To enhance line capacity in high-speed railways without new infrastructure, virtual coupling train sets (VCTSs) enable reduced inter-train distances via real-time communication and cooperative control. However, unknown disturbances and model uncertainties challenge VCTS performance, often causing chattering, slow convergence, and poor disturbance rejection. This paper proposes a novel finite-time extended state observer-based nonsingular terminal sliding mode (FTESO-NTSM) control strategy. The method integrates a nonsingular terminal sliding mode surface with a hyperbolic tangent-based reaching law to ensure fast convergence and chattering suppression, while a finite-time extended state observer estimates and compensates for lumped disturbances in real time. Lyapunov analysis rigorously proves finite-time stability. Numerical simulations under different initial statuses are conducted to validate the effectiveness of the proposed method. The results show that the maximum observation error achieves 0.0087 kN. The speed chattering magnitudes reach 0.00087 km/h, 0.0017 km/h, 0.0026 km/h, and 0.0034 km/h for the leading train and three followers, respectively. Furthermore, the convergence time of the followers is 56 s, 130 s, and 76 s, respectively. The results highlight that the proposed method can significantly improve line capacity and transportation efficiency.
Khulud Salem Alshudukhi, Mamoona Humayun, Aala Oqab Alsalem
et al.
Wireless communication and the Internet of Things (IoT) are integrated for the formulation of an emerging Intelligent Transportation System (ITS) for the interaction of vehicles and to enhance road safety. The emerging network manages the traffic flow, real-time data analytics, and resource control for the development of urban transportation systems and smart cities. Extensive research has been conducted on the development of efficient routing response time for the IoT-ITS environment; however, the rapid changes in the network topologies still lead to unmanageable congestion and communication holes. Moreover, it is also often threatened due to high urban mobility and incurs additional transmission with excessive overhead. Such concepts are not able to maintain secure interactions among vehicles and expose confidential data to malicious devices while interacting on unpredictable channels. This research proposes a trust-aware edge-assisted model to secure the vehicular network and offers a more reliable system with optimal routing performance. The global trust model is maintained based on network conditions using localized computing and attaining data privacy and coherence. Furthermore, a blockchain ledger is included along with trust to ensure tamper-proof and transparent computing across the boundaries of the IoT-ITS environment. The proposed model is compared with Graph-Based Trust-Enabled Routing (GBTR) and Bacteria for Aging Optimization Algorithm (BFOA), and the results revealed significant performance for network throughput by 50% and 62.5%, end-to-end delay by 33.3% and 37.5%, routing overhead by 34% and 38.7%, and false positive rate by 67.9% and 68.5% over the dynamic network infrastructure.
A combinatorial optimization problem is a problem finding an optimal combination of variables that maximizes or minimizes an objective function while satisfying given constraints. Such problems arise in various fields, including transportation and communication. Many combinatorial optimization problems are NP-hard, meaning that the number of possible combinations grows exponentially as the number of variables increases. As a result, it is often difficult to solve large-scale combinatorial optimization problems for conventional classical computers. Recently, Ising machines, including quantum annealers, have gained attention as a promising architecture for efficiently solving combinatorial optimization problems. One real-world example of a combinatorial optimization problem is university course selection. Many students manually choose their courses, but this process is time consuming and labor intensive due to the large number of available options. To address this, in this paper, we formulate course selection as a combinatorial optimization problem, which we define as a Personalized Course Selection Problem (PCSP). To solve it efficiently, we use an Ising machine, a specialized computer designed for combinatorial optimization. We propose a Quadratic Unconstrained Binary Optimization (QUBO) formulation for solving PCSP and solve the problem with an Ising machine. Experimental evaluations demonstrate that, for the largest instance tested, the proposed method solves the problem 20X times faster than the conventional simulated annealing while achieving the same total course cost.
Jerawat Sopajarn, Apidet Booranawong, Surachate Chumpol
et al.
