Hasil untuk "Transportation and communication"

Ao Lei, H. Cruickshank, Yue Cao et al.

M. B. Mollah, Jun Zhao, D. Niyato et al.

Internet of Vehicles (IoV) is an emerging concept that is believed to help realize the vision of intelligent transportation systems (ITSs). IoV has become an important research area of impactful applications in recent years due to the rapid advancements in vehicular technologies, high throughput satellite communication, the Internet of Things, and cyber–physical systems. IoV enables the integration of smart vehicles with the Internet and system components attributing to their environments, such as public infrastructures, sensors, computing nodes, pedestrians, and other vehicles. By allowing the development of a common information exchange platform between vehicles and heterogeneous vehicular networks, this integration aims to create a better environment and public space for the people as well as to enhance safety for all road users. Being a participatory data exchange and storage, the underlying information exchange platform of IoV needs to be secure, transparent, and immutable in order to achieve the intended objectives of ITS. In this connection, the adoption of blockchain as a system platform for supporting the information exchange needs of IoV has been explored. Due to their decentralized and immutable nature, IoV applications enabled by blockchain are believed to have a number of desirable properties, such as decentralization, security, transparency, immutability, and automation. In this article, we present a contemporary survey on the latest advancement in blockchain for IoV. Particularly, we highlight the different application scenarios of IoV after carefully reviewing the recent literature. We also investigate several key challenges where blockchain is applied in IoV. Furthermore, we present the future opportunities and explore further research directions of IoV as a key enabler of ITS.

Xiang Cheng, Dongliang Duan, Shijian Gao et al.

With the unprecedented development of smart vehicles and roadside units equipped with wireless connectivity, the transportation system is undergoing revolutionary changes in the past decade or two. Bearing safety and efficiency as the utmost objectives, the vehicular environments are witnessing explosive increase of various sensors onboard vehicles and equipped at transportation infrastructures. On the one hand, these sensors are destined to be wirelessly connected to provide more comprehensive situational awareness for transportation purposes. On the other hand, the abundance of sensor data of the environment can potentially shed light on the channel propagation characteristics that lie at the core of any communications system design. The integrated sensing and communications (ISACs) is henceforth both necessary and natural in vehicular communications networks (VCN). Different from existing ISAC works that target generic environments but are limited to dual-function radar-communications (DFRC), in this article we focus on transportation scenarios and applications but take a wholistic view of ISAC possibilities. First, we argue that, even though many sensors in transportation settings are nonradio-frequency (RF)-based, functional ISAC (fISAC) is feasible and necessary, in both communication-centric (CC) or sensing-centric (SC) modes. To facilitate this, the concept of synesthesia is introduced to ISAC to accommodate “machine senses” in the RF and non-RF formats. We then zoom in to RF-based sensors and propose the so-termed signaling ISAC (sISAC), with either unified-hardware (UH) or separate-hardware (SH) platforms, and delineate the unique issues arising in transportation settings. Several transportation-specific case studies are included to demonstrate these various ISAC regimes. Toward the end, the relationships of these ISAC subcategories are discussed with a roadmap laid out.

Quy Vu Khanh, Nam Vi Hoai, Linh Dao Manh et al.

Communication technologies are developing very rapidly and achieving many breakthrough results. The advent of 5th generation mobile communication networks, the so-called 5G, has become one of the most exciting and challenging topics in the wireless study area. The power of 5G enables it to connect to hundreds of billions of devices with extreme-high throughput and extreme-low latency. The 5G realizing a true digital society where everything can be connected via the Internet, well known as the Internet of Things (IoT). IoT is a technology of technologies where humans, devices, software, solutions, and platforms can connect based on the Internet. The formation of IoT technology leads to the birth of a series of applications and solutions serving humanity, such as smart cities, smart agriculture, smart retail, intelligent transportation systems, and IoT ecosystems. Although IoT is considered a revolution in the evolution of the Internet, it still faces a series of challenges such as saving energy, security, performance, and QoS support. In this study, we provide a vision of the Internet of Things that will be the main force driving the comprehensive digital revolution in the future. The communication technologies in the IoT system are discussed comprehensively and in detail. Furthermore, we also indicated indepth challenges of existing common communication technologies in IoT systems and future research directions of IoT. We hope the results of this work can provide a vital guide for future studies on communication technologies for IoT in 5G.

