Search Results (123)

This paper provides the design and simulation of a lightweight cryptographic protocol on smart house IoT devices using ChaCha20 and Ascon-AEAD128. The protocol, implemented in Python 3.14.6 and tested on a virtual ESP32 platform using Wokwi and CloudAMQP, uses stream cipher encryption with authenticated message tagging to provide confidentiality and integrity. Two conditions, normal and tampered transmission, were experimented to confirm tag validation and successful decryption. Findings affirmed sound detection of tampering and unauthorized access prevention, proving usefulness of current AEAD ciphers on limited devices. The protocol highlights cryptographic systems that prioritize computationally efficiency and robustness, which is essential in smart homes that have limited power and memory. The hybrid design provides confidentiality and authenticity using a minimal overhead by utilizing ChaCha20 to provide lightweight encryption and Ascon-AEAD128 as authentication. The resilience to the replay and modification attacks was demonstrated in experiments based on message queues and injected packet modifications simulating real-world conditions. Even though benchmarking of hardware was not carried out, the simulated values reveal stability and flexibility to use in low-power systems. This article emphasizes the necessity of authenticated encryption as default, which is consistent with the NIST standards and reflects the appropriateness of Ascon to new IoT security requirements. It also creates a reconfigurable structure that can be used in other highly constrained systems, such as healthcare monitoring and industrial IoT. The main contribution is the gap between theoretical cryptography and practical IoT security provided by the practical prototype. Further development will include tests on physical ESP32 modules and fine performance profiling, yet already, the current implementation proves a scalable, secure model of smart home IoT. Finally, this research demonstrates that the demand of reliable and low-power-based communication in resource constrained networks can be met efficiently without sacrificing device performance by means of lightweight cryptography. In addition to the cryptographic protocol design, this study integrates a machine learning-based intrusion detection layer trained on the Edge-IIoTset dataset, in which Logistic Regression, Decision Tree, Random Forest, and Gradient Boosting classifiers are evaluated to complement the encryption–authentication framework with anomaly-aware monitoring of network traffic. Full article

Sand production constrains artificial-lift reliability, shutdown management, and energy efficiency. This study develops an integrated experimental framework linking particle settling, bulk sand transport, shutdown-related plugging, and electrical demand using 12 anonymized Kumkol wells. The database contains 4380 daily records, 720 laboratory tests, 84 shutdown/restart events, 3600 energy points, 4800 high-frequency restart samples, and 132 maintenance events. In 480 settling column tests, Stokes yielded R² = 0.937 and MAPE = 24.8%; one-parameter calibration improved R² to 0.981 and MAPE to 11.3%, with leave-one-well-out MAPE of 11.4%. In 240 flow loop tests, the full-data non-unstable threshold was 1.052 m/s; nested held-out well accuracy was 87.9%, and the strict stable criterion yielded 83.8%. Shutdown duration was the dominant field predictor: plug odds rose 3.87-fold per 10 h, and nested threshold validation yielded 78.6% accuracy. For ESPs, the ratio-based SEC minimum occurred near 0.773 m/s. A denominator-free active power model controlling for pressure rise, VFD frequency, sand concentration, and well effects achieved R² = 0.851 and retained a positive velocity coefficient in all leave-one-well-out fits. The results define a locally calibrated energy–transport operating window; laboratory transport thresholds are not claimed as direct field setpoints without hydrodynamic scaling and on-well verification. The flow loop threshold block comprises only four independent wells; accordingly, its held-out results are treated as small-cluster evidence, and the numerical velocities remain laboratory reference constraints rather than direct field settings. Full article