Vehicle-to-vehicle (V2V) communications are important for intelligent transportation system (ITS) development for driving safety, traffic efficiency, and the development of autonomous vehicles. V2V communication channels, environments, mobility patterns, and mobility speed significantly affect the accuracy of autonomous vehicle control. In this paper, we propose a versatile system-level framework that can be used for investigation, experimentation, and verification to expedite the development of autonomous vehicles. Once vehicle functionality, communication channels, and driving scenarios were modelled, experiments with different mobility speeds and communication channels were set up to measure the communication quality and the effects on the vehicle’s following control. In our experiment, the leader vehicle was set to travel through a high-building environment with a constant speed of 36 km/h and suddenly changed lanes in front of the follower vehicle. The speed of the follower vehicle ranged from 40 km/h to 80 km/h. The experimental results show that the quality of single-input and single-output (SISO) communication is less efficient than multiple-input and multiple-output (MIMO) communication. The quality of SISO communication between vehicles with a speed difference of 4 km/h (leader 36 km/h and follower 40 km/h) had a link quality worse than 0.85, which caused unstable control in the follower vehicle speed. However, it was also found that if the speed of the follower vehicle increased to 80 km/h, the link quality of SISO communication was better, close to 0.95, due to the decreased distance between the vehicles, resulting in better control. Moreover, it was found that the impact of SISO communication can be overcome by using the MIMO communication technique and selecting the best input signal at each time. MIMO communication has less signal loss, allowing the follower vehicle to make correct decisions throughout the movement.
V2I collaborative perception improves awareness of the dynamic driving environment by exchanging multi-viewpoint information through communication, establishing itself as a key element of intelligent transportation systems. Despite its advantages, this method requires a balance between communication bandwidth and perception performance. To address this challenge, we propose a map-mask designed to align with perceptual spatial features, enabling precise background filtering to isolate critical areas for communication. During the sender’s compression phase, the map-mask filters out background elements and extracts key features from critical areas, significantly reducing communication bandwidth consumption. During the receiver’s decompression phase, the map-mask restores scene context and enhances spatial information surrounding critical areas, ensuring the preservation of perception performance. Based on this map alignment, we develop Mapcooper, a unified collaborative perception framework that optimizes the balance between communication bandwidth and perception performance. We evaluated Mapcooper’s effectiveness via extensive experimentation using the large-scale V2X-Seq-SPD dataset. The results demonstrate that Mapcooper outperforms existing collaborative perception approaches with respect to perceptual accuracy while minimizing communication transmission costs.
Christophe Eward Kattouw, Christophe Eward Kattouw, Karina Aase
et al.
IntroductionSignificant gaps exist between the preferred and the existing service ecosystem for senior citizens living at home emphasizing the need for transformation. This study aimed to develop design proposals for the preferred service ecosystem for senior citizens living at home.MethodsNine service design workshops were conducted with multiple stakeholder groups (n = 58), including senior citizens (aged 67 years or older), carers, health care professionals, municipal home care managers, municipal advisers, a bus driver, and representatives from the regional transportation provider. Stakeholders identified timely home care provision and age-friendly mobility as key areas for improvement. An inductive thematic analysis was applied to the workshop data.ResultsDesign proposals were developed including prototypes and design principles across seven domains: (1) enabling self-reliance; (2) housing and buildings; (3) urban mobility; (4) collaboration and education; (5) real-time communication; (6) resource organization and flexible scheduling; and (7) methods and tools to reshape patterns of thought. Design principles focused on enabling sufficient time and resources, improving accessibility, and changing mental models. These proposals may support senior citizens’ self-reliance, free up time and resources for service providers, and create time buffers for service delays.DiscussionThe design proposals demonstrate considerable interconnectedness and transferability across timely home care provision and age-friendly mobility. They may improve predictability and adaptive capacity for both senior citizens and service providers.
Maath A. Albeyar, Ikram Smaoui, Hassene Mnif
et al.
