Search Results (19)

This article studies the cooperative underwater payload transportation problem for multiple unmanned underwater vehicles (UUVs) operating in a constrained workspace with both static and dynamic obstacles. A novel cooperative formation control algorithm has been presented in this paper for the transportation of a large payload in underwater scenarios. More precisely, by using the advantages of multi-UUV formation cooperation, based on rigidity graph theory and backstepping technology, the distance between each UUV, as well as the UUV and the transport payload, is controlled to form a three-dimensional rigid structure so that the load remains balanced and stable, to coordinate the transport of objects within the feasible area of the workspace. Moreover, a neural network (NN) is utilized to maintain system stability despite unknown nonlinearities and disturbances in the system dynamics. In addition, based on the interfered fluid flow algorithm, a collision-free motion trajectory was planned for formation systems. The control scheme also performs real-time formation reconfiguration according to the size and position of obstacles in space, thereby enhancing the flexibility of cooperative handling. The uniform ultimate boundedness of the formation distance errors is comprehensively demonstrated by utilizing the Lyapunov stability theory. Finally, the simulation results show that the UUVs can quickly form and maintain the desired formation, transport the payload along the planned trajectory to shuttle in multi-obstacle environments, verify the feasibility of the method proposed in this paper, and achieve the purpose of the collaborative transportation of large underwater payload by multiple UUVs and their targeted delivery. Full article

Urban air mobility (UAM) is a revolutionary approach to transportation in densely populated cities. UAM involves using small, highly automated aircraft to transport passengers and goods at lower altitudes within urban and suburban areas, aiming to transform how people and parcels move within these environments. On average, UAM can reduce travel times by 30% to 40% for point-to-point journeys, with even greater reductions of 40% to 50% in major cities in the United States and China, compared to land transport. UAM includes advanced airborne transportation options like electric vertical takeoff and landing (eVTOL) aircraft and unmanned aerial vehicles (UAVs or drones). These technologies offer the potential to ease traffic congestion, decrease greenhouse gas emissions, and substantially cut travel times in urban areas. Studying the applications of eVTOLs and UAVs in parcel delivery and passenger transportation poses intricate challenges when examined through the lens of operations research (OR). By OR approaches, we mean mathematical programming, models, and solution methods addressing eVTOL- and UAV-aided parcel/people transportation problems. Despite the academic and practical importance, there is no review paper on eVTOL- and UAV-based optimization problems in the UAM sector. The present paper, applying a systematic literature review, develops a classification scheme for these problems, dividing them into routing and scheduling of eVTOLs and UAVs, infrastructure planning, safety and security, and the trade-off between efficiency and sustainability. The OR methodologies and the characteristics of the solution methods proposed for each problem are discussed. Finally, the study gaps and future research directions are presented alongside the concluding remarks. Full article

In recent years, the Internet of Vehicles (IoV) has been found to be of huge potential value in the promotion of the development of intelligent transportation systems (ITSs) and smart cities. However, the traditional scheme in IoV has difficulty in dealing with an uncertain environment, while reinforcement learning has the advantage of being able to deal with an uncertain environment. Spectrum resource allocation in IoV faces the uncertain environment in most cases. Therefore, this paper investigates the spectrum resource allocation problem by deep reinforcement learning after using spectrum sensing technology in the ITS, including the vehicle-to-infrastructure (V2I) link and the vehicle-to-vehicle (V2V) link. The spectrum resource allocation is modeled as a reinforcement learning-based multi-agent problem which is solved by using the soft actor critic (SAC) algorithm. Considered an agent, each V2V link interacts with the vehicle environment and makes a joint action. After that, each agent receives different observations as well as the same reward, and updates networks through the experiences from the memory. Therefore, during a certain time, each V2V link can optimize its spectrum allocation scheme to maximize the V2I capacity as well as increase the V2V payload delivery transmission rate. However, the number of SAC networks increases linearly as the number of V2V links increases, which means that the networks may have a problem in terms of convergence when there are an excessive number of V2V links. Consequently, a new algorithm, namely parameter sharing soft actor critic (PSSAC), is proposed to reduce the complexity for which the model is easier to converge. The simulation results show that both SAC and PSSAC can improve the V2I capacity and increase the V2V payload transmission success probability within a certain time. Specifically, these novel schemes have a 10 percent performance improvement compared with the existing scheme in the vehicular environment. Additionally, PSSAC has a lower complexity. Full article

