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The Internet of Things (IoT) has become a fundamental paradigm in modern communication systems, enabling the large-scale interconnection of sensors, actuators, and embedded computing platforms. This paper presents a decision-oriented framework for the selection of energy-sensitive wireless communication platforms in IoT systems. The proposed approach combines systematic measurement, structured feature engineering, and lightweight regression models to predict energy consumption and current demand for different hardware platforms and wireless technologies, including ESP32- and NORA-based devices utilizing Wi-Fi, Bluetooth Low Energy (BLE) and LoRa communication. The results confirm that simple and interpretable regression models can provide robust guidance for platform and technology selection in realistic real-world scenarios, without incurring the complexity associated with detailed physical-layer or protocol-level simulations. Full article

Securing IoT networks presents fundamental challenges rooted in hardware constraints: firmware is often non-upgradeable and every security boundary is fixed at manufacture. Machine learning-based intrusion detection offers a scalable response, yet nearly all published systems assume clean training data and clean inference conditions. Production IoT environments satisfy neither assumption. Sensors degrade, packets drop, and adversaries deliberately corrupt telemetry streams to evade detection. The framework described here is built around that reality. The proposed framework is distinguished from prior work by four design decisions. First, three encoding branches, a residual DNN, a 1D-CNN, and a BiLSTM, are run in parallel and are fused by concatenation, each capturing structural patterns in tabular traffic data that the others miss. Second, a dual-view consistency loss trains the model under simultaneous feature masking and Gaussian noise, penalizing prediction divergence between two independently corrupted views of the same sample. Third, we introduce entropy-weighted attention: rather than fixed learned weights, per-feature importance is adjusted dynamically from information entropy measured across training batches, giving higher-entropy features stronger influence because they carry more discriminative variation. Fourth, branch-dropout regularization randomly silences entire branches during training, forcing each to develop independently useful representations instead of co-adapting. Class imbalance is handled through severity-aware loss weighting which scales contributions by the operational cost of missing each attack category, not purely by inverse frequency. On UNSW-NB15, the full model achieves 99.99% accuracy, 100% precision, 99.97% recall, and a false-negative rate of 2.65 × 10⁻⁴—the lowest across all compared architectures. Full article

Low-cost IoT-based air quality sensors enable dense monitoring networks but suffer from significant measurement noise and instability particularly in dynamic environments. Conventional fixed-window smoothing reduces noise but introduces a trade-off between signal stability and temporal responsiveness, often attenuating short-term pollution events. This paper proposes an adaptive filtering algorithm that dynamically adjusts the averaging window size based on short-term signal variability. The method relies on real-time variance estimation to balance noise suppression and sensitivity to rapid changes without increasing computational complexity. The approach is implemented within an IoT-based monitoring framework and evaluated using parallel measurements with a certified reference device. Comparative analysis against a certified reference device demonstrates strong agreement, with Pearson correlation coefficients reaching r = 0.88 for PM_2.5 and r = 0.86 for PM₁₀, and low error levels (RMSE ≈ 2.1–2.2 µg/m³). The proposed adaptive filtering approach preserves temporal dynamics while improving signal stability and robustness compared to raw and fixed-window filtering. In addition, this method improves event detection stability, achieving low false alarm rates and near real-time response (latency < 1 sampling interval), supporting RPA-based workflow triggering. The results show that the proposed adaptive filtering provides an efficient and lightweight solution for real-time signal processing on resource-constrained devices, making it suitable for large-scale deployment in environmental monitoring systems. Full article

This Special Issue based on eight Articles/Reviews focuses on low-cost chemical sensor technologies, bio-chemical sensors, advanced active materials, sensing nanomaterials, sensor nodes, wireless sensor networks for chemical sensing, functional characterization, miniaturized transducers, advanced proofs of concept, and chemical detection applications. Promising advanced materials such as metal oxide nanostructures, carbon nanomaterials, composite heterostructures, multilayered coatings, and more have been explored for chemical sensing applications and environmental sustainability. Sensing solutions have been applied in the context of bio-chemical detection and gas monitoring, representing the current state of the art. Full article

With the growing security requirements of sensor nodes in Internet of Things (IoT) systems, conventional silicon-circuit-based physical unclonable functions (PUFs) still face limitations in circuit overhead, design complexity, and system integration. To address these challenges, this paper proposes a lightweight gas sensor PUF (GS-PUF) design based on a Ni-doped SnO₂ nanoscale gas sensor array. The proposed method exploits both the unavoidable process randomness introduced during sensor fabrication and the device-to-device electrical response variations induced by gas–material interactions as entropy sources, thereby enabling high-quality PUF response generation. At the device level, Ni-SnO₂ nanomaterials are prepared by electrostatic spray deposition (ESD), and an indirectly heated gas sensor array is constructed to enhance the sensitivity and stability of the sensing response. At the algorithmic level, a random resistance balancing algorithm based on multi-sensor combinational comparison is proposed. By randomly comparing the summed resistances of multiple sensor clusters, a 128-bit multi-bit PUF response is generated, while the uniformity and independence of the output bits are effectively improved. Experimental results demonstrate that the proposed GS-PUF exhibits excellent randomness, uniqueness, and reliability: the information entropy of the PUF responses is greater than 0.99, approaching the ideal value; the probabilities of output bits “1” and “0” are 0.4988 and 0.5012, respectively, indicating a well-balanced distribution; the inter-device uniqueness reaches 49.8%, close to the ideal value of 50%; all items in the NIST randomness test suite are passed, with all p-values exceeding 0.01 and the minimum p-value being 0.0368, confirming a high level of statistical randomness confidence. In addition, long-term measurements under fixed laboratory conditions show that the PUF response reliability remains above 96%. Compared with other sensor-based PUFs, the proposed method provides a lightweight sensing-security integration approach for IoT sensor nodes by reusing intrinsic gas-sensor response variations and avoiding an additional dedicated silicon PUF circuit. Full article

