Search Results (183)

Vehicle-to-everything (V2X) communication is a fundamental technology in the development of intelligent transportation systems, encompassing vehicle-to-vehicle (V2V), infrastructure (V2I), and pedestrian (V2P) communications. This technology enables connected and autonomous vehicles (CAVs) to interact with their surroundings, significantly enhancing road safety, traffic efficiency, and driving comfort. However, as V2X communication becomes more widespread, it becomes a prime target for adversarial and persistent cyberattacks, posing significant threats to the security and privacy of CAVs. These challenges are compounded by the dynamic nature of vehicular networks and the stringent requirements for real-time data processing and decision-making. Much research is on using novel technologies such as machine learning, blockchain, and cryptography to secure V2X communications. Our survey highlights the security challenges faced by V2X communications and assesses current ML and blockchain-based solutions, revealing significant gaps and opportunities for improvement. Specifically, our survey focuses on studies integrating ML, blockchain, and multi-access edge computing (MEC) for low latency, robust, and dynamic security in V2X networks. Based on our findings, we outline a conceptual framework that synergizes ML, blockchain, and MEC to address some of the identified security challenges. This integrated framework demonstrates the potential for real-time anomaly detection, decentralized data sharing, and enhanced system scalability. The survey concludes by identifying future research directions and outlining the remaining challenges for securing V2X communications in the face of evolving threats. Full article

Non-orthogonal multiple access (NOMA) technology can effectively improve spectrum efficiency, unmanned aerial vehicle (UAV) communication has the advantage of flexible deployment, and reconfigurable intelligent surface (RIS) can intelligently control the wireless transmission environment. Traditional communication systems have problems such as limited coverage and low spectrum efficiency in complex scenarios. However, a key challenge in deploying RIS-assisted NOMA-UAV communication systems lies in how to jointly optimize the UAV flight trajectory, power allocation strategy, and RIS phase offset to achieve the maximum average achievable rate for users. The non-convex nature of the optimization complicates the problem, making it challenging to find an efficient solution. Based on this, this paper presents a RIS-assisted NOMA-UAV communication system consisting of one UAV, one RIS, and two ground users. To achieve the maximum average rate for users, the UAV flight trajectory, power allocation strategy, and RIS phase offset are jointly optimized. For the non-convex problem, we decompose it into three sub-problems based on its inherent structural characteristics and use an alternating iterative approach to gradually converge to a feasible solution. The simulation results demonstrate that the proposed scheme offers significant advantages in the application scenario. Compared to other benchmark schemes, it delivers superior performance improvements to the communication system and offers higher practical value. Full article

The integration of shared autonomous vehicles into historic urban areas presents both opportunities and challenges. In heritage-rich environments like very old Asian (such as Suzhou old town, which serves as a use case example) or European (especially Mediterranean coastal cities) areas—characterized by narrow alleys, dense development, and sensitive cultural landscapes—shared autonomous vehicle adoption raises critical spatial and social questions. This study employs a qualitative, user-centered approach based on the ripple model to examine residents’ perceptions across four dimensions: residential patterns, parking land use, regional accessibility, and street-level infrastructure. Semi-structured interviews with 27 participants reveal five key findings: (1) public trust depends on transparent decision-making and safety guarantees; (2) shared autonomous vehicles may reshape generational residential clustering; (3) the short-term parking demand remains stable, but the long-term reuse of space is feasible; (4) shared autonomous vehicles could enhance accessibility in historic cores; (5) transport systems may evolve toward intelligent, human-centered designs. Based on these insights, the study proposes three strategies: (1) transparent risk assessment using explainable artificial intelligence and digital twins; (2) polycentric development to diversify land use; (3) hierarchical street retrofitting to balance mobility and preservation. While this study is limited by its qualitative scope and absence of simulation, it offers a framework for culturally sensitive, small-scale interventions supporting sustainable mobility transitions in historic urban contexts. Full article

