Engineering

Abstract: Variable-speed induction motor drives spend most of their service life at partial load, where rated-flux field-oriented control (FOC) is inefficient and where loss-minimizing control (LMC) recovers a large part of the loss. LMC, however, is brittle in two ways that matter in industry: it is tuned isothermally, so as the windings heat the rotor-resistance drift detunes the field orientation and corrupts torque; and it treats the loss-optimal flux as a quasi-static set-point, so an abrupt load rise from a light-load, low-flux condition forces a slow flux rebuild that throttles torque. This paper proposes a thermally-adaptive economic model predictive controller (TA-EMPC) that retains the energy optimum of LMC while removing both weaknesses. A temperature-coupled total-loss model (machine copper and core loss plus inverter conduction and switching loss) is minimized over a finite horizon subject to a torque-delivery constraint; a reduced-order two-node thermal observer updates the loss-defining resistances online without a temperature sensor; and a load-demand-aware flux-reservation term pre-magnetizes the machine ahead of anticipated torque rises. In simulation on a representative 7.5 kW drive, TA-EMPC matched the energy of static LMC to within 0.3% across pump, conveyor, and fast-cycling duty profiles—both saving 1.4–2.3% of cycle energy relative to rated-flux FOC, and up to about 14 efficiency points at very light load—while, unlike LMC, holding the steady torque error below 0.5% when the winding temperature rose to about 90 °C (a resistance increase of roughly 26%) (against an 8% error for the non-adaptive scheme) and reducing the torque undershoot during a light-to-heavy load step from about 23% to near zero. An operation-count analysis indicates that the condensed quadratic-program formulation with move blocking is executable within the 100 µs sampling interval on a production digital signal controller for the chosen control horizon (analytical estimate; on-target measurement is future work). The contribution is thus energy-efficient operation delivered with the torque robustness that loss minimization alone does not provide.

Abstract: The convergence of industrial communication networks and electric drive systems has increased the range of risks associated with induction motor operation, with abnormal motor operation potentially arising from either physical motor faults or malicious cyber operations. This study examines the impact of coordinated network attacks on a three-phase induction motor drive system in a real-time laboratory environment through a PLC–SCADA controlled environment. The following operating conditions were investigated experimentally: normal operation, stator disturbance, rotor abnormalities, false data injection attack, replay-based communication manipulation, and cyber-physical events. In the attack scenarios, significant differences were observed in motor speed, electromagnetic torque, stator current distortion, and communication latency compared with normal operating conditions. To differentiate between actual machine failures and cyber-induced anomalies, an explainable AI-based diagnostic framework was introduced that employs both electrical and network-layer features. Experimental results revealed that the proposed model achieved an overall classification accuracy of 93.17%, with precision and recall> 97% across most operating classes. When subjected to a coordinated attack, communication latency rose from 4.8ms under normal operation to 37.6ms, and the current THD increased from 3.2% to 14.7%. The proposed framework also successfully distinguished cyber-attack-induced abnormal behavior from genuine motor faults at a 96.9% detection rate, which is lower than that of conventional AI classifiers. Explainability analysis also showed that the top features that affect the diagnostic decision process were packet delay, stator current distortion, torque oscillation, and rotor speed deviation. The results demonstrate the necessity of incorporating cybersecurity awareness into intelligent fault diagnosis systems for modern induction motors in industrial cyber-physical environments.

Abstract: In this paper, a numerical method was used to study the dielectric recovery characteristics underwater discharge. The Keller-Miksis equation was used to study the bubble evolution, considering the internal thermodynamic parameters. The temperature and pressure inside the bubble was estimated the second breakdown voltage. The calculated results showed the precise values for second breakdown voltage and recovery time. The second breakdown voltage was determined by the variation in internal temperature and pressure in gradual expansion-contraction stage, as well as in the initial collapse stage. The calculated voltages were in good agreement with the experimental results. The viscosity has effect on the recovery time, the lower viscosity results in a considerably shorter recovery time, while surface tension has little effect.

