Search Results (587)

Water scarcity in semi-arid regions makes efficient irrigation scheduling a priority, yet farm-level adoption of soil-moisture monitoring remains limited by the cost, low portability and installation complexity of commercial sensing systems. This study presents the development and field validation of WaziSense, a low-cost, solar-powered Internet-of-Things (IoT) smart tensiometer, developed within the OSIRRIS platform for soil-water monitoring and irrigation scheduling. The device couples a Watermark granular-matrix sensor and a DS18B20 temperature probe to an ATmega328P microcontroller (Arduino Pro-Mini, 3.3 V, 8 MHz) with long-range LoRa communication and a maximum-power-point-tracking (MPPT) solar-charging stage, logging soil matric potential and soil temperature every 15 min. An open-source edge/cloud stack (WaziGate, WaziApp) retrieves weather forecasts from an open API and runs an automated machine learning (AutoML) regression pipeline that forecasts soil-water dynamics and the time to a user-defined threshold, from which irrigation is scheduled and its applied volume verified by a flow meter. The system was deployed at three bioclimatic sites in Tunisia (durum wheat at Cherfech, citrus at Nabeul, apple at Sbeitla), with tensiometers installed at 20 and 40 cm depths, and validated against commercial 10HS capacitive probes coupled to a ZL6 data logger, with which the co-located readings were significantly correlated (r = 0.81). Calibrated readings showed a strong relationship between soil–water content and soil–water potential (R² = 0.99), and the edge forecasting model reproduced soil–water dynamics on unseen data (Sbeitla apple site, 5-day horizon) with R² = 0.73, RMSE = 0.35, MAE = 0.23 and MPE = 12.52%. With a material cost under about 90 EUR per node and fully open-source hardware and software, WaziSense is one to two orders of magnitude cheaper than commercial monitoring stations, offering an affordable, reproducible and scalable tool for data-driven irrigation in water-limited agriculture. Full article

Hydrogen leakage detection and mapping are essential in hydrogen-rich environments to ensure safe utilization in industrial and commercial applications. In this study, a wireless IoT-enabled hydrogen leak-mapping system was developed using machine learning and a LoRa-coupled wireless sensor network. A miniature model of a hydrogen production system was used, featuring a functioning electrolyzer that generates pure hydrogen by splitting water. To perform efficient leakage mapping, the leak location and watch time were varied, and six readings from commercial hydrogen gas sensors were recorded for better analysis. The relative sensor responses recorded by the six hydrogen sensors were used as input features for the machine learning models. The model accuracy was approximately 88.13%. LoRa communication technology was also used to demonstrate its use in harsh conditions, along with the IoT protocol, to deliver data over the Internet for better accessibility and monitoring. The developed localization technology enables safe monitoring of hazardous, highly flammable hydrogen gas, and machine learning can help prevent fatal accidents. Full article

As artificial intelligence (AI) technologies advance, traditional craftsmen face new challenges: innovating using digital tools while preserving cultural authenticity and heritage knowledge. The “hybrid artisan,” who strategically integrates AI-based design tools with traditional craft, emerges as a response to this tension. This article addresses research questions regarding how integrating generative AI technologies into design processes influences: (1) artisans’ productivity and product quality; (2) cultural authenticity and heritage preservation; (3) sustainable business models in creative entrepreneurship. The research methodology employs a convergent design with mixed methods, combining: (a) a systematic literature review (SLR) guided by the preferred reporting items for systematic reviews and meta-analyses (PRISMA 2020, n = 33 articles, 2022–2025); and (b) a qualitative survey (n = 13 artisans, Romania; semi-structured questionnaire, 34 items). The literature review identifies three dominant human–AI collaboration models: task-level cooperation, process-level coordination, and system-level co-creation. Diffusion models fine-tuned with low-rank adaptation (LoRA) and generative adversarial networks (GANs) achieve cultural authenticity scores of 73–95% while reducing design time by 30–70%. Empirical data reveal paradoxes: artisans value authentic creativity and sustainability (4 of 13 respondents (31%) rate sustainability as “extremely important”) but adopt AI cautiously (6 of 13 respondents (46%) report that they were not familiar with AI tools). Those using AI report 15–40% productivity gains without a proportional increase in sales, suggesting that market recognition of AI-assisted crafts remains uneven and that sustainability benefits are not yet clearly linked to AI use in practice. The successful “hybrid artisan” model relies on collaborative rather than autonomous AI positioning, explicit cultural safeguards in system design, and transparent communication with consumers about AI involvement. This research provides a conceptual heuristic, points to new research directions, and outlines policy implications for understanding when and how AI-assisted craft practices may support cultural integrity while also accepting that such benefits are context-dependent and not universally validated. Full article

