Search Results (266)

Satellite direct-to-cell connectivity can extend 5G Internet of Things (IoT) services into areas without terrestrial coverage by reusing terrestrial mobile-service spectrum. This study examines aggregate interference at a non-terrestrial network (NTN) satellite receiver in selected bands within 694–2690 MHz, where terrestrial duplex arrangements differ between regions. A satellite receiving an uplink may also receive terrestrial downlink emissions from another country using TDD or an overlapping FDD downlink. Six geographical case studies combine satellite motion, population-based terrestrial deployments, and aggregate-interference calculations over a 180 kHz receiver bandwidth. Results are presented as interference-to-noise ratio (I/N) complementary cumulative distribution functions and geographical maps, with nadir and random off-nadir pointing cases. In the evaluated TDD cases, BS contributions dominate the upper part of the aggregate-interference distributions, and elevated interference extends beyond the emitting countries. These findings identify cross-border interference pathways under the assumed deployments, loading, spectral coupling, and antenna patterns. They do not establish that duplex arrangement outweighs deployment density independently of these assumptions. The adopted −6 dB I/N threshold is a screening reference; service availability, link performance, and the effectiveness of mitigation techniques are not evaluated. Full article

Growing consumer demand for sustainable food has pushed regenerative agriculture (RA) to the forefront of green marketing, often promoted through claims that outpace verifiable environmental outcomes. Because RA is defined by outcome-based principles rather than standardized certification, a perception–reality gap can emerge, as herbicide reliance in no-till systems or logistical inefficiencies erode farm-level gains. Using qualitative systemic analysis and scenario modeling, this study examines structural barriers to RA implementation across the European agri-food sector, comparing three maturity-based scenarios—Idealized Marketing, Mature EU Markets (Germany/France), and Transitional Economy (Romania)—through an author-calibrated Green Claim Veracity Index (Vi) built on four sustainability KPIs. Results show that logistical complexity in mature markets can dilute on-farm carbon benefits by approximately 20%, while in transitional economies, fragmented land ownership and a “Logistical Paradox” may neutralize or reverse these gains. The study proposes CTSAF (Converging Technologies for Sustainable Agri-Food), integrating IoT, AI, including satellite-based cross-verification against the “Oracle Problem”, and blockchain for end-to-end traceability aligned with the proposed EU Green Claims Directive and an emerging Digital Product Passport framework. The findings suggest that verifiable sustainability is a system-wide property rather than an isolated farm-level outcome, offering policymakers concrete levers for transparency and smallholder digital inclusion. Full article

Severe conditions in hard-to-reach regions, where terrestrial networks are limited, make data backhaul from these areas challenging. In this context, advances in the Space-IoT have created new opportunities for data collection and monitoring using LEO satellites. To address this issue, this paper presents an orbit-aware S&F architecture for data collection from an intelligent ground terminal located in remote areas using a 3U CubeSat orbiting at 500 km altitude in a Sun-synchronous orbit. The designed ground terminal integrates an ESP32 microcontroller and a 433 MHz LoRa module and is enhanced with an embedded satellite pass prediction, Doppler pre-correction, and an adaptive LoRa strategy. The CubeSat utilizes a TOTEM SDR receiver, onboard data buffering, and an S-band downlink with variable coding for throughput enhancement. The orbital model for satellite prediction is validated using Ansys STK software version 12, while UHF and S-band links are evaluated using time-varying link-budget analysis. The adaptive transmission is compared with fixed configurations in terms of usable contact time and data delivered per pass. Results indicate that the proposed system demonstrates the feasibility of a Store-and-Forward mission co-design that combines precise orbit prediction, Doppler compensation, and adaptive transmission for future Space-IoT applications. Full article

Reliable ship-to-shore backhaul is essential for maritime Internet of Things (IoT) data delivery, yet cellular and satellite connectivity varies by route, operating phase, and qualification criterion. This study analyses 606,625 georeferenced monitoring records collected during a nine-month, 12-voyage campaign aboard a container ship operating between Korea and Southeast Asia, with the cellular–geostationary Earth orbit (GEO) satellite analysis limited to the period of active very small aperture terminal (VSAT) service. During sailing, cellular attachment was reported for 70.3% of valid cellular state records, but only 28.0% satisfied the adopted technology-specific received power criteria, with leg-level qualified fractions ranging from 77.1% (Incheon–Busan) to 17.3% (Shanghai–Ho Chi Minh). Within the joint-analysis window, either the cellular criterion was satisfied or a valid GEO probe response was observed in 99.4% of records, although the residual gap reached 3.1% on Laem Chabang–Ho Chi Minh. Under retrospective cellular-first allocation, raising the candidate VSAT signal-to-noise ratio (SNR) threshold from 6 to 7 dB increased the store-and-forward share from 6.7% to 25.7% without reducing the median round-trip time (RTT) of the retained GEO records, and the longest buffered interval grew from 2.02 to 9.86 h. These results show that route segment, operating phase, state definition, and threshold selection materially influence link allocation and buffering implications; live traffic steering and application-level availability were not evaluated. Full article