Reliable structural monitoring for smart buildings and construction sites requires more than sensor acquisition; it requires sensing and communication paths that remain traceable, recoverable, and compatible with platform-side data processing under heterogeneous field constraints. This study presents a dual-path IoT sensing and communication framework that deliberately separates high-data-rate vibration monitoring from low-data-rate inclination-status monitoring while maintaining common requirements for preservation of available time information, data-source identification, and backend interpretability. The smart-building path integrates an ADXL355 triaxial accelerometer, ESP32-S3, Power over Ethernet (PoE), and Message Queuing Telemetry Transport (MQTT) for 200 Hz vibration acquisition, together with a second-order 10 Hz low-pass filter, 40-record batching, and a Flash LittleFS-based store-and-recovery mechanism that interleaves live and replayed records after reconnection. The construction-site path combines an SCL3300-D01 inclinometer with LoRaWAN, baseline-referenced relative-angle estimation, and a hysteresis state machine with distinct alarm and recovery thresholds. In a 24 h validation, four vibration nodes delivered all 69,120,000 expected records, and four forced-outage trials recovered all offline records while live transmission continued. Frequency-domain analysis confirmed attenuation of high-frequency components while retaining the dominant low-frequency response. The inclination path demonstrated quantifiable angle accuracy, correct alarm/recovery transitions, continuous LoRaWAN frame delivery over the observed interval, and correct backend decoding. The results show that path-specific communication design, combined with a common traceability concept, supports prototype functionality under the reported test conditions, not immediate construction-site deployment. Full 3D visual synchronization, BIM/GIS asset mapping, and digital-twin platform interfacing were not implemented and remain future development tasks. Full article

This paper presents a maritime Internet of Things anti-theft architecture based on an ESP32 onboard node, local motion analysis, and LoRa mesh communication. The proposed detection pipeline uses a one-dimensional convolutional autoencoder trained on stationary vessel data and applied to sliding windows of X- and Z-axis angular velocity measurements. During deployment, a calibration phase estimates the reconstruction-error threshold from the local motion profile of the moored vessel, reducing the dependence on labelled theft examples. The communication layer combines an Elliptic Curve Cryptography setup phase with symmetric payload encryption for alert packets, while ESP32 hardware security features are used to protect firmware and stored credentials. The mesh network uses controlled flooding. A Godot-based simulation environment was used to generate stationary and towing scenarios under different wave configurations. In the current simulation campaign, towing windows produced a higher mean reconstruction error than stationary windows. The results support the feasibility of the architecture and also show that event-level alert logic is required to aggregate window-level anomaly scores into reliable alarms. Full article

Artificial intelligence can extend the functional capabilities of embedded sensors by extracting application-level information from physical measurements. This study investigates whether footstep-induced floor vibrations acquired with a single triaxial MEMS accelerometer contain sufficient information to characterize pedestrian path orientation and travel sense. A custom sensing platform based on an ADXL355 accelerometer and an ESP32 microcontroller was developed to acquire the structural vibration response at 4 kSPS. Lightweight temporal features were processed using a Random Forest classifier. The primary assessment used leakage-aware event-level cross-validation, with complete footsteps as the data-partitioning units. Under this more conservative protocol, discrimination of the complete A–K movement-label set was poor, whereas a compact 12-dimensional descriptor representation achieved 73.18% accuracy, 71.52% balanced accuracy, and 70.98% macro-F1 for X/Y path-orientation classification. Reliable positive/negative travel-sense discrimination could not be demonstrated from isolated footsteps. For historical comparison, the original sample-level procedure yielded 97.09% accuracy, but this value is retained only as a within-sequence reference because densely sampled observations contain strongly overlapping information. The findings provide proof-of-concept evidence that a single floor-mounted MEMS accelerometer can capture coarse pedestrian path-orientation information without cameras or spatially distributed vibration-sensor networks. Feature extraction and classifier inference were performed offline; broader validation across participants, sessions, floor structures, and realistic disturbances is required before deployment as an embedded edge AI sensing node. Full article

Environmental conditions such as inadequate lighting, excessive noise, and poor indoor air quality can negatively affect students’ health, comfort, and academic performance. Although recent Internet of Things (IoT) technologies have enabled low-cost environmental monitoring systems, most studies provide limited validation of the embedded firmware responsible for deterministic multi-sensor acquisition and real-time operation. This paper proposes and virtually validates an energy-efficient embedded firmware architecture for a low-cost IoT environmental monitoring node for smart classrooms. The system integrates ESP32-based firmware with MQ-135, LDR, and KY-037 sensors, local alarm management, LCD visualization, and HTTP-based wireless communication. Hardware–software co-simulation was performed in Proteus Virtual System Modeling (VSM) before physical implementation. Ten experimental scenarios evaluated firmware execution, sensor integration, alarm coordination, fault handling, IoT communication, and power consumption. The proposed architecture maintained stable deterministic execution under all scenarios, with instantaneous current consumption ranging from 100 mA to 280 mA, 100% successful HTTP transmissions (HTTP status code 200), and an average communication latency of 378 ms. These results demonstrate that virtual prototyping provides an effective framework for early firmware verification, reducing development risks while supporting the deployment of reliable and scalable IoT environmental monitoring systems for smart classroom applications. Full article