Industrial Internet of Things (IIoT) systems are increasingly deployed in heavy industrial facilities to support real-time monitoring, automation, and safety-critical operations. Long-range low-power communication technologies such as LoRaWAN are widely used for large-scale sensor connectivity due to their long communication range and energy efficiency. However, dense industrial deployments may experience reduced communication reliability due to network congestion, packet collisions, and challenging propagation conditions caused by metallic infrastructure and electromagnetic interference. To address these limitations, this paper proposes RSrSF-LoRa, an integrated implementation of the reserved spreading factor (rSF) mechanism within the RS-LoRa framework (lightweight scheduling), designed to improve reliability for critical traffic. The study presents a comparative performance evaluation of RSrSF-LoRa and LoRaWAN in a smelter IIoT deployment, assessing packet delivery ratio (PDR), throughput, fairness, energy consumption and scalability under varying node densities and gateway configurations. Single-gateway scenarios with 100, 500, and 1000 nodes, and seven-gateway scenarios with 1000 and 2100 nodes, are evaluated as communication distances increase. The results indicate that while throughput and fairness remain comparable across approaches, RSrSF-LoRa improves alarm message reliability in dense single-gateway deployments, extending the acceptable PDR from 700 m to 900 m. Alarm nodes in RSrSF-LoRa consume slightly more energy due to reserved transmission, but overall energy consumption remains comparable. These findings provide design insights for reliable and energy-aware industrial IoT networks in smelter environments. Full article

In high-mobility LoRa communications, carrier frequency offset (CFO) stemming from low-cost crystal oscillators causes signal energy to disperse across frequency bins. To quantify the impact of CFO and sampling time offset (STO) induced by sampling rate conversion on the symbol error rate (SER), this paper derives approximate SER bounds under Additive White Gaussian Noise (AWGN) channels. Simulations verify these bounds, revealing that low spreading factors (SFs) combined with high STO and CFO induce significant energy leakage and performance degradation. Furthermore, Low Rate Optimization (LRO) is employed to enhance signal robustness. The proposed SER bounds are shown to hold for LRO-enhanced systems, with simulations confirming that LRO effectively improves overall performance. Full article

Advanced Metering Infrastructure (AMI) systems require bidirectional wireless communication for remote meter reading, disconnection, and demand–response management across large numbers of smart meters. LoRaWAN Class-C is a strong candidate for large-scale AMI due to its long range, low infrastructure cost, and native downlink support, but gateway placement must simultaneously satisfy uplink Pure-ALOHA capacity, downlink duty-cycle limits, and geographic coverage constraints in non-uniform device distributions. This paper proposes a greedy capacity-coverage gateway placement algorithm for a LoRaWAN AMI deployment serving 89,350 smart meters in Lam Luk Ka district, Pathum Thani Province, Thailand, based on real GIS building-footprint data. The algorithm seeds each gateway at the densest unserved 300 m grid cell, assigns devices within the planning radius R up to the tighter of the uplink Pure-ALOHA capacity and the downlink duty-cycle capacity, both computed for a 98% packet delivery target, and continues placing gateways until 98% of devices are geographically covered. For the 15 min reporting interval, the algorithm yields K* = 137 gateways, only 7% above the arithmetic lower bound of 128, and requires 2.9× fewer gateways than iterative K-means under the same placement constraints. An interval-based scenario analysis across five reporting periods of 5, 10, 15, 30, and 60 min reveals that 15 min is the crossover design point where longer intervals are limited by the downlink duty-cycle and shorter intervals are limited by uplink Pure-ALOHA collision, making 15 min the point at which both constraints are simultaneously near-binding. RX2 reconfiguration from SF10 to SF7 is shown to be essential: the default configuration yields only 121 devices/gateway at the 15 min rate, requiring 739 gateways, while SF7 reconfiguration raises capacity 5.8x to 702 devices/gateway. A Monte Carlo simulation with Urban Okumura–Hata path loss and log-normal shadowing validates the placement, achieving a mean uplink PDR of 98.0% and downlink PDR of 99.9%, both meeting the 98% design target. Class-C energy consumption is 112,787 mJ per 900 s reporting cycle, 434x that of Class-A (260 mJ), confirming that Class-C bidirectional AMI is feasible only for mains-powered smart meters. Full article