The increasing demand for sustainable, affordable smart city infrastructure has heightened the need for low-cost near-real-time water quality monitoring systems. In this study, we propose Water-QI, a low-cost Internet of Things (IoT)-based environmental monitoring platform that combines budget-friendly sensors with deep learning for water quality index (WQI) assessment and forecasting. The sensing platform measures five key physicochemical parameters, namely temperature, total dissolved solids (TDS), pH, turbidity, and electrical conductivity, enabling continuous multi-parameter monitoring in urban water environments. To model temporal variations in water quality under both cloud-based and edge-oriented deployment scenarios, we evaluate multiple gated recurrent unit (GRU) architectures with different widths and depths. Experiments are conducted at two temporal resolutions, hourly and minute-level, in order to examine the trade-off between predictive accuracy and edge computational latencies. In the hourly scenario, the single-layer GRU with 64 units achieved the best overall balance, reaching a validation RMSE of 0.0281 and a test $R^2$ of 0.9820, while deeper stacked GRU models degraded performance substantially. In the minute-resolution scenario, shallow wider GRU models produced the best results, with the single-layer GRU with 512 units attaining the lowest validation RMSE (0.025548) and the 256-unit variant achieving nearly identical accuracy with much lower inference cost. The results show that increasing the GRU model length can yield improvements at high temporal granularity, whereas increasing the GRU layer depth consistently harms convergence and generalization. Overall, the findings indicate that shallow GRU architectures provide the most practical solution for accurate, low-cost, and scalable water quality forecasting. In particular, the 64-unit GRU is the most suitable choice for hourly periodic interval operation, while the 256-unit GRU offers the best edge computational speed and accuracy trade-off for minute-level near-real-time inference on resource-constrained devices. Full article

The rapid growth of Internet of Things (IoT) applications over 5G networks demands secure, low-latency data transmission while operating under strict resource constraints. However, existing studies have relied on simulations or partial implementations that fail to capture real 5G features, thus producing overly optimistic elucidations of cryptographic performance. In addition, the absence of end-to-end validation across system layers introduces an opaque flow effect, where transparency lacks across the full transmission path. To address this gap, this paper presents a fully integrated end-to-end 5G IoT security framework that introduces a modified RC4-NL (nonlinear) algorithm to enhance the security of lightweight stream ciphers while preserving computational efficiency. Environmental sensor data is encrypted on a Raspberry Pi 4B and transmitted over a commercial 5G standalone network using a Quectel FG50V module to a Multi-access Edge-Computing (MEC) server. A web-based dashboard built with FastAPI, accessed securely through an Ngrok tunnel, performs real-time decryption and visualization on 5G-connected mobile devices. This architecture eliminates the opaque flow effect and enables realistic performance evaluation, thereby avoiding the optimistic elucidations observed in simulation-based studies. This work experimentally evaluates cryptographic algorithms named Ascon, ChaCha20, AES, standard RC4, and the proposed RC4-NL under the same conditions. Experimental findings indicate that modified RC4-NL achieved an encryption time of 977 µs, a decryption time of 456 µs, and provides a lower power consumption of 0.40 watts, thus giving a proper trade-off between efficiency and enhanced security compared to standard RC4. Full article

This paper presents a low-cost Internet-of-Things (IoT) telemetry and alerting system for monitoring and protecting sea turtle nests. The proposed platform integrates temperature, humidity, vibration, ultrasonic proximity, and ambient light sensors into an autonomous sensing node based on the ESP8266 microcontroller. Measurements are transmitted wirelessly to a cloud backend for real-time visualization and rule-based alert generation. The system is designed to support continuous nest-level monitoring and rapid response to environmental and anthropogenic threats such as overheating, artificial light exposure during hatching, and physical disturbance. In contrast to approaches that require extensive historical datasets or machine-learning models, the proposed solution employs transparent threshold-based rules that provide reliable operation without training data. The platform emphasizes low cost, ease of deployment, and scalability, making it suitable for large-scale conservation deployments across multiple nesting sites. It provides conservation practitioners with actionable situational awareness that complements existing field-based monitoring and protection strategies. Full article

