Hasil untuk "Transportation and communication"

A. Cook, S. Hussey

Honghao Gao, Can Liu, Youhuizi Li et al.

Vehicular ad hoc networks (VANETs) have been widely used in intelligent transportation systems (ITSs) for purposes such as the control of unmanned aerial vehicles (UAVs) and trajectory prediction. However, an efficient and reliable data routing decision scheme is critical for VANETs due to the feature of self-organizing wireless multi-hop communication. Compared with wireless networks, which are unstable and have limited bandwidth, wired networks normally provide longer transmission distances, higher network speeds and greater reliability. To address this problem, this paper proposes a reliable VANET routing decision scheme based on the Manhattan mobility model, which considers the integration of roadside units (RSUs) into wireless and wired modes for data transmission and routing optimization. First, the problems of frequently moving vehicles and network connectivity are analyzed based on road networks and the motion information of vehicle nodes. Second, an improved greedy algorithm for vehicle wireless communication is used for network optimization, and a wired RSU network is also applied. In addition, routing decision analysis is carried out in accordance with the probabilistic model for various transmission ranges by checking the connectivity among vehicles and RSUs. Finally, comprehensive experiments show that our proposed method can support real-time planning and improve network transmission performance compared with other baseline protocol approaches in terms of several metrics, including package delivery ratio, time delay and wireless hops.

Yi Tao, Zhen Gao, Zhiao Zhu et al.

Against the backdrop of the global drive to advance the green transformation of the information and communications technology (ICT) industry and leverage technological innovation to facilitate the achievement of Net-Zero carbon goals, research into Rydberg atomic receivers (RAREs) is gaining significant interest. RAREs leverage the electron transition phenomenon for signal reception, offering significant advantages over conventional radio frequency receivers in terms of miniaturized antenna design, high sensitivity, robust interference resistance, and compact form factors, which positions them as a competitive alternative for meeting zero-carbon communication demands. This article systematically elaborates on the basic principle, state-of-the-art progress, and novel experiments of RAREs in quantum wireless communication and sensing. In this first-of-its-kind work, we experimentally verify the RARE-based orthogonal frequency division multiplexing transmission and reveal the potential of deep learning design in optimizing quantum wireless systems. Finally, we delve into the prospect of integrating RARE with existing cutting-edge application scenarios, while mapping out critical pathways for developing Rydberg-based wireless systems.

Botond Bertok, Márton Frits, Károly Kalauz et al.

In traditional industry setups, Automated Guided Vehicles (AGVs) follow trajectories planned together with the layout of the storage or production facility and supported by fixed markers on the floor or on the walls. Traffic rules manage the avoidance of multiple vehicles, while fleet management gets movement and transportation commands completed as soon as possible. In contrast, recent developments in navigation and advanced computing, sensor, and communication capabilities make their free movement safe and manageable. Detailed route planning and scheduling can guarantee that the vehicles keep a safe distance in time and space. A recent challenge of electric AGVs is that their charging may take several hours, which must be factored into their schedule. This has made minimal energy demand a key objective alongside earliest delivery and strictly meeting the deadlines. This paper presents a method for detailed routing and scheduling of AGV fleets to minimize energy consumption while considering battery levels and charging times. The optimization method is illustrated by a case study where multiple delivery tasks are performed by synchronized movement of vehicles on a complex warehouse layout. In the optimal solution, the scheduled waiting times for collision avoidance are utilized by the vehicles to pre-charge their batteries.

Matthew W. Bhagat-Conway

Betweenness centrality measures the importance of a link or node in a network based on how often it falls on the shortest path between other nodes. It was originally introduced in social network analysis, where it is interpreted as the ability an actor has to moderate communication between others in the network. In transportation, it is often used as a measure of the resilience impact of a particular link. In this article, I argue that betweenness centrality is not a theoretically valid measure of resilience, and provide hypothetical networks where it clearly fails to identify resilience issues.

Ruimin Ke, Yifan Zhuang, Ziyuan Pu et al.