R. Pradhan, Mak B. Arvin, Mahendhiran Nair

Abstract In many countries, urbanization is seen as an important policy tool to ensure sustained economic growth. While urbanization can lead to positive economic outcomes, unfettered migration into urban areas without appropriate infrastructure support such as information and communication technology (ICT) infrastructure and transportation infrastructure can lead to negative side effects such as congestion, formation of slums, and other diseconomies of scale. The latter can actually lower economic growth. This study examines the relationships between urbanization, transportation infrastructure, ICT infrastructure, and economic growth in the G-20 countries from 1961 to 2016. Using the panel vector error-correction model, the study shows that there is a myriad of temporal causal relationships between the four variables in both the short and long run. The key policy implication of these results is that long-run economic growth in the G-20 countries depends on the co-development of policies in creating a vibrant urban ecosystem that is enabled by intelligent transportation systems and underpinned by a sound ICT infrastructure plan.

Yijing Lin, Hongyang Du, Dusist Niyato et al.

The construction of virtual transportation networks requires massive data to be transmitted from edge devices to Virtual Service Providers (VSP) to facilitate circulations between the physical and virtual domains in Metaverse. Leveraging semantic communication for reducing information redundancy, VSPs can receive semantic data from edge devices to provide varied services through advanced techniques, e.g., AI-Generated Content (AIGC), for users to explore digital worlds. But the use of semantic communication raises a security issue because attackers could send malicious semantic data with similar semantic information but different desired content to break Metaverse services and cause wrong output of AIGC. Therefore, in this paper, we first propose a blockchain-aided semantic communication framework for AIGC services in virtual transportation networks to facilitate interactions of the physical and virtual domains among VSPs and edge devices. We illustrate a training-based targeted semantic attack scheme to generate adversarial semantic data by various loss functions. We also design a semantic defense scheme that uses the blockchain and zero-knowledge proofs to tell the difference between the semantic similarities of adversarial and authentic semantic data and to check the authenticity of semantic data transformations. Simulation results show that the proposed defense method can reduce the semantic similarity of the adversarial semantic data and the authentic ones by up to 30% compared with the attack scheme.

Wenjun Xu, Yimeng Zhang, Fengyu Wang et al.

The Internet of Vehicles (IoV) is expected to become the central infrastructure to provide advanced services to connected vehicles and users for higher transportation efficiency and security. A variety of emerging applications/services bring explosively growing demands for mobile data traffic between connected vehicles and roadside units (RSUs), imposing the significant challenge of spectrum scarcity in the IoV. In this article, we propose a cooperative semantic-aware architecture to convey essential semantics from collaborated users to servers for lessening data traffic. In contrast to current solutions that are mainly based on piling up highly complex signal processing techniques and multiple access capabilities in terms of syntactic communications, this article puts forth the idea of semantic-aware content delivery in the IoV. Specifically, the successful transmission of essential semantics of the source data is pursued rather than the accurate reception of symbols regardless of their meaning, as in conventional syntactic communications. To assess the benefits of the proposed architecture, we provide a case study of an image retrieval task for vehicles in intelligent transportation systems (ITSs). Simulation results demonstrate that the proposed architecture outperforms existing solutions, with fewer radio resources, especially in a low-signal-to-noise-ratio (SNR) regime, which can shed light on the potential of the proposed architecture in extending applications in extreme environments.

Yagmur Yigit, Mohamed Amine Ferrag, Mohamed C. Ghanem et al.