Vehicular ad hoc networks (VANETs) can bolster road safety through the proactive dissemination of emergency messages (EMs) among vehicles, effectively reducing the occurrence of traffic-related accidents. It is difficult to transmit EMs quickly and reliably due to the high-speed mobility of VANET and the attenuation of the wireless signal. However, poor network design and high vehicle mobility are the two most difficult problems that affect VANET’s network performance. The real-time traffic situation and network dependability will also be significantly impacted by route selection and message delivery. Many of the current works have undergone studies focused on forwarder selection and message transmission to address these problems. However, these earlier approaches, while effective in forwarder selection and routing, have overlooked the critical aspects of communication overhead and excessive energy consumption, resulting in transmission delays. To address the prevailing challenges, the proposed solutions use edge computing to process and analyze data locally from surrounding cars and infrastructure. EDGE-RSUs are positioned by the side of the road. In intelligent transportation systems, this lowers latency and enhances real-time decision-making by employing proficient forwarder selection techniques and optimizing the dissemination of EMs. In the context of 5G-enabled VANET, this paper introduces a novel routing protocol, namely, the supercluster-based urban multi-hop broadcast and best forwarder selection protocol (UMB-BFS). The improved twin delay deep deterministic policy gradient (IT3DPG) method is used to select the target region for emergency message distribution after route selection. Clustering is conducted using modified density peak clustering (MDPC). Improved firefly optimization (IFO) is used for optimal path selection. In this way, all emergency messages are quickly disseminated to multiple directions and also manage the traffic in VANET. Finally, we plotted graphs for the following metrics: throughput (3.9 kbps), end-to-end delay (70), coverage (90%), packet delivery ratio (98%), packet received (12.75 k), and transmission delay (57 ms). Our approach’s performance is examined using numerical analysis, demonstrating that it performs better than the current methodologies across all measures.
Sriramulu Bojjagani, Y. P. Reddy, Thati Anuradha
et al.
Intelligent transport systems amalgamated with advanced technologies are an important element of the automotive industry, including critical infrastructure and transportation. Internet of Vehicles (IoV) is the modern technological framework designed for intelligent transportation. IoV creates a network of information relations among vehicles, thus contributing to reduced congestion, roadside infrastructure, driver/traveller safety, and traffic efficiency through wireless communication and sensing technology. However, a significant challenge in IoV applications is security, as criminals could potentially exploit these applications. It is clear that despite increasing industry awareness, the potential danger posed by security vulnerabilities and cyber threats is high. In this study, we have designed a new system called AKAP-IoV, which supports secure communication, mutual authentication, and key management among vehicles, roadside units, and fog and cloud servers. AKAP-IoV was tested and verified using Scyther and Tamarin to ensure its resistance to cyber threats. Furthermore, we conducted a formal security analysis using the Real-or-Random (RoR) oracle model to assess security properties logically. In addition, a detailed, comprehensive comparative study was considered to evaluate the performance, functionality, efficiency and security features supported by AKAP-IoV compared to those of recently developed schemes.
Traffic flow is crucial for the efficient and safe operation of transportation systems. Understanding and managing traffic flow can help alleviate congestion, reduce travel time, and enhance transportation safety. In order to better identify traffic flow in a traffic network, we propose a new method that uses roadside units (RSUs) for path flow reconstruction. Roadside units (RSUs) are vital transportation facilities in cooperative vehicle infrastructure systems. They utilize modern communication technologies to exchange information directly with intelligent connected vehicles and their influence on accurate path flow reconstruction and average travel time are respectively analyzed. Considering the path flow uncertainty in traffic networks, a two-stage stochastic model is formulated, which aims to balance RSU deployment cost and value of reduced travel time. On the first stage, we solve a fully path flow reconstruction problem; On the second stage, we calculates the reduction on average travel time under different scenarios. To effectively handle the characteristics of the second stage model, we employ the integer L-shaped algorithm for solution. Numerical experiments suggest that (1) Expanding the size of scenarios has little impact on experimental results, which indicating that this model has good applicability; (2) some links play important roles in path flow reconstruction.
