Vehicle Network Research Articles

Optimizing Traffic Scheduling in Autonomous Vehicle Networks Using Machine Learning Techniques and Time-Sensitive Networking

Optimizing Traffic Scheduling in Autonomous Vehicle Networks Using Machine Learning Techniques and Time-Sensitive Networking

Optimal dispatching of regional power grid considering vehicle network interaction.

When large-scale electric vehicles are connected to the grid for unordered charging, it will seriously affect the stability and security of the power system. To solve this problem, this paper proposes a regional power network optimization scheduling method considering vehicle network interaction. Initially, based on the user behavior characteristics and charging and discharging characteristics of electric vehicles, a charging and discharging behavior model of electric vehicles was established. Based on the Monte Carlo sampling algorithm, the scheduling upper and lower limits of each scheduling cycle of electric vehicles were described, and the scheduling potential of each scheduling cycle of electric vehicles was obtained. Then, the electricity price is then used as an incentive parameter to guide EV users to charge during periods of low electricity prices and participate in discharge during periods of peak electricity prices. Aiming at the highest economic efficiency, the best consumption effect of new energy and the smoothest demand-side power curve of regional power grid, a three-objective optimal dispatching model was established. In the later stage, uncertainty factors are taken into consideration by introducing the concept of interval numbers, and an interval multi-objective optimization dispatching model is established. The two dispatching models are solved by NSGA-II algorithm and improved NSGA-II algorithm, and the Pareto solution set is obtained. Finally, based on the analytic Hierarchy Process (AHP), the optimal scheduling scheme is determined. The Monte Carlo sampling method is used to simulate the user side charging demand, and the effectiveness of this method is verified. In addition, the results of the interval multi-objective optimization model and the deterministic multi-objective optimization model are compared, and it is proved that the solution results of the interval multi-objective model are more adaptive, practical and robust to the uncertain factors.

Flavors of the Next Generation of Unmanned Aerial Vehicles Networks

Flavors of the Next Generation of Unmanned Aerial Vehicles Networks

A model for preventing radio interference in the communication system of unmanned aerial vehicles

Problem statement. The communication network of unmanned aerial vehicles (UAVs) has proven to be a promising model capable of operating independently and as a repeater to increase coverage and communication efficiency. However, densely distributed ground base stations with common communication frequencies inevitably generate interference within the transmission channel. The influence of interference can be effectively eliminated by implementing optical communication in free space in the communication network of an unmanned aerial vehicle. Goal. Development of a modal for effective elimination of radio interference for unmanned aerial vehicles through the use of a mixed millimeter-wave radio frequency communication system. Results. The simulation results show that the proposed network significantly increases the probability of troubleshooting and achieves an increase in the signal-to-noise ratio by approximately 4.6 dB at an average frequency of character errors, especially in scenarios where radio frequency lines are subject to severe interference or unfavorable weather conditions, unlike a fully radio frequency communication network. Practical significance. Improved communication reliability for UAV systems with an effective signal-to-noise ratio reduces the likelihood of packet loss and the bit error rate (BER) of transmitted data.

Vertical Switching Algorithm for Unmanned Aerial Vehicle in Power Grid Heterogeneous Communication Networks

The rapid development of wireless network technology has led to the coexistence of various heterogeneous wireless networks (HWNs). To ensure that users enjoy normal and diversified services, research on vertical switching technology has become an inevitable trend. However, most current vertical switching algorithms only consider static situations or single services, which are not suitable for power grid scenarios. This paper studies the vertical switching problem of wireless heterogeneous networks for unmanned aerial vehicles (UAVs) performing inspection tasks in power grid scenarios. In this model, a UAV for power grid inspection needs to plan its flight trajectory, avoid obstacles, and find the optimal trajectory to reach each inspection point. Throughout the UAV inspection process, we must ensure the quality of communication services for the UAV. The UAV dynamically selects different networks for access at different locations, presenting a dynamic network selection and vertical switching problem. This paper proposes a method that combines trajectory planning and network selection, which first utilizes the A-star algorithm to obtain suitable trajectories, and then evaluates and judges networks based on the Fuzzy Analytic Hierarchy Process (FAHP) to determine the most appropriate network. It is worth noting that this paper considers three service requirements and seven network attributes under three types of heterogeneous wireless networks. Numerical results show that this method can better meet the requirements of UAV inspection tasks and reduce the number of switches, thus addressing the issue of terminal vertical switches in power grid scenarios.