The rapid proliferation of Internet of Things (IoT) technologies and smart grids has substantially intensified the cybersecurity challenges associated with smart energy meters (SEMs). The data collected by the plugs are transmitted via a wireless communication protocol to a smart electricity meter that acts as a local gateway. This meter centralizes the information from the various sensors, may perform data pre-processing, aggregation, or validation operations, and then forwards the information to a central server. The main contributions of this system can be categorized into two key aspects. First, the implementation of a centralized wireless local energy consumption network using the Wi-Fi protocol to coordinate smart plugs over distances of up to 20 m. Second, the real-time acquisition of power characteristics and the remote control (ON/OFF switching) of household appliances for direct appliance-level submetering purposes. Data collected by the smart meter are transmitted to a processing unit through a Semtech SX1276 LoRa transceiver communication link. The central server constitutes the processing and storage layer of the system: it receives the collected data, archives it in a dedicated database, and makes it available through analysis, visualization, and decision-support tools. This architecture enables real-time monitoring of energy consumption, anomaly detection, optimization of electrical resource use, and the development of effective energy management strategies for smart electrical grids. Although current smart meter architectures incorporate multi-layer protection mechanisms at the hardware, communication, and data levels, additional security measures are required to counter advanced cyber threats aimed at data interception and manipulation. This paper improves the security framework of smart energy meters by integrating a chaos-based encryption layer to ensure secure data transmission. Chaotic systems exhibit intrinsic properties such as sensitivity to initial conditions, pseudo-randomness, and ergodicity, which render them particularly suitable for cryptographic applications. The proposed framework employs a Lorenz-based chaotic encryption module to secure SEM-utility data exchanges. Full article

To address the problems of complex process knowledge sources, heterogeneous representations, dispersed semantic associations, and limited reusability in the domain of machining distortion of thin-walled parts, this study proposes a knowledge graph construction method for the workpiece machining distortion domain, together with an intelligent decision-making framework driven by the collaboration of knowledge graphs and large language models. First, a domain ontology model is established around core concepts, including workpiece objects, deformation-driving factors, analytical resources, analytical methods, and optimization knowledge, thereby providing a unified semantic foundation for domain knowledge organization. Second, considering the characteristics of domain texts, such as dense technical terminology, ambiguous entity boundaries, and complex relation expressions, a dual-channel knowledge extraction method integrating BERT-BiLSTM-CRF and Universal Information Extraction (UIE) is developed to achieve high-precision extraction of entities and relations from unstructured texts. Knowledge fusion is further carried out through cross-validation, entity disambiguation, coreference resolution, and semantic alignment, and the extracted knowledge is ultimately stored and organized in Neo4j. Furthermore, an intelligent decision-making framework based on the collaboration of knowledge graphs and large language models is constructed. In this framework, a LoRA-tuned Qwen model is employed for user intent recognition and key information extraction, RapidFuzz WRatio is adopted for similar-node retrieval, and local subgraph construction, Label Propagation-based community detection, Betweenness Centrality-based key-node analysis, and evidence fusion are integrated to support process recommendation and intelligent question answering. Based on the proposed framework, an intelligent decision-making system is further developed for process recommendation and intelligent question answering in machining distortion scenarios. Experimental results show that the proposed dual-channel knowledge extraction model achieves an F1-score of 0.88, demonstrating its effectiveness in knowledge acquisition for the machining distortion domain. The constructed knowledge graph contains 4639 entities and 5822 relations, enabling a systematic representation of machining distortion knowledge. Case studies further demonstrate that the proposed method can generate interpretable recommendation results under complex process constraints in real industrial query scenarios. Overall, the proposed approach provides a feasible pathway for the structured organization, intelligent retrieval, and decision support of workpiece machining distortion knowledge. Full article

