Md. Noor-A.-Rahim, Zilong Liu, Haeyoung Lee
et al.
We are on the cusp of a new era of connected autonomous vehicles with unprecedented user experiences, tremendously improved road safety and air quality, highly diverse transportation environments and use cases, and a plethora of advanced applications. Realizing this grand vision requires a significantly enhanced vehicle-to-everything (V2X) communication network that should be extremely intelligent and capable of concurrently supporting hyperfast, ultrareliable, and low-latency massive information exchange. It is anticipated that the sixth-generation (6G) communication systems will fulfill these requirements of the next-generation V2X. In this article, we outline a series of key enabling technologies from a range of domains, such as new materials, algorithms, and system architectures. Aiming for truly intelligent transportation systems, we envision that machine learning (ML) will play an instrumental role in advanced vehicular communication and networking. To this end, we provide an overview of the recent advances of ML in 6G vehicular networks. To stimulate future research in this area, we discuss the strength, open challenges, maturity, and enhancing areas of these technologies.
Cyber-physical systems (CPSs) empower the integration of physical processes and cyber infrastructure with the aid of ubiquitous computation resources and communication capabilities. CPSs have permeated modern society and found extensive applications in a wide variety of areas, including energy, transportation, advanced manufacturing, and medical health. The security of CPSs against cyberattacks has been regarded as a long-standing concern. However, CPSs suffer from extendable vulnerabilities that are beyond classical networked systems due to the tight integration of cyber and physical components. Sophisticated and malicious cyberattacks continue to emerge to adversely impact CPS operation, resulting in performance degradation, service interruption, and system failure. Secure state estimation and control technologies play a vital role in warranting reliable monitoring and operation of safety-critical CPSs. This article provides a review of the state-of-the-art results for secure state estimation and control of CPSs. Specifically, the latest development of secure state estimation is summarized in light of different performance indicators and defense strategies. Then, the recent results on secure control are discussed and classified into three categories: 1) centralized secure control; 2) distributed secure control; and 3) resource-aware secure control. Furthermore, two specific application examples of water supply distribution systems and wide-area power systems are presented to demonstrate the applicability of secure state estimation and control approaches. Finally, several challenging issues are discussed to direct future research.
Seyed Mehran Dibaji, Mohammad Pirani, D. Flamholz
et al.
Abstract The comprehensive integration of instrumentation, communication, and control into physical systems has led to the study of Cyber-Physical Systems (CPSs), a field that has recently garnered increased attention. A key concern that is ubiquitous in CPS is a need to ensure security in the face of cyber attacks. In this paper, we carry out a survey of systems and control methods that have been proposed for the security of CPS. We classify these methods into three categories based on the type of defense proposed against the cyberattacks: prevention, resilience, and detection & isolation. A unified threat assessment metric is proposed in order to evaluate how CPS security is achieved in each of these three cases. Also surveyed are the risk assessment tools and the effect of network topology on CPS security. Furthermore, an emphasis has been placed on power and transportation applications in the overall survey.
Cooperation among the traffic signals enables vehicles to move through intersections more quickly. Conventional transportation approaches implement cooperation by pre-calculating the offsets between two intersections. Such pre-calculated offsets are not suitable for dynamic traffic environments. To enable cooperation of traffic signals, in this paper, we propose a model, CoLight, which uses graph attentional networks to facilitate communication. Specifically, for a target intersection in a network, CoLight can not only incorporate the temporal and spatial influences of neighboring intersections to the target intersection, but also build up index-free modeling of neighboring intersections. To the best of our knowledge, we are the first to use graph attentional networks in the setting of reinforcement learning for traffic signal control and to conduct experiments on the large-scale road network with hundreds of traffic signals. In experiments, we demonstrate that by learning the communication, the proposed model can achieve superior performance against the state-of-the-art methods.
Modern cyber physical systems (CPSs) has widely being used in our daily lives because of development of information and communication technologies (ICT). With the provision of CPSs, the security and privacy threats associated to these systems are also increasing. Passive attacks are being used by intruders to get access to private information of CPSs. In order to make CPSs data more secure, certain privacy preservation strategies such as encryption, and k-anonymity have been presented in the past. However, with the advances in CPSs architecture, these techniques also need certain modifications. Meanwhile, differential privacy emerged as an efficient technique to protect CPSs data privacy. In this paper, we present a comprehensive survey of differential privacy techniques for CPSs. In particular, we survey the application and implementation of differential privacy in four major applications of CPSs named as energy systems, transportation systems, healthcare and medical systems, and industrial Internet of things (IIoT). Furthermore, we present open issues, challenges, and future research direction for differential privacy techniques for CPSs. This survey can serve as basis for the development of modern differential privacy techniques to address various problems and data privacy scenarios of CPSs.