In most railway divisions the results of scheduled inspections of automation and telemechanics field devices are still recorded on paper. Such records reach engineering management with a delay, are easy to lose, and are difficult to verify. This paper examines the data transmission core of a digital inspection complex in which passive RFID tags identify both the equipment and the personnel, and the inspection record is delivered to a cloud server over a hybrid cellular architecture combining a failure-prone GSM channel with a reliable CDMA channel. To quantify the stability of such a system, a continuous-time Markov chain model is constructed in which the link is represented as an M/M/2/K queue with one unreliable server: both channels carry traffic in parallel, and during a GSM outage the CDMA channel alone sustains service. Records already admitted are preserved across a channel switch; only records arriving at a full shared buffer are rejected. This residual overflow loss stays below 0.1% at routine load with a buffer of m ≥ 5 and reaches about 2.8% only under post-incident overload. The model parameters were measured on an operating ESP32-based scanner complex piloted at Hamza station on 46 point machines. Calculations for three load scenarios show that increasing the local buffer beyond m = 5 yields diminishing returns while the delay grows, and that resilience is governed primarily by the presence of the redundant channel and adequate buffering, with the primary-channel recovery rate a secondary factor. Full article

As the photovoltaic (PV) generation sector expands, ensuring reliable maximum power point tracking (MPPT) in embedded controllers becomes increasingly important. However, many MPPT studies rely on idealized simulation assumptions that neglect practical sensing limitations, including ADC quantization, finite measurement resolution, sensor noise, offset, ripple, and scaling constraints. This paper presents a sensing-aware simulation-based digital twin framework for photovoltaic arrays that integrates dynamic environmental excitation with a virtual instrumentation layer and firmware-level execution on an ESP32 microcontroller (Espressif Systems, Shanghai, China). Unlike conventional model-in-the-loop simulations, the proposed framework ensures that the embedded control algorithm operates on reconstructed and quantized measurements rather than on ideal internal model states. The platform is evaluated using an Incremental Conductance MPPT implementation as a representative embedded workload. The results demonstrate that the proposed simulation-based digital twin framework enables repeatable firmware-level experimentation while exposing instrumentation-induced effects on tracking stability, control dynamics, and measurement-driven behavior under non-ideal sensing conditions. Furthermore, it provides structured datasets to support early-stage development, tuning, and validation of embedded MPPT algorithms before physical laboratory deployment. Full article

Plant factories with artificial light (PFAL) enable precise environmental control for vertical indoor agricultural production systems. However, their multi-layer configuration often creates stagnant air zones with significant temperature and humidity gradients. While ventilation systems are essential for mitigating these issues, their effective design requires accurate and distributed climate monitoring. The deployment of distributed microclimate sensors in PFAL environments remains challenging when multiple identical I2C sensors are required, particularly in low-cost monitoring architectures. Because of their fixed I2C addresses, these devices cannot be connected directly to the same bus without conflicts, prompting the need for multiple controllers and thereby increasing system costs. This study evaluates the implementation of a low-cost, multiplexed IoT sensor network for PFAL microclimate monitoring based on an ESP32 microcontroller and an I2C multiplexer (TCA9548A). This network enables simultaneous operation of five SHT20 temperature and humidity sensors at different levels within a PFAL structure. The multiplexing architecture generated coherent multipoint measurements, demonstrating its practical suitability for microclimate monitoring in PFAL environments. However, the system’s performance was compromised by its reliance on the local Wi-Fi network, resulting in intermittent connectivity failures and significant data loss during internet outages. The presented findings and observed limitations underscore the need for complementary strategies, such as local data buffering or a dedicated private network, to ensure reliable long-term monitoring in smart agricultural environments. Full article

