Search Results (346)

This paper describes the design and implementation of an automated irrigation control system that uses data collected by a wireless sensor network. Each sensor node, built on a custom-designed printed circuit board, includes sensors for light intensity, temperature, and a custom soil moisture sensor. Data is transmitted to a central control node via ESP-NOW, where it is processed and compared with configurable thresholds retrieved from Google Sheets over Wi-Fi. Irrigation is triggered automatically when conditions meet the remotely defined thresholds. A key contribution is the development and testing of a custom soil moisture sensor, with results compared to commercial models. The system supports low-power operation through deep sleep modes, enabling long-term field deployment. The novelty lies in the complete integration of hardware, software, and cloud-based control, providing a flexible and low-cost solution for precision agriculture. The system can be deployed in greenhouses or open fields and serves as a platform for future research in smart irrigation. The fundamental aspect is a very user-friendly solution for any farmer attributable to easy accommodation to the Google Sheets interface, no maintenance cost over the cloud account, and up to 45 days of battery life or a built-in alternative for solar power. Full article

Predictive maintenance (PdM) is central to Industry 5.0 strategies for reducing unplanned downtime in rotating machinery. This work proposes and evaluates, as a proof of concept on a controlled single-machine testbed, a multimodal TinyML edge architecture for PdM designed to remain compatible across the application plane’s evolution toward sixth-generation (6G) networks. Three complementary modalities run local inference on commercial off-the-shelf smart sensor nodes—vibration, acoustic, and thermography—with an embedded gateway bridging per-modality decisions to a serverless cloud back-end. Using real vibration data from a controlled static-unbalance protocol, five anomaly-detection model variants, operating on ten frequency-independent time-domain features extracted from 6 s windows, are benchmarked on the actual Cortex-M4F target; the INT8-quantized fully connected autoencoder, scored by per-window reconstruction error, reaches F1 = 0.9807 with 254 µs inference latency and a 6056 B Flash footprint, well within the microcontroller budget. In a second acquisition session with the remounted sensor, the frozen model retains perfect fault recall, and a short per-installation healthy-baseline recalibration restores F1 = 0.975 without any weight retraining. The acoustic modality is classified in-sensor on log-Mel filterbank energies by the Syntiant NDP120 neural coprocessor, and the thermographic modality by a lightweight binary CNN on 96 × 96 px frames. A preliminary intra-session late-fusion analysis suggests that a logistic-regression meta-learner over the three modality confidence scores can improve on single-modality baselines when no single modality already saturates, motivating multimodal sensing primarily for robustness and redundancy. An end-to-end latency experiment shows that the cloud-uplink leg dominates the budget (79–88%), establishing edge-first inference as a necessary condition for 6G URLLC gains to be observable at the application level. All experiments are conducted over Wi-Fi and MQTT with no 5G or 6G radio, so 6G compatibility is presented as a forward-looking roadmap rather than a tested capability. Full article

Future IoT, smart-city, and 6G ecosystems will increasingly use UAVs as autonomous nodes, creating demand for rapid yet traceable mission-to-airframe design workflows. This study evaluates whether a large language model (LLM) with retrieval-augmented generation (RAG) can convert a cargo-UAV mission brief into source-grounded preliminary sweep-angle ranges and whether one instantiated geometry remains compatible with downstream physics-based checks. An LLM first proposed broad ranges; a RAG stage, over a curated aerospace corpus, narrowed the three sweep intervals from a combined width of 25° to 7° (72% aggregate reduction) before CAD instantiation. The resulting tailless blended-wing geometry was screened with a SolidWorks 2025 Flow Simulation database spanning speed, angle of attack, sideslip, and elevon deflections. In the neutral-elevon, near-zero-sideslip slice, lift crossed the CAD-derived weight between 4° and 8° at 100 m/s and between 0° and 4° at 200 m/s. A three-level mesh check preserved force and moment signs. A nominal 6-DoF mission at 120 m/s over approximately 32 km remained within the prescribed corridor, with late mean cross-track and altitude-path errors of 10.7 m and 7 m. The contribution is a traceable early-stage design workflow, not a claim of aerodynamic optimality, flight readiness, controller robustness, or certification-level validation. Full article