Cloud computing has been a main-stream computing service for years. Recently, with the rapid development in urbanization, massive video surveillance data are produced at an unprecedented speed. A traditional solution to deal with the big data would require a large amount of computing and storage resources. With the advances in Internet of things (IoT), artificial intelligence, and communication technologies, edge computing offers a new solution to the problem by processing all or part of the data locally at the edge of a surveillance system. In this study, we investigate the feasibility of using edge computing for smart parking surveillance tasks, specifically, parking occupancy detection using the real-time video feed. The system processing pipeline is carefully designed with the consideration of flexibility, online surveillance, data transmission, detection accuracy, and system reliability. It enables artificial intelligence at the edge by implementing an enhanced single shot multibox detector (SSD). A few more algorithms are developed either locally at the edge of the system or on the centralized data server targeting optimal system efficiency and accuracy. Thorough field tests were conducted in the Angle Lake parking garage for three months. The experimental results are promising that the final detection method achieves over 95% accuracy in real-world scenarios with high efficiency and reliability. The proposed smart parking surveillance system is a critical component of smart cities and can be a solid foundation for future applications in intelligent transportation systems.

Junhua Wang, K. Zhu, E. Hossain

As one of the most promising applications in future Internet of Things, Internet of Vehicles (IoV) has been acknowledged as a fundamental technology for developing the Intelligent Transportation Systems in smart cities. With the emergence of the sixth generation (6G) communications technologies, massive network infrastructures will be densely deployed and the number of network nodes will increase exponentially, leading to extremely high energy consumption. There has been an upsurge of interest to develop the green IoV towards sustainable vehicular communication and networking in the 6G era. However, as a special mobile ad-hoc network, the energy cost in an IoV system involves the communication and computation energy in addition to the fuel consumption and the electricity cost of moving vehicles. Moreover, the energy harvesting technology, which is likely to be adopted widely in 6G systems, will complicate the optimization of energy efficiency in the entire system. Current studies focus only on part of the energy issues in IoV systems without a comprehensive discussion of the state-of-the-art energy-efficient approaches and the influence of the development of 6G networks on green IoV. In this paper, we present the main considerations for green IoV from five different scenarios, including the communication, computation, traffic, Electric Vehicles (EVs), and energy harvesting management. The literature relevant to each of the scenarios is compared from the perspective of energy optimization (e.g., with respect to resource allocation, workload scheduling, routing design, traffic control, charging management, energy harvesting and sharing) and the related factors affecting energy efficiency (e.g., resource limitation, channel state, network topology, traffic condition). In addition, we introduce the potential challenges and the emerging technologies in 6G for developing green IoV systems. Finally, we discuss the research trends in designing energy-efficient IoV systems.

Zisang Xu, W. Liang, Kuan Ching Li et al.

Abstract A fundamental layer of smart cities, the Internet of Vehicles (IoV) can significantly improve transportation efficiency, reduce energy consumption, and traffic accidents. However, because of the vehicle and the RoadSide Units (RSU) use wireless channels for communication, the risk of information being leaked or tampered is highly increased. Therefore, secure and reliable authentication and key agreement protocol is the masterpiece of IoV security. As most of the existing authentication protocols pertain to a centralized structure and single Trusted Authority (TA) network model, all vehicles involved can only perform mutual authentication with one TA through the intermediate node RSU, and thus, the efficiency of these centralized authentication protocols is easily affected by TA’s communication and computing resource bottlenecks. In this article, a blockchain-based authentication and key agreement protocol is designed for the multi-TA network model, moving the computing load of TA down to the RSU to improve the efficiency of authentication. In addition, blockchain technology is used for multiple TAs to manage the ledger that stores vehicle-related information, which results in vehicles that can easily achieve cross-TA authentication. Both formal and informal security analysis and simulation results from ProVerif show that the proposed protocol is secure. Comparisons with other existing work show that the proposed protocol has less computational overhead, higher authentication efficiency, and can resist various common attacks.

Gang Sun, Lingzhi Sheng, Long Luo et al.

