Search Results (875)

The Smart Readiness Indicator (SRI) provides a European framework for assessing the capacity of buildings to optimise operation, respond to occupants and interact with energy grids. Its current catalogue covers nine domains: heating, cooling, domestic hot water, ventilation, lighting, dynamic envelope, electricity, electric vehicle charging, and monitoring and control. This Perspective argues that this boundary is too narrow for complex buildings, where services such as lifts, security, fire safety, swimming pools, irrigation, outdoor lighting, kitchens, laundries, refrigeration, laboratories, and information and communication technology (ICT) rooms may significantly affect energy use, water use, safety, resilience, maintenance and user experience. The paper proposes five additional service families for SRI 2.0: mobility and accessibility; safety and security; water and outdoor environmental services; outdoor and site infrastructure; and special energy-intensive and facility services. These should be introduced as optional, building-type-specific modules assessed through familiar SRI functionality levels. This would make the SRI more realistic, actionable and relevant. Full article

Demand response (DR) can improve power system flexibility and support low-carbon operation, yet the potential of Internet of Vehicles (IoV) communication infrastructures remains limited by the coupling between traffic distribution and the communication network load. Existing traffic-only or communication-only approaches do not exploit this cross-domain flexibility. This study proposes a sustainable traffic–communication–energy coordinated DR framework that uses dynamic route compensation to reshape communication loads. A Stackelberg mechanism coordinates the IoV operator and vehicle users while a large language model (LLM) generates and corrects adaptive compensation candidates under heterogeneous operating conditions. Compared with the no-guidance and static-compensation baselines, the proposed dynamic strategy redistributes traffic and communication loads, releases communication-side DR capacity, and improves both operator and user performance. Compared with the non-LLM decision baseline, LLM assistance increases the load regulation rate from 21.5% to 36.2%, raises cost savings from 16.8% to 28.5%, and increases the user response rate from 72.4% to 88.7%. The framework provides a sustainable pathway for using existing IoV communication infrastructure as a flexible demand-side resource. Full article

The rapid evolution of connected and autonomous vehicles has significantly increased the importance of automotive telemetry as a fundamental component of intelligent transportation systems. Modern vehicles continuously generate large volumes of operational, environmental, and behavioral data that must be transmitted, processed, and analyzed in real time to support applications such as predictive maintenance, remote diagnostics, cooperative mobility, and autonomous driving. The growing demand for reliable and large-scale data exchange has highlighted the critical role of advanced communication infrastructures in connected mobility ecosystems. This review provides a comprehensive overview of automotive telemetry technologies with a particular focus on communication solutions enabling long-range data transmission. The study examines the architecture of automotive telemetry systems, connected vehicles, and the Internet of Vehicles, followed by an analysis of Vehicle-to-Everything (V2X) communication, fifth-generation (5G) mobile networks, satellite communication systems, and emerging hybrid 5G–satellite architectures. In addition, the review discusses Software Defined Vehicles, Digital Twin technologies, artificial intelligence integration, and cybersecurity challenges associated with highly connected transportation environments. The analysis demonstrates that no single communication technology can satisfy all requirements of future intelligent mobility systems. Instead, hybrid communication architectures combining terrestrial and non-terrestrial networks are expected to provide the reliability, coverage, scalability, and low-latency performance required for next-generation connected transportation. Finally, key research challenges and future development directions are identified, emphasizing the growing convergence of telemetry, communication networks, artificial intelligence, digital twins, and cybersecurity within future connected mobility ecosystems. Full article

The ambitious roadmap for a sustainable transport system adopted by the European Commission (EC) by 2050 includes the deployment of an extensive Electric Vehicle Charging Stations (EVCSs) infrastructure, which introduces significant challenges for distribution power grids. High power demand, particularly from fast-charging systems, may lead to network overloading and voltage unbalance. In addition, recent measurement campaigns highlight substantial changes in grid impedance and the emergence of resonance phenomena, together with the injection and propagation of high-frequency conducted disturbances. These effects extend over a wide frequency range, up to several hundreds of kHz, causing degradation, aging and malfunction of network assets, in particular Power Line Communications. This paper provides a comprehensive and updated review of the impact of EVCSs on electrical grids, covering power flow, power quality, stability, and impedance-related interactions. Particular attention is given to the role of power-electronic converters, high-frequency emissions, and the associated challenges in measurement and standardization. The analysis highlights that EVCS integration fundamentally alters the nature of electrical loads, requiring new approaches for grid planning, monitoring, and regulation. The study identifies key research gaps and outlines future directions to ensure the reliable and sustainable integration of electromobility into modern power systems. Full article