An Adaptive Deployment Scheme of Unmanned Aerial Vehicles in Dynamic Vehicle Networking for Complete Offloading

An Adaptive Deployment Scheme of Unmanned Aerial Vehicles in Dynamic Vehicle Networking for Complete Offloading

Bifurcation analysis and control study of improved full-speed differential model in connected vehicle environment

Abstract In recent years, the traffic congestion problem has become more and more serious, and the research on traffic system control has become a new hot spot. Studying the bifurcation characteristics of traffic flow systems and designing control schemes for unstable pivots can alleviate the traffic congestion problem from a new perspective. In this work, the full-speed differential model considering the vehicle network environment is improved in order to adjust the traffic flow from the perspective of bifurcation control, the existence conditions of Hopf bifurcation and saddle-node bifurcation in the model are proved theoretically, and the stability mutation point for the stability of the transportation system is found. For the unstable bifurcation point, a nonlinear system feedback controller is designed by using Chebyshev polynomial approximation and stochastic feedback control method. The advancement, postponement, and elimination of Hopf bifurcation are achieved without changing the system equilibrium point, and the mutation behavior of the transportation system is controlled so as to alleviate the traffic congestion. The changes in the stability of complex traffic systems are explained through the bifurcation analysis, which can better capture the characteristics of the traffic flow. By adjusting the control parameters in the feedback controllers, the influence of the boundary conditions on the stability of the traffic system is adequately described, and the effects of the unstable focuses and saddle points on the system are suppressed to slow down the traffic flow. In addition, the unstable bifurcation points can be eliminated and the Hopf bifurcation can be controlled to advance, delay, and disappear, so as to realize the control of the stability behavior of the traffic system, which can help to alleviate the traffic congestion and describe the actual traffic phenomena as well.

Research on vehicle electromagnetic compatibility testing based on LTE-V2X direct communication

Abstract With the continuous development of intelligent networked vehicle technology, the electromagnetic environment inside and outside the vehicle is becoming more and more complicated. As the key technology of vehicle-road coordination, the electromagnetic reliability of V2X communication will affect the driving safety and ride comfort of intelligent networked vehicles. Based on V2X communication, this paper introduces the mainstream V2X technology and related standards at home and abroad. Taking the vehicle based on LTE-V2X direct communication technology as the object, the construction of the communication scene, electromagnetic emission, and immunity test method in the semi-dark room are studied, and the test results and matters needing attention in the test are given. This research can well support the formulation of vehicle networking standards and help improve the intelligent networked vehicle standards and regulations system.

Internet of Things (IoT) and its Utilisation in Marine Logistics

The marine logistics industry is the backbone of global trade, responsible for the transport of more than 90% of goods traded worldwide. However, the industry faces challenges in terms of improving efficiency, reducing costs, and ensuring supply chain security and visibility. The Internet of Things (IoT), which refers to a network of physical devices, vehicles, and other objects equipped with sensors, software, and internet connectivity, is emerging as a potential solution to address these issues. This research aims to explore the role and utilization of IoT in improving the performance of the marine logistics industry. The methodology used includes a comprehensive literature review, in-depth interviews with industry experts, and case study analyses of IoT implementations in marine logistics companies. The results revealed several opportunities and benefits from IoT implementation, such as real-time container and cargo tracking, efficient vessel fleet management, route optimization and logistics planning, and improved supply chain security and safety. However, IoT implementation also faces challenges related to data security, interoperability, investment costs, and regulation. This research provides in-depth insights into how IoT can be leveraged to optimize marine logistics operations and improve industry competitiveness, as well as recommended solutions to overcome the challenges faced.