Abstract: Smart buildings increasingly depend on dense, distributed sensing infrastructures to improve energy efficiency, indoor environmental quality and operational flexibility. However, large-scale IoT/WSN deployment is still constrained by wiring effort, battery maintenance and limited access to sensing locations. Energy harvesting (EH) offers a promising approach toward low-maintenance and partly autonomous sensing, but its practical value in building automation depends on more than the output of individual transducers. This article presents a structured review of EH for IoT/WSN and edge-enabled building automation, focusing on smart-building, Building Management System (BMS) and Building Automation and Control System (BACS) contexts. Light-based, thermoelectric, mechanical, RF/wireless-power-transfer and hybrid harvesting technologies are interpreted through a system-oriented chain linking energy sources, power management, storage, communication, adaptive operation, gateways, diagnostics and edge intelligence. The synthesis shows that EH is most promising for low-duty-cycle environmental monitoring, envelope and façade sensing, occupancy and human-building interaction, airflow-related sensing, technical monitoring and retrofit automation. The main challenges concern the transition from device autonomy to sensing-service autonomy, complete-node evaluation under real building conditions, interoperability with supervisory systems and diagnostic interpretation of intermittent operation. Further research is also needed on lifecycle value assessment and safe transferability toward mission-critical, military-inspired and closed ecological infrastructure applications.

Abstract: Improving building energy efficiency while maintaining collective thermal comfort remains challenging in multi-occupant shared spaces, where occupants may differ in thermal sensation, activity, and clothing conditions. This study develops an edge-cloud Internet of Things system for dynamic group thermal comfort control and energy optimization. The proposed system uses an HUB 8735 ULTRA edge-computing board for localized multi-point environmental sensing and non-invasive visual extraction of occupancy, posture, and clothing-related features. A 4-day field data collection campaign with structured questionnaires was conducted to obtain occupants’ Thermal Sensation Votes (TSV) as ground-truth labels. A machine-learning thermal sensation model was trained using cloud GPU resources, compressed from Float32 to INT8 through post-training quantization, and deployed on the edge device. The embedded controller applies a two-time-scale rolling-horizon strategy with deterministic grid search to identify temperature setpoints that minimize group discomfort, while an event-triggered rolling-horizon control logic with a 10% Group PPD comfort deadband reduces frequent compressor switching. Field experiments under a single-blind comparative protocol showed that the system achieved a mean TSV of -0.12, reduced comfort variation relative to a 25 °C baseline, and decreased electricity consumption by 1.16 kWh, corresponding to an 11.1% energy reduction. These results indicate that the edge-cloud framework can support coordinated thermal comfort control and energy-saving operation in shared indoor environments.

Abstract: The concept of computational sustainability in urban environments emphasizes integrating data engineering, artificial intelligence (AI), and computational techniques to harmonize environmental, social, and economic needs while advancing the United Nations (UN) Sustainable Development Goals (SDGs). A bioelectromagnetically-conscious microenvironment represents an architectural and spatial design approach that incorporates various advanced disciplines to align with natural biological rhythms and regulate electromagnetic (EM) fields, ultimately fostering a balance between human health and environmental well-being. As cities and high-density urban regions are increasingly saturated with EM fields from various sources, the integration of computational sustainability and bioelectromagnetics becomes essential to enhance the occupant experience when interacting with buildings and spaces. This study addresses this EM anthropocene influence by presenting an EM exposure mapping (EMEM) that involves a data acquisition (DAQ), interpolation heatmaps, and machine learning (ML) algorithms for validation and prediction. A case study is conducted on a microenvironment that comprises three buildings on the University of Ottawa campus in Canada. The above correlation between techniques serves as a robust pathway for architects, engineers, and policymakers for promoting designs that are mindful of visualization and management of EM ambient influences. The outcomes serve not only scientific and educational purposes but also bolster adaptive urban design, compliance with safety standards, and building certification systems. All the above aim at fostering smarter and more livable communities and establishing a pathway for advancing EM sustainability.

Abstract: Soft mechanical inductive sensors are emerging as a promising solution for advanced robotics, healthcare, and wearable devices, offering high precision, adaptability, and environmental robustness. These sensors leverage coil-based designs to achieve resilience against temperature variations, humidity, and mechanical wear, making them suitable for long-term operation in challenging environments. Existing reviews tend to focus on a single aspect of coil-based inductive sensors, thereby lacking a comprehensive, unified analysis of the field. This review addresses this gap by analyzing recent developments in soft mechanical inductive sensors, from theoretical concepts and practical design to their use cases. The review focuses on the working principles, design strategies, fabrication techniques, electronic interfaces, and, finally, the applications of coil-based soft mechanical inductive sensors. Key applications in soft robotics, prosthetics, and haptic devices are examined, highlighting the transformative potential of these sensors across diverse domains. Finally, the review discusses critical challenges, including sensitivity optimization, durability, and environmental interference, and outlines future directions to further advance this promising technology.