Autonomous vehicles (AVs) rely on various types of sensor technologies to perceive the environment and to make logical decisions based on the gathered information similar to humans. Under ideal operating conditions, the perception systems (sensors onboard AVs) provide enough information to enable autonomous transportation and mobility. In practice, there are still several challenges that can impede the AV sensors’ operability and, in turn, degrade their performance under more realistic conditions that actually occur in the physical world. This paper specifically addresses the effects of different weather conditions (precipitation, fog, lightning, etc.) on the perception systems of AVs. In this work, the most common types of AV sensors and communication modules are included, namely: RADAR, LiDAR, ultrasonic, camera, and global navigation satellite system (GNSS). A comprehensive overview of their physical fundamentals, electromagnetic spectrum, and principle of operation is used to quantify the effects of various weather conditions on the performance of the selected AV sensors. This quantification will lead to several advantages in the simulation world by creating more realistic scenarios and by properly fusing responses from AV sensors in any object identification model used in AVs in the physical world. Moreover, it will assist in selecting the appropriate fading or attenuation models to be used in any X-in-the-loop (XIL, e.g., hardware-in-the-loop, software-in-the-loop, etc.) type of experiments to test and validate the manner AVs perceive the surrounding environment under certain conditions.
Abstract Accurate, low-latency traffic forecasting is a cornerstone capability for next-generation Intelligent Transportation Systems (ITS). This paper investigates how emerging 6G-era network context specifically per node slice-bandwidth and channel-quality indicators can be fused with spatio-temporal graph models to improve short-term freeway speed prediction while respecting strict real-time constraints. Building on the METR-LA benchmark, we construct a reproducible pipeline that (i) cleans and temporally imputes loop-detector speeds, (ii) constructs a sparse Gaussian-kernel sensor graph, and (iii) synthesizes realistic per-sensor 6G signals aligned with the traffic time series. We implement and compare four model families: Spatio-Temporal GCN (ST-GCN), Graph Attention ST-GAT, Diffusion Convolutional Recurrent Neural Network (DCRNN), and a novel 6G-conditioned DCRNN (DCRNN6G) that adaptively weights diffusion by slice-bandwidth. Our evaluation systematically explores four feature regimes (speeds only; channel quality only; slice bandwidth only; both features), and includes hyperparameter sweeps, ablation studies, and latency profiling on commodity CPUs to reflect edge deployment realities. Empirical results reveal three central findings. First, diffusion-recurrent modeling (DCRNN) produces the best accuracy latency trade-off for large-scale freeway forecasting: it attains test RMSE $$\approx 0.036$$ with average inference latency $$\approx 24$$ ms, comfortably meeting real-time requirements. Second, naïve incorporation of simulated 6G metrics provides only marginal RMSE gains for ST-GCN/ST-GAT and does not improve DCRNN when conditioned simply on bandwidth or CQI; in many cases, small accuracy gains are offset by notable latency penalties. Third, error diagnostics (sensor-wise RMSE, MAE heatmaps, error histograms) expose a small subset of spatially localized hard sensors and episodic time windows that dominate tail errors, indicating where targeted modules (anomaly detectors, incident-aware submodels) could yield outsized improvements. The main contributions of this work are: (1) the first end-to-end benchmarking of 6G-conditioned spatio-temporal GNNs on METR-LA with real-time latency analysis; (2) the introduction and empirical evaluation of a bandwidth conditional diffusion cell (DCRNN6G); and (3) extensive ablation, hyperparameter, and diagnostic studies that quantify both the potential and limitations of network aware fusion for ITS. We conclude by outlining concrete research directions, heterogeneous cross-graph fusion, dynamic adjacency learning, probabilistic forecasting, and real 5G/6G testbed validation that will be critical to realize truly co-optimized transportation and communication systems.
Bhupesh Chand, Frezer Ayele, Ian Pineiro-Dakers
et al.
The growing number of older bridges has resulted in an increase in structural flaws, demanding frequent inspections and maintenance. Structural degradation accelerates post-damage recovery, emphasizing the necessity of preventive interventions. The use of Uncrewed Aerial Vehicle Systems (UASs) for bridge inspections represents a significant development in structural health monitoring (SHM). Traditional inspection methods are labor-intensive, time-consuming, expensive, and require access to high or difficult-to-reach areas, posing safety risks to inspectors. This study focuses on identifying drones that can efficiently support bridge inspection activities. Key factors influencing UAS selection include flight performance, flying modes, cost, sensor capabilities, payload capacity, and controller communication. The primary objective of this paper is to provide guidance to inspectors and transportation agencies regarding the capabilities and limitations of commercially available drones. It also outlines potential cost considerations associated with drone selection, including pilot skill level, platform cost, and sensor integration. These factors may vary depending on the type and complexity of the bridge being inspected. By addressing these aspects, this paper aims to assist decision-makers in making informed choices regarding the use of UASs for bridge inspection applications.