Multi-objective task offloading for highly dynamic heterogeneous Vehicular Edge Computing: An efficient reinforcement learning approach

Vehicular Edge Computing (VEC) provides a flexible distributed computing paradigm for offloading computations to the vehicular network, which can effectively solve the problem of limited vehicle computing resources and meet the on-vehicle computing requests of users. However, the conflict of interest between vehicle users and service providers leads to the need to consider a variety of conflict optimization goals for computing offloading, and the dynamic nature of vehicle networks, such as vehicle mobility and time-varying network conditions, make the offloading effectiveness of vehicle computing requests and the adaptability to complex VEC scenarios challenging. To address these challenges, this paper proposes a multi-objective optimization model suitable for computational offloading of dynamic heterogeneous VEC networks. By formulating the dynamic multi-objective computational offloading problem as a multi-objective Markov Decision Process (MOMDP), this paper designs a novel multi-objective reinforcement learning algorithm EMOTO, which aims to minimize the average task execution delay and average vehicle energy consumption, and maximize the revenue of service providers. In this paper, a preference priority sampling module is proposed, and a model-augmented environment estimator is introduced to learn the environmental model for multi-objective optimization, so as to solve the problem that the agent is difficult to learn steadily caused by the highly dynamic change of VEC environment, thus to effectively realize the joint optimization of multiple objectives and improve the decision-making accuracy and efficiency of the algorithm. Experiments show that EMOTO has superior performance on multiple optimization objectives compared with advanced multi-objective reinforcement learning algorithms. In addition, the algorithm shows robustness when applied to different environmental settings and better adapting to highly dynamic environments, and balancing the conflict of interest between vehicle users and service providers.

Исследование алгоритмов организации сетей АНПА с использованием программной среды моделирования

Development of a network of autonomous underwater vehicles (AUV) is a complex multifactorial task. Solution of this problem requires the fulfillment of a number of stringent requirements, including, requirements for fault tolerance, efficiency of information exchanges, energy efficiency, etc. As a result, it is very important to use simulation for test proposed solutions of AUV network design. The purpose of current research is development and test a software environment for designing AUV networks. The simulation environment is implemented within the framework of an object-oriented approach in Python. The main results obtained include the established principles for implementing a software environment for simulation of AUV networks. The layout of the hardware and software for underwater communication network developed in Concern CSRI Elektropribor is the basis of the developed simulation model. The results of testing the software are presented using the example of solving the problem of ensuring the efficiency of information exchanges. The developed software environment makes it possible to increase the efficiency of AUV network development.

Addressing environmental stochasticity in reconfigurable intelligent surface aided unmanned aerial vehicle networks: Multi-task deep reinforcement learning based optimization for physical layer security

Deep Reinforcement Learning (DRL) is a powerful tool for optimizing communications in reconfigurable intelligent surface (RIS)-assisted millimeter-wave unmanned aerial vehicle (UAV) systems, particularly for ensuring physical layer security by dynamically maximizing signal strength for legitimate users while minimizing leakage towards eavesdroppers. However, existing approaches often overfit the DRL agent to a non-stochastic environment, where the trajectories of users and eavesdroppers are fixed. This results in a single-task DRL agent that cannot generalize beyond the training environment/task. In response, this study introduces a novel multi-task DRL framework to address this limitation. Firstly, recognizing that not all tasks share the same solution, we proposed a reward-driven clustering method to group similar tasks based on their reward scores. This enables the training of a shared multi-task DRL agent for each cluster. Each cluster can be treated as an expertise, and the cluster-specific multi-task DRL agents are considered the 'experts'. Meanwhile, we leverage federated learning (FL) to speed up the training of the shared multi-task DRL agent. By combining these two techniques, our reward-driven clustered FL autonomously groups similar tasks together rapidly. Simulation results demonstrate a reduction in the number of agents from 50 to 10 specialized 'experts', while achieving a 10X speedup compared to conventional single-task DRL training. Furthermore, the study presents a mixture of experts (MoE) model that unifies these 'experts' into a single model. The MoE is trained to select the most suitable 'expert' when encountering new tasks. Our MoE exhibits robustness by effectively generalizing to 50 novel tasks without fine-tuning.