The global greenhouse effect and air pollution problems have been deteriorating in recent years. The power generation in the future is expected to shift from fossil fuels to renewables, and many countries have also announced the ban on the sale of vehicles powered by fossil fuels in the next few decades, to effectively alleviate the global greenhouse effect and air pollution problems. In addition to electric vehicles (EVs) that will replace traditional fuel vehicles as the main ground transportation vehicles in the future, unmanned aerial vehicles (UAVs) have also gradually and more recently been widely used for military and civilian purposes. The recent literature estimated that UAVs will become the major means of transport for goods delivery services before 2040, and the development of passenger UAVs will also extend the traditional human ground transportation to low-altitude airspace transportation. In recent years, the literature has proposed the use of renewable power supply, battery swapping, and charging stations to refill the battery of UAVs. However, the uncertainty of renewable power generation cannot guarantee the stable power supply of UAVs. It may even be very possible that a large number of UAVs need to be charged during the same period, causing congestion in charging stations or battery swapping facilities and delaying the arranged schedules of UAVs. Although studies have proposed the method of that employing moving EVs along with wireless charging technology in order to provide electricity to UAVs with urgent needs, the charging schemes are still oversimplified and have many restrictions. In addition, different charging options should be provided to fit the individual need of each UAV. In view of this, this work attempts to meet the mission characteristics and needs of various UAVs by providing an adaptive flight path and charging plan attached to individual UAVs, as well as reducing the power load of the renewable power generation during the peak period. We ran a series of simulations for the proposed flight path and charging mechanism to evaluate its performance. The simulation results revealed that the solutions proposed in this work can be used by UAV operators to fit the needs of each individual UAV. Full article

Vehicle ad hoc networks (VANETs) is an emerging ad hoc network technology with a promising future but significant limitations, particularly in communication networks. An effective routing system can significantly enhance the performance of VANETs. However, creating an effective routing protocol in an urban context to transport the data packet to the destination is still challenging due to complex road conditions, sporadic connectivity among vehicles, frequent disconnections, and quick changes in network topology. To enhance the accuracy of routing decisions in an urban setting, we propose a novel “multiple attributes decision-making junction selection routing (MADMJSR)” routing protocol based on vehicle position for VANETs. Formerly, most of the research was focused on static weight-based attribute selection for communication rather than optimal values, which led to a drop in junction decision-making accuracy. Our proposed scheme considers the multiple attribute selection and the optimal weight value assigned to each attribute, which significantly improves the routing decision accuracy. The results show a high packet delivery ratio (PDR) gain within minimum latency and less network load. Furthermore, a thorough discussion of the functioning of our proposed protocol is presented along with a detailed performance evaluation in comparison to other protocols. Full article

With progressive technological advancements, the time for electric vehicles (EVs) and unmanned aerial vehicles (UAVs) has finally arrived for the masses. However, intelligent transportation systems need to develop appropriate protocols that enable swift predictive communication among these battery-powered devices. In this paper, we highlight the challenges in message routing in a unified paradigm of electric and flying vehicles (EnFVs). We innovate over the existing routing scheme by considering multi-objective EnFVs message routing using a novel modified genetics algorithm. The proposed scheme identifies all possible solutions, outlines the Pareto-front, and considers the optimal solution for the best route. Moreover, the reliability, data rate, and residual energy of vehicles are considered to achieve high communication gains. An exhaustive evaluation of the proposed and three existing schemes using a New York City real geographical trace shows that the proposed scheme outperforms existing solutions and achieves a $90\%+$ packet delivery ratio, longer connectivity time, shortest average hop distance, and efficient energy consumption. Full article