The vehicle-to-vehicle (V2V) communication driven by the fifth generation (5G) cellular mobile network with the features of ultra-high reliability and low latency provides promising solutions to various applications in the intelligent transportation system (ITS). To improve the resource utilization and guarantee the quality-of-service (QoS), users in 5G vehicular networks have to select appropriate communication modes and control their own transmission power. However, the highly dynamic network topology and channel status pose challenges to the mode selection. In this paper, we propose a scheme for joint mode selection and power adaptation based on the game theoretic approach with the objective of maximizing the overall system throughput. We consider the transmission requirements of multi-priority packets of different vehicular applications, where packets with higher priority have more stringent latency constraints. The segmented auction method with reserve price is performed to select modes for the vehicular users (VUEs) and the Stackelberg gaming model is introduced to solve the problem of cochannel interference. We compare our approach with three existing methods in extensive simulations. The results show that our approach outperforms the existing methods in terms of network performance, including the network throughput, resource utilization and QoS violation rate.

L. Ang, K. Seng, Gerald Ijemaru et al.

The Internet of Vehicles (IoV) is a convergence of the mobile Internet and the Internet of Things (IoT), where vehicles function as smart moving intelligent nodes or objects within the sensing network. This paper gives two contributions to the state-of-the-art for IoV technology research. First, we present a comprehensive review of the current and emerging IoV paradigms and communication models with an emphasis on deployment in smart cities. Currently, surveys from many authors have focused concentration on the IoV as only serving applications for intelligent transportation like driver safety, traffic efficiency, and infotainment. This paper presents a more inclusive review of the IoV for also serving the needs of smart cities for large-scale data sensing, collection, information processing, and storage. The second component of the paper presents a new universal architecture for the IoV which can be used for different communication models in smart cities to address the above challenges. It consists of seven layers: vehicle identification layer, object layer, inter-intra devices layer, communication layer, servers and cloud services layer, big data and multimedia computation layer, and application layer. The final part of this paper discusses various challenges and gives some experimental results and insights for future research direction such as the effects of a large and growing number of vehicles and the packet delivery success rate in the dynamic network structure in a smart city scenario.

Manjiang Hu, Chongkang Li, Yougang Bian et al.

Vehicle platoon control based on vehicle-to-vehicle (V2V) communication is one of the promising technologies to improve the performance of transportation systems. This paper presents a distributed controller to optimize a vehicle platoon’s fuel consumption by combining the switching feedback control and economic model predictive control (EMPC) methods. The closed-loop dynamics involving switching feedback gains with the constant time headway (CTH) policy are established firstly, and the multiple-predecessor following (MPF) communication topology is considered. In order to obtain the economy optimal feedback gain, we design a local optimal control problem for each vehicle, based on which the average dwell time is defined and the distributed EMPC algorithm is designed. Based on linear matrix inequalities (LMIs) and the Lyapunov theorem, the feedback gain selection method and the lower bound for average dwell time that guarantees asymptotic stability are analyzed rigorously. Then a modified algorithm that can ensure string stability is designed. Numerical simulations show a maximum of 6.84% fuel benefit compared with pure tracking-oriented methods.

Wei Wang, Neng Zhu, Tao Yan et al.

Exosomes have been considered as novel and potent vehicles of intercellular communication, instead of “cell dust”. Exosomes are consistent with anucleate cells, and organelles with lipid bilayer consisting of the proteins and abundant lipid, enhancing their “rigidity” and “flexibility”. Neighboring cells or distant cells are capable of exchanging genetic or metabolic information via exosomes binding to recipient cell and releasing bioactive molecules, such as lipids, proteins, and nucleic acids. Of note, exosomes exert the remarkable effects on lipid metabolism, including the synthesis, transportation and degradation of the lipid. The disorder of lipid metabolism mediated by exosomes leads to the occurrence and progression of diseases, such as atherosclerosis, cancer, non-alcoholic fatty liver disease (NAFLD), obesity and Alzheimer’s diseases and so on. More importantly, lipid metabolism can also affect the production and secretion of exosomes, as well as interactions with the recipient cells. Therefore, exosomes may be applied as effective targets for diagnosis and treatment of diseases. Video abstract

F. Pasqualetti, Florian Dorfler, F. Bullo

Zicong Hong, Wuhui Chen, Huawei Huang et al.

