Search Results (23)

This paper investigates the integration of distributed computing and edge Artificial Intelligence (edge AI) as foundational enablers of sixth-generation (6G) mobile networks. Through a systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, encompassing over 200 peer-reviewed papers, architectural proposals, and standardization documents retrieved from IEEE Xplore, Scopus, Web of Science, MDPI, arXiv, ITU-R, 3GPP, and ETSI, this study provides a structured computational analysis of architectural approaches that integrate distributed computing paradigms and edge AI as core enablers of 6G. The analysis examines the evolution from cloud-centric to edge-centric computing, key edge AI techniques—including Federated Learning (FL), Split Learning (SL), and edge-adapted Large AI Models (LAMs)—and their role in enabling intelligent orchestration, resource optimization, and context-aware services. The comparative analysis demonstrates that edge computing architectures reduce end-to-end latency by 85–95% relative to cloud-centric deployments (under conditions of MEC servers within 1 km and 5G NR fronthaul), while federated learning with gradient compression achieves communication overhead reductions of up to 99% under IID data distributions and stable channel conditions. The results indicate that the tight integration of distributed computing and edge AI enhances network responsiveness, scalability, and adaptability, while also revealing persistent challenges related to orchestration complexity, resource constraints, security, and interoperability. The study concludes that holistic computational architectures and AI-native design principles are essential for the effective realization of 6G networks and for guiding future research and standardization efforts. Full article

A Quantum Key Distribution (QKD)-assisted Physical Layer Security (PLS) scheme for Multiple-Input Multiple-Output (MIMO) wireless links is proposed and numerically evaluated. The framework utilizes high-rate quantum keys to generate unitary precoding matrices for channel estimation preamble encryption, alongside a constellation-based encryption methodology for the data payload. Integration of the QKD is facilitated by a practical Key Management System (KMS) that orchestrates key synchronization and ensures seamless interoperability with the QKD infrastructure. By securing both the preamble and payload portions of the transmission frame, the proposed scheme prevents unauthorized entities from acquiring critical knowledge of transceiver functionalities. Furthermore, the framework leverages high-entropy QKD-derived keys to reseed a pseudo-random number generator (PRNG), providing a symmetric-key encryption layer that enhances data confidentiality. Numerical evaluation results obtained within a simulated residential wireless environment demonstrate that the proposed architecture yields enhanced security at the cost of a minor degradation in reception performance, driven by a small noise amplification penalty and a marginal elevation in the peak-to-average power ratio (PAPR). Full article

This systematic literature review (SLR) evaluates the existing literature on the benefits, implementation challenges, and ethical concerns associated with Artificial Intelligence (AI)-driven Knowledge Management Systems (KMS) across industries. The SLR followed PRISMA guidelines to identify studies from Scopus, Web of Science, JSTOR, and Google Scholar, using inclusion and exclusion criteria. Critical Appraisal Skills Programme (CASP) checklists were used to assess methodological quality and risk of bias in the included studies, and a structured narrative synthesis was employed to synthesize the findings. The review of 21 articles reveals benefits like improved knowledge capture and creation, storage, retrieval, personalization, and efficient dissemination, which lead to effective decision-making and performance improvements. The implementation barriers are categorized as organizational, technological, ethical, and financial, which generate a lack of trust, inability to manage, lack of interoperability, and monetary constraints. These barriers can be overcome by adopting Kotters’ Eight Stage Change Model, developing interoperability frameworks, evolving ethics benchmarks and standard guidelines for governance, and using viability analyses that incorporate both financial and non-financial considerations. In addition to bridging the gap between AI and KMS theories, the paper also provides practical and actionable insights about managing implementation and governance challenges. Full article