Urban intelligent transportation systems increasingly rely on vehicle-to-infrastructure (V2I) communication for green-light optimal speed advisory (GLOSA) services. However, conventional GLOSA systems are vulnerable to roadside unit (RSU) coverage gaps and communication instability in mixed-traffic environments. In this paper, we propose a heterogeneous communication network cooperation framework that integrates decentralized multi-hop vehicle-to-vehicle (V2V) relaying with conventional V2I communication to extend signal phase and timing (SPaT) dissemination beyond direct RSU coverage. A lightweight gradient-based speed synchronization mechanism supports real-time trajectory adaptation with low computational overhead. The framework is evaluated through microscopic SUMO simulations with explicit communication impairment modeling across varied traffic densities and connected autonomous vehicle (CAV) penetration levels (10–70%). The results demonstrate reductions in travel time reductions of up to 22%, stop frequency of up to 95%, and CO₂ emission exceeding 18% relative to V2I-only GLOSA under 70% CAV penetration. At the lower bound of 10% CAV penetration, the framework still achieves measurable improvements of approximately 4–6% in travel time and 15–20% in stop frequency, confirming practical benefit even under minimal connected-vehicle adoption. The proposed framework maintains advisory continuity through distributed relay dissemination, offering a scalable and communication-resilient enhancement to intelligent transportation coordination in heterogeneous environments. Full article

The rapid expansion of electric mobility is reshaping both transport infrastructure and power-system operation. This narrative and critical review examines the connected evolution of new energy vehicle (NEV) markets, charging and battery-swapping infrastructure, policy mechanisms, and vehicle-to-grid (V2G) systems. In this paper, NEV includes battery electric vehicles (BEVs), plug-in hybrid electric vehicles (PHEVs), and fuel-cell electric vehicles (FCEVs); conventional non-plug-in hybrid electric vehicles are discussed only where regional statistics require clarification. Peer-reviewed studies, official statistics, policy documents, market reports, and technical standards available through June 2026 are synthesized thematically and compared across China, Europe, the United States, and selected emerging markets. The review distinguishes verified 2025 observations from scenario-based projections, evaluates policy instruments by their outcomes and limitations, and extends the V2G discussion to bidirectional charger requirements, interoperability, aggregation, DSO-TSO coordination, battery degradation, cybersecurity, and economic viability. Unlike reviews centered on a single technology or region, the proposed market–infrastructure–policy–V2G framework explains how market structure, infrastructure governance, standards, and electricity-market design jointly shape commercialization pathways. The synthesis indicates that infrastructure scale alone is insufficient: utilization, grid hosting capacity, interoperable communication, credible revenue stacking, and equitable access determine whether charging, battery swapping, and V2G can deliver system-level value. Full article

Vehicular Ad Hoc Networks (VANETs) are essential components of Intelligent Transportation Systems (ITS), allowing communication exchanges between vehicles and road infrastructure elements. These networks face challenges from vehicular mobility, including frequent topology changes, link instability, and variable wireless channel quality. This paper presents an extensive evaluation of Software-Defined Networking (SDN) integrated with two traffic engineering technologies, Multi-Protocol Label Switching (MPLS) and Segment Routing (SR), applied to the AODV and OLSR routing protocols. Nine incremental configurations are evaluated for each protocol, ranging from the default protocol through MPLS-enhanced forwarding, SDN-based centralized optimization, combined SDN-MPLS and SDN-SR integration, to advanced configurations using distance-based IS-IS weighted topology metrics with both Fixed and Adaptive metric computation approaches. Two distinct SDN topology construction methods are compared: a Protocol-based approach that uses routing table entries with equal hop-count metrics, and a distance-based approach using IS-IS weighted metrics. The simulation uses a realistic urban topology with 50 vehicles and 5 RSUs, evaluated across several traffic patterns, representing different application types. Results demonstrate that SR with distance-based IS-IS metrics achieves the highest Packet Delivery Ratio (PDR) and lowest delay by leveraging RSU infrastructure as reliable forwarding relays. Moreover, the proposed SDN-SR framework reduces routing overhead and control-plane signaling, improving network resource utilization and thereby indicating its potential to enhance the energy efficiency of vehicular communication infrastructures. Full article