Nowadays, academic research, disaster mitigation, industry, and transportation apply the cooperative multi-agent concept. A cooperative multi-agent system is a multi-agent system that works together to solve problems or maximise utility. The essential marks of formation control are how the multiple agents can reach the desired point while maintaining their position in the formation based on the dynamic conditions and environment. A cooperative multi-agent system closely relates to the formation change issue. It is necessary to change the arrangement of multiple agents according to the environmental conditions, such as when avoiding obstacles, applying different sizes and shapes of tracks, and moving different sizes and shapes of transport objects. Reinforcement learning is a good method to apply in a formation change environment. On the other hand, the complex formation control process requires a long learning time. This paper proposed using the Deep Dyna-Q algorithm to speed up the learning process while improving the formation achievement rate by tuning the parameters of the Deep Dyna-Q algorithm. Even though the Deep Dyna-Q algorithm has been used in many applications, it has not been applied in an actual experiment. The contribution of this paper is the application of the Deep Dyna-Q algorithm in formation control in both simulations and actual experiments. This study successfully implements the proposed method and investigates formation control in simulations and actual experiments. In the actual experiments, the Nexus robot with a robot operating system (ROS) was used. To confirm the communication between the PC and robots, camera processing, and motor controller, the velocities from the simulation were directly given to the robots. The simulations could give the same goal points as the actual experiments, so the simulation results approach the actual experimental results. The discount rate and learning rate values affected the formation change achievement rate, collision number among agents, and collisions between agents and transport objects. For learning rate comparison, DDQ (0.01) consistently outperformed DQN. DQN obtained the maximum −170 reward in about 130,000 episodes, while DDQ (0.01) could achieve this value in 58,000 episodes and achieved a maximum −160 reward. The application of an MEC (model error compensator) in the actual experiment successfully reduced the error movement of the robots so that the robots could produce the formation change appropriately.
Reinforcement learning (RL) methods for energy saving and greening have recently appeared in the field of autonomous driving. In inter-vehicle communication (IVC), a feasible and increasingly popular research direction of RL is to obtain the optimal action decision of agents in a special environment. This paper presents the application of reinforcement learning in the vehicle communication simulation framework (Veins). In this research, we explore the application of reinforcement learning algorithms in a green cooperative adaptive cruise control (CACC) platoon. Our aim is to train member vehicles to react appropriately in the event of a severe collision involving the leading vehicle. We seek to reduce collision damage and optimize energy consumption by encouraging behavior that conforms to the platoon’s environmentally friendly aim. Our study provides insight into the potential benefits of using reinforcement learning algorithms to improve the safety and efficiency of CACC platoons while promoting sustainable transportation. The policy gradient algorithm used in this paper has good convergence in the calculation of the minimum energy consumption problem and the optimal solution of vehicle behavior. In terms of energy consumption metrics, the policy gradient algorithm is used first in the IVC field for training the proposed platoon problem. It is a feasible training decision-planning algorithm for solving the minimization of energy consumption caused by decision making in platoon avoidance behavior.
Ibrahim A. Nemer, Tarek R. Sheltami, Slim Belhaiza
et al.
Unmanned Aerial Vehicles (UAVs) are considered an important element in wireless communication networks due to their agility, mobility, and ability to be deployed as mobile base stations (BSs) in the network to improve the communication quality and coverage area. UAVs can be used to provide communication services for ground users in different scenarios, such as transportation systems, disaster situations, emergency cases, and surveillance. However, covering a specific area under a dynamic environment for a long time using UAV technology is quite challenging due to its limited energy resources, short communication range, and flying regulations and rules. Hence, a distributed solution is needed to overcome these limitations and to handle the interactions among UAVs, which leads to a large state space. In this paper, we introduced a novel distributed control solution to place a group of UAVs in the candidate area in order to improve the coverage score with minimum energy consumption and a high fairness value. The new algorithm is called the state-based game with actor–critic (SBG-AC). To simplify the complex interactions in the problem, we model SBG-AC using a state-based potential game. Then, we merge SBG-AC with an actor–critic algorithm to assure the convergence of the model, to control each UAV in a distributed way, and to have learning capabilities in case of dynamic environments. Simulation results show that the SBG-AC outperforms the distributed DRL and the DRL-EC3 in terms of fairness, coverage score, and energy consumption.
