important research topic due to its direct effect on intelligent transportation systems (ITS) development. There are many challenges in the IoV environment, such as communication, big data and best route assigning. In this paper, an effective IoV architecture is proposed. This architecture has four main objectives. The first objective is to utilize a powerful communication scheme in which three tiers of coverage tools — Internet, satellite, high-altitude platform (HAP) — are utilized. Therefore, the vehicles maintain a continuous connection to the IoV environment everywhere. The second objective is to apply filtering and prioritization mechanisms to reduce the detrimental effects of IoV big data. The third objective is to assign the best route for a vehicle after determining its real-time priority. The fourth objective is to analyze the IoV data. The proposed architecture performance is measured using a simulation environment that is created by the NS-3 package. The simulation results proved that the proposed IoV architecture has a positive impact on the IoV environment according to the performance metrics: energy, success rate of route assignment, filtering effect, data loss, delay, usage of coverage tools and throughput.

Highly dynamic Internet of Vehicles spectrum sharing can share spectrum owned by vehicle-to-infrastructure links through multiple workshop links to achieve efficient resource allocation. Aiming at the problem that the rapid variations in channel states in highly dynamic vehicular environments can make it challenging for base stations to gather and manage information about instantaneous channel states, we present a multi-agent deep reinforcement learning-based V2X spectrum access algorithm. The algorithm is designed to optimize the throughput of V2I user under V2V user delay and reliability constraints, and uses the experience gained from interacting with the communication environment to update the Q network to improve spectrum and power allocation strategies. Implicit collaborative agents are trained through an improved DQN model combined with dueling network architecture and long short-term memory network layers and public rewards. With lagged Q-learning and concurrent experience replay trajectories, the training process was stabilized and the non-stationarity problem caused by concurrent learning of multiple agents was resolved. Simulation results demonstrate that our presented algorithm achieves a mean successful payload delivery rate of 95.89%, which is 16.48% greater than that of the randomized baseline algorithm. Our algorithm obtains approximately the optimal value and shows performance close to the centralized brute force algorithm, which provides a better strategy for further minimizing the signaling overhead of the Internet of Vehicles communication system.