Digital twins (DTs) are increasingly adopted to enhance the monitoring, management, and planning of urban infrastructure. While DT development for buildings is well established, applications to urban road networks remain limited, particularly in integrating heterogeneous geospatial datasets into semantically rich, multi-scale representations. This study presents a methodology for developing a BIM-based DT of urban roads by integrating geospatial data from Mobile Mapping System (MMS) surveys with semantic information from municipal geodatabases. The approach follows a multi-modal (point clouds, imagery, vector data), multi-scale and multi-level framework, where ‘multi-level’ refers to modeling at different scopes—from a city-wide level, offering a generalized representation of the entire road network, to asset-level detail, capturing parametric BIM elements for individual road segments or specific components such as road sign and road marker, lamp posts and traffic light. MMS-derived LiDAR point clouds allow accurate 3D reconstruction of road surfaces, curbs, and ancillary infrastructure, while municipal geodatabases enrich the model with thematic layers including pavement condition, road classification, and street furniture. The resulting DT framework supports multi-scale visualization, asset management, and predictive maintenance. By combining geometric precision with semantic richness, the proposed methodology delivers an interoperable and scalable framework for sustainable urban road management, providing a foundation for AI-ready applications such as automated defect detection, traffic simulation, and predictive maintenance planning. The resulting DT achieved a geometric accuracy of ±3 cm and integrated more than 45 km of urban road network, enabling multi-scale analyses and AI-ready data fusion. Full article

The growing frequency of extreme weather, earthquakes, fires, and environmental hazards underscores the need for real-time monitoring and predictive management at the urban scale. Conventional three-dimensional spatial information systems, which rely on orthophotos and ground surveys, often suffer from computational inefficiency and data overload when processing large and heterogeneous datasets. To address these limitations, this study introduces a three-dimensional GeoHash-based geocoding algorithm designed for lightweight, real-time, and attribute-driven digital twin operations. The proposed method comprises five integrated steps: generation of 3D GeoHash grids using longitude, latitude, and altitude coordinates; integration with GIS-based urban 3D models; level optimization using the Shape Overlap Ratio (SOR) with a threshold of 0.90; representative object labeling through weighted volume ratios; and altitude correction using DEM interpolation. Validation using a testbed in Sillim-dong, Seoul (10.19 km²), demonstrated that the framework achieved approximately 9.8 times faster 3D modeling performance than conventional orthophoto-based methods, while maintaining complete object recognition accuracy. The results confirm that the 3D GeoHash framework provides a unified spatial key structure that enhances data interoperability across querying, visualization, and simulation. This approach offers a practical foundation for operational digital twins, supporting high-efficiency 3D mapping and predictive disaster management toward resilient and data-driven urban systems. Full article

The rapid growth of urban populations intensifies congestion, air pollution, and energy demand. Green mobility is central to sustainable smart cities, and the Internet of Things (IoT) offers a means to monitor, coordinate, and optimize transport systems in real time. This paper presents an Internet of Things (IoT)-based architecture integrating heterogeneous sensing with edge–cloud orchestration and AI-driven control for green routing and coordinated Electric Vehicle (EV) charging. The framework supports adaptive traffic management, energy-aware charging, and multimodal integration through standards-aware interfaces and auditable Key Performance Indicators (KPIs). We hypothesize that, relative to a static shortest-path baseline, the integrated green routing and EV-charging coordination reduce (H1) mean travel time per trip by ≥7%, (H2) CO₂ intensity (g/km) by ≥6%, and (H3) station peak load by ≥20% under moderate-to-high demand conditions. These hypotheses are tested in Simulation of Urban MObility (SUMO) with Handbook Emission Factors for Road Transport (HBEFA) emission classes, using 10 independent random seeds and reporting means with 95% confidence intervals and formal significance testing. The results confirm the hypotheses: average travel time decreases by approximately 9.8%, CO₂ intensity by approximately 8%, and peak load by approximately 25% under demand multipliers ≥1.2 and EV shares ≥20%. Gains are attenuated under light demand, where congestion effects are weaker. We further discuss scalability, interoperability, privacy/security, and the simulation-to-deployment gap, and outline priorities for reproducible field pilots. In summary, a pragmatic edge–cloud IoT stack has the potential to lower congestion, reduce per-kilometer emissions, and smooth charging demand, provided it is supported by reliable data integration, resilient edge services, and standards-compliant interoperability, thereby contributing to sustainable urban mobility in line with the objectives of SDG 11 (Sustainable Cities and Communities). Full article