Vehicular ad hoc networks (VANETs), as a key communication component of the Internet of Vehicles (IoV), enable vehicles and roadside infrastructure to exchange information efficiently, thereby supporting road safety and traffic management. However, because these communications take place over open wireless channels, VANETs are exposed to message forgery, replay, identity disclosure, and unauthorised access by revoked vehicles. To address these issues, this paper proposes EPAF, an efficient privacy-preserving batch authentication scheme with revocation support for fog-enabled VANETs. EPAF uses roadside fog nodes to distribute update keys and report information related to misbehaving vehicles, thereby reducing reliance on remote centralised processing. Rather than assuming ideal tamper-proof devices that store system-wide secrets, EPAF requires protected storage only for vehicle-local certificates, limiting the impact of compromising an individual vehicle device. The scheme employs batch verification to authenticate multiple messages from different vehicles in a single procedure, reducing verification overhead in message-intensive traffic conditions. It further introduces an update-key mechanism through which legitimate vehicles obtain current authentication keys, whereas revoked vehicles are prevented from generating valid authentication messages in subsequent revocation periods. Under the honest-authority model, the security analysis establishes the EUF-CMA security of an authentication packet in the random-oracle model and separately addresses conditional identity privacy, traceability, and unlinkability across different pseudonym periods. Performance evaluation examines the trade-off among authentication efficiency, communication overhead, and revocation performance, showing that EPAF is a practical solution for fog-enabled vehicular communication. Full article

Unmanned aerial vehicles (UAVs) have become essential cyber-physical platforms for applications such as surveillance, infrastructure inspection, emergency response, and intelligent transportation. However, their tight coupling among sensing, communication, control, actuation, and swarm coordination also exposes them to sophisticated cyber-physical attacks that are difficult to detect using conventional intrusion detection systems. Existing machine learning, deep learning, graph-based, and federated intrusion detection approaches generally rely on statistical feature representations or temporal patterns, providing limited capability to model causal dependencies among interacting UAV subsystems and to generalize across heterogeneous operating environments. To address these limitations, this paper proposes Fed-CGIDS-UAV, a federated causal graph learning framework for cross-domain intrusion detection in cyber-physical UAV networks. The proposed framework models each telemetry window as a typed causal graph in which nodes represent navigation, sensing, communication, control, actuation, and swarm states, while directed edges capture stable operational dependencies. Intrusions are detected by identifying violations of these learned causal relationships, and the framework provides interpretable node-edge explanations to support root-cause analysis. Furthermore, federated learning enables collaborative model training across distributed UAV clients without sharing raw telemetry, thereby preserving data privacy while improving robustness under heterogeneous operating conditions. The proposed framework was implemented and experimentally evaluated in a controlled simulation environment covering four UAV operating domains and six representative attack classes. All experiments were repeated over five independent runs using different random seeds, and the reported results correspond to the measured average performance. The proposed framework was implemented using Python 3.12 (Python Software Foundation, Wilmington, DE, USA) and PyTorch 2.3 (Meta Platforms, Menlo Park, CA, USA). UAV flight data were generated using Microsoft AirSim 1.9.1 (Microsoft Corporation, Redmond, WA, USA), integrated with PX4 Autopilot v1.14 (Dronecode Foundation, San Francisco, CA, USA) and Gazebo Sim 11 (Open Source Robotics Foundation, Mountain View, CA, USA). Within this simulation-based evaluation, Fed-CGIDS-UAV achieved an accuracy of 0.968, an F1-score of 0.956, and an internal–external stability gap (IESG) of 0.028, outperforming conventional machine learning, deep learning, graph-based, and centralized causal baselines while maintaining competitive computational latency. Although these results demonstrate the effectiveness of the proposed framework under controlled simulation conditions, validation using real-flight UAV telemetry remains an important direction for future research. These results demonstrate that integrating causal graph learning with federated optimization provides an effective and interpretable solution for privacy-preserving intrusion detection in heterogeneous cyber-physical UAV environments. Full article

