Search Results (164)

This paper presents the development and experimental validation of a low-cost IoT-enabled turbidity monitoring platform intended for laboratory-scale bioprocess applications. The proposed system was designed as a modular turbidity acquisition subsystem that can be integrated into broader bioreactor automation platforms. The hardware architecture is based on an ESP8266 microcontroller, a TS-300B optical turbidity sensor, a resistive voltage divider for analog signal conditioning, an OLED display for local visualization, and a Google Sheets-based cloud logging solution. A blank-based relative Turbidity Index was defined in order to compensate for optical configuration and environmental variations. The embedded firmware implements multi-sample averaging, blank calibration, serial command control, local display updates, CSV logging, and optional cloud transmission through HTTP requests. The calibration procedure was performed using serial dilutions of a yeast suspension, and the obtained data were fitted using a nonlinear power-law model and a log-log representation. An additional comparison with OD600 reference measurements showed a monotonic relationship between the proposed Turbidity Index and conventional optical-density measurements. The system was further validated through a yeast-based monitoring experiment performed under consistent optical conditions. The results showed the capability of the platform to acquire, process, visualize, and store turbidity-related data over an extended interval. The proposed platform provides a practical, affordable, and reproducible solution for turbidity monitoring and IoT-based data acquisition in small-scale bioprocess applications. Full article

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

Structural health monitoring (SHM) systems based on Internet of Things (IoT) technologies have become an effective approach for continuous monitoring of bridge infrastructures. However, many wireless monitoring systems relying on LoRaWAN or contention-based communication suffer from packet collisions, unpredictable latency, and limited scalability when multiple sensing nodes operate simultaneously. To address these limitations, this study proposes a soft real-time LoRa-based IoT architecture for bridge SHM using a time division multiple access (TDMA) communication framework implemented on an embedded real-time platform. The proposed system integrates distributed vibration sensing nodes, a TDMA-enabled LoRa communication layer, an ESP32-based gateway, and a web-based monitoring database for remote visualization and analysis. The architecture leverages FreeRTOS (v10.4.3) for system-level task scheduling, enabling concurrent execution of sensing, communication, and networking processes across the dual-core ESP32-WROOM-32D platform. Experimental results obtained using a laboratory-scale cable-stayed bridge model demonstrate stable multi-node communication with a packet delivery ratio exceeding 95% and predictable TDMA-scheduled transmission cycles with TDMA slots of 100–200 ms under the evaluated operating conditions. The experiments validate end-to-end operation using a representative three-node deployment, while broader scalability is evaluated analytically through the TDMA capacity model and identified as future work for larger physical deployments. Full article

This article presents the design and implementation of a control system for an adaptive light luminaire with variable light distribution. The developed solution enables dynamic shaping of the light distribution characteristics by simultaneously controlling the geometry of the optical system and the spatial distribution of the emitted light flux. The system utilizes two cooperating control mechanisms. The first is implemented by four independently controlled reflectors with adjustable angles of inclination. The second is based on the independent control of eight sections of LED light sources. The coordination of both systems enables the implementation of various operating scenarios, including symmetric, asymmetric, and adaptive configurations, with variants of narrow and wide beam distribution. The central unit of the system is an ESP32 microcontroller that performs control functions, generates PWM signals, and coordinates the operation of the actuators. The system was implemented as a dedicated embedded system. The main contribution of this work is the implementation and experimental validation of an embedded control platform integrating mechanical beam shaping and segmented LED control within a single adaptive lighting system. As part of the work, predefined control scenarios for lighting system configuration were developed and experimentally tested. The developed solution increases the functionality of adaptive lighting systems and may contribute to reducing energy consumption by directing light only where required. However, the quantitative evaluation of the energy savings was beyond the scope of the present study. Full article