Automatic license plate recognition (ALPR) plays an important role in applications such as intelligent traffic systems, vehicle access control in specific areas, and law enforcement. The main novelty brought by the present research consists in the development of an automatic vehicle license plate recognition system adapted to the Romanian context, which integrates the YOLOv12 detection architecture with the PaddleOCR library while also providing functionalities for recognizing the type of vehicle on which the license plate is mounted and identifying the county of registration. The integration of these functionalities allows for an extension of the applicability range of the proposed solution, including for addressing issues related to restricting access for certain types of vehicles in specific areas, as well as monitoring vehicle traffic based on the county of registration. The dataset used in the study was manually collected and labeled using the makesense.ai platform and was made publicly available for future research. It includes 744 images of vehicles registered in Romania, captured in real traffic conditions (the training dataset being expanded by augmentation). The YOLOv12 model was trained to automatically detect license plates in images with vehicles, and then it was evaluated and validated using standard metrics such as precision, recall, F1 score, mAP@0.5, mAP@0.5:0.95, etc., proving very good performance. Experimental results demonstrate that YOLOv12 achieved superior performance compared to YOLOv11 for the analyzed issue. YOLOv12 outperforms YOLOv11 with a 2.3% increase in precision (from 97.4% to 99.6%) and a 1.1% improvement in F1 score (from 96.7% to 97.8%). Full article

The integration of intelligent voice-control systems represents a critical pathway for enhancing driver comfort and reducing cognitive distraction in modern vehicles. Currently, voice assistants capable of accessing real-time vehicular data (e.g., engine parameters) or controlling actuators (e.g., door locks) remain exclusive to premium brands. While aftermarket solutions like Amazon’s Echo Auto provide multimedia functionality, they lack access to critical vehicle systems. To address this gap, we develop a novel architecture leveraging the OBD-II port to enable voice-controlled telematics and actuation in mass-production vehicles. Our system interfaces with a Toyota Hilux (2020) and Mazda CX-3 SUV (2021), utilizing an MCP2515 CAN controller for engine control unit (ECU) communication, an Arduino Nano for data processing, and an ESP01 Wi-Fi module for cloud transmission. The Blynk IoT platform orchestrates data flow and provides user interfaces, while a Voiceflow-programmed Alexa skill enables natural language commands (e.g., “unlock doors”) via Alexa Auto. Experimental validation confirms the successful real-time monitoring of engine variables (coolant temperature, air–fuel ratio, ignition timing) and secure door-lock control. This work demonstrates that high-end vehicle capabilities—previously restricted to luxury segments—can be effectively implemented in series-production automobiles through standardized OBD-II protocols and IoT integration, establishing a scalable framework for next-generation in-vehicle assistants. Full article

This study presents a new intelligent aquatic farming surveillance system that tackles real-time monitoring challenges in the industry. The main technical break-throughs of this system are evident in four key aspects: First, it achieves the smooth integration of remotely operated vehicles (ROVs), sensors, and real-time data transmission. Second, it uses a mobile communication architecture with buoy relay stations for distributed edge computing. This design supports future upgrades to Beyond 5G and satellite networks for deep-sea applications. Third, it features a multi-terminal control system that supports computers, smartphones, smartwatches, and centralized hubs, effectively enabling monitoring anytime, anywhere. Fourth, it incorporates a cost-effective modular design, utilizing commercial hardware and innovative system integration solutions, making it particularly suitable for farms with limited resources. The data indicates that the system’s 4G connection is both stable and reliable, demonstrating excellent performance in terms of data transmission success rates, control command response delays, and endurance. It has successfully processed 324,800 data transmission events, thoroughly validating its reliability in real-world production environments. This system integrates advanced technologies such as the Internet of Things, mobile communications, and multi-access control, which not only significantly enhance the precision oversight capabilities of marine farming but also feature a modular design that allows for future expansion into satellite communications. Notably, the system reduces operating costs while simultaneously improving aquaculture efficiency, offering a practical and intelligent solution for small farmers in resource-limited areas. Full article

Artificial intelligence is increasingly deployed as a vehicle for “social good” in agriculture, ostensibly advancing the UN Sustainable Development Goals whilst uplifting smallholders. This study examines how such claims materialise through a selective case study analysis of eleven Thai Agricultural AI providers, analysing governance practices and impact framing. The research develops the “hybrid intersection” concept as an analytical lens for understanding how Agricultural AI simultaneously delivers genuine social benefits whilst reproducing structural constraints that limit transformative change. Findings reveal that “social good” becomes operationalised primarily through economic gains, reflecting farmers’ immediate financial predicament and market-driven innovation constraints. Governance practices prioritise functional trust over procedural safeguards, reflecting institutional pressures to demonstrate immediate value. The study reveals two systemic tensions: data commodification models enabling free farmer access whilst extracting behavioural surplus for third-party monetisation, and market optimisation approaches delivering incremental improvements whilst leaving structural challenges unaddressed. Thailand’s Agricultural AI landscape thus embodies a “hybrid intersection” where genuine social good coexists with constrained transformation, providing analytical tools for understanding similar patterns in other Southern contexts. Full article