Digital television is an integral part of modern society, and the quality of the service its has exceeded all expectations. Television stations are divided into national and regional licensing categories, governed by the broadcasting regulations of each European Union member state. DVB-T gap fillers are used to provide and enhance the television signal in rural areas using satellite transport streams (TS) as feeds. However, for regional television stations—particularly in areas lacking network coverage—retransmitting their transport streams via standard digital terrestrial reception and rebroadcasting is often insufficient. This direct Re-transmission method frequently suffers from severe signal intermittency and broadcast interruptions. This paper presents the design, field deployment, and long-term evaluation (8760 h) of an ultra-low-cost, license-exempt DVB-over-IP gap filler architecture. The proposed system integrates Commercial-Off-The-Shelf (COTS) devices to convert a pristine DVB-T transport stream into an IP data stream, transmit it via a 5.64 GHz wireless bridge to bypass natural obstacles, and accurately reconstruct the digital TV signal at the remote gap filler. Empirical results demonstrate that the proposed IP stream method yields a 95.51% reduction in total annual downtime, elevating link availability from 86.26% to 99.38%. The system virtually eliminates environmental signal interruptions, with the only recorded downtime caused by a local power outage. Notably, this robust reliability is achieved at approximately 5% of the CEcapital expenditure (CapEx) required for conventional professional microwave backhaul solutions. Furthermore, the residual bandwidth of the wireless IP backbone provides a ready-made foundation for deploying future municipal network services, such as local Wi-Fi hotspots and LoRa-based Internet of Things (IoT) telemetry networks. Full article

Wildfires are becoming more frequent, severe, and long-lasting, driving rapid growth in sensor-based and artificial intelligence (AI)-enabled systems for early detection and risk assessment. This article presents a systematic literature review, based on 169 studies screened from 7511 records, of wildfire monitoring approaches using satellite and aerial remote sensing, fixed cameras, wireless sensor networks, and Internet of Things (IoT) platforms combined with machine learning (ML) and deep learning (DL) models for ignition detection, fire-weather indices, spread prediction, and burned-area mapping. The review organizes existing work by sensing modality, spatial and temporal scale, learning task, model type, and deployment architecture, and identifies the environmental drivers most commonly used across systems, including temperature, humidity, vegetation state, drought indices, and smoke or air quality. Based on this analysis, the review summarizes key technical challenges, including data sparsity in remote regions, high false-alarm rates, limited edge resources, and difficulty fusing heterogeneous data streams in real time. As an exploratory future direction, the review discusses bioindicator signals from wildlife and managed species, using honeybee colonies as a case example. Current bee bioacoustic studies support the detection of colony states and environmental stress proxies, but they do not yet validate wildfire or smoke detection. Therefore, this review proposes bee bioacoustics only as a potential complementary contextual signal for future hybrid wildfire monitoring systems. Full article

In this paper, the solar irradiance is investigated in a part of central Serbia, where a low-cost IoT sensor network was deployed at three locations: a mountain slope, an open field near a village, and an obstructed position in the city center. All three locations are in the same NASA POWER grid cell. After multi-stage quality control, 26,145 valid daytime records were compared to the satellite reference. The satellite assigns identical values to all three positions but the measured mean daytime irradiances are 267.7, 351.5 and 19.6 W/m², respectively. The rural station is in the best agreement with the reference (R² = 0.567); the mountain station suffers from a persistent shading bias (MBE = −138.9 W/m²); the signal at the urban station is attenuated by surrounding buildings and vegetation by a factor of ~12. Also, 25 regression models (gradient boosting, recurrent, convolutional, fully connected and graph-based) were trained on the 25-year monthly NASA POWER record for the same cell. XGBoost obtained R² = 0.998, the hybrid TCN-GNN R² = 0.968 and a plain ReLU network R² = 0.912, and a seasonal model with calendar features was used to reconstruct the satellite reference for two months of 2026 not yet available in the archive. The deployed hardware, network and energy behavior are documented quantitatively: per-site link delivery ratios of 96.8%, 83.5% and 58.2%, the photovoltaic harvesting record of the nodes, and the absence of any energy-aware transmission scheduling. Two of the trained models were compiled for the ESP32 nodes and measured on the deployment hardware: a depth-limited gradient-boosted corrector runs in 46.3 µs using 11.1 kB of flash, and an INT8 fully connected network in 138.1 µs using 3.0 kB, while the graph hybrid cannot be converted for microcontroller execution at all. This defines an edge–cloud partition in which local inference performs bias correction and fault detection while the cloud tier retains the heavy models and periodic retraining. To the best of the authors’ knowledge, this is the first multi-site ground-based IoT irradiance record for central Serbia with such different terrain types. Full article