In 2024, a significant Salmonella Umbilo outbreak was reported across the European Union and beyond, traced to contaminated vegetables originating from the Province of Salerno (Italy). Subsequent on-site inspections in the production area revealed a mismanaged manure storage tank, which became the focus of a GIS-based investigation aimed at locating nearby animal establishments. Within a 1-km radius—encompassing both the tank and the contaminated greenhouses—three buffalo farms were identified. Farm inspections revealed buffalo calves exhibiting enteric symptoms. Fecal samples collected from these animals led to the isolation of S. Umbilo genomically linked to the 2024 multi-country outbreak, as well as other serotypes. To thoroughly investigate, data from official EU and Italian databases were analyzed, to detect the presence of S. Umbilo in vegetables, buffalo, and other livestock within the Province of Salerno. However, the lack of access to critical data needed to clarify the epidemiological links at the human–animal–environment interface has hindered the full reconstruction of the outbreak dynamics. These limitations underscore the urgent need to implement One Health strategies by promoting interdisciplinary collaboration among veterinarians, physicians, food technologists, biologists and other professionals; leveraging official open access databases; and adopting emerging technologies as interoperable data systems and drone surveillance. Full article

Small water bodies are widely spread and play crucial roles in supporting regional agricultural and aquaculture activities. PlanetScope imagery has a high resolution (3 m) with daily global coverage and has obviously enhanced small water body mapping. Recent studies have demonstrated the effectiveness of deep learning for mapping small water bodies using PlanetScope; however, a persistent challenge remains in the scarcity of high-quality, manually annotated water masks used for model training, which limits the generalization capability of data-driven deep learning models. In this study, we propose a transfer learning framework that leverages Sentinel-2 data to improve PlanetScope-based small water body mapping, capitalizing on the spectral interoperability between PlanetScope and Sentinel-2 bands and the abundance of open-source Sentinel-2 water masks. Eight state-of-the-art segmentation models have been explored. Additionally, this paper presents the first assessment of the VMamba model for small water body mapping, building on its demonstrated success in segmentation tasks. The models were pre-trained using Sentinel-2-derived water masks and subsequently fine-tuned with a limited set (1292 image patches, 256 × 256 pixels in each patch) of manually annotated PlanetScope labels. Experiments were conducted using 5648 image patches and two areas of 9636 km² and 2745 km², respectively. Among the evaluated methods, VMamba achieved higher accuracy compared with both CNN- and Transformer-based models. This study highlights the efficacy of combining global Sentinel-2 datasets for pre-training with localized fine-tuning, which not only enhances mapping accuracy but also reduces reliance on labor-intensive manual annotation in regional small water body mapping. Full article

Background: This paper presents TheraSense, a system developed within the Supporting Mental Health in Young People: Integrated Methodology for cLinical dEcisions and evidence (Smile) and Sensor Enabled Affective Computing for Enhancing Medical Care (SenseCare) projects. TheraSense is designed to enhance teleconsultation services by leveraging deep learning for real-time emotion recognition through facial expressions. It integrates with the Knowledge Management-Ecosystem Portal (SenseCare KM-EP) platform to provide mental health practitioners with valuable emotional insights during remote consultations. Method: We describe the conceptual design of TheraSense, including its use case contexts, architectural structure, and user interface layout. The system’s interoperability is discussed in detail, highlighting its seamless integration within the teleconsultation workflow. The evaluation methods include both quantitative assessments of the video-based emotion recognition system’s performance and qualitative feedback through heuristic evaluation and survey analysis. Results: The performance evaluation shows that TheraSense effectively recognizes emotions in video streams, with positive user feedback on its usability and integration. The system’s real-time emotion detection capabilities provide valuable support for mental health practitioners during remote sessions. Conclusions: TheraSense demonstrates its potential as an innovative tool for enhancing teleconsultation services. By providing real-time emotional insights, it supports better-informed decision-making in mental health care, making it an effective addition to remote telehealth platforms. Full article