An identity authentication and key agreement protocol for the internet of vehicles based on trusted cloud-edge-terminal architecture

The continuous progression in cloud computing, edge computing, and associated technologies has notably hastened the progress of vehicle networking technology. This advancement is increasingly assuming a crucial role in enhancing driving safety, optimizing traffic management, and revolutionizing traffic control methodologies. The principal aim of Internet of Vehicles (IoV) technology is to establish a secure, convenient, and efficient novel driving paradigm, enabling intelligent transportation through wireless communication connecting roadside units and vehicles. Nevertheless, this wireless communication method is susceptible to potential attacks, including remote control, information monitoring, and identity simulation. Given this situation, effective authentication is required to address this security concern. Thus, this study proposes an identity authentication and key negotiation protocol grounded in a trusted cloud-edge-terminal architecture. This protocol facilitates mutual authentication, generates secure session keys for communication, guarantees the security of vehicle communication, and supports functionalities including privacy protection and password alteration for vehicle users. Time tree technology is employed for managing the edge nodes, facilitating the sharing of vehicle certification information among these nodes, and enhancing certification efficiency. Formal security analysis and informal security analysis are conducted to demonstrate the security of the proposed protocol, evaluating its security and practicality. Theoretical comparisons and experimental results demonstrate the outstanding computational and communication performance of the proposed protocol.

A Review of Anomalous Behavior Detection in Internet of Vehicles

The intelligent transportation system with the Internet of Vehicles as its core is gradually penetrating into the lives of urban residents, but it has also exposed security threats such as remote control of vehicles and leakage of personal information of car owners. Compared to the security issues at the level of vehicle end devices and vehicle networking service platforms, the article focuses on the security issues of abnormal behavior in vehicle networking. Based on this, the article reviews the relevant research on abnormal behavior detection mechanisms in the Internet of Vehicles environment in recent years. Firstly, the definition of abnormal behavior was analyzed, and the basic framework for detecting abnormal behavior was provided; Then, the classification of abnormal behavior detection mechanisms was discussed from three aspects: deep learning based abnormal behavior detection, spatiotemporal fusion based abnormal behavior detection, and visual based abnormal behavior detection; Finally, the unresolved technical issues and future research trends in the current abnormal behavior detection mechanism for the Internet of Vehicles were summarized.

A Survey on Reputation Systems for UAV Networks

The proliferation of unmanned aerial vehicle (UAV) networks is increasing, driven by their capacity to deliver automated services tailored to the varied demands of numerous smart city applications. Trust, security, and privacy remain paramount in the public domain. Traditional centralized network designs fall short of ensuring device authentication, data integrity, and privacy within the highly dynamic and adaptable environments of UAV networks. Decentralized reputation systems have emerged as a promising solution for enhancing the reliability and trustworthiness of data and communications within these networks while safeguarding UAV security. This paper presents an exhaustive survey of trust and reputation systems, exploring existing frameworks and proposed innovations alongside their inherent challenges. The crucial role of reputation systems is to strengthen trust, security, and privacy throughout these networks, and various strategies can be incorporated to mitigate existing vulnerabilities. As a useful resource for researchers and practitioners seeking to advance the state of the art in UAV network security, we hope this survey will spark further community discussion and stimulate innovative ideas in this burgeoning field.

Research on UAV-assisted Vehicle networking task unloading strategy based on multi-agent reinforcement learning

Research on UAV-assisted Vehicle networking task unloading strategy based on multi-agent reinforcement learning

Cost-aware task offloading in vehicular edge computing: A Stackelberg game approach

With the popularity of vehicular communication systems and mobile edge vehicle networking, intelligent transportation applications arise in Internet of Vehicles (IoVs), which are latency-sensitive, computation-intensive, and requiring sufficient computing and communication resources. To satisfy the requirements of these applications, computation offloading emerges as a new paradigm to utilize idle resources on vehicles to cooperatively complete tasks. However, there exist several obstacles for realizing successful task offloading among vehicles. For one thing, extra cost such as communication overhead and energy consumption occurs when a task is offloaded on a service vehicle, it is unlikely to expect the service vehicle will contribute its resources without any reward. For another, since there are many vehicles around, both user vehicles and service vehicles are trying to strike a balance between cost and profit, through matching the perfect service/user vehicles and settled with optimal offloading plan that is beneficial to all parties. To solve these issues, this work focuses on the design of effective incentive mechanisms to stimulate vehicles with idle resources to actively participate in the offloading process. A fuzzy logic-based dynamic pricing strategy is proposed to accurately evaluate the cost of a vehicle for processing the task, which provides insightful guidance for finding the optimal offloading decision. Meanwhile, the competitive and cooperation relations among vehicles are thoroughly investigated and modeled as a two-stage Stackelberg game. Particularly, this work emphasizes the social attributes of vehicles and their effect on the offloading decision making process, multiple key properties such as the willingness of UV to undertake the task locally, the reputation of UV and the satisfaction of SV for the allocated task proportion, are carefully integrated in the design of the optimization problem. A distributed algorithm with applicable complexity is proposed to solve the problem and to find the optimal task offloading strategy. Extensive simulations are conducted on real-world scenarios and results show that the proposed mechanism achieves significant performance advantages in terms of vehicles' utilities, cost, completion delay under varied network and channel environment, which justifies the effectiveness and efficiency of this work.