Quantum communication is emerging as a foundation for next-generation networks, offering unprecedented capabilities in security, entanglement-based connectivity, and distributed computation. However, the classical Open Systems Interconnection (OSI) model, designed for deterministic, error-tolerant systems, is incompatible with quantum phenomena such as decoherence, probabilistic entanglement, and the no-cloning theorem. This paper surveys and redefines the OSI model for quantum networking in the context of 7G systems. We propose a Quantum-Converged OSI stack by extending the classical seven-layer model with two additional layers: (i) Layer 0, the Quantum Substrate, responsible for entanglement management, coherence preservation, and teleportation; and (ii) Layer 8, the Cognitive Intent Plane, which enables AI- and QML-driven orchestration. The survey synthesizes over 150 research works published between 2018 and 2025, classifying them by OSI layer, enabling technologies (e.g., Quantum Key Distribution, Quantum Error Correction, and Post-Quantum Cryptography), and application domains such as satellite quantum links, quantum IoT, and federated edge systems. We further provide a taxonomy of cross-layer enablers and discuss simulation tools, including NetSquid, QuNetSim, and QuISP. Finally, an evaluation framework with quantum-native metrics, such as entropy throughput, coherence latency, and entanglement fidelity, is introduced, along with open challenges for programmable stacks, digital twins, and AI-defined quantum agents. The specific and novel contribution of this work is a Quantum-Converged OSI stack that extends the classical seven-layer model with two additional layers: Layer 0, the Quantum Substrate, responsible for entanglement management, coherence preservation, and teleportation; and Layer 8, the Cognitive Intent Plane, which enables AI- and QML-driven orchestration. Unlike prior technology-centric surveys, the proposed framework classifies over 150 research works by OSI layer, maps enabling technologies (QKD, QEC, PQC) and application domains (satellite quantum links, quantum IoT, federated edge systems) to their functional layers, and introduces a quantum-native evaluation framework based on entropy throughput, coherence latency, and entanglement fidelity. This layer-resolved synthesis, together with the formal definition of cross-layer quantum-native metrics, constitutes the principal novelty distinguishing this survey from existing quantum-networking reviews. Full article

High-precision urban 3D localization is a critical foundation for vehicular Internet of Things (IoT) applications, yet conventional Global Navigation Satellite System (GNSS)-based localization is vulnerable to signal obstruction, multipath effects, and non-line-of-sight propagation in complex environments. To improve localization reliability, this paper proposes a consistency-guided quantitative–qualitative fusion (CG–QQF) framework with vision-based terrain constraints. The framework integrates heterogeneous quantitative sensors, including an absolute positioning source, inertial measurement unit (IMU), wheel encoders, and a steering angle sensor, for continuous metric state estimation, while a monocular camera provides qualitative terrain-slope information. Rather than treating visual perception as a direct metric observation, the proposed method introduces it as a conditional structural constraint that is activated only when it is consistent with the quantitative estimate, thereby regularizing the localization solution and suppressing vertical drift. Although ultra-wideband (UWB) positioning is adopted as the absolute positioning source in the experimental platform, it serves as a generic positioning module and can be replaced by GNSS-based techniques such as real-time kinematic (RTK) and precise point positioning (PPP). In an indoor scaled proof-of-concept experiment over a controlled four-lap dataset, CG–QQF achieves a 3D RMSE of 0.0575 m and a vertical MAE of 0.0042 m. Its 3D RMSE is approximately 4.0% lower than quantitative sensor fusion (QSF), 37.9% lower than absolute-positioning/inertial fusion (ABS–INS), and 49.9% lower than vision-assisted quantitative fusion (VA–QF). These results demonstrate that consistency-triggered qualitative constraints can complement metric sensor fusion without directly introducing uncertain visual measurements, providing a practical mechanism for improving the robustness and vertical stability of heterogeneous localization systems. Full article