In the realm of Intelligent Transportation Systems (ITS), vehicular communication technologies such as Dedicated Short-Range Communications (DSRC), Cellular Vehicle-to-Everything (C-V2X), and LoRa 2.4 GHz play crucial roles in enhancing road safety, reducing traffic congestion, and improving transport efficiency. This article explores the integration of these communication protocols within smart intersections, emphasizing their capabilities and synergies. DSRC, based on IEEE 802.11p, provides reliable short-range communication with data rates up to 27 Mbps and latencies below 50 ms, ideal for real-time safety applications. C-V2X leverages LTE and 5G networks, offering broader coverage up to 10 km and supporting data rates up to 100 Mbps, with latencies as low as 20 ms in direct communication mode (PC5). LoRa 2.4 GHz, known for its long-range (up to 15 km in rural areas, 1–2 km in urban settings) and low-power characteristics, offers data rates between 0.3 and 37.5 kbps, suitable for non-critical data exchange and infrastructure monitoring. The study evaluates the performance and interoperability of these technologies in urban environments, focusing on data latency, transmission reliability, and scalability. Experimental results from simulated and real-world scenarios show that DSRC maintains reliable communication within 1 km with minimal interference. C-V2X demonstrates superior scalability and coverage, maintaining robust communication over several kilometers in high-density urban settings. LoRa 2.4 GHz exhibits excellent penetration through urban obstacles, maintaining connectivity and efficient data transmission with packet error rates below 10%. Full article

This paper demonstrates the use of software-defined networking (SDN) orchestration to integrate regionally separated networks in which different network parts use incompatible key management systems (KMSs) managed by different SDN controllers to ensure end-to-end QKD service provisioning to deliver the QKD keys between geographically different QKD networks. The study focuses on scenarios in which different parts of the network are managed separately by different SDN controllers, requiring an SDN orchestrator to coordinate and manage these controllers. In practical network deployments, operators often utilize multiple vendors for their network equipment. This practice also enables the expansion of the QKD network’s coverage by interconnecting various QKD networks equipped with devices from different vendors. However, as coordinating different parts of the QKD network is a complex task, this paper proposes the implementation of an SDN orchestrator which acts as a central entity to manage multiple SDN controllers, ensuring end-to-end QKD service provisioning to address this challenge. For instance, when there are multiple border nodes to interconnect different networks, the SDN orchestrator calculates the path in advance for the end-to-end delivery of keys between initiating and target applications belonging to different networks. This path selection requires the SDN orchestrator to gather information from each SDN controller managing the respective parts of the QKD network. This work shows the practical implementation of SDN orchestration for interoperable KMS in commercial QKD networks in South Korea. By employing an SDN orchestrator, it becomes possible to coordinate multiple SDN controllers and ensure the efficient and secure delivery of QKD keys between different QKD networks with varying vendor equipment. Full article

A new satellite-enabled interoperable service has been developed to provide high spatiotemporal and continuous time series of Growing Degree Days (GDDs) at the field. The GDDs are calculated from MSG-SEVIRI data acquired by the EUMETCast station operated by IAASARS/NOA and downscaled on-the-fly to increase the initial coarse spatial resolution from the original 4–5 km to 1 km. The performance of the new service SENSE-GDD, in deriving reliable GDD timeseries at dates very close to key phenological stages, is assessed using in situ air temperature measurements from weather stations installed in Gerovassiliou Estate vineyard at Epanomi (Northern Greece) and an apple orchard at Agia (Central Greece). Budburst, pollination, and the start of veraison are selected as key phenological stages for the vineyards, whilst budburst and pollination for the apple orchard. The assessment shows that SENSE-GDD provided uninterrupted accurate measurements in both crop types. A distinct feature is that the proposed service can support decisions in non-instrumented crop fields in a cost-effective way, paving the way for its extended operational use in agriculture. Full article