Indoor evacuation under emergency conditions remains a challenging problem due to dynamic hazards, uncertain infrastructure availability, and variability in human behavior. Traditional evacuation systems rely heavily on centralized architectures, making them vulnerable to communication failures and delayed global decision making. To address these limitations, this paper proposes a novel framework termed Federated Edge-Semantic Learning for Decentralized Resilient Evacuation (FESL-DRE). The proposed framework distributes evacuation intelligence across edge nodes, enabling autonomous decision making without dependence on a central controller. It integrates semantic reasoning to transform raw sensor data into interpretable environmental states, federated learning to model behavioral patterns in a privacy-preserving manner, and a gossip-based coordination mechanism to propagate hazard information across neighboring nodes. An adaptive routing strategy is developed to account for hazard levels, crowd density, and human behavioral variability. The framework is evaluated using a simulation-based environment under dynamic hazard conditions and varying levels of node failure. Experimental results demonstrate that FESL-DRE achieves superior performance compared to classical and centralized adaptive methods, with improvements in evacuation success rate, reduced blocked movement attempts, and enhanced resilience under moderate infrastructure degradation. Furthermore, the proposed approach maintains low communication overhead and demonstrates promising scalability characteristics within the evaluated simulation environment. The results highlight the potential of decentralized intelligence for evacuation support and provide a foundation for future validation in realistic smart building and IoT-enabled environments. Full article

Smart-port decarbonisation requires operations-research decision support under day-ahead uncertainty. We present a rolling-horizon Model Predictive Control Energy Management System, formulated as a Mixed-Integer Linear Program with five forecast streams, and benchmark it against a deterministic rule-based controller on an identical configuration. A full-year proof-of-concept at the Port of Ancona (8760 hourly steps over the 2024 Italian Day-Ahead Market, 6.5 MWp PV, 1.0 MWh BESS) combines realised 2024 market, photovoltaic and auxiliary-demand series with a post-AFIR projected cold-ironing demand—the dominant load—and is therefore an operational proof-of-concept rather than a fully metered baseline. The principal MPC outcome is structural: anticipatory dispatch raises the mean BESS state of charge from 13.6% to 46.0% and cuts residence at the minimum SoC from 81% to 6% of hours. The forecasting layer attains sub-7% sMAPE on cold-ironing-loaded demand and 9–18% on the remaining streams (seasonal MASE₂₄ ≤ 0.74 on demand and price streams). At the relay-constrained 0.08 C pilot, the realised savings is 0.44% (€14,463 yr⁻¹; 95% moving-block bootstrap CI [€12,842, €15,742]); benchmarked against an enhanced rule-based controller that is itself permitted price-threshold grid charging, the residual value of predictive optimisation is €5652 yr⁻¹ (0.17%), with the remainder of the gap being the value of enabling grid charging. A C-rate sweep shows the savings doubling to 0.93% at 0.5 C, and a direct 20 MWh/±10 MW simulation yields a €0.57 M yr⁻¹ gross arbitrage savings whose net value, after a realistic battery-degradation penalty, is substantially smaller. Controller-level operational CO₂ rises marginally (+6.2 t, +0.13%), an effect distinct from—and dwarfed by—the system-level cold-ironing decarbonisation. The framework is reproducible in open-source Python (PuLP/HiGHS) from the actual data and is portable to other single-node smart city energy hubs. Full article