Precision agriculture (PA) has emerged as a data-driven approach for improving maize (Zea mays L.) production through the integration of remote sensing, Geographic Information Systems (GISs), Global Navigation Satellite Systems (GNSSs), the Internet of Things (IoT), and machine learning (ML). This systematic review evaluates the application of PA for yield optimisation and resource-use efficiency in maize production between 2020 and 2026. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, 464 records were identified from Scopus and Web of Science, of which 129 studies met the inclusion criteria. The reviewed literature comprised field experiments (28.6%), remote sensing and PA integration studies (25.5%), machine learning applications (19.4%), climate-informed PA strategies (23.4%), and soil degradation studies (3.1%). Remote sensing and integrated multi-technology systems were the most extensively investigated approaches, followed by ML-based models for yield prediction and crop monitoring. Overall, PA technologies enhanced maize productivity, nutrient-use efficiency, water-use efficiency, and yield prediction accuracy through site-specific management and data-driven decision support. Despite its considerable potential, the adoption of PA remains constrained by high implementation costs, technical complexity, and limited data availability. Collectively, these findings demonstrate that precision agriculture provides an effective framework for sustainable maize intensification by improving productivity, optimising resource-use efficiency, and strengthening resilience to climate variability. Full article

The Internet of Things (IoT) market is currently undergoing a period of unprecedented, rapid evolution, leading to the enabling of novel and diverse applications spanning both the civilian and industrial sectors. A significant proportion of these emerging use cases, particularly those in domains such as maritime communications and forestry management, require service continuity and connectivity within geographically remote regions, where conventional terrestrial infrastructure is often absent or economically unfeasible. To bridge this coverage gap and achieve truly ubiquitous connectivity, the recent 3GPP initiative to extend 5G services into Non-Terrestrial Segments (NTNs) holds substantial promise. This expansion is crucial for ensuring that massive Machine-Type Communication (mMTC) services can be reliably provisioned globally. This paper aims to detail the progress in standardization and academic activities towards the design and deployment of NTN-based Narrowband IoT (NB-IoT) systems, which are the leading NTN mMTC enabler in the 3GPP portfolio. We will specify the challenges faced by these systems and outline the solutions proposed thus far. We conclude the paper with a discussion on already operational systems and lessons learned from their deployment and operation. Full article

Although prehistoric rock art shelters in remote locations require continuous environmental monitoring for preventive conservation, this need is frequently hindered by the absence of terrestrial connectivity. In this study, we present and validate a hybrid Internet of Things (IoT) monitoring architecture integrating low-power Long-Range Wide-Area Network (LoRaWAN) wireless ground-based sensors, edge computing, and dual terrestrial–satellite connectivity, designed for continuous climatic monitoring in heritage areas without terrestrial connectivity coverage. This proof of concept was conducted at the rock art shelter of Los Toros del Barranco de las Olivanas (Tormón, Teruel, Spain), a United Nations Educational, Scientific and Cultural Organization (UNESCO) World Heritage Site within the Albarracín Cultural Park (PCA), using Geostationary Earth Orbit (GEO) satellite connectivity provided by EchoStar Mobile. Laboratory and outdoor tests at the Universitat Politècnica de València (UPV) achieved a satellite data delivery rate of 98.6%, while field deployment under adverse conditions—partial vegetation and terrain obstructions, cloudy and rainy weather—yielded a delivery rate of 78.2%, with 100% delivery from sensors to the edge gateway. The system operates below 10 W, enabling autonomous solar-powered deployment. Reliability can be raised above 95% by having the system confirm that each message is received and by managing the order in which messages are sent. The architecture reduces on-site visits by an estimated 75%, significantly lowering the carbon footprint of field campaigns. The proposed system is flexible, scalable, and compatible with future Non-Terrestrial Networks (NTNs), demonstrating that a satellite IoT constitutes a technically feasible solution for continuous environmental monitoring of remote heritage sites worldwide. Full article