Abstract: This technical note presents a low-cost Internet of Things (IoT)-based prototype that combines real-time electrical monitoring, local visualization, cloud connectivity, and threshold-based demand response for electric power distribution education. The system integrates an ESP32 DevKit V1, a PZEM-004T V3.0 meter with a 100 A current-transformer clamp, a 16×2 I2C liquid-crystal display, a Waveshare 1.3-inch OLED display, a relay module, and a 12 V RGB fan representing a controllable non-critical load. Voltage, current, active power, and accumulated energy are read at the AC input and exchanged with a Blynk IoT dashboard through virtual datastreams. WiFiManager enables network provisioning without embedding the local Wi-Fi credentials in the firmware. In automatic mode, active power is compared with a remotely adjustable limit; an exceedance disconnects the fan, activates a short audible alarm, and reports a demand-response event. A recovery threshold equal to 70% of the selected limit provides hysteresis. During a 15 min laboratory stability observation, the prototype continuously reported valid measurements without abnormal heating, odor, acoustic noise, or communication interruption. A representative 14-reading serial-monitor snapshot yielded 213.6-214.4 V, 0.05 A, and 4.3-4.4 W, with mean values of 214.04 V and 4.37 W. The results support the prototype as an educational demonstrator rather than a utility-grade meter; calibration against a reference instrument and longer data logging remain necessary for metrological claims.

Abstract: As distribution systems evolve toward active networks, the integration of rooftop solar photovoltaic (PV) systems has become essential yet technically challenging. This study investigates the impact of high-level PV penetration on the low-voltage (LV) distribution network of the Northland Su residential complex in Keçiören, Ankara. A detailed network model was developed in OpenDSS and cross-validated against a custom MATLAB Newton-Raphson load-flow script, using actual electrical parameters extracted from approved construction drawings. The system comprises three transformer-feeder branches — A1+A2+A3 (800 kVA, 347.36 kW), B1+B2 (315 kVA, 151.56 kW) and C1+C2 (315 kVA, 147.57 kW) — with a total aggregate demand of approximately 718 kW. Steady-state and 24-hour quasi-static load-flow analyses were performed across three scenarios: Base Case (S1, 0% PV), Moderate Penetration (S2, 50% PV) and High Penetration (S3, 100% PV, up to 160/110/84 kWp per block). The results show that voltage profiles remain within the ±0.95–1.05 pu operating band in every scenario, with a maximum voltage rise of only +0.67% (MATLAB) / +0.360% (OpenDSS) at the B1+B2 block under 100% PV. Midday PV generation reduces transformer loading substantially, yet the evening demand peak persists unchanged. It is concluded that the existing infrastructure can safely host up to 100% rooftop PV penetration under Ankara’s May insolation profile without grid reinforcement, while battery energy storage is recommended to shift the midday surplus to the evening peak.

Abstract: Reliable electric load forecasting is an important engineering problem for power-system planning, grid stability, and mission-critical energy management. This paper presents a symmetry-aware simulation and modelling framework for medium-range electric load forecasting under noisy and uncertain operating conditions. The proposed approach combines the Multi-Model Partitioning Filter (MMPF) with Nonlinear Autoregressive Exogenous (NARX) submodels, while two adaptive optimization strategies, Genetic Algorithm-based Resource Allocation (GARA) and Particle Swarm Optimization (PSO), are used to optimize the contribution weights of the parallel predictors. The modelling process uses real commercial power-system data and evaluates the forecasting framework over two-month horizons. To simulate realistic engineering disturbances, correlated symmetric Gaussian noise is injected into the testing phase under moderate and heavy noise scenarios. The cyclic symmetry of temporal variables, such as hour and month, is preserved through unit-circle encoding, while the symmetry and asymmetry of residual error symmetric distributions are examined through scatter-plot analysis. The results show that both GARA and PSO improve the robustness of the hybrid MMPF-NARX model, but PSO consistently achieves lower MAPE values, smoother convergence, and lower computational burden. The optimal configuration is obtained with nine NARX submodels, beyond which additional model complexity offers no meaningful performance gain. Overall, the study shows that symmetry-aware modelling, adaptive optimization, and noise-based simulation can support more reliable forecasting in modern power-system engineering applications.