Accurate thermal management is crucial for ensuring the safety, longevity, and performance of lithium-ion batteries, especially in compact embedded systems like USB chargers, power banks, and IoT nodes. Despite extensive research on predictive thermal models and intelligent control frameworks, their implementation in resource-constrained microcontroller-class devices has been limited. Existing strategies in the literature, such as threshold-based or PID logic, cloud-enabled analytics, machine learning models, and observer-based estimators, are often reactive, computationally intensive, or dependent on external infrastructure, making them unsuitable for low-power, standalone applications. This study introduces a novel Scalable Embedded Thermal Intelligence architecture designed for real-time battery thermal regulation in locally executable, without cloud dependency, low-cost platforms. Unlike conventional methods, the proposed system operates entirely on-device using closed-form models implemented on an ESP32 microcontroller. It combines two synergistic algorithms: a static preemptive model that calculates a safe C-rate at startup based solely on ambient and initial battery temperature, and a dynamic disturbance-aware model that monitors temperature rise per SOC step and adjusts airflow or current adaptively without requiring high memory, floating-point units, or supervisory control. The architecture achieves sub-second response times, <7% RAM, and <25% Flash usage, and does not need cloud connectivity, simulation backend, or complex thermal-management infrastructures such as liquid cooling circuits, phase-change systems, or cloud-supervised architectures. The significant contribution of this work is not the introduction of a new electrochemical–thermal formulation, but the effective integration and application of previously validated closed-form thermal predictors on low-cost microcontroller-class hardware, designed for anticipatory battery thermal regulation while adhering to strict computational limitations. Compared to traditional battery thermal management systems using PCM, liquid-cooling circuits, or cloud-based predictive estimators, the proposed approach eliminates the need for complex thermal hardware, fluidic systems, external computing infrastructure and resource-efficient edge operation. This makes the system suitable for deployment in real-world embedded applications like USB-C smart charging cables, compact IoT power banks, and portable medical devices, where form factors, energy efficiency, and cost are critical. The proposed SETI framework offers a firmware-integrated architecture and a firmware-integrated solution that provides a lightweight embedded alternative for predictive thermal regulation for distributed energy systems and miniaturized electronics. Full article

English proficiency is vital for non-native speakers’ career development, yet classroom instruction alone cannot meet practical demands, making informal digital learning of English (IDLE) increasingly important. Artificial intelligence (AI), with conversational and multimodal functions, offers new opportunities for IDLE. However, existing research on AI-mediated IDLE has predominantly focused on language majors and often relied on a single methodological lens, neglecting STEM undergraduates and the complex network dynamics among motivational factors. However, research has largely focused on language majors, leaving STEM majors underexplored. Guided by the Hedonic-Motivation System Adoption Model (HMSAM), this study analyzed data from 413 Chinese STEM majors using partial least squares structural equation modeling (PLS-SEM, SmartPLS 4.0) and psychological network analysis (PNA, R 4.5.3). PLS-SEM results showed that enjoyment was the strongest direct predictor of AI-IDLE, followed by focused immersion, perceived usefulness, and curiosity. Control contributed indirectly via focused immersion, while boredom was non-significant. Perceived ease of use influenced AI-IDLE only through cognitive and emotional pathways. The model explained 58.1% of the variance. PNA further identified enjoyment, focused immersion, and control as central nodes, while the link between perceived usefulness and AI-IDLE was non-significant. These findings suggest that Chinese STEM undergraduates’ AI-IDLE is primarily driven by intrinsic hedonic motivations rather than utilitarian evaluations. The study provides empirical support for designing AI tools that enhance enjoyment and control to foster STEM students’ extracurricular English engagement. Full article

The innate–adaptive immune interface is a decisive control point determining whether therapeutic interventions induce durable protection, antitumor immunity, inflammatory, or immune tolerance. Many immunotherapies fail in translation because immunity is often treated as a single-output system rather than a spatially and temporally organized network shaped by tissue context, antigen-presenting cell fate, biomolecular conditioning, and metabolic state. This review introduces the immunoscape framework as a nanobiotechnology-oriented model for linking immune-state mapping with controllable translational variables, including delivery route, release kinetics, first-contact immune cells, lymphatic routing, biomolecular corona identity, antigen-presenting cell fate, and safety-gate assessment. Unlike systems immunology, which primarily describes immune networks, or conventional immune engineering, which often focuses on selected payloads, targets, or platforms, the immunoscape framework provides a design layer for predicting context-dependent immune outcomes. We discuss two converging strategies for reprogramming this interface: natural biologics, including beta-glucans, polyphenols, microbial metabolites, and extracellular vesicles; and smart nanomaterials, including lipid nanoparticles, biomimetic vesicles, lymph node-targeted platforms, and stimulus-responsive nanoarchitectures. We further propose translational design rules to guide clinically realistic immune-reprogramming nanomedicines for cancer, infectious, inflammatory, and regenerative applications. Full article