There exist various challenges in constructing a large in-city project, such as narrow construction sites, limited surrounding roads, heavy construction periods and tasks, various types of vehicles, and affected cargo transport. Considering construction needs, transportation characteristics, and site conditions, this paper puts forward the overall planning for modular integrated construction (MiC) transportation and on-site assembly. Meanwhile, the traffic organization and transportation scheduling method are designed for smart construction sites and different engineering materials are coordinated in the space-time dimension during the overall period from construction delivery. Meanwhile, an integer programming model is developed to solve the truck scheduling matching problem between the supply side and the construction side. The weighted loss time of the truck is set as the optimization objective function, and time, space, and material type are the constraints. For this model, this paper proposes an operations scheduling solution method by combining operations research and actual field construction scheduling experience. The traditional empirical scheduling method and the proposed operations research scheduling model are compared through a case study of actual engineering scheduling data. The experimental results show that the operations research scheduling model is better than the traditional empirical scheduling method at different traffic levels. In addition, the implementation of the scheme is guaranteed through measures such as pre-data analysis, management framework, and information technology equipment. The planning and scheduling cover the whole process of MiC module transportation and on-site assembly, which have practical guiding significance for the project and ensure the timely success and acceptance of the project. Full article

To meet the global climate goals agreed upon regarding the Paris Agreement, governments and institutions around the world are investigating various technologies to reduce carbon emissions and achieve a net-negative energy system. To this end, integrated solutions that incorporate carbon utilization processes, as well as promote the transition of the fossil fuel-based energy system to carbon-free systems, such as the hydrogen economy, are required. One of the possible pathways is to utilize CO₂ as the base chemical for producing a liquid organic hydrogen carrier (LOHC), using CO₂ as a mediating chemical for delivering H₂ to the site of usage since gaseous and liquid H₂ retain transportation and storage problems. Formic acid is a probable candidate considering its high volumetric H₂ capacity and low toxicity. While previous studies have shown that formic acid is less competitive as an LOHC candidate compared to other chemicals, such as methanol or toluene, the results were based on out-of-date process schemes. Recently, advances have been made in the formic acid production and dehydrogenation processes, and an analysis regarding the recent process configurations could deem formic acid as a feasible option for LOHC. In this study, the potential for using formic acid as an LOHC is evaluated, with respect to the state-of-the-art formic acid production schemes, including the use of heterogeneous catalysts during thermocatalytic and electrochemical formic acid production from CO₂. Assuming a hydrogen distribution system using formic acid as the LOHC, each of the production, transportation, dehydrogenation, and CO₂ recycle sections are separately modeled and evaluated by means of techno-economic analysis (TEA) and life cycle assessment (LCA). Realistic scenarios for hydrogen distribution are established considering the different transportation and CO₂ recovery options; then, the separate scenarios are compared to the results of a liquefied hydrogen distribution scenario. TEA results showed that, while the LOHC system incorporating the thermocatalytic CO₂ hydrogenation to formic acid is more expensive than liquefied H₂ distribution, the electrochemical CO₂ reduction to formic acid system reduces the H₂ distribution cost by 12%. Breakdown of the cost compositions revealed that reduction of steam usage for thermocatalytic processes in the future can make the LOHC system based on thermocatalytic CO₂ hydrogenation to formic acid to be competitive with liquefied H₂ distribution if the production cost could be reduced by 23% and 32%, according to the dehydrogenation mode selected. Using formic acid as a LOHC was shown to be less competitive compared to liquefied H₂ delivery in terms of LCA, but producing formic acid via electrochemical CO₂ reduction was shown to retain the lowest global warming potential among the considered options. Full article