Critical National Infrastructures (CNIs)—including energy grids, water systems, transportation networks, and communication frameworks—are essential to modern society yet face escalating cybersecurity threats. This review paper comprehensively analyzes AI-driven approaches for Critical Infrastructure Protection (CIP). We begin by examining the reliability of CNIs and introduce established benchmarks for evaluating Large Language Models (LLMs) within cybersecurity contexts. Next, we explore core cybersecurity issues, focusing on trust, privacy, resilience, and securability in these vital systems. Building on this foundation, we assess the role of Generative AI and LLMs in enhancing CIP and present insights on applying Agentic AI for proactive defense mechanisms. Finally, we outline future directions to guide the integration of advanced AI methodologies into protecting critical infrastructures. Our paper provides a strategic roadmap for researchers and practitioners committed to fortifying national infrastructures against emerging cyber threats through this synthesis of current challenges, benchmarking strategies, and innovative AI applications.

Nandhini Vineeth, H S Guruprasad

The transmission of video frames among vehicles is a crucial global requirement in current road traffic conditions. Video data in vehicles may serve passengers with information as well as entertainment and, crucially, provide the drivers with dynamic information about the traffic conditions on the road ahead, assisting in reaching their destination safely at the earliest convenience using an optimal route. Intelligent Transportation System through its subsystem Vehicular Ad-hoc Networks (VANETs) make these necessities a reality. Inter-sessions carrying video data that exhibit multi-hop scenarios are encountered in the real world when people in a place of emergency communicate with the fire station/doctors/police. In such situations, the earlier the communication can occur, the smaller the damage. This work aimed to reduce the end-to-end delay (EED) experienced in VANETs when unicasting video streams among vehicles involved in inter-sessions by considering the packet delivery ratio (PDR) as a supporting parameter. The technique Network Coding was applied in VANETs and the possible recoder behaviors were simulated. An algorithm – Dynamic Recoder Recommender (DRR) – was designed and implemented to select an appropriate recoder on the fly in a path decided by the Adhoc On-Demand Vector routing protocol. This implementation outperformed the conventional network coding in terms of EED and PDR when applied with random recoders.

Alessandro Agnetis, Ilaria Salvadori

<i>Background</i>: This paper addresses a new class of scheduling problems in the context of machines subject to (unrecoverable) interruptions; i.e., when a machine fails, the current and subsequently scheduled work on that machine is lost. Each job has a certain processing time and a reward that is attained if the job is successfully completed. <i>Methods</i>: For the failure process, we considered the linear risk model, according to which the probability of machine failure is uniform across a certain time horizon. <i>Results</i>: We analyzed both the situation in which the set of jobs is given, and that in which jobs must be selected from a pool of jobs, at a certain selection cost. <i>Conclusions</i>: We characterized the complexity of various problems, showing both hardness results and polynomial algorithms, and pointed out some open problems.

Akid Abrar, Sagar Dasgupta, Mizanur Rahman et al.

Transportation Cyber-Physical Systems (TCPS) integrate physical elements, such as transportation infrastructure and vehicles, with cyber elements via advanced communication technologies, allowing them to interact seamlessly. This integration enhances the efficiency, safety, and sustainability of transportation systems. TCPS rely heavily on cryptographic security to protect sensitive information transmitted between vehicles, transportation infrastructure, and other entities within the transportation ecosystem, ensuring data integrity, confidentiality, and authenticity. Traditional cryptographic methods have been employed to secure TCPS communications, but the advent of quantum computing presents a significant threat to these existing security measures. Therefore, integrating Post-Quantum Cryptography (PQC) into TCPS is essential to maintain secure and resilient communications. While PQC offers a promising approach to developing cryptographic algorithms resistant to quantum attacks, artificial intelligence (AI) can enhance PQC by optimizing algorithm selection, resource allocation, and adapting to evolving threats in real-time. AI-driven PQC approaches can improve the efficiency and effectiveness of PQC implementations, ensuring robust security without compromising system performance. This chapter introduces TCPS communication protocols, discusses the vulnerabilities of corresponding communications to cyber-attacks, and explores the limitations of existing cryptographic methods in the quantum era. By examining how AI can strengthen PQC solutions, the chapter presents cyber-resilient communication strategies for TCPS.