Wearable electrocardiographic technologies have become increasingly important for continuous cardiac monitoring; however, most existing portable systems are limited by the number of recorded leads or provide only basic signal acquisition without advanced automated analysis. This study presents a third-generation wearable twelve-lead electrocardiographic platform developed for multilead ECG acquisition and automated spatial ECG analysis. Compared with the previous generation, the hardware modification primarily consists of architectural consolidation: functions previously distributed across an STM32 microcontroller and separate wireless communication modules are integrated into a single ESP32-S3-based architecture, while the ECG acquisition principle, ten-electrode configuration, and sampling rate remain unchanged. The main methodological contribution of the present work is the software pipeline for lead-specific ST₈₀ measurement and analysis of ST-segment deviations across anatomically contiguous leads. The system uses an ADS1298 analog front-end for synchronized multichannel ECG acquisition. The host software performs digital preprocessing, R-peak detection, ECG feature extraction, twelve-lead reconstruction, lead-specific ST₈₀ measurement, contiguous-lead analysis, and generation of a preliminary computer-assisted ECG report. The developed platform was clinically evaluated using sequential recordings acquired with the proposed system and a reference clinical electrocardiograph. Quantitative comparison of automated PR, QRS, QT, and QTc measurements in 30 paired recordings demonstrated positive correlations with the reference BTL Flexi 12 ECG (r = 0.756–0.820, all p < 0.001), with mean absolute errors ranging from 2.53 ms for QRS duration to 10.40 ms for the QT interval. The system successfully recorded diagnostically interpretable twelve-lead ECGs in all participants and produced stable signal quality suitable for clinical assessment. The software automatically identified ECG waves and intervals, reconstructed twelve-lead recordings, evaluated ST-segment deviations across individual leads, and localized ischemia-related changes according to standard anatomical lead groups. Integration of signal acquisition, processing, visualization, and automated interpretation into a single telemedicine-oriented platform reduced hardware complexity while maintaining reliable multichannel ECG monitoring. The proposed wearable platform demonstrates the feasibility of combining compact embedded hardware with automated multilead ECG analysis for remote cardiovascular monitoring. The presented architecture provides a practical foundation for telemedicine applications and may support earlier recognition of clinically significant electrocardiographic abnormalities during ambulatory monitoring. Full article

This paper presents a secure data transmission framework for intelligent IoT sensor networks, specifically designed for an electronic nose system used in food quality analysis. The proposed architecture integrates a heterogeneous sensor array with an ESP32-based processing unit and GSM/GPRS communication module. To ensure data confidentiality over public mobile networks, a hybrid security scheme combining cryptographic encryption (ChaCha20) and chaotic modulation (Brusselator + DCSK) is introduced. The data packet is first encrypted using the ChaCha20 stream cipher, then converted into a bipolar bit stream. The chaotic system used to generate a chaotic signal is a nonlinear Brusselator model. The system was implemented on a LILYGO TTGO T-Call V1.4 (ESP32 + SIM800L) and tested for discrimination of olive oil and sunflower oil mixtures. Experimental results demonstrate 98.7% classification accuracy using a neural network, 96% first-try transmission success rate over GPRS with 2.3 s average packet transmission time, and 99% overall reliability under RSSI above −85 dBm. Full article

Accurate crack width measurement is essential for the condition assessment of reinforced concrete (RC) bridges, yet most unmanned aerial vehicle (UAV) inspections remain limited to pixel-level observations that cannot be converted into reliable metric units without an external scale reference. This paper presents a lightweight, drone-agnostic UAV-mounted metrology system that enables standards-aligned metric crack width measurement directly from inspection imagery. The payload integrates a focusable diffractive optical element (DOE) red laser that projects a cross pattern of known angular geometry, three TF-Luna time-of-flight (ToF) distance sensors, and an ESP-WROOM-32 microcontroller that provides dual-rate sampling, Bluetooth Low Energy (BLE) streaming, and on-board logging. A two-stage calibration links the synchronized distance measurements to the physical length of the projected cross, yielding an image-specific pixel-to-millimeter scale that is applied to pixel-level crack widths obtained from a vision-based segmentation pipeline. The system is field-deployed on the Puente Huamani Bridge in Pisco, Peru, where measurements of 39 cracks classified under AASHTO MBEI condition states are compared against independent manual measurements by six inspectors. The proposed system reduces measurement variability across all condition states (CS), lowering the average coefficient of variation from 0.36 to 0.10 for fine CS1 cracks, from 0.27 to 0.11 for CS2, and from 0.22 to 0.07 for CS3. Cross-platform adaptability is demonstrated through an additional deployment on a DJI Matrice 350 RTK at the Low Level Bridge in Edmonton, Canada. The results indicate that the system provides a practical, low-cost, and scalable solution for repeatable, standards-aligned UAV-based bridge crack assessment. Full article