In this paper, we investigate a simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted non-orthogonal multiple access (NOMA) communication system where the STAR-RIS is mounted on an unmanned aerial vehicle (UAV) with adjustable altitude. Due to severe blockages in urban environments, direct links from the base station (BS) to users are assumed unavailable, and signal transmission is realized via the STAR-RIS. We formulate a joint optimization problem that maximizes the system sum rate by jointly optimizing the UAV’s altitude, BS beamforming vectors, and the STAR-RIS phase shifts, while considering Rician fading channels with altitude-dependent Rician factors. To tackle the maximum achievable rate problem, we adopt a block-wise optimization framework and employ semidefinite relaxation and gradient descent methods. Simulation results show that the proposed scheme achieves up to 22% improvement in achievable rate and significant reduction in bit error rate (BER) compared to benchmark schemes, demonstrating its effectiveness in integrating STAR-RIS and UAV in NOMA networks. Full article

Distribution network operators face the complex challenge of maintaining stable electricity access for diverse consumers while balancing economic constraints, user comfort, and the impact of stochastic events, particularly the increasing integration of renewable energy sources and electric vehicles. To address these challenges, this paper introduces a novel decision-making system for energy management within smart energy meters, leveraging a specifically designed fuzzy inference system. This fuzzy inference system autonomously interprets real-time energy consumption patterns and responds to control commands from distribution network operators, optimizing energy flow at the consumer level. Unlike generic energy management approaches, this study provides a detailed mathematical model of the proposed low-cost fuzzy inference system-based system, explicitly outlining its rule base and inference mechanisms. Simulation studies conducted under varying load conditions and renewable generation profiles demonstrate the system’s effectiveness in achieving a balanced response to grid demands and user needs, yielding a quantifiable reduction in peak demand during simulated stress scenarios. Furthermore, experimental validation on resource-constrained embedded platforms confirms the practical feasibility and real-time performance of the proposed system on low-cost smart energy meter hardware. The differential contribution of this work lies in its provision of a computationally efficient and readily implementable fuzzy logic-based solution tailored for the limitations of low-cost smart energy meters, offering a viable alternative to more complex artificial intelligence algorithms. The findings underscore the necessity and justification for optimizing algorithm code for resource-constrained smart energy meter deployments to facilitate widespread adoption of advanced energy management functionalities. Full article

As electric vehicle (EV) adoption grows, efficient and accessible charging infrastructure is essential. This paper introduces a modular simulation environment to evaluate charging point configurations and operational strategies. The simulation incorporates detailed models of electrical consumers and user behaviour, leveraging real-world data to simulate charging scenarios. A rule-based control strategy is applied to assess six configurations for a supermarket parking lot charging point. Key findings include the highest profit being achieved with two fast chargers. In scenarios with a 50 kW grid connection limit, combining fast chargers with stationary battery storage proves effective. Conversely, mobile charging robots generate lower revenue, though grid peak limitations have minimal impact. The study highlights the potential of the simulation environment to optimise charging layouts, refine operational strategies, and develop energy management algorithms. This work demonstrates the utility of the simulation framework for analyzing diverse charging solutions, offering insights into cost efficiency and user satisfaction. The results emphasise the importance of tailored strategies to balance grid constraints, profitability, and user needs, paving the way for intelligent EV charging infrastructure development. Full article

Vehicular Internet of Things (V-IoT) networks, sustained by a high-density deployment of roadside units and sensor-equipped vehicles, are currently at the edge of next-generation intelligent transportation system evolution. However, offering stable, low-latency, and energy-efficient communication in such heterogeneous and delay-prone environments is challenging due to limited spectral resources and diverse quality of service (QoS) requirements. This paper presents a Priority-Aware Spectrum Management (PASM) scheme for IoT-based vehicular networks. This dynamic spectrum access scheme integrates interweave, underlay, and coexistence modes to optimize spectrum utilization, energy efficiency, and throughput while minimizing blocking and interruption probabilities. The algorithm manages resources efficiently and gives proper attention to each device based on its priority, so all IoT devices, from high to low priority, receive continuous and reliable service. A Continuous-Time Markov Chain (CTMC) model is derived to analyze the proposed algorithm for various network loads. Simulation results indicate improved spectral efficiency, throughput, delay, and overall QoS compliance over conventional access methods. These findings establish that the proposed algorithm is a scalable solution for dynamic V-IoT environments. Full article