Eduardo R. Manika, Emilio C. G. Wille, Joilson Alves

Vehicle mobility has a significant impact on wireless communication between vehicles (buses) in Public Transportation Systems (PTS). Nevertheless, the transportation literature does not provide satisfactory models for bus movements because they are influenced by a variety of factors (itineraries, timetables, etc.). Custom-made mobility models that take these issues into account require a great deal of effort and may render simulations unfeasible. This article considers a tool (EMMS) that automatically inserts PTS information into a mobility simulator in order to undertake a complete statistical analysis of vehicular density, trip duration, and vehicle-to-vehicle interaction. In light of opportunistic communication services, this analysis is of the utmost importance.

Mozhgan Nasr Azadani, A. Boukerche

The advent of in-vehicle networking systems as well as state-of-the-art sensors and communication technologies have facilitated the collection of large volume and almost real-time data on vehicles and drivers, thus opening up future possibilities. Processing and analyzing this data provides unprecedented opportunities to offer remarkable insights and solutions for driving behavior analysis (DBA). Characterizing driving behavior plays a key role in a variety of research areas such as traffic safety, the development of automated vehicles, energy and fuel management, risk assessment, and driver identification and profiling. Advances in DBA-based driver inattention or drunk driver detection can help reduce fatal car crashes, and understanding the driving style (e.g. eco-friendly or aggressive) of drivers can contribute to fuel management and risk assessment of the drivers. These facts have led to a growing interest in addressing DBA challenges. This paper aims to present the state-of-the-art methodologies for DBA and provide a clear roadmap about the main current and future trends in DBA. To this end, we propose categorizing the current research on driving behavior based on the types of data employed for the analysis, the ultimate goals of the analysis, and the techniques based on which the driving data are modeled. We provide an overview of different data resources and available datasets for DBA. Moreover, we discuss the application of DBA along with the key research challenges in this field and potential future directions.

Samuel Onimisi Dawodu, Islam Ahmad, Ibrahim Ahmad et al.

The advent of 5G technology represents a paradigm shift in the realm of communication and security, ushering in a new era of connectivity and capabilities. This paper provides a comprehensive review of the far-reaching implications of emerging 5G technology on communication and security landscapes. The 5G technology promises unprecedented data speeds and low latency, revolutionizing the way we communicate and interact with digital content. Its enhanced bandwidth and connectivity are poised to transform various industries, including healthcare, manufacturing, and transportation. This review explores how 5G's ultra-fast data transfer rates will enable real-time communication, paving the way for innovations like augmented reality and Internet of Things (IoT) applications. Additionally, the integration of 5G into smart cities will redefine urban infrastructure, enhancing efficiency and sustainability. However, the widespread adoption of 5G also raises concerns about security vulnerabilities. The study delves into the potential risks associated with 5G technology, such as increased attack surfaces and the need for robust encryption protocols. It examines the evolving threat landscape and the imperative for proactive cybersecurity measures to safeguard sensitive information and critical infrastructure. Furthermore, the paper explores the geopolitical implications of 5G technology, as nations vie for dominance in the race to deploy and control this transformative connectivity. The competitive landscape of global telecommunications is shifting, with implications for international relations and economic competitiveness. This review highlights the transformative potential of emerging 5G technology in communication and security realms. While promising unprecedented connectivity and technological advancements, it underscores the critical importance of addressing security challenges to fully realize the benefits of this groundbreaking technology. Stakeholders must collaborate to navigate the complexities and capitalize on the vast opportunities presented by the 5G revolution.

Palak Bagga, A. Das, Mohammad Wazid et al.