Abstract: Surface electromyography (sEMG) is the dominant control modality for myoelectric hand prostheses, yet a persistent gap remains between high offline classification accuracy and real-time, on-device control: models are often large, evaluated under leaky window-level splits, and reported with a single inflated metric that hides instantaneous behaviour. Our contribution is primarily methodological: a strictly leak-safe within-user protocol (calibrate on a user's earlier repetitions, test on a held-out later one, split by contiguous recording segment so no overlapping window crosses the split), paired with honest dual reporting of per-window (instantaneous) and per-execution balanced accuracy alongside the rest false-activation rate. We instantiate it with a compact (28k-parameter, 27.5 kB int8) decoder for six hand grips plus rest: a depthwise-separable CNN with a causal, population-learned, zero-parameter transition grammar that reasons over per-window evidence. On NinaPro DB2, five calibration repetitions give 94.0% per-execution balanced accuracy at 2.2% false-activation and 75.2% per-window; a wider 20–450 Hz acquisition band adds 2.6 per-window points (Wilcoxon p=0.005) at the same footprint. On 12 trans-radial amputees (NinaPro DB3, DB7) accuracy is 63.8–74.2% with high between-subject variance and no evidence that the deep decoder's able-bodied advantage over a classical baseline transfers. Most consequentially, on NinaPro DB6 we expose the cross-session gap the within-session protocol cannot see: per-execution accuracy collapses from 93.4% (day 1) to 38.8% on day-5 re-donning, but a brief per-don recalibration fully restores it, statistically level with the within-session ceiling at every separation, turning the collapse into an explicit deployment recipe. We position the work honestly against recent ultra-low-power and foundation-model decoders, reporting compute cost rather than claiming on-device measurements. Code and configurations: https://github.com/seanb9/emg-leaksafe-benchmark.

Abstract: Reliable battery operation requires not only detecting accelerated degradation, but also knowing whether the available history is sufficient to localize that transition. We analyse a public cycle-level dataset comprising 14 reconstructed lithium-ion battery records, using the charging-to-discharge duration ratio as an empirical degradation indicator. A linear reference, continuous broken-stick regression, smoothing-spline curvature analysis, an aggregate Bayesian smooth-transition model, and a hierarchical Bayesian smooth-hinge model are compared. The hierarchical model estimates battery-specific transition midpoints and slope changes under partial pooling. Its population transition midpoint is 553.9 cycles (95% highest-density interval: 547.1–560.9), with substantial between-battery variability (posterior median standard deviation: 142.0 cycles); all batteries have posterior probability one of a positive slope increment. Removing approximately half the observations throughout the trajectories widens uncertainty but preserves the acceleration conclusion and nearly preserves battery ordering. In contrast, truncating late-life observations strongly destabilizes transition localization and extrapolative coverage. The practical implication is that distributed sparse monitoring can remain informative, whereas dense early-life data cannot replace continued observation through the post-transition regime. The framework supports retrospective, uncertainty-aware screening and confirmation of accelerated ageing in battery-monitoring data, but it is not a prospective changepoint or remaining-useful-life predictor.

Abstract: The selection of georeferenced meteorological databases is a critical technical and financial factor in photovoltaic (PV) system sizing. This study evaluates how database choice affects PV generation estimates and economic feasibility by combining bibliometric screening, technical validation, financial indicators, and multicriteria analysis. A bibliometric review of 5,658 documents indexed in Scopus and Web of Science supports the selection of seven databases, which are compared across ten Brazilian locations. PV generation is estimated using both a simplified sizing approach and a higher-temporal-resolution model based on hourly irradiance, ambient temperature, and module thermal coefficients, enabling comparison with measured generation in a three-year case study in João Pessoa. Performance is assessed using MAE, MAPE, RMSE, R2, NPV, payback, and the Analytic Hierarchy Process. NASA POWER shows the strongest overall multicriteria performance, ranking first in seven of the ten locations. Database choice causes variations of up to 4.70 years in payback and BRL 105,709.36 in NPV for the same system, equivalent to USD 21,082.84 at the May 22, 2026 exchange rate. The simplified CRESESB-based method overestimates annual generation by 6.79%–12.64%, whereas NASA POWER reaches a best annual deviation of −1.66% in 2024.