Agriculture is increasingly challenged by water scarcity, climate change, and rising energy demands, requiring more efficient and sustainable irrigation solutions. Conventional irrigation systems often lack the capability to adapt their operation to changing soil conditions and renewable energy availability, resulting in inefficient water and energy use. This study proposes a digital twin-based framework for energy management and irrigation optimization in photovoltaic (PV)-powered smart agriculture systems using Internet of Things (IoT) soil monitoring. The proposed system integrates a physical irrigation infrastructure, an IoT monitoring network, a fuzzy logic control layer, and a digital twin environment that periodically synchronizes the virtual model with IoT measurements to support the system representation and decision-making. The digital twin models soil moisture, temperature, nutrient levels, PV energy generation, battery state of charge, and irrigation water consumption. The virtual representation was periodically aligned with the physical system using measurements transmitted through the long-range (LoRa)-based network. An energy-aware irrigation scheduling strategy was developed to optimize irrigation timing based on soil conditions, battery status, and solar energy availability. The framework was evaluated using field data collected in a real apple orchard through an ESP32-based IoT platform and a standalone PV-powered irrigation system; quantitative experimental validation was performed for the soil twin. The results demonstrate high soil twin synchronization accuracy, with an overall RMSE of 1.47 percentage points and R² of 0.981, based on experimental field measurements. The energy twin and irrigation twin were evaluated using experimentally acquired sensor data together with model-based performance assessment, demonstrating the potential of the proposed digital twin framework for integrated water–energy management in smart agriculture. Full article

Industrial supervision can be expanded through the Internet of Things (IoT) without moving the control logic outside the PLC. This study evaluates a level-monitoring and control architecture using a point-to-point LoRa link between two Siemens S7-1200 PLCs; LoRaWAN is used solely as a conceptual architectural reference, and no gateway, network server, or OTAA/ABP procedures were implemented. An Arduino Uno with an Ethernet Shield W5100 exchanges variables with the PLC through Modbus TCP and transfers them via UART to Heltec LoRa ESP32 modules. Factory I/O simulates the process, and Adafruit IO provides remote supervision. The field campaign covered twelve locations between 10 and 120 m and 1200 frames. Reception, packet loss, RSSI, SNR, and the latency value calculated by the firmware were recorded. Overall reception was 94.17%, packet loss was 5.83%, and the mean latency value was 727.17 ms. The main contribution is the separation of local control from wireless communication and the quantitative evaluation of the link. Because the PLC maintained control when frames were lost, the solution is suitable for supervising slow processes, but not for critical loops. The results are specific to the evaluated radio and firmware configuration. Full article

Wearable sensors provide significant potential for continuous animal monitoring. However, the practical implementation of such technologies on livestock farms for animal monitoring raises significant challenges related to power efficiency, robustness, and reliability. In this research paper, we propose the design, implementation, and field testing of a low-energy, low-cost, multi-sensor wearable collar specifically designed for continuous monitoring of ruminants using LoRa. The proposed collar is based on a modular hardware platform that incorporates inertial sensing, temperature sensing, and wireless communication, with special attention to sensor choice, location on the body, casing, and mounting mechanism. Reduced weight, environmental protection, and long-term wearability without affecting animal behavior are the primary focus in the hardware design. Power optimization management strategies, including sleep mode and duty cycling functionality, are implemented to maximize autonomy and battery lifetime and are evaluated under realistic operating scenarios. Field deployment was conducted in a ruminant farm, where the wearable devices operated flawlessly for a long time period. Characteristic sensor data are collected, including accelerometer readings induced by animal movement, variability in received signal strength (RSSI), and animal temperature. The system demonstrates stable operation, satisfactory data completeness and consistency on multi-day basis. The knowledge acquired brings into focus the practical challenges and design issues associated with the use of wearable sensors in livestock and offers insights into designing efficient sensor collars for precision livestock farming. Full article