Suresh Kumar Sharma , Seema, Rajwant Singh Rao
et al.
Vehicular Ad-hoc Network (VANET) is an essential part of futuristic Intelligent Transportation Systems. VANET can improve the overall traffic control system and reduce road accident deaths by providing remote health monitoring in hazardous conditions to outdoor patients. Nowadays, vehicles have become so intelligent that they can sense patient health data and transmit it to a nearby ambulance or hospital in emergency or road accident situations. Health professionals can provide appropriate treatment without wasting critical time in further testing. Developing an efficient and reliable routing solution is a significant research problem for VANET based health monitoring applications because of time-sensitives. Routing approaches to reduce the transmission delay for critical applications are based on topological, geographical, clustering, and flooding techniques. This article has evaluated and compared widely used topological and geographical routing protocols for data-based VANETs health monitoring applications. A comprehensive analysis is performed on Ad hoc On-Demand Distance Vector (AODV), Destination-Sequenced Distance-Vector (DSDV), Optimized Link State Routing (OLSR), Greedy Perimeter Stateless Routing (GPSR), Greedy Perimeter Stateless Routing-Modified (GPSR-M), and Max duration-Minangle Greedy Perimeter Stateless
Routing (MM-GPSR) protocols with different numbers of nodes, CBR connections, communication range and
packet size on Network Simulator (NS-3.23) and Simulation of Urban Mobility (SUMO) platforms. Experimental results give useful knowledge in analyzing routing protocols for VANET's data-based smart health monitoring
applications.
Abdullah Akram Bajwa, H. Mokhlis, S. Mekhilef
et al.
An electrical power system is considered as a critical infrastructure (CI), the epicenter of a nation's economy, security, and health. It is interlinked with other CIs such as gas and water supplies and transportation and communication systems. A failure in the power system will immensely affect the functionality of these CIs. Therefore, enhancing power system resilience is crucially needed to ensure continuous operation of these CIs. One of the possible approaches to improve the resilience in a power system is by integrating microgrids in the power system. Microgrids have proven to have self-healing and resilient capabilities in such extreme events which inflict damage out of the conventional scope of failures. Operational flexibility and controllability make microgrids a viable solution for resilience enhancement. This paper reviews the concept of resilience in power systems and the functions of microgrids in enhancement of resilience. The most current studies in improving power system resilience through microgrids are reviewed by highlighting their advantages and limitations.An electrical power system is considered as a critical infrastructure (CI), the epicenter of a nation's economy, security, and health. It is interlinked with other CIs such as gas and water supplies and transportation and communication systems. A failure in the power system will immensely affect the functionality of these CIs. Therefore, enhancing power system resilience is crucially needed to ensure continuous operation of these CIs. One of the possible approaches to improve the resilience in a power system is by integrating microgrids in the power system. Microgrids have proven to have self-healing and resilient capabilities in such extreme events which inflict damage out of the conventional scope of failures. Operational flexibility and controllability make microgrids a viable solution for resilience enhancement. This paper reviews the concept of resilience in power systems and the functions of microgrids in enhancement of resilience. The most current studies in improving power system resilience throug...
Abstract Internet of Things (IoT) has emerged from the proliferation of smart and inter-connected devices ranging from tiny sensors to complex Fog and Cloud nodes, various networking technologies, and communication protocols. These IoT devices permeate in our lives through various applications including smart homes, healthcare, defence, transportation, and so forth. Although IoT provides a way of interaction among the physical world objects and the Internet, these connected devices have created a new dimension of security challenges associated with the vulnerabilities present in them. These challenges can be tackled to some extent by deploying a rigid authentication and access control model. In this paper, we propose a novel light-weight authentication and authorization framework suitable for distributed IoT environment using Elliptical Curve Cryptography (ECC) and Message Queuing Telemetry Transport (MQTT). Moreover, we implement the scheme, and analyse and compare its various security and performance aspects with other schemes.