Achieving global food security requires better use of natural, genetic, and importantly, human resources—knowledge. Technology must be created, and existing and new technology and knowledge deployed, and adopted by farmers and others engaged in agriculture. This requires collaboration amongst many professional communities world-wide including farmers, agribusinesses, policymakers, and multi-disciplinary scientific groups. Each community having its own knowledge-associated terminology, techniques, and types of data, collectively forms a barrier to collaboration. Knowledge management (KM) approaches are being implemented to capture knowledge from all communities and make it interoperable and accessible as a “group memory” to create a multi-professional, multidisciplinary knowledge economy. As an example, we present KM efforts at the US Department of Agriculture. Information and Communications Technology (ICT) is being developed to capture tacit and explicit knowledge assets including Big Data and transform it into curated knowledge products available, with permissions, to the agricultural community. Communities of Practice (CoP) of scientists, farmers, and others are being developed at USDA and elsewhere to foster knowledge exchange. Marrying CoPs to ICT-leveraged aspects of KM will speed development and adoption of needed agricultural solutions. Ultimately needed is a network of KM networks so that knowledge stored anywhere can be used globally in real time. Full article

With the advent of the “ear of high-speed railways”, the space–time distance between cities has drastically decreased. The opening of high-speed railways has not only increased the factor-flow speed across regions, but has also reduced the cost of factor movement. However, there is some controversy about whether high-speed railways can be used to promote regional economic growth. The Wuhan–Guangzhou high-speed railway is the earliest section of the Beijing–Guangzhou high-speed railway, and it was the first high-speed railway in China with a real speed of 350 km per hour. The Wuhan–Guangzhou high-speed railway opening spawned a three-hour economic circle in the south of China, injected new vitality into the economic and social development along the line, and had an important impact on accelerating regional economic interoperability and integration. Taking this into account, this paper took the Wuhan–Guangzhou high-speed railway as the case study. Firstly, this paper used the theory of system dynamics to analyze the mechanism of the impact of high-speed railway opening on regional economic growth. Secondly, the accessibility model was used to analyze the impact of the high-speed railway opening on the accessibility of cities along the line, and a difference-in-differences model (DID) was used to explore the impact of the high-speed railway opening on regional economic growth. Finally, on the basis of the analysis of the previous mechanism, the mechanism of the impact of the high-speed railway on regional economic growth is discussed. The results of this study show the following: (1) Since the opening of the high-speed railway, the accessibility of cities along the line has grown by more than 60%. (2) The opening of the high-speed railway has promoted regional economic growth, mainly through the influence mechanism of “increasing the total cargo volume”. This study not only scientifically and quantitatively analyzed the impact of the opening of high-speed rail on regional economic growth, taking the Wuhan–Guangzhou high-speed railway as an example for empirical analysis, but also further summarized the high-speed railway construction achievements in China and provides reference experience for planning the opening of high-speed railways. Full article

Some of the advantages of using Worldwide Interoperability Microwave Access (WiMAX) technology at the last-mile level as an access technology include an extensive range of 50 km Line of Sight (LOS), 5 to 15 km Non-Line of Sight, and fewer infrastructure installations compared to other wireless broadband access technologies. Despite positive investments in ICT fiber infrastructure by developing countries, including Botswana, servicing end-users is subjected to high prices and service disparities. The alternative, the Wi-Fi hotspot initiative by the Botswana government, falls short as a solution for last-mile connectivity and access. This study used OPNET simulation Modeler 14.5 to investigate whether Botswana’s national broadband project could adopt WiMAX IEEE 802.16e as an access technology. Therefore, using the experimental method, the simulation evaluated the WiMAX IEEE 802.16e/m over three subscriber locations in Botswana. The results obtained indicate that the deployment of the WiMAX IEEE 802.16e standard can solve most of the deployment issues and access at the last-mile level. Although the findings suggest that WiMAX IEEE 802.16e is more suitable for high-density areas, it could also solve rural areas’ infrastructure development challenges and provide the required high-speed connectivity access. However, unlike the Wi-Fi initiative, which requires more infrastructure deployment and relies less on institutional and regulatory frameworks, the deployment of WiMAX IEEE 802.16e necessitates institutional and regulatory standards. Full article