Internet of Vehicles (IoV), a distributed network involving connected vehicles and Vehicular Ad Hoc Networks (VANETs), allows connected vehicles to communicate with other Internet-connected entities in real time. The communications among these entities (e.g. vehicles, pedestrians, fleet management systems, and road-side infrastructure) generally take place via an open channel. In other words, such an open communication can be targeted by the adversary to eavesdrop, modify, insert fabricated (or malicious) messages, or delete any data-in-transit; thus, resulting in replay, impersonation, man-in-the-middle, privileged-insider, and other related attacks. In addition to security, anonymity and untraceability are two other important features that should be achieved in an authentication protocol. In this paper, we propose a new mutual authentication and key agreement protocol in an IoV-enabled Intelligent Transportation System (ITS). Using both formal and informal security analysis, as well as formal security verification using an automated verification tool, we show that the proposed scheme is secure against several known attacks in an IoV-enabled ITS environment. Furthermore, a detailed comparative analysis shows that the proposed scheme has low communication and computational overheads, and offers better security and functionality attributes in comparison to seven other competing schemes. We also evaluate the performance of the proposed scheme using NS2.

T. Tran, Callum Parker, Xinyan Yu et al.

With the rise of autonomous vehicles (AVs) in transportation, a pressing concern is their seamless integration into daily life. In multi-pedestrian settings, two challenges emerge: ensuring unambiguous communication to individual pedestrians via external Human-Machine Interfaces (eHMIs), and the influence of one pedestrian over another. We conducted an experiment (N=25) using a multi-pedestrian virtual reality simulator. Participants were paired and exposed to three distinct eHMI concepts: on the vehicle, within the surrounding infrastructure, and on the pedestrian themselves, against a baseline without any eHMI. Results indicate that all eHMI concepts improved clarity of communication over the baseline, but differences in their effectiveness were observed. While pedestrian and infrastructure communications often provided more direct clarity, vehicle-based cues at times introduced uncertainty elements. Furthermore, the study identified the role of co-located pedestrians: in the absence of clear AV communication, individuals frequently sought cues from their peers.

A. K. Sangaiah, A. Javadpour, F. Ja’fari et al.

Each group in a data-driven automobile network has its cluster head. A group can communicate with each other and members of other groups once it has been founded. Vehicles belonging to each group near the other group allow intergroup communication. Because nodes in automotive networks move so quickly, routing in these networks is a complex problem to solve. Each cluster in hierarchical clustering can be partitioned into multiple sub-clusters. Put another way, and the data is stored in a cluster, which is then divided into more clusters. The data is stored directly in separate clusters in non-hierarchical approaches. A dendrogram is a type of hierarchical tree. We anticipate increasing information sharing in clusters by properly clustering vehicles on the road and establishing clusters of the desired size in the relevant dendrogram. We can select clusters of the necessary extent and compare the Quality of Service (QoS) network’s outcomes by breaking the dendrogram at different levels. The findings reveal that the suggested method outperforms AIVISN in delay, PDR, overhead, and Drooped packets compared to AIVISN, 7.12%, 12.21%,8.32%, and 7.34%, respectively.

Anna Konovalenko, Lars Magnus Hvattum

<i>Background:</i> The dynamic vehicle routing problem (DVRP) is a complex optimization problem that is crucial for applications such as last-mile delivery. Our goal is to develop an application that can make real-time decisions to maximize total performance while adapting to the dynamic nature of incoming orders. We formulate the DVRP as a vehicle routing problem where new customer requests arrive dynamically, requiring immediate acceptance or rejection decisions. <i>Methods:</i> This study leverages reinforcement learning (RL), a machine learning paradigm that operates via feedback-driven decisions, to tackle the DVRP. We present a detailed RL formulation and systematically investigate the impacts of various state-space components on algorithm performance. Our approach involves incrementally modifying the state space, including analyzing the impacts of individual components, applying data transformation methods, and incorporating derived features. <i>Results:</i> Our findings demonstrate that a carefully designed state space in the formulation of the DVRP significantly improves RL performance. Notably, incorporating derived features and selectively applying feature transformation enhanced the model’s decision-making capabilities. The combination of all enhancements led to a statistically significant improvement in the results compared with the basic state formulation. <i>Conclusions:</i> This research provides insights into RL modeling for DVRPs, highlighting the importance of state-space design. The proposed approach offers a flexible framework that is applicable to various variants of the DVRP, with potential for validation using real-world data.