The concept of the industrial Internet of things (IIoT) is being widely applied to service provisioning in many domains, including smart healthcare, intelligent transportation, autopilot, and the smart grid. However, because of the IIoT devices’ limited onboard resources, supporting resource-intensive applications, such as 3D sensing, navigation, AI processing, and big-data analytics, remains a challenging task. In this paper, we study the multi-hop computation-offloading problem for the IIoT–edge–cloud computing model and adopt a game-theoretic approach to achieving Quality of service (QoS)-aware computation offloading in a distributed manner. First, we study the computation-offloading and communication-routing problems with the goal of minimizing each task's computation time and energy consumption, formulating the joint problem as a potential game in which the IIoT devices determine their computation-offloading strategies. Second, we apply a free–bound mechanism that can ensure a finite improvement path to a Nash equilibrium. Third, we propose a multi-hop cooperative-messaging mechanism and develop two QoS-aware distributed algorithms that can achieve the Nash equilibrium. Our simulation results show that our algorithms offer a stable performance gain for IIoT in various scenarios and scale well as the device size increases.

Linfeng Shen, Guanzhen Wu, Cong Zhang et al.

The rise of 5G communication has transformed the telecom industry for critical applications. With the widespread deployment of 5G base stations comes a significant concern about energy consumption. Key industrial players have recently shown strong interest in incorporating energy storage systems to store excess energy during off-peak hours, reducing costs and participating in demand response. The fast development of batteries opens up new possibilities, such as the transportation area. An effective method is needed to maximize base station battery utilization and reduce operating costs. In this trend towards next-generation smart and integrated energy-communication-transportation (ECT) infrastructure, base stations are believed to play a key role as service hubs. By exploring the overlap between base station distribution and electric vehicle charging infrastructure, we demonstrate the feasibility of efficiently charging EVs using base station batteries and renewable power plants at the Hub. Our model considers various factors, including base station traffic conditions, weather, and EV charging behavior. This paper introduces an incentive mechanism for setting charging prices and employs a deep reinforcement learning-based method for battery scheduling. Experimental results demonstrate the effectiveness of our proposed ECT-Hub in optimizing surplus energy utilization and reducing operating costs, particularly through revenue-generating EV charging.

Ahmet Burak Ozyurt, Shreesh Mohalik, John S. Thompson

With the increasing frequency and intensity of natural disasters, there is a necessity for advanced technologies that can provide reliable situational awareness and communication. Conventional systems are often inadequate due to unreliable infrastructure, power grid failures, high investment costs and scalability challenges. This paper explores the potential of ad-hoc mesh joint radar and communication (JRC) networks as a scalable, resilient, energy-efficient solution for disaster management that can operate independently of conventional infrastructure. The proposed JRC network enhances disaster response by integrating target detection (such as identifying vital signs, hazardous leaks, and fires) with communication capabilities to ensure efficient information dissemination under intense clutter conditions. Key performance metrics, including data rate, Signal-to-Clutter and Noise Ratio (SCNR), probability of detection, and false alarm rate, are used to assess performance. An optimization approach is proposed to provide an energy-efficient resource allocation scheme. The results show the performance of ad-hoc mesh JRC systems, underscoring their potential to enhance disaster management efforts by addressing unique operational challenges.

Shunli Hong, Youming Li, Xiaolong Zhang et al.