Electricity is fundamental for all modern infrastructures, while disruptions in transmission networks could result in severe failures in healthcare, transportation, communication, industry, and all related to public safety. However, inspection of overhead lines and their components is costly and labor-intensive. The convergence of Unmanned Aerial Vehicles (UAVs), advanced imaging sensors, and deep-learning-based Computer Vision has reshaped this domain. To this end, this work presents a systematic review of Computer Vision applications in electric power transmission line inspection. From an initial 1493 Scopus records, 148 studies published between 2018 and 2026 were retained through a transparent, multi-stage screening and quality-scoring process based on PRISMA guidelines. The reviewed literature was synthesized across four axes: (1) monitoring platforms and sensor technologies, (2) Computer Vision approaches per vision task, (3) datasets, metrics, and evaluation practices, and (4) synthesis of results and industrial adoption. The analysis of the literature confirms the dominance of You Only Look Once (YOLO) family models for real-time edge deployment and the rising adoption of Transformer architectures, while exposing persistent gaps in dataset availability, domain generalization, and field validation. The review concludes with the open challenges and concrete future directions toward fully autonomous, robust, and sustainable inspection systems. Full article

Digitalization and sustainability are increasingly important in container terminal development, yet their operational-level integration remains insufficiently explained in medium-sized terminals. This study examines how digital systems support sustainability-related outcomes in a medium-sized container terminal in the Port of Klaipėda, Lithuania. An exploratory qualitative case study was conducted using document analysis and semi-structured interviews with three key informants representing operational, planning/administrative, and technical or sustainability-related functions. Qualitative content analysis was applied to identify digital technologies, operational mechanisms, sustainability outcomes, implementation barriers, and managerial responses. The findings show that the terminal operating system, vehicle booking system, automated gate solutions, and port community system support sustainability mainly indirectly, through improved planning, information exchange, gate coordination, reduced congestion, and more efficient resource use. However, these effects are constrained by system fragmentation, limited interoperability, manual data transfer, uneven access to real-time information, and insufficiently targeted staff training. The strongest documented sustainability effects relate to LED lighting and solar energy infrastructure, while other digital-green links remain mainly qualitative or potential. The study provides exploratory, case-based evidence on how digitalization and sustainability are operationally connected in the analyzed medium-sized container terminal. Rather than proposing a new general theory, the study offers an analytical classification structure that helps distinguish documented, indirect and potential digitalization–sustainability links. The findings provide practical recommendations for system integration, staff training and sustainability performance monitoring. Full article

The automotive industry is evolving toward Software-Defined Vehicles (SDVs) enabled by centralized computing, cloud integration, and Over-the-Air (OTA) updates. Yet, prevailing SDV and Internet of Vehicles (IoV) simulators often treat each vehicle as a single monolithic node, obscuring the interplay between internal vehicle modules and the surrounding infrastructure in dense urban scenarios. This work proposes a modular SDV reference architecture embedded in a Software-Defined Internet of Vehicles (SD-IoV) framework together with a Software-in-the-Loop (SiL) co-simulation testbed built on Objective Modular Network Testbed in C++ (OMNeT++), Simulation of Urban MObility (SUMO), and Vehicles in Network Simulation (Veins). The architecture decouples perception, communication, decision, and actuation into typed replaceable modules and instantiates them across six co-existing agent types: an SDV; two human-driver vehicle classes with cognition modelled as a multi-stage Eye–Ear–Brain–Hand–Foot pipeline with reaction-delay sampling; a public transport bus; a Roadside Unit (RSU); and a Traffic Light (TL). Three platform-level mechanisms connect the agents to the infrastructure: a single shared world model with a three-layer line-of-sight funnel that serves visual-sensor queries and reuses the building polygons of the wireless shadowing model; a dual-CPU mobile-fog node implementing a cycles-per-frequency workload model with explicit end-to-end latency decomposition; and a three-plane intersection coordination fabric that combines 802.11p wireless with a wired RSU-to-TL star and a wired peer mesh between adjacent TLs. The initial results confirm that the implemented message paths and module interactions behave as specified, including directional Signal Phase and Timing (SPaT) reception, cross-junction handover, bus-side fog-offload latency accounting, and passive identification of Vehicle-to-Everything (V2X)-silent vehicles. Several architecture elements are specified but deliberately not exercised in the present evaluation and remain design targets for future work: the Roadside Unit (RSU) route planning and fog computing companion (and any multi-tier offloading comparison), non-line-of-sight SPaT reception, and a safety violation detection layer. Within the above scope, the testbed is positioned as a reusable foundation for module-level SDV research and as a basis for future extensions such as Joint Communication and Sensing (JCAS), energy-aware driving, and Hardware-in-the-Loop (HiL) integration. Full article