Abstract: The large-scale integration of renewable energy sources and power electronic devices has introduced increasing complexity to power quality disturbances in smart grids. While machine learning and deep learning methods have demonstrated high accuracy in disturbance detection and classification, their inherent black-box nature limits interpretability and limits operator trust. Existing Explainable Artificial Intelligence (XAI) techniques often suffer from high computational overhead. To address these challenges, this paper proposes an Evolving Explainable Artificial Intelligence (E-XAI) framework for power quality disturbance detection and interpretation in smart grids. The proposed framework employs a Genetic Algorithm (GA) to automatically optimize power quality features, reducing computational complexity while preserving model interpretability. By using this approach, machine learning models can not only identify disturbances but also provide transparent explanations of the key contributing factors, enabling grid operators to gain actionable insights. Experimental results demonstrate that the E-XAI framework reduces the computational cost of XAI techniques while improving classification stability. This work provides an effective pathway toward explainable and intelligent power quality disturbance analysis, addressing the critical research gap in lightweight XAI methods for smart grid environments.

Abstract: With the rapid growth of electric vehicles (EVs), high charging demand has in-creased uneven station utilization, and pressure on distribution networks. Therefore, this paper proposes a collaborative method for capacity planning and charging scheduling of multiple charging stations (CSs) considering time-of-use (TOU) pricing and energy storage system (ESS). A total system cost model is established, including transformer and ESS costs. Secondly, a deep neural network-guided im-proved sparrow search algorithm (DNN-ISSA) is proposed to optimize the number of chargers and parking spaces by predicting the initial capacity center. Furthermore, a charging scheduling algorithm is proposed to optimize user charging time by introducing a TOU price response function to modify charging probabilities. A case study of 36 CSs in Jinan shows that the proposed method reduces average charging time by 15.7, 15.4, and 15.2 minutes for 1,000, 5,000, and 10,000 demand points, while lowering the total system cost from 73.92 to 70.36 million yuan. The convergence value of DNN-ISSA reduces by 15.05%, 21.67%, and 11.61% compared with the sparrow golden-sine optimization algorithm (SSA-GSA), particle swarm optimization algorithm (PSO), and sparrow-particle swarm optimization algorithm (SSA-PSO), respectively. The proposed method enhances energy utilization, mitigates peak loads, and supports low-carbon EV charging operation.

Abstract: High-speed electric rotating machines enhance power density and eliminate gearboxes in waste heat recovery microturbines, but conventional designs face high manufacturing costs and complex cooling requirements. This study presents the development, experimental validation, and comparative analysis of high-speed configurations. Initially, a lower-speed induction machine prototype operating at 13,000 rpm was built using standardized components to experimentally validate numerical loss models. Experimental testing of the initial prototype confirmed a total loss of 7.89 kW, closely matching the simulated 7.75 kW. Leveraging these findings, two next-generation topologies of decreased size, an induction machine and a surface permanent magnet machine, were designed and evaluated using finite element method and conjugate heat transfer simulations under sinusoidal and pulse-width modulation excitations. At a 14,000 rpm operational point, the surface permanent magnet prototype outperformed the induction machine configuration, ensuring the lower temperatures of the permanent magnet machine and achieving 63.2 kW of mechanical power and 96.21% efficiency compared to the induction machine's 52.4 kW and 94.64%.This paper builds upon the microturbine generator project introduced at the ICPGEEC 2025 conference, by presenting lighter, higher-speed machine designs.

Abstract: Recent advances in miniaturized neural-interfacing probes have led to broadening of the knowledge on brain functions via recording of local field potentials and provide a great framework for interacting directly with neurons by different means of brain stimulation, typically by delivering electrical stimuli. Although fabrication of bidirectional implantable neural probes based on MEMS materials and methods is already a mature technology, the fact that electrical brain stimulation relies solely on a good electrical contact between the conductive electrode and the living tissue limits the lifespan of such implants, mainly due to foreign body inflammatory response and scar-tissue growth on the electrode-tissue interface. As a solution, we propose a neural probe for state-of-the-art biopotential recording, with a monolithic integrated miniaturized piezoelectric resonator for neural modulation which is fully encapsulated in biocompatible material.Theoretical analysis on piezoelectric resonating membranes is presented and its limitations are assessed and compared to finite element analysis (FEA), which is then used to tune the resonant frequency of the vibrating membrane to the desired value for neural stimulation. As well the acoustic output and penetration depth of the generated signal are analyzedFinally, the MEMS fabrication methodology for the proposed neural probe is presented.