Wearable technologies offer practical opportunities for assistive communication and educational support in introductory sign language learning. This paper presents an IoT-enabled smart glove for deterministic static sign language recognition over a bounded vocabulary of 15 isolated static gestures, comprising digits (0–9) and five vowel handshapes (A, E, I, O, U). The system is intended for foundational static gesture and posture practice and is not designed or validated for dynamic gestures, coarticulated signing, continuous sign language recognition, or sentence-level translation. The prototype integrates five 2.2-inch (55.9 mm) resistive flex sensors and an MPU6050 3-axis accelerometer, performs acquisition, exponential moving average filtering, user-specific calibration, normalization, and deterministic classification on a NodeMCU ESP32 board, and transmits selected processed variables to Arduino Cloud through MQTT for remote monitoring. A 10 s calibration routine maps user-specific open-hand and closed-fist responses into normalized flex-sensor ranges, allowing the same deterministic rule structure to operate across participants without model retraining. Experimental evaluation with 10 healthy adult participants aged 20–41 years (mean age: 27 years), all familiar with sign language and all providing written informed consent, produced a balanced dataset of 1500 labeled steady-state sensor vectors. The class-averaged recognition rate was 92.8%, and leave-one-subject-out validation produced a subject-wise accuracy of $92.80\pm 2.03\%$, with individual participant accuracies ranging from 90.00% to 96.00%. The local embedded processing pipeline required less than 2 ms per cycle, the complete path including MQTT visualization produced approximately 150 ms end-to-end latency, and the device operated for up to 14 h using a 3.7 V, 1000 mAh Li-Po battery. The results indicate that calibrated deterministic sensor fusion can provide a low-cost, low-latency, edge-executed solution for bounded static sign-language gesture learning tasks while maintaining stable short-term subject-wise performance under controlled experimental conditions. Full article

Existing cross-chain bridges over-rely on static collateralization and post-event penalties, leaving them vulnerable to concealed on–off attacks and rational group collusion. To address these limitations, this paper proposes a Dynamic Trust Evaluation and Risk Control (DTERC) mechanism for heterogeneous cross-chain relay nodes. First, DTERC develops a multidimensional trust quantification model that combines temporal decay, robust multi-observer latency aggregation, verification accuracy, online stability, and an asymmetric one-strike penalty triggered only by cryptographic evidence. Second, DTERC constructs a threshold-aware N-player evolutionary game model to characterize the k-of-N signature structure of cross-chain relay consensus and introduces a dynamic staking function to reduce the economic incentive for collusion under bounded attack-value and parameter conditions. Third, DTERC designs a threshold-preserving FastPath mechanism to reduce redundant verification for low-risk transactions while retaining committee-level confirmation and challenge-based fallback. The empirical evaluation combines multi-agent simulation, smart-contract prototype testing, whitelist-compromise stress tests, malicious-oracle robustness analysis, network-jitter experiments, repeated trials, and parameter-sensitivity analysis. The results show that, under the tested settings, DTERC reduces the malicious transaction success rate to 0.15% under a 50% initial collusion scenario, lowers core contract Gas overhead by 35.7%, and reduces average end-to-end latency by approximately 10% in benign FastPath conditions. These findings indicate that DTERC improves the security–efficiency trade-off of heterogeneous cross-chain relay networks while making its assumptions and limitations explicit. Full article