Abstract A blind signal-to-interference-plus-noise ratio (SINR) estimation method is proposed for orthogonal frequency division multiplex (OFDM) systems in the presence of impulse noise over multipath fading channels. According to the multipath channel order and length of cyclic prefix (CP), the received signal on each OFDM symbol is divided into six data intervals which reveal that there is inter-symbol interference (ISI) in the 1st and 6th intervals and no ISI in the 2nd to 5th intervals. The data in the 1st, 2nd, and 3rd intervals, respectively, are replicated data in the 4th, 5th, and 6th intervals. Based on these observations, first, the multipath channel order can be estimated by the autocorrelation functions of the data in the 1st, 2nd, 4th, and 5th intervals based on likelihood function. Second, utilizing the properties that there is no ISI and autocorrelation functions of noise plus interference are zero in the 2nd data interval, the signal powers can be estimated from the correlation coefficients of the received signals in this interval. Third, since the CP data on the 2nd interval are a copy of the data in the 5th interval, the interference plus noise power can be obtained based on the autocorrelation function of the data difference between these two intervals. Based on the above analysis, a closed-form expression of SINR estimation is obtained. Furthermore, closed-form expressions for the normalized mean square errors (NMSE) and the Cramer–Rao lower bound (CRLB) for the proposed SINR estimation are also derived. Both computer simulation and theoretical analysis results show that the proposed method achieves a lower NMSE than the cyclic correlation-based method and NMSE of the proposed method is closer to the CRLB.

Mingxing Peng, Xusen Guo, Xianda Chen et al.

To ensure safe driving in dynamic environments, autonomous vehicles should possess the capability to accurately predict lane change intentions of surrounding vehicles in advance and forecast their future trajectories. Existing motion prediction approaches have ample room for improvement, particularly in terms of long-term prediction accuracy and interpretability. In this study, we address these challenges by proposing a Lane Change-Large Language Model (LC-LLM), an explainable lane change prediction model that leverages the strong reasoning capabilities and self explanation abilities of Large Language Models (LLMs). Essentially, we reformulate the lane change prediction task as a language modeling problem, processing heterogeneous driving scenario information as natural language prompts for LLMs and employing supervised fine-tuning to tailor LLMs specifically for lane change prediction task. Additionally, we finetune the Chain-of-Thought (CoT) reasoning to improve prediction transparency and reliability, and include explanatory requirements in the prompts during the inference stage. Therefore, our LC-LLM not only predicts lane change intentions and trajectories but also provides CoT reasoning and explanations for its predictions, enhancing its interpretability. Extensive experiments based on the large-scale highD dataset demonstrate the superior performance and interpretability of our LC-LLM in lane change prediction task. To the best of our knowledge, this is the first attempt to utilize LLMs for predicting lane change behavior. Our study shows that LLMs can effectively encode comprehensive interaction information for understanding driving behavior.

Ismail Abushaikha, Munirah Sarhan Alqahtani, Omar M. Bwaliez et al.

<i>Background</i>: This study examines the degree to which cognitive supply chain management (CSCM) indirectly enhances supply chain risk management (SCRM), addressing the lack of specific empirical research concerning the underlying mechanisms of this relationship. Specifically, this study tests the CSCM-SCRM relationship using the mediating roles of supply chain forecasting (SCF), supply chain synchronization (SCS), and supply chain transparency (SCT). <i>Methods</i>: For this quantitative research, a survey was conducted among 287 respondents of pharmaceutical companies operating in Saudi Arabia. Convenience sampling was conducted, and the collected data were then analyzed via partial least squares structural equation modeling (PLS-SEM) through SmartPLS 4 software. The dynamic capabilities theory (DCT) and information processing theory (IPT) were integrated to develop the conceptual framework of this study. <i>Results</i>: The findings indicate that CSCM does not exert a direct impact on SCRM. Instead, CSCM significantly enhances SCF, SCS, and SCT. Among these, both SCF and SCT have a direct positive impact on SCRM and act as significant mediators in the CSCM–SCRM relationship. In contrast, SCS neither directly impacts SCRM nor plays a mediating role in this relationship. Based on this study, the positive outcomes of CSCM on SCRM come about via SCF and SCT rather than SCS. <i>Conclusions</i>: This study contributes to the literature by empirically validating a model that integrates CSCM, SCF, SCS, SCT, and SCRM in the context of Saudi pharmaceutical companies. It further contributes to the pharmaceutical practitioners by establishing that CSCM exerts an indirect positive effect on SCRM via information-intensive capabilities.

Nejdet Dogru, A. Subasi