Reliable data collection is essential for disaster-oriented Internet of Things (IoT) systems, where damaged terrestrial communication infrastructure often leaves sensed data buffered at disconnected end devices. In Unmanned Aerial Vehicle (UAV)-Internet of Things device (IoTD) collaborative data collection, random UAV faults and limited energy and buffer resources further complicate mission execution, making fault-tolerant scheduling crucial for robust data recovery. To address these issues, a unified framework is developed by integrating dynamic UAV reliability modeling, Maximum Distance Separable (MDS)-coded fault-tolerant backup, and collaborative scheduling optimization. Within this framework, a data fault-tolerance mechanism, termed MFTB, and a bilevel collaborative scheduling algorithm, termed LP-DCFS, are proposed. Simulation results indicate that, in the evaluated scenarios, the proposed methods achieve better overall performance than the considered baselines. In a representative high-load, high-failure scenario, MFTB reduces data loss by 4.8% and 37.5% compared with Buffer-Limited Retransmission (BLR) and Replication, respectively, while LP-DCFS increases the amount of recovered data by 33.9%, 32.3%, and 53.1% compared with ACEPSO, ADE-DMRM, and DQN, respectively. Under the modeled independent random crash and non-return faults and the evaluated simulation settings, these results suggest that coordinating failure-risk characterization, data-protection mechanisms, and task-scheduling strategies can improve the robustness and data-recovery capability of disaster-oriented UAV-assisted data collection. Full article

Wireless power transfer (WPT) is becoming an attractive solution for charging electric vehicles (EVs). It offers greater convenience, reduces mechanical wear, and supports the development of automated and dynamic charging systems. However, the effectiveness of WPT-based EV chargers does not depend only on power electronics, coil design, and energy efficiency. Reliable wireless communication between the vehicle and the charging infrastructure is also essential. This communication link allows the system to identify and authenticate the vehicle, support coil alignment, regulate power transfer, monitor battery conditions, supervise safety, and manage possible faults. This review discusses the role of wireless communication technologies in the control of WPT EV charging systems. It also presents the main EV charging methods, the basic principles of WPT, its applications in electric vehicles, and key standards such as IEC 61980 and ISO 15118. In addition, the review compares Wi-Fi, Bluetooth, NFC, Zigbee, V2X, and cellular communication in terms of their suitability for WPT control. It also highlights major challenges, including electromagnetic interference, latency, cybersecurity, coil misalignment, system complexity, and standardization issues. Overall, future WPT EV chargers will need secure, reliable, and low-latency communication integrated with control and power transfer systems to achieve safe, efficient, and intelligent wireless charging. Full article

Smart and sustainable mobility increasingly relies on distributed sensing, low-power communication technologies, and cloud-based ICT platforms. This article presents a comprehensive scientific analysis of the technological foundations of sensitized mobility, reviewing the state of the art in embedded sensing, distributed systems, and communication paradigms for future mobility challenges. The research project “MOST” and in particular its subgroup “Spoke 5” falls within this framework of sustainable and sensorized mobility, with numerous activities in data collection, analysis, and field experimentation. In order to allow data collection, retention and analysis, one of the challenges that we must address is the definition of an adequate ICT architecture. The core contribution of this work is the presentation of the MOST ICT architecture, designed as a containerized, scalable, and resilient infrastructure capable of integrating heterogeneous data coming from field-deployed systems. In addition, it discusses the primary research challenges encountered in the definition and development of the presented architecture by examining two representative case studies within the MOST-Spoke 5 research project: renewable energy charging stations for light electric vehicles and cyclists monitoring systems. Full article