Insung Lee, Duk Kyung Kim

Vehicle-to-everything (V2X) communication is a pivotal technology for advanced driving, encompassing autonomous driving and Intelligent Transportation Systems (ITS). Beyond direct vehicle-to-vehicle (V2V) communication, vehicle-to-infrastructure (V2I) communication via Road Side Unit (RSU) can play an important role for efficient traffic management and enhancement of advanced driving, providing surrounding vehicles with proper road information. To accommodate diverse V2X scenarios, heterogeneous traffic with varied objectives, formats, and sizes needs to be supported for V2X communication. We tackle the challenge of resource allocation for heterogeneous traffic in the RSU-deployed V2X communications, proposing a decentralized Multi-Agent Reinforcement Learning (MARL) based resource allocation scheme with limited shared resources. To reduce the model complexity, RSU is modeled as a collection of virtual agents with a small action space instead of a single agent selecting multiple resources at the same time. A weighted global reward is introduced to incorporate traffic heterogeneity efficiently. The performance is evaluated and compared with random, 5G NR mode 2, and optimal allocation schemes in terms of Packet Reception Ratio (PRR) and communication range. The proposed scheme nearly matches the performance of the optimal scheme and significantly outperforms the random allocation scheme in both underload and overload situations.

Zenghua Huang, Shirong Ge, Yonghua He et al.

Despite the pressure of carbon emissions and clean energy, coal remains the economic backbone of many developing countries due to its abundant resources and widespread distribution. The stable supply of coal is also vital for the global economy and remains irreplaceable in the future global energy structure. China has been a major contributor to annual coal output, accounting for nearly 50% worldwide since 2014. However, despite implementing intelligent coal mining technology, China’s coal mining industry still employs over 1.5 million underground miners, posing significant safety risks associated with underground mining operations. Therefore, the introduction of coal mining robots in underground mines is an urgently needed scientific and technological solution for upgrading China’s and even the world’s coal energy industry. The working face needs a shearer, hydraulic support, a scraper conveyor, and other equipment for coordination. The deep integration of intelligent technology with factors such as “humans, machines, the environment, and management” in the workplace is the core content of intelligent coal mines. This paper puts forward an advanced framework for robot technology systems in coal mining, including single robots, robotized equipment, robot crowds, and unmanned systems. The framework clarifies the common key technologies of coal mining robot research and development and the cross-integration with new technologies such as 5G, the industrial internet, big data, artificial intelligence, and digital twins to improve the autonomous and intelligent application of coal mining robots. By establishing a scientific and complete standard system for coal mining robots, we aim to achieve the customized research and development and standardized production of various types of robot. A specific analysis is conducted on the research progress of common key technologies such as the explosion-proof design, mechanical system innovation, power drive, intelligent sensing, positioning and navigation, and underground communication of coal mining robots. The current research and application status of various types of coal mining robots in China are summarized. A new direction for future coal mining robot research and development is proposed. Robotic mining systems should be promoted to enhance the overall intelligence level and efficiency of mining equipment. To develop human–machine environment-integrated robots to improve the autonomy and collaboration level of coal mining robots, the digital twinning of the entire mine robot system should be accelerated; the normalized operation level of coal mine robots should be improved; research on coal mining robots, shield support robots, and transportation robots should be performed; intelligence should be achieved in fully mechanized mining faces; and equipment shield support for fully mechanized mining faces should be provided. The practical process of implementing coal mining robotization is summarized in this paper, and the technical and engineering feasibility of the coal mining machine population is verified.