Cloud-dependent irrigation platforms can support remote monitoring, but their use in precision fertigation is limited when local decisions must be made quickly and reliably. Network delay, temporary disconnection, and the use of single-point measurements may all reduce the ability of a system to respond to spatial variation in soil moisture and nutrient demand. In this work, an edge-controlled precision fertigation system was developed by combining multi-parameter soil sensing, spatial task scheduling, and a 6-DOF robotic manipulator. The ESP32 controller runs a preemptive FreeRTOS scheduler, allowing sensor acquisition, inverse-kinematics calculation, and pump actuation to be handled as separate tasks. A Kalman filter was used to smooth soil moisture measurements, and a hysteresis-based control strategy was adopted to reduce false triggering and repeated pump switching. To improve fertigation safety, a hardware–software interlock was added so that fertilizer delivery is always accompanied by water delivery. Hardware-in-the-Loop simulation and a 14-day field deployment were used to evaluate the system. The controller achieved an end-to-end latency of less than 38 ms and maintained operation during network interruptions through cached local parameters. After calibration, the robotic end-effector positioning error was reduced to ±2.4 mm. The hysteresis strategy lowered daily pump cycling by 71%. Based on prototype duty-cycle data and seasonal extrapolation, the projected seasonal water use and fertilizer demand were 44% and 38% lower, respectively, than those estimated for a uniform application. These values should be interpreted as model-based projections rather than direct season-long measurements. During 72 h of continuous operation, no Modbus faults were observed, and RTOS heap fragmentation remained stable. Overall, the results suggest that edge-based deterministic control can provide a practical route for precision fertigation where both spatial variability and intermittent connectivity must be considered. Full article

This study presents a compact affective sensing system that converts physical touch into intuitive multimodal feedback through dynamic facial expressions and optional audio responses. The system was developed to support non-verbal, human-centered interaction in embedded human–machine interfaces, where tactile input can be directly associated with emotional perception. The hardware platform is based on the M5Stack CoreS3, integrating a force-sensitive resistor (FSR), an ESP32-S3 microcontroller, an embedded LCD, and a built-in speaker. Pressure signals are acquired using a simple voltage divider circuit and digitized through the built-in 12-bit analog-to-digital converter (ADC) of the ESP32-S3. To improve signal stability, a simple moving average (SMA) filter is applied for noise reduction. The normalized pressure signal is classified into multiple pressure regions and mapped to emotional states. Smooth facial transitions are generated by continuously interpolating geometric facial parameters, including mouth curvature, eye shape, and eyebrow angle, without relying on pre-rendered images. Experimental evaluation demonstrated stable pressure detection, low-latency response, and intuitive emotional feedback across a wide operating range. User evaluation results further indicated that the combination of visual and auditory feedback enhanced realism, anthropomorphic perception, and interaction quality. The proposed system demonstrates the potential of tactile affective sensing for applications in assistive communication, education, and empathetic human–robot interaction systems. Full article

Intrusion detection in IoT and IIoT networks must operate under tight resource constraints, yet most published machine learning-based IDS solutions report accuracy on held-out data without addressing whether the trained model can actually run on the target hardware. We address this gap with an end-to-end study spanning dataset preprocessing, model training, INT8 quantisation, and physical execution on two real microcontrollers. Five supervised classifiers—Logistic Regression, Decision Tree (depth 5), Random Forest, XGBoost, and LightGBM—plus an MLP deep learning baseline are evaluated on binary and ten-class intrusion detection tasks using the TON_IoT network dataset. A 5-fold stratified cross-validation confirms stable performance across splits, with LightGBM reaching $F_1=0.9993\pm 0.0001$. Models are then exported through three quantisation pipelines: m2cgen C code generation for the two lightest classifiers, TensorFlow Lite Micro full-integer INT8 for the MLP (9.34× size reduction to 13.03 KB), and a custom post-training INT8 binary format for XGBoost and LightGBM (18.91× compression for LightGBM to 73.85 KB). All five quantised models are deployed to an Arduino Mega 2560 (ATmega2560, 16 MHz, 8 KB SRAM) and an ESP32-C3 SuperMini (RISC-V, 160 MHz, 400 KB SRAM) and benchmarked on physical hardware across 500 timed inferences per model (250 per input class), with firmware predictions confirmed to match the Python 3.11 float model on both test vectors. The Decision Tree achieves 5.6 µs inference on the ESP32-C3; LightGBM INT8 ($F_1=0.9992$) provides the best accuracy–size trade-off among ensemble models. Cross-platform comparison reveals that the RISC-V device is 5.8–7.8× faster than the 8-bit AVR for identical model code. A cross-domain evaluation using CIC-IoT-Dataset2023 identifies large normalised distribution shifts (up to $\delta =5.95$ in packet asymmetry), quantifying the generalisation gap that remains an open challenge. Full article