This paper investigates the growing problem of abandoned maritime containers and the lack of effective reverse logistics to manage them: <i>Background:</i> The research highlights the significant environmental impact and economic burdens caused by the imbalance of container inflow and outflow, which leads to the accumulation of containers in storage yards; <i>Methods:</i> The study used the Delphi Method, gathering insights from a panel of experts in container transport and maintenance. The goal was to identify key challenges and potential solutions for improving container reverse logistics in Portugal; <i>Results:</i> The results confirm the urgent need for efficient reverse logistics strategies to address the container imbalance. The experts reached over 60% consensus on the importance of developing logistics systems and improving communication between ports. Implementing these strategies would not only reduce economic costs but also significantly lower environmental pollution; <i>Conclusions:</i> The paper concludes that a strategic shift toward effective reverse logistics is essential for enhancing the sustainability and operational efficiency of the maritime transport sector.
Transportation and communication, Management. Industrial management
Background. Ultra-Reliable Low Latency Communication (URLLC) as a service offered by fifth and sixth generation (5G/6G) wireless systems is a technological response to the needs of various mission-critical applications that require reliable data transmission with low latency. These applications also include Intelligent Transportation Systems services, which, among other things, provide connectivity and autonomous vehicle control. The combination of high reliability and low latency requirements in URLLC usage scenarios creates a security problem for URLLC data transmission that cannot be solved using conventional complex cryptographic methods based on a secret key. The article discusses in detail the approach to using physical layer security mechanisms (physical layer security - PLS) as a powerful alternative to classical cryptographic security methods for URLLC, and also proposes the application of the wire-tap channel concept in URLLC with an analysis of the efficiency that can be achieved for physical layer security.
Objective. The aim of the article is to provide an overview of information security solutions in URLLC usage scenarios, as well as to propose a constructive method for information protection for reliable data transmission with low latency without the use of cryptographic mechanisms based on a secret key.
Methods. Theoretical research in the field of the branch channel concept was used to create solutions that allow data protection with information-theoretic stability in URLLC usage scenarios for providing IoT, connected car and autonomous driving services.
Results. The article examines in detail the data security issues in ultra-reliable low latency communication (URLLC) as a service offered by fifth and sixth generation (5G/6G) wireless systems. It is determined that URLLC is a technological response to the needs of various critical applications that require reliable signal transmission with low latency, and among these applications are Intelligent Transportation Systems services, which, among other things, provide connectivity and autonomous vehicle control. It is shown that the combination of high reliability and low latency requirements in URLLC scenarios creates a security problem for URLLC data transmission that cannot be solved using conventional complex cryptographic methods based on a secret key. The feasibility of using physical layer security mechanisms (PLS) as a powerful alternative to classical cryptographic security methods for URLLC is substantiated. The approach to applying the concept of a wire-tap channel in URLLC is considered in detail, as well as the results that can be achieved for physical layer security, and the influence of code parameters for probabilistic cryptographic transformations in accordance with the concept of a wire-tap channel on PLS URLLC. Estimates of the effectiveness of PLS URLLC for finite block length codes are provided.
Conclusions. An effective way to ensure data security for ultra-reliable low-latency physical layer link (PLS URLLC) of fifth-generation 5G wireless systems in the field of connected cars and vehicles of 4-5 levels of automation can be approaches based on the concept of a tapped channel ("wire-tap channel").
The success execution of the scheduling tasks in a marshalling station is crucial to the whole railway transportation, which is the bottleneck of the railway network. However, the reliability performance evaluation of the scheduling tasks quantitatively are not performed yet. In order to tackle this problem, a Phased Mission System (PMS) modeling methodology is proposed to describe the tasks as several consecutive phases, which enables quantitative evaluation of each phase with heterogeneous configurations among phases. In the paper, the work flow inside a marshalling station is analyzed first. Then the fault tree models of different phases are constructed considering the functional relation of subsystems and equipment. The numerical analysis is carried out based on the Binary Decision Diagram (BDD) calculation method, according to relevant reliability parameters of the basic equipment. A case study is conducted to illustrate the evaluation process with a marshalling station layout from both local railway administration and marshalling station view. The result shows that the number of outdoor equipment have big influence to the scheduling tasks and the fault tolerant design helps to improve the performance during phases.