Low Earth Orbit (LEO) Non-Terrestrial Networks (NTNs) are emerging as a promising connectivity solution for Internet of Things (IoT) sensing applications deployed in remote, isolated, or infrastructure-limited environments. However, sparse LEO constellations inherently lead to intermittent connectivity, long service gaps, and frequent disruptions, challenging conventional 5G architectures that assume continuous end-to-end availability. This paper presents and experimentally validates a distributed 5G Core architecture enhanced with Store-and-Forward (S&F) capabilities to enable reliable delivery of IoT sensing data over intermittently connected LEO-NTN scenarios. The proposed architecture distributes selected 5G Core functions between ground and satellite nodes and introduces an S&F module capable of locally buffering uplink IoT data during periods without feeder-link connectivity and forwarding them once the ground connection is restored. A functional prototype is implemented using Open5GS, UERANSIM, and an emulated satellite node, and experimentally evaluated under representative intermittent-connectivity conditions. The results demonstrate that the proposed architecture successfully preserves and delivers IoT sensing data across temporary link disruptions. The experimental findings confirm the feasibility of integrating S&F mechanisms into distributed 5G Core architectures and provide practical insights into the design of resilient IoT sensing services over sparse LEO constellations. Full article

Monitoring vegetation-index dynamics in tropical regions remains challenging due to persistent cloud contamination, landscape heterogeneity, and the lack of standardised and reproducible analytical workflows. This paper presents an open-source, fully reproducible end-to-end methodology for multitemporal vegetation monitoring using Sentinel-2 Level-2A (L2A) Bottom-of-Atmosphere (BOA) reflectance imagery. The methodology was applied to Military Grid Reference System (MGRS) tile T18NVL in the Colombian Eje Cafetero region (4.43°N–5.43°N, 74.91°W–75.90°W) for the 2017–2025 period. The proposed workflow integrates storage-efficient direct extraction of spectral reflectance from compressed Standard Archive Format for Europe (SAFE) archives using the Geospatial Data Abstraction Library (GDAL) /vsizip/ interface, per-pixel cloud and shadow masking based on the Sentinel-2 Scene Classification Layer (SCL), computation of Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil-Adjusted Vegetation Index (SAVI), and Normalized Difference Moisture Index (NDMI) spectral indices, a diagnostic Random Forest experiment based on threshold-labelled spectral classes, non-parametric Mann–Kendall trend analysis with Sen’s slope estimation, and external agreement assessment against European Space Agency (ESA) WorldCover 10 m 2020 and Google Earth Pro reference data. The methodology reduced per-scene I/O time by approximately 98% without additional disk overhead while retaining a median of 57.9% valid pixels under a mean scene cloud fraction of 35.4%. Mann–Kendall analysis detected no statistically significant long-term trend in any of the four vegetation indices. Seasonal NDVI peaks during September–November were consistent with the bimodal regional precipitation regime, supporting temporal coherence in the satellite-derived vegetation-index response. External agreement was low, with an Overall Accuracy (OA) of 20.2% against ESA WorldCover and 19.9% against Google Earth Pro, indicating systematic over-prediction of woody and mixed-canopy vegetation classes. These results show that single-date optical spectral indices are insufficient for reliable thematic separation of shade-grown coffee, secondary forest, and dense forest within heterogeneous tropical landscapes. The complete version-controlled codebase is publicly available to support methodological reproducibility and adaptation across data-scarce tropical regions. Full article

This article proposes, to the best of our knowledge, the first dual-port compact dual-polarized ultrawideband wearable textile antenna covering lower UHF bands for off-body communications in Internet-of-Things-based wireless body area network (IoT-based WBAN) scenarios. The antenna covers key bands for diverse services, including the 470–510 MHz LoRa WAN, 700 MHz offline emergency communication, 900 MHz NB-IoT, and 1–1.2 GHz satellite internet bands. The antenna adopts a square-ring loaded wide slot structure and a multi-mode resonant feeding structure to achieve ultrawideband operation. Moreover, it utilizes oppositely placed advanced microstrip feeding networks to excite the horizontal and vertical polarization modes, respectively, and four narrow slots around the wide slot to extend the current path, thus enabling a compact size of 0.30 × 0.28 × 0.0035 λ_l³ (where λ_l is the largest operating wavelength). Measured −10 dB impedance bandwidths are 119.1% (0.35–1.38 GHz) for Port 1 and 115.9% (0.39–1.37 GHz) for Port 2 on the human body, with more than 19 dB port isolation over the operating band. The measured average gains are about 4.21 dBi for Port 1 and 3.54 dBi for Port 2 on the human body, respectively. Specific absorption rate analysis confirms compliance with the IEEE C95.1 limit at 0.5 W input power. Wireless transmission experiments at IoT bands further validate reliable off-body links with excellent signal-to-noise ratios for both polarizations. The antenna shall be very attractive for off-body communications in IoT-based WBAN scenarios. Full article