The concept of sustainable transportation of goods as the primary paradigm for designing contemporary logistics systems assumes the use of energy-efficient and affordable modes of transport in a way that guarantees the most cost-efficient variant of the delivery scheme. That especially applies to road transport deliveries, where the number of alternatives for organizing the transportation process is numerous and the choice of the optimal solution is complicated by the multiple stochastic influences of the environment on the technological processes. In this paper, we contribute to solving the problem of shaping the sustainable delivery schemes by proposing an approach to shape the complete set of alternative transport and technological schemes for packaged cargo delivery by road transport. The developed mathematical model allows estimating the efficiency of each alternative delivery scheme for the given request and chooses the best variant that minimizes the total costs of all participants in the delivery process. The proposed algorithms are implemented in the C# programming language within the frame of a class library for modeling transport delivery processes. A case of transport processes for Delivery Ltd. (Kharkiv, Ukraine) is applied to illustrate the procedure of using the developed approach to choose the optimal transport and technological schemes for long-distance deliveries. As the result of simulating the goods transportation processes, we show the regression models that represent dependencies of the total costs for the implementation of a delivery scheme from the parameters of demand for the transportation of goods. These regression models allow estimating the most efficient delivery schemes based on the functional analysis of the obtained dependencies for the given demand parameters. Full article

One of the most sought-after applications of cellular technology is transforming a vehicle into a device that can connect with the outside world, similar to smartphones. This connectivity is changing the automotive world. With the speedy growth and densification of vehicles in Internet of Vehicles (IoV) technology, the need for consistency in communication amongst vehicles becomes more significant. This technology needs to be scalable, secure, and flexible when connecting products and services. 5G technology, with its incredible speed, is expected to power the future of vehicular networks. Owing to high mobility and constant change in the topology, cooperative intelligent transport systems ensure real time connectivity between vehicles. For ensuring a seamless connectivity amongst the entities in vehicular networks, a significant alternative to design is support of handoff. This paper proposes a scheme for the best Road Side Unit (RSU) selection during handoff. Authentication and security of the vehicles are ensured using the Deep Sparse Stacked Autoencoder Network (DS2AN) algorithm, developed using a deep learning model. Once authenticated, resource allocation by RSU to the vehicle is accomplished through Deep-Q learning (DQL) techniques. Compared with the existing handoff schemes, Reinforcement Learning based on the MDP (RL-MDP) has been found to have a 13% lesser decision delay for selecting the best RSU. A higher level of security and minimum time requirement for authentication is achieved using DS2AN. The proposed system simulation results demonstrate that it ensures reliable packet delivery, significantly improving system throughput, upholding tolerable delay levels during a change of RSUs. Full article

Rapid and tremendous advances in wireless technology, miniaturization, and Internet of things (IoT) technology have brought significant development to vehicular ad hoc networks (VANETs). VANETs and IoT together play a vital role in the current intelligent transport system (ITS). However, a VANET is highly vulnerable to various security attacks due to its highly dynamic, decentralized, open-access medium, and protocol-design-related concerns. Regarding security concerns, a black hole attack (BHA) is one such threat in which the control or data packets are dropped by the malicious vehicle, converting a safe path/link into a compromised one. Dropping data packets has a severe impact on a VANET’s performance and security and may cause road fatalities, accidents, and traffic jams. In this study, a novel solution called detection and prevention of a BHA (DPBHA) is proposed to secure and improve the overall security and performance of the VANETs by detecting BHA at an early stage of the route discovery process. The proposed solution is based on calculating a dynamic threshold value and generating a forged route request (RREQ) packet. The solution is implemented and evaluated in the NS-2 simulator and its performance and efficacy are compared with the benchmark schemes. The results showed that the proposed DPBHA outperformed the benchmark schemes in terms of increasing the packet delivery ratio (PDR) by 3.0%, increasing throughput by 6.15%, reducing the routing overhead by 3.69%, decreasing the end-to-end delay by 6.13%, and achieving a maximum detection rate of 94.66%. Full article