This work proposes guidelines for coding a grafcet on a microcontroller, following the specifications described in standard IEC 60848:2013. While previous works exist for coding a simple grafcet on platforms like Arduino or STM32, they overlook implementing timers, hierarchical structure, and other advanced resources included in the IEC 60848:2013. Throughout this study, procedures for implementing the majority of the resources described in the standard using pseudocode and C++ are presented. These resources include timers, stored actions, macrosteps, enclosures, and forcing orders, among others. Previously, it has been necessary to identify ambiguities, vagueness, inaccuracies, and the non-modular approach of the IEC 60848:2013 standard that impeded a proper and unequivocal implementation, proposing improvements and interpretations to make it more unequivocal and exportable. Examples are included to illustrate how to apply the proposed guidelines, and a template to assist in coding any grafcet is made available in a repository. As a result, this article provides designers with a complete set of tools and resources to unequivocally implement concurrent discrete-event processes. The implementation on several microcontrollers exhibits exceptional memory efficiency under scaled stress-testing conditions (100 steps and 25 timers): less than 2% of the RAM for STM32F411RE and ESP32, and less than a 1.5% increase in ROM when implementing the analysis of 100 transitions and the definition of 25 timers. This proves its scalability for complex discrete-event applications. Cycle times depend on whether there is access to macrostep expansions and enclosures but are around 80 microseconds for the STM32F411 and 20 microseconds for the ESP32. 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

The operational safety and longevity of Lithium-ion Nickel Manganese Cobalt Oxide (NMC) battery packs depend on the early detection of gradual cell degradation rather than reactive fault protection. Conventional Battery Management Systems (BMS) predominantly rely on fixed threshold-based mechanisms, which are insufficient for identifying long-term abnormal trends at the individual cell level preceding failure. This studyproposes an intelligent IoT-based battery monitoring and visualization framework for trend-oriented abnormal behavior analysis in a 72 V, 20 cell NMC battery pack. A JK-BMS performs cell voltage acquisition, while an ESP32-S3 microcontroller operates as an IoT gateway, wirelessly collecting high-resolution cell level data via Bluetooth Low Energy (BLE). The data are transmitted to a Home Assistant platform, which provides centralized time-series visualization and comparative cell analytics. The primary contribution is a heuristic anomaly detection algorithm that evaluates temporal voltage trends of individual cells, with emphasis on instability within the critical operating range of 3.0–3.5 V. Unlike conventional threshold-based approaches, the proposed method detects repeated abnormal patterns over time. A frequency-based alert mechanism categorizes battery health into normal, warning, and critical states based on cumulative anomaly occurrences, enabling progressive degradation assessment. Experimental results demonstrate that the proposed framework effectively identifies early-stage degradation patterns that remain undetected by conventional BMS logic. The system supports predictive maintenance, enhances operational safety, and provides a scalable, cost-effective solution for advanced battery health monitoring in electric mobility and distributed energy storage applications. Full article

The accurate characterization of low-power near-infrared LEDs typically requires costly radiometric equipment, limiting broader accessibility. This study proposes a low-cost indirect method for comparative NIR LED characterization based on the thermal response of black-coated aluminum absorbing targets monitored by a commercial MLX90614 contactless temperature sensor integrated with an ESP32 acquisition system. The absorbed optical power was estimated from a steady-state energy-balance model combining convective and radiative heat transfer, with geometry-dependent effective coefficients derived for 10 mm and 15 mm diameter targets. Experiments were conducted using 850 nm and 940 nm LEDs at drive currents between 30 mA and 100 mA. The absorbed power increased linearly with the drive current and electrical input power across all configurations, with R² values of 0.995–0.997 and 0.996–0.999, respectively. The 15 mm targets exhibited higher capture ratios (10.4–11.9%) compared to the 10 mm targets (8.4–9.4%). The combined measurement uncertainty ranged from 13% at high drive currents to nearly 70% at low drive currents, with the temperature-rise sensitivity being the dominant factor; within the recommended operating range (≥70 mA for 10 mm and ≥80 mA for 15 mm targets), the uncertainty remained below 25%. The proposed platform enables reliable comparative characterization of low-power NIR emitters using exclusively off-the-shelf components. Full article

This work investigates a novel GaCl₃·AlCl₃/H₂O catalytic system for the synthesis of low-molecular weight polyisobutylene (LPIB). Catalytic performance was improved by employing a dual Lewis acid system, which outperformed the conventional single component (GaCl₃/H₂O) catalyst in terms of both reaction rate and yield. In accordance with the optimized reaction conditions, the conversion of monomer was found to be 97%, thereby achieving low molecular weight polyisobutylene (LPIB) with a number average molecular weight (M_n) of 3900 g/mol. Density functional theory (DFT) calculations revealed a lower proton transfer barrier (5.8 kcal/mol) in the dual Lewis acid catalytic structure compared to its single component counterpart. Subsequent theoretical analyses, incorporating electrostatic potential (ESP), independent gradient model based on Hirshfeld partition (IGMH), and distortion/interaction analysis, attributed this observed kinetic advantage to a higher positive ESP extremum and enhanced interaction between the IB fragment and the Lewis-acidic active center. Together, these results establish the GaCl₃·AlCl₃/H₂O dual Lewis acid system as an enhanced catalytic platform over the conventional GaCl₃/H₂O system for efficient IB polymerization toward LPIB synthesis. Full article