Airborne viruses are a key driver of infectious disease transmission, highlighting the importance of reliable detection in public health surveillance. As atmospheric viral concentrations are very low, a sampler with a high viral collection efficiency is essential. Although multiple approaches for evaluating collection efficiency have been applied using various samplers, no standardized sampler has yet been developed. We previously developed a wet-type electrostatic precipitator (WT-ESP) and successfully collected severe acute respiratory syndrome coronavirus 2 from the public environment. However, its efficiency for quantitative collection of airborne viruses remains unclear. This study aimed to clarify the collection efficiency of the WT-ESP. We evaluated collection efficiency via two different approaches: direct spray, where virus-containing aerosols were sprayed directly into the WT-ESP inlet, and indirect spray, where aerosols were dispersed into a closed space and then collected using the sampler. The direct spray tests achieved 20.1–50.2% collection efficiency, whereas the indirect spray test achieved an efficiency <12%. These findings highlight that electrostatic precipitation has an advantage of enhancing collection efficiency compared with values reported for impingers in previous studies and provide preliminary insights into the collection efficiencies under direct and indirect spray conditions, providing foundational data that bridge the gap between both methods. Full article

Background: Wearable sensing and local wireless communication architectures offer significant potential for accessible rehabilitation technologies. The objective of this exploratory study was to design, implement, and describe an interdisciplinary, wireless inertial sensing platform—the Virtual Therapist Platform (VTP)—for real-time postural monitoring and exergame-based rehabilitation in a controlled laboratory setting. Methods: A laboratory proof-of-concept evaluation was conducted across three integrated domains: (1) technical characterisation of the dual-node ESP-NOW architecture (30-min test at 50 packets/s per node via an ESP32-C6 gateway) for real-time trunk tracking; (2) formative usability and acceptability assessment of the VTP interface across two independent cohorts of fourth-year Physiotherapy students ($n=25$ per cohort; 2024 and 2025) using a standardized protocol; and (3) a descriptive mapping of the four-phase interdisciplinary development process integrating Physiotherapy and Mechatronics Engineering inputs. Results: The two-node ESP-NOW test demonstrated high communication performance (99.68% packet delivery ratio (PDR), 8.03 ms mean latency, 1.98 ms jitter, and 99.68 ± 0.52 packets/s throughput). Subjective usability increased from a median score of 85.0 (IQR: 80.0–92.5) in 2024 to 100.0 (IQR: 100.0–100.0) in 2025. Conclusions: The dual-node architecture provided reliable local ESP-NOW packet transmission, while subjective evaluations demonstrated high system usability and acceptability. This interdisciplinary baseline lays the groundwork for future evaluations in clinical environments. Full article

Reliable drinking-water production is difficult on remote islands where brackish-water delivery is intermittent, technical personnel are scarce, and reverse-osmosis plants are manually operated. This study presents an operational characterization of an Internet of Things (IoT) retrofit deployed in Santa Cruz, Galápagos; it is not a controlled before-and-after effectiveness evaluation. An ESP32-based M5Stack Tough controller, distributed ESP-NOW sensing nodes, relay–contactor interfaces, a binary pressure permissive, and a ThingSpeak cloud layer were integrated without replacing the existing pumps and membranes. The exported primary-flow channel contained 4,603,989 numeric observations, including 500 pre-official test readings. Operational analyses used 4,603,489 numeric observations from the official monitoring period; 4,603,340 values remained after nominal-range filtering, and positive flow had a median of 12 L/min (interquartile range: 11–15 L/min). Among 332 logged high-pressure commands, 326 were preceded by a low-pressure command (98.2% unbounded command-state consistency), whereas 275 occurred within a 120 s analytical bound (82.8%). The median low-to-high command delay was 27 s (interquartile range: 11–70 s). Four organizational representatives completed a published 41-item Industry 4.0 maturity instrument before and after deployment; the self-reported overall mean was 0.26 at baseline and 1.95 post-deployment, and these results are interpreted descriptively. Energy-consumption and production data were confidential and unavailable to the authors, while water-quality variables were not measured. The contribution is therefore a long-duration, local-first legacy retrofit with auditable telemetry and explicit limitations, rather than a claim of optimized desalination performance. Full article