Abstract: Personal voice activity detection (PVAD) identifies whether each detected speech frame originates from a designated target speaker. Modern PVAD systems are typically trained offline and then deployed with frozen model parameters and a fixed, pre-enrolled speaker embedding, leaving them unable to adapt to distribution shifts at inference time such as unseen acoustic environments, changing speaking styles, or mismatches between enrollment and test conditions. Test-time training (TTT) and test-time adaptation have shown promise in language, vision, and several speech tasks, yet their behavior on PVAD has not been studied. In this work, we present an empirical study of two complementary test-time adaptation mechanisms built on top of the recently proposed FDE-Mamba backbone. The first is a VAD-gated TTT adapter, which instantiates the TTT-Linear formulation within the personalization pathway and augments it with a VAD-probability gate and exponential moving-average stabilization, adapting an internal weight matrix on the speaker-conditioned feature stream of each test utterance. The second is TEA (Test-time Embedding Adaptation), a scheme that keeps all model parameters frozen and instead adapts the target speaker d-vector itself via self-supervised objectives at inference time, directly targeting enrollment–test mismatch. We evaluate both mechanisms on the LibriSpeech PVAD benchmark across two backbones (LSTM-based FDE-RNN and Mamba-based FDE-Mamba), reporting category-wise average precision, mean average precision (mAP), accuracy, recall, precision, and real-time factor. Our results show that test-time adaptation yields consistent but modest gains over the FDE-Mamba baseline (e.g., mAP from 0.9605 to 0.9641 and precision from 0.881 to 0.899), while slightly reducing recall and increasing inference cost when TEA is enabled. Through ablation studies, we quantify the independent and combined contribution of each component, characterize the recall–precision trade-off introduced by adaptation, and isolate the effect of post-hoc Gaussian smoothing so that its benefit is not conflated with that of the adaptation mechanisms. These findings, together with a discussion of their limitations and cost–benefit profile, provide a measured baseline and design insights for future work on adaptive PVAD, particularly under stronger acoustic and enrollment mismatches than those captured by the LibriSpeech protocol.

Abstract: Artificial high-reflectivity ground covers are a potential strategy to increase rear-side irradiance and energy yield in bifacial photovoltaic systems, especially in utility-scale plants with single-axis trackers. This paper reports field evidence from a pilot plant in southern Brazil (27.4°S, 48.4°W), where four reflective covers—a white film, a pearl-white film, a black-and-pearl film, and a white geomembrane—were evaluated against a gray gravel reference. The study combines albedo and spectral characterization, rear-to-front irradiation ratios, energy-yield comparisons, soiling assessment, thermal analysis, and operational observations. Broadband albedo increased from 25% for gray gravel to 53–58% for the reflective films and 72% for the geomembrane. Reflective films increased the rear-to-front irradiation ratio to around 20% and delivered energy gains close to 9%, while the geomembrane achieved the highest irradiance enhancement and gains exceeding 10%. Inverter current limitations led to clipping, indicating that measured gains may underestimate the full energy potential of the reflective covers. Estimated thermal losses were insignificant compared with measured gains, while soiling and fixation methods affected long-term feasibility. The results confirm the technical potential of reflective covers, while showing that utility-scale deployment must consider not only optical performance, but also optical stability, electrical limitations, cleaning and anchoring requirements, drainage adaptations, operation and maintenance practices, and cost constraints.

Abstract: Indoor environmental quality in university classrooms significantly influences the overall well-being of occupants. However, implementing continuous monitoring networks is often restricted by high equipment costs. This paper presents the architectural design, firmware implementation, and virtual validation of a low-cost, multi-sensor IoT system aimed at monitoring key environmental parameters: air quality index indicators, ambient light, and acoustic distress indicators. The system architecture centers on an ESP32 microcontroller, integrated with simulated response curves for an MQ-135 sensor, an LDR, and a sound detection module. Due to physical deployment constraints, the system was strictly validated using electronic simulation software (Proteus VSM). The simulation results demonstrate a reliable firmware execution with an algorithm latency under 10 ms during multi-trigger alarm states. Furthermore, a virtual power consumption analysis was conducted, revealing an average current draw of 100 mA under normal operations and a peak dynamic current draw spanning from 130 mA up to 280 mA during high-priority alarm cycles. The proposed virtual framework establishes a technically viable, open-source blueprint for rapid-deployment environmental telemetry before transitioning to physical scaling.