Solar energy has emerged as a promising alternative to fossil fuels for mitigating climate change; however, efficient photovoltaic (PV) operation requires continuous monitoring and accurate energy forecasting. This study proposes an intelligent IoT-based photovoltaic monitoring and short-term energy prediction system integrating real-time sensing, solar tracking, and machine learning techniques. A small-scale experimental prototype based on a 10 W photovoltaic panel was implemented to collect real-time data, including voltage, current, temperature, humidity, ultraviolet radiation, and dust accumulation during a 30-day monitoring period under outdoor conditions. The acquired data were transmitted through an IoT architecture based on the Arduino Uno and ESP32, programmed using Arduino IDE, and integrated with the Blynk cloud platform for real-time monitoring and analysis. To evaluate predictive performance, Random Forest, XGBoost, and LSTM models were trained and compared for photovoltaic energy forecasting. Experimental results showed that XGBoost achieved the best predictive performance, obtaining the lowest error values (MAE = 0.00077, RMSE = 0.001103) and the highest coefficient of determination (R² = 0.918), outperforming the other evaluated models. In addition, the proposed system enabled effective remote monitoring and degradation analysis associated with environmental conditions. The results demonstrate the potential of integrating IoT and machine learning for accurate short-term photovoltaic energy forecasting in small-scale experimental environments. Nevertheless, further long-term and large-scale validation is required to evaluate system robustness under operating conditions. Full article

This paper presents a compact wearable patch antenna operating in the 2.4 GHz ISM band for biomedical Internet of Things (IoT)-based healthcare monitoring applications. The proposed antenna is intended for integration with wearable biomedical sensors in order to support real-time physiological data transmission in remote patient monitoring systems. The antenna was designed on an FR4 substrate to achieve good impedance matching and stable radiation performance. The antenna showed good performance, with a reflection coefficient of −39.56 dB and a gain of 3.01 dB. SAR analysis confirmed compliance with IEEE and ICNIRP safety standards for wearable applications. In addition, the antenna prototype was fabricated and experimentally validated using a vector network analyzer (VNA), showing good agreement between simulated and measured results. Furthermore, the proposed system was implemented by integrating an ESP32 microcontroller with a MAX30100 physiological sensor, where the sensor is responsible for acquiring real-time biomedical data, including heart rate and blood oxygen saturation (SpO₂). The ESP32 processes the acquired data and enables wireless transmission through the proposed antenna to a smartphone and laptop using the Blynk IoT platform, which allows real-time remote monitoring and visualization of physiological parameters. The obtained results confirm the suitability of the proposed antenna for wearable biomedical devices, remote healthcare monitoring, and IoT-enabled healthcare applications. Full article

Environmental monitoring in hazardous indoor zones conventionally relies on fixed-sensor networks or manual inspections, both of which suffer from spatial blind spots and increased human exposure risks. This paper addresses the problem of transforming sparse, mobile sensor measurements into spatially resolved risk assessments in GPS-denied environments. We propose a Hazard Index (HI) framework that normalizes environmental parameters against established safety thresholds into a unified, graduated risk metric with $O\left(N\right)$ computational complexity, where N is the number of monitored parameters. The framework is designed for multi-parameter hazard assessment; the present work validates the computational pipeline, spatial mapping methodology, and classification logic through single-parameter CO₂ detection ($N=1$) deployed on a LiDAR-guided robotic platform integrating an MQ-135 gas sensor interfaced via a NodeMCU ESP8266 microcontroller. Experimental validation across a 144 sq ft indoor area achieved a trajectory-following RMSE of 0.54 ft relative to planned waypoints using Hector SLAM without odometry, detected CO₂ concentrations ranging from 0.02% to 0.25%, and identified a hazardous region encompassing eight measurement points ($\mathrm{HI}\ge 1.0$) using a three-tier classification scheme (Safe, Elevated, Hazardous) within 225 s of active mapping. The framework provides a lightweight computational footprint suitable for real-time evaluation on an NVIDIA Jetson Nano. The proposed approach establishes a cost-effective, reproducible methodology for autonomous indoor environmental monitoring, with the modular architecture designed for future expansion to multi-parameter sensing. Full article