Smart grid private communication networks need to support heterogeneous services with varying requirements for reliability, security, bandwidth, and controllability. In such networks, service function chains (SFCs) can provide customized network services by deploying virtual network functions (VNFs) over a shared substrate infrastructure. However, sharing physical servers among different service categories may conflict with the physical isolation requirement between critical grid services and common grid services. To address this problem, this paper investigates physical-isolation-aware SFC deployment for smart grid private communication networks. We first formulate an integer nonlinear programming (INLP) model that maximizes the network resource usage revenue while considering server resource constraints, link bandwidth constraints, flow conservation constraints, virtual link mapping constraints, server energy consumption, and physical isolation constraints. The nonlinear constraints are then linearized into an integer linear programming (ILP) model, which can be solved by an optimizer and used as a benchmark. To reduce the computational cost, we propose a private-network-oriented service function chain isolation deployment (PNO-SSID) algorithm. The proposed algorithm selects a revenue-aware subset of SFC requests, determines the service category to be preferentially processed, selects server nodes based on VNF-layer traffic cost, deploys VNFs using a matching-game-based method, and maps virtual links based on shortest paths. Simulation results show that PNO-SSID requires much less execution time than CPLEX while achieving close revenue in small-scale cases. Compared with online profit maximization (OLPM) variants using different request preprocessing strategies, PNO-SSID achieves higher network resource usage revenue and request acceptance ratio under physical isolation constraints. A prototype platform based on a fifth-generation non-standalone private network and the OAI platform further validates the feasibility of server-level isolated core network service chain deployment under the considered service-category separation requirement. Full article

Poor indoor air quality, inadequate ventilation, and unnoticed local disturbances can reduce occupant well-being and compromise practical safety in smart-home and small-building environments. Although low-cost Internet-of-Things (IoT) sensing technologies are widely available, many monitoring systems remain focused on single-modality sensing and do not jointly evaluate environmental conditions, vibration activity, communication reliability, and gateway-side interpretation within one framework. This study presents the design, implementation, and proof-of-concept evaluation of a low-cost, privacy-conscious, non-imaging IoT-based indoor environment and safety-awareness monitoring framework built with ESP32/Arduino sensor nodes and a Raspberry Pi gateway. The system integrates carbon dioxide, temperature, humidity, gas-resistance/VOC-trend indication, and vibration sensing with MQTT-based communication and edge-side analytics. Controlled subsystem experiments showed that CO₂ concentration differentiated ventilation conditions, increasing from 395.47 ppm in the valid empty/open-door baseline to 1083.16 ppm in the closed occupied condition. Vibration states were distinguished using root-mean-square acceleration features across calm, surface-disturbance, footstep, play, and jump conditions. MQTT evaluation using 1000-message batches showed no observed message loss or duplicates across the tested QoS/network combinations, although latency and throughput varied by network configuration and QoS level. QoS 1 provided a practical balance between low latency and protocol-level delivery assurance in the tested local/Wi-Fi setting. A final integrated validation run further demonstrated synchronized acquisition from indoor environmental, vibration, and outdoor CO₂ reference publishers through the same Raspberry Pi gateway, with zero missing or duplicate sequence flags across the three streams. Overall, the findings indicate that lightweight open-source IoT hardware can support a reproducible building-level sensing and edge-analytics prototype for indoor environment and safety-awareness monitoring. Broader deployment in standard-sized rooms, multi-room buildings, and smart-city infrastructure remains future work. Full article

Wireless sensor networks (WSNs), IoT-enabled sensing, and mobile platforms are increasingly used in precision agriculture, but fixed stations cannot fully capture within-field or canopy-level variability. This study developed and greenhouse-tested a low-cost modular tracked robot as a wireless environmental-sensing and telemetry research node for future crop-monitoring applications, rather than as a fully validated autonomous field robot. An open-source tracked chassis was extended with Raspberry Pi edge computing, a Cube Orange autopilot, RTK-capable GNSS, 5G/VPN/MAVLink communication, and BME280, BH1750, MLX90614, RGB camera, and LiDAR-ready sensing. The platform measured 35 × 25 × 40 cm, weighed 6.4 kg, operated from a 12 V supply, and provided about 4 h of runtime under favorable conditions. Sensor data were logged locally and could be transmitted remotely, while telemetry was visualized in QGroundControl. The environmental sensing layer was compared with a calibrated Libelium Smart Agriculture Pro station in a greenhouse using 70 synchronized samples per variable across three sessions. Because the two nodes were placed close to one another but were not strictly co-located, the comparison quantifies operational sensing differences under greenhouse microclimatic gradients rather than pure laboratory sensor error. Regression was retained only as a trend-tracking metric, while method-comparison interpretation was added using bias and Bland–Altman limits of agreement. The pressure channel showed strong trend tracking (R² = 0.992, RMSE = 0.024 hPa), whereas air temperature (R² = 0.756, RMSE = 2.537 °C) and relative humidity (R² = 0.817, RMSE = 5.024%) were suitable mainly for exploratory microclimate mapping and relative trend monitoring unless local calibration is applied. The title, claims and conclusions were therefore narrowed to greenhouse sensing-layer validation and future crop-monitoring deployment. Full article