With the increasing penetration of distributed photovoltaic-energy storage system (PV-ESS) access distribution networks, the safe and stable operation of the system has brought a huge impact, in which the voltage regulation of PV-ESS distribution networks is more prominent. This paper comprehensively reviews the voltage over-run mechanism in the PV-ESS distribution network and combs through the current mainstream voltage regulation strategies, of which two strategies of direct voltage regulation and current optimization are summarized. At the same time, this paper discusses the advantages and limitations of centralized, distributed, multi-timescale, voltage-reactive joint optimization and other regulation methods and focuses on the analysis of heuristic algorithms and algorithms based on deep reinforcement learning in the voltage regulation of the relevant research progress. Finally, this paper points out the main challenges currently facing voltage regulation in PV-ESS distribution networks, including cluster dynamic partitioning technologies, multi-timescale control of hybrid voltage regulation devices, and synergistic problems of demand-side resources, such as electric vehicle participation in voltage regulation, etc., and gives an outlook on future research directions. The aim of this paper is to provide a theoretical basis and practical guidance for voltage regulation of PV-ESS distribution networks and to promote the intelligent construction and sustainable development of power grids. Full article

Human–Machine Interfaces (HMIs) in traditional automobiles are essential in connecting drivers, passengers, and vehicle systems. In automated vehicles, the HMI has become a critical component. A well-designed HMI facilitates effective human oversight, enhances situational awareness, and mitigates risks associated with system failures or unexpected scenarios. Simultaneously, it serves as a crucial safeguard against cyber threats, preventing unauthorized access and ensuring the integrity of vehicular operations in increasingly connected environments. This narrative review delves into the evolving landscape of automotive HMI design, emphasizing its role in enhancing user experience (UX) and safety. By exploring usability challenges, technological advancements, and the integration of rapidly evolving technologies such as AI (Artificial Intelligence), AR (Augmented Reality), and gesture-based controls, this study highlights how effective HMIs minimize cognitive load while maintaining functionality. Significant attention is given to the new challenges that arise from technological advancements in terms of security and safety. Full article

Smart cities are urban areas that use contemporary technology to improve citizens’ overall quality of life. These modern digital civil hubs aim to manage environmental conditions, traffic flow, and infrastructure through interconnected and data-driven decision-making systems. Today, many applications employ intelligent sensors for real-time data acquisition, leveraging visualization to derive actionable insights. However, despite the proliferation of such platforms, challenges like high data volume, noise, and incompleteness continue to hinder practical visual analysis. As missing data is a frequent issue in visualizing those urban sensing systems, our approach prioritizes their correction as a fundamental step. We deploy a hybrid imputation strategy combining SARIMAX, k-nearest neighbors, and random forest regression to address this. Building on this foundation, we propose an interactive web-based pipeline that processes, analyzes, and presents the sensor data provided by Basel’s “Smarte Strasse”. Our platform receives and projects environmental measurements, i.e., NO₂, O₃, PM_2.5, and traffic noise, as well as mobility indicators such as vehicle speed and type, parking occupancy, and electric vehicle charging behavior. By resolving gaps in the data, we provide a solid foundation for high-fidelity and quality visual analytics. Built on the Flask web framework, the platform incorporates performance optimizations through Flask-Caching. Concerning the user’s dashboard, it supports interactive exploration via dynamic charts and spatial maps. This way, we demonstrate how future internet technologies permit the accessibility of complex urban sensor data for research, planning, and public engagement. Lastly, our open-source web-based application keeps reproducible, privacy-aware urban analytics. Full article

The rapid expansion of distributed systems such as the Internet of Things (IoT) has increased the need for robust authentication and data integrity mechanisms to ensure public security in dynamic environments. This article presents a hierarchical multiparty digital signature (HMPS) technique designed to address the unique challenges of resource-constrained and decentralized systems. By integrating a modified ElGamal-based individual signature with linear encryption and hierarchical aggregation, HMPS delivers enhanced security through collaborative and layered signing processes. A key application is demonstrated in intelligent vehicle surveillance, where the scheme ensures the authenticity and integrity of commands and data in multi-level communication scenarios. Comprehensive security analysis confirms resistance to forgery, single points of failure, and unauthorized access. HMPS exhibits superior computational efficiency, scalability, and energy efficiency, as evidenced by comparative performance evaluations with state-of-the-art techniques. These results highlight HMPS as a highly effective solution for secure, real-time IoT applications, providing a pathway to more resilient and trustworthy distributed systems. Full article