Water quality management in large-scale institutional infrastructures faces significant challenges due to the high latency of manual sampling and the energy–connectivity trade-offs in traditional IoT deployments. This paper proposes HydroSense AI, a robust three-tier IoT framework designed for real-time multi-parameter water quality monitoring and predictive analytics. The system integrates a heterogeneous sensing layer (pH, TDS, turbidity, and temperature) with a hybrid communication architecture, utilising Long Range (LoRa) technology for low-power transmission over long ranges (manufacturer-rated for line-of-sight distances of up to 16 km, and validated up to 2 km within a dense campus environment in this study), bridged via an ESP32-based gateway to the cloud. To address the critical issue of energy autonomy in remote sensing nodes, we implement a hardware-synchronised duty-cycling mechanism using a DS3231 Real-Time Clock (RTC), enabling precise deep-sleep scheduling and significantly extending battery operational life. Beyond data acquisition, the framework incorporates AI-driven trend-forecasting and anomaly-detection models to provide early warnings of water degradation through a Telegram-integrated alert system. Experimental validation over an extended deployment period demonstrates high measurement stability, with the forecasting model achieving a one-step (10-min) normalised RMSE of 0.0063 (equivalent to 0.033 pH units) for pH and 0.0298 (17.0 ppm) for TDS on a held-out test partition; a benchmark against persistence and ARIMA baselines is also provided. A complete measured energy decomposition of the deployed node is reported: hardware-synchronised duty cycling reduces the quiescent current to 18.2 μA, and with a 12 s acquisition window at 112 mA on a 10-min cycle, the mean current is 2.26 mA, corresponding to an estimated 46 days of unattended operation on a 2500 mAh cell. Critically, the acquisition window accounts for 99.2% of the per-cycle energy budget and the sleep interval for only 0.8%, so quiescent current—the figure of merit most often reported as evidence of low-power design—is shown not to be the binding constraint for sensor-dominated nodes of this class. The results indicate that the proposed hybrid architecture offers a 99.8% packet delivery ratio for sustainable water management. Full article

Precision agriculture and sustainable farming practices require autonomous environmental monitoring systems capable of operating in remote areas with limited energy and connectivity. However, the high cost of existing professional technology remains a significant barrier to widespread adoption. This study presents the development of a solar-powered smart sensing node designed for autonomous operation that integrates TinyML and dual-mode wireless connectivity via LoRaWAN and Wi-Fi for intelligent monitoring. The system features a custom-designed cup anemometer and multispectral sensing capabilities integrated into a compact single-tower architecture. All structural components, including radiation shields and a modular PVC frame, were designed for low-cost manufacturing and mass production. A single hermetic housing protects the core control electronics and is designed to improve durability in harsh outdoor environments. A Multi-Layer Perceptron model was implemented on the edge to enable intelligent data fusion and compensation, while a dynamic sampling strategy optimized power consumption. Experimental results demonstrate the feasibility of the proposed architecture through adaptive spectral acquisition over a daily illumination cycle, embedded MLP-based sensor fusion, and telemetry-oriented data compression that substantially reduces the number of transmitted samples. The main contribution of this work is a system-level architecture that integrates sensing, embedded intelligence, solar-energy harvesting, hybrid wireless communication, and telemetry optimization into a compact, low-cost, and field-deployable prototype IoT platform for sustainable precision agriculture. Full article

Measuring the water table level is a critical factor in irrigated agriculture in arid regions, as it can significantly influence the exchange of water and nutrients with crops. This work presents the design, implementation, and field validation of an open-source, solar-powered IoT device for autonomous groundwater level monitoring, combining long-range low-power LoRa communication, a non-contact pressure-based level sensor using the trapped-air capillary method, and an efficient power management stage that seamlessly switches between solar and battery power. Unlike existing commercial leveloggers, which are costly and lack integrated wireless telemetry and solar-based autonomy, the proposed platform is presented as a fully open-source, low-cost alternative purpose-built for unattended deployment in areas without grid power or cellular coverage. The system was validated through a multi-day field trial and dedicated communication tests, demonstrating a stable power conversion efficiency of 84–90%, a five-day autonomous operation without any deep-discharge event, high linearity ($R^2$ = 0.9998) of the level module over a 0–2 m range with a resolution of approximately 1.94 mm per ADC count, and a reliable LoRa link of up to 8.51 km in an urban/suburban environment despite non-line-of-sight conditions. With an estimated hardware cost of approximately $100 USD per unit, the device represents a low-cost, low-maintenance tool capable of generating knowledge about water resources to optimize irrigation and crop management in the face of climate change. 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