Vehicular ad-hoc network (VANET) is a technology that allows ubiquitous mobility to mobile users. Inter-vehicle communication is an integral component of intelligent transportation systems that enables a wide variety of applications where vehicles interact and cooperate with each other, from safety applications to non-safety applications. VANETs applications have different needs (e.g., latency, reliability, delivery priorities, etc.) in terms of delivery effectiveness. In the last decade, named data networking (NDN) gained the attention of the research community for effective content retrieval and dissemination in mobile environments such as VANETs. In NDN, the content’s name has a vital role in storing and retrieving the content effectively and efficiently. In NDN-based VANETs, adaptive content dissemination solutions must be introduced that can make decisions related to forwarding, cache management, etc., based on context information represented by a content name. In this context, our main contributions are two-fold: (i) we present the hierarchical context-aware content-naming (CACN) scheme for NDN-based VANETs that enables naming the safety and non-safety applications, and (ii) we present a decentralized context-aware notification (DCN) protocol that broadcasts event notification information for awareness within the application-based geographical area. Simulation results show that the proposed DCN protocol succeeds in achieving reduced transmissions, bandwidth, and energy compared to existing critical contents dissemination protocols. Full article

The lithium ion battery, with its high energy density and low reduction potential, continues to enchant researchers and dominate the landscape of energy storage systems development. However, the demands of technology in modern society have begun to reveal limitations of the lithium energy revolution. A combination of safety concerns, strained natural resources and geopolitics have inspired the search for alternative energy storage and delivery platforms. Traditional liquid electrolytes prove precarious in large scale schemes due to the propensity for leakage, the potential for side reactions and their corrosive nature. Alternative electrolytic materials in the form of solid inorganic ion conductors and solid polymer matrices offer new possibilities for all solid state batteries. In addition to the engineering of novel electrolyte materials, there is the opportunity to employ post-lithium chemistries. Utility of multivalent cation (Ca${}^{2+}$, Mg${}^{2+}$, Zn${}^{2+}$ and Al${}^{3+}$) transport promises a reduction in cost and increase in safety. In this review, we examine the current research focused on developing solid electrolytes using multivalent metal cation charge carriers and the outlook for their application in all solid state batteries. Full article

As a key technology of intelligent transportation systems (ITS), vehicular ad hoc networks (VANETs) have been promising to provide safety and infotainment for drivers and passengers. To support different applications about traffic safety, traffic efficiency, autonomous driving and entertainment, it is important to investigate how to effectively deliver content in VANETs. Since it takes resources such as bandwidth and power for base stations (BSs) or roadside units (RSUs) to deliver content, the optimal pricing strategy for BSs and the optimal caching incentive scheme for RSUs need to be studied. In this paper, a framework of content delivery is proposed first, where each moving vehicle can obtain small-volume content files from either the nearest BS or the nearest RSU according to the competition among them. Then, the profit models for both BSs and RSUs are established based on stochastic geometry and point processes theory. Next, a caching incentive scheme for RSUs based on Stackelberg game is proposed, where both competition sides (i.e., BSs and RSUs) can maximize their own profits. Besides, a backward introduction method is introduced to solve the Stackelberg equilibrium. Finally, the simulation results demonstrate that BSs can obtain their own optimal pricing strategy for maximizing the profit as well as RSUs can obtain the optimal caching scheme with the maximum profit during the content delivery. Full article

Microfluidic structures and devices have been studied over decades for the transport of liquid through internal channels using versatile microfabrication schemes such as surface and bulk micromachining technologies. One challenge in consideration of the device design involves the breakthrough of microfluidic reservoir and channels being substantially limited in two-dimensional (2D) geometry. However, recent progress of the emerging 3D printing technologies has showed great potential to overcome this problem in a simple manner. This paper comprehensively reports an additive manufacturing of polylactic acid (PLA) layers to significantly improve the complexity in the formation of the 3D microfluidic structures as compared to conventional micro-manufacturing techniques. Moreover, a handheld mechatronic device with a small height of ~10 mm, assembled with a thin planar atomizer and a micro controller, was produced and demonstrated for generation of droplets (~6 μm in diameter). Both the analytical and experimental results indicated that the grids of channel microstructures were simply varied by different line widths (300–500 μm) and spacing (250–400 μm) 3D printed within the device, thereby providing the design capability for capillary flow. In this regard, a variety of complex micro devices fabricated via computer-aided design (CAD) and the 3D printing method could be applied for more applications than ever, such as microfluidic delivery of biomedical materials and health care devices of a small size. Full article