On Evaluating Black-Box Explainable AI Methods for Enhancing Anomaly Detection in Autonomous Driving Systems.

The recent advancements in autonomous driving come with the associated cybersecurity issue of compromising networks of autonomous vehicles (AVs), motivating the use of AI models for detecting anomalies on these networks. In this context, the usage of explainable AI (XAI) for explaining the behavior of these anomaly detection AI models is crucial. This work introduces a comprehensive framework to assess black-box XAI techniques for anomaly detection within AVs, facilitating the examination of both global and local XAI methods to elucidate the decisions made by XAI techniques that explain the behavior of AI models classifying anomalous AV behavior. By considering six evaluation metrics (descriptive accuracy, sparsity, stability, efficiency, robustness, and completeness), the framework evaluates two well-known black-box XAI techniques, SHAP and LIME, involving applying XAI techniques to identify primary features crucial for anomaly classification, followed by extensive experiments assessing SHAP and LIME across the six metrics using two prevalent autonomous driving datasets, VeReMi and Sensor. This study advances the deployment of black-box XAI methods for real-world anomaly detection in autonomous driving systems, contributing valuable insights into the strengths and limitations of current black-box XAI methods within this critical domain.

Dynamic task offloading and service caching based on game theory in vehicular edge computing networks

Vehicular edge computing (VEC) enables task offloading from vehicle to edge servers deployed on Road Side Units (RSUs) and to vehicle with idle resources, which is considered as a promising vehicle network architecture. However, efficient task offloading and task resource cache management in the VEC network is challenging. In this work, we formulate a joint task offloading and service caching problem (JTOSCP) for VEC with edge-vehicle cooperation, aiming to minimize the total task delay-energy value and cost value of all vehicles while guaranteeing task processing delay tolerance. The formulated JTOSCP is proven as NP-hard problem. Due to the complexity of JTOSCP, we propose a cooperative task service caching and task offloading algorithm named CO-MATCH. In this algorithm, we design the dynamic programming-based service caching (DPSC) Algorithm to incentivize edge services and volunteer vehicles to cache, which takes into account both service caching preferences and social similarity. The Many-to-One Matching Game (MOMG) Algorithm is proposed to stimulate edge device games and achieve the optimal task offloading. Then, the CO-MATCH method is proven to be stable. Simulation results demonstrate that our proposed approach can significantly improve the quality of service of user vehicles compared to the other methods.

Drive-by Environmental Sensing Strategy to Reach Optimal and Continuous Spatio-Temporal Coverage Using Local Transit Network

Monitoring environmental features, such as air pollution, carbon dioxide emissions, noise, and heat, gives cities key data-driven insights to advise sustainable policies and city design. However, given the high variability of the environmental data, achieving good spatio-temporal resolution and coverage remains a major challenge. Even in well-monitored cities, such as Amsterdam, environmental sensors are usually placed in very few fixed locations, implying limited spatial coverage and an inability to adapt to changes in the urban environment. As cities evolve, they experience shifts in pollution sources, and fixed sensors might not adequately capture these changes without a costly and time-consuming reconfiguration process. To monitor the environmental qualities of Amsterdam’s roads, we present a “drive-by” sensing solution for a structured network of vehicles, meaning that sensors are designed to be deployed on buses and tramways, the trajectories and schedules of which are known. We propose a deployment strategy that combines the available fleets to reach optimal spatio-temporal coverage for different environmental features. For example, by optimizing the deployment of sensors on public transit vehicles, our proposal significantly enhances the monitoring of pollution-sensitive areas in Amsterdam. Depending on the desired spatio-temporal granularity and noting that one vehicle only hosts one sensor, the required number of sensors to be deployed on the structured network varies between 43 and 142, with the latter achieving the finest possible resolution.