The increasing integration of distributed energy resources (DERs), including photovoltaic systems, battery energy storage systems, electric vehicles, and flexible loads, is transforming modern power systems and creating new challenges for coordination, control, and cybersecurity. Conventional Virtual Power Plant (VPP) architectures typically rely on centralized energy management systems, which may face scalability limitations, communication bottlenecks, cybersecurity risks, and reduced resilience to failures. This paper presents a blockchain-enabled decentralized Virtual Power Plant framework for secure and autonomous coordination of distributed energy resources. The proposed architecture combines blockchain technology, smart contracts, IoT-based communication infrastructure, and decentralized energy management functions within a unified multi-layer coordination framework. Smart contracts automate energy scheduling, peer-to-peer transaction validation, and settlement processes, reducing dependence on centralized control entities. Lightweight blockchain consensus mechanisms are employed to improve scalability while limiting computational overhead. The effectiveness of the proposed framework is evaluated through simulation-based case studies involving decentralized DER coordination, peer-to-peer energy trading, and resilience assessment under node-failure conditions. Its performance is compared with that of a conventional centralized VPP architecture in terms of scalability, coordination reliability, communication overhead, transaction transparency, and fault tolerance. The results indicate that the decentralized framework improves operational resilience, coordination transparency, and scalability under increasing DER participation while maintaining satisfactory energy balancing performance. Although blockchain-based coordination introduces additional transaction latency, the proposed approach enhances cybersecurity, reduces dependence on centralized control structures, and provides a flexible foundation for future intelligent smart-grid applications. Full article

As cloud computing proliferates, outsourced data faces severe security threats, yet existing searchable encryption (SE) schemes rely on classical hardness assumptions, centralized trust authorities, and static access control, leaving critical gaps in quantum resistance, single-point-of-failure prevention, and dynamic permission management. To address these limitations, we propose BL-ABSE, a blockchain-enhanced, lattice-based attribute-based searchable encryption framework. BL-ABSE employs the Ring Learning With Errors (RLWE) problem as its security foundation and applies the Number Theoretic Transform (NTT) to reduce polynomial multiplication from $O\left(n^2\right)$ to $O(nlogn)$. To eliminate single-point trust risks, the framework further integrates a $(t,n)$ threshold key protocol across an edge-node consortium governed by Practical Byzantine Fault Tolerance (PBFT) consensus. A smart-contract-maintained on-chain revocation list enables permission withdrawal via a single blockchain transaction without re-encryption. Experimental evaluation demonstrates that commitment generation requires approximately 23 ms at $n=1024$, search latency scales linearly at roughly 29 µs per record, and revocation completes in approximately 2 s regardless of system scale. Formal security proofs under the quantum polynomial-time (QPT) adversary model reduce six security properties—index indistinguishability, query privacy, threshold key security, Byzantine fault tolerance, audit immutability, and revocation immediacy—to the hardness of RLWE and the Short Integer Solution (SIS) problems. To the best of our knowledge, BL-ABSE is the first framework to simultaneously achieve post-quantum security, attribute-based access control, decentralized key management, instant revocation, and immutable auditing within a single unified framework. We further conduct threshold parameter verification, end-to-end revocation latency decomposition, blockchain throughput stress testing, search-pattern leakage quantification, and communication/storage overhead analysis, providing a comprehensive evaluation of both performance and security trade-offs. We explicitly characterize the search-pattern leakage inherent in the deterministic commitment design as a correctness–privacy trade-off and discuss mitigation directions. Full article