This study developed and evaluated a cloud-based smart irrigation platform (DSmart Farming) integrating low-cost sensors and IoT technology for automated irrigation control in community greenhouses of Puen Jai Insee, organic group in Sa Kaeo Province. The system combined soil moisture, air temperature, and relative humidity sensors, with a LoRa32-based control unit in each greenhouse and a central web-based management application linked to a MariaDB database on a cloud server. Five vegetable crops, including cherry tomato, broccoli, cabbage, Chinese kale, and kale, were grown over two distinct seasons under four irrigation strategies in a completely randomized design with three replications: three smart irrigation treatments based on soil moisture thresholds (on/off at 40/50%, 45/55%, and 50/60%) and a farmer-managed conventional irrigation control. The smart irrigation system maintained root-zone moisture within the target range (approximately 50–60%) and moderated greenhouse microclimate, preventing daytime temperatures from exceeding 40 °C, in contrast to 40–45 °C peaks in the conventional greenhouses. Across crops, smart irrigation increased yields by 20–29% while reducing water use by 41–60% compared to conventional practice, leading to income increases of 20–56%, depending on the crop. Bacterial soft rot caused by Pectobacterium carotovorum subsp. carotovorum occurred only under conventional irrigation, whereas no soft rot or other major diseases were detected in smart-irrigated greenhouses. These results demonstrate that the DSmart Farming system can enhance water use efficiency, avoid disease incidence, and improve the productivity and profitability of organic greenhouse vegetable production in water-limited smallholder systems. Full article

Agricultural soils are a major source of greenhouse gas (GHG) emissions, particularly carbon dioxide (CO₂) and methane (CH₄), highlighting the need for cost-effective and field-applicable monitoring solutions. This study developed and evaluated a low-cost real-time monitoring system for soil CO₂ and CH₄ emissions by integrating surface emission chambers, low-cost gas sensors, a solar-powered energy supply, and IoT-based wireless communication. Three acrylic chambers with different heights (40, 60, and 80 cm) were fabricated to investigate the influence of chamber geometry on measurement performance. System performance was assessed through simultaneous measurements against a Biogas 5000 analyzer under simulated conditions and during field deployment in a sugarcane cultivation area in Khon Kaen Province, Thailand. Relative agreement was used to compare the developed system with the reference instrument. The results showed that relative agreement varied with chamber height for both gases. Under simulated conditions, the 80 cm chamber achieved the highest overall relative agreement for CO₂ and CH₄, underscoring the importance of sufficient headspace volume in chamber-based measurements. Field experiments confirmed the system’s capability for continuous CO₂ monitoring in an agricultural environment. However, CH₄ emissions were not detected during the study period, likely due to drought-induced, well-aerated soil conditions. The developed system demonstrated stable autonomous operation, low energy consumption, and ease of installation, making it suitable for long-term field applications. Overall, the proposed platform provides a practical and scalable approach for real-time soil GHG monitoring and offers strong potential for integration into precision agriculture and climate-smart farming systems to support GHG mitigation strategies. Full article

Water quality has become a major concern for public health, agriculture, and industry, necessitating reliable and continuous monitoring. Conventional monitoring methods are often time-consuming, rely on manual sampling, and involve complex equipment or procedures, making them unsuitable for real-time applications. This study presents an Internet of Things (IoT)-based system for real-time water quality monitoring using ESP32 hardware integrated with the ThingSpeak platform. The system enhances the accuracy of turbidity and pH measurements using advanced sensor calibration techniques. Nephelometric methods and glass electrodes are employed for turbidity detection and pH sensing, respectively, across various water types—including tap water, groundwater, wastewater, saline water, and treated water—to address issues such as environmental drift and measurement inaccuracies. The turbidity sensor was calibrated using a standard six-point method with formazin solutions (0–1064 NTU), whereas pH calibration utilized a three-point approach with NIST-traceable buffer solutions (pH 4, 7, and 10). The results indicate that turbidity measurement errors, initially ranging from 15.75% to 422%, were reduced to below 10% after calibration. Similarly, pH accuracy was significantly improved across all tested water matrices. The system enables real-time data visualization via ThingSpeak, and the implementation of multi-point calibration ensures high data reliability for continuous monitoring. Overall, this approach offers an accurate, efficient, and practical solution for real-time water quality management. Full article

This paper proposes a compact axial-ratio-enhanced wideband circularly polarized rectenna for ambient RF energy harvesting. The proposed rectenna is designed to operate across the mainstream Wi-Fi (2.45 GHz) and 5G (2.6 GHz and 3.5 GHz) communication bands, achieving efficient RF energy capture and effective direct current (DC) conversion. From a design perspective, the proposed approach is developed based on parasitic-element-enabled current redistribution for broadband circular polarization and nonlinear-aware multi-stage impedance matching for wideband rectification. The receiving antenna is based on a crossed-dipole configuration integrated with quarter-ring elements. By employing techniques such as slotting and incorporating additional parasitic patches, a fractional 3-dB axial ratio bandwidth (ARBW) of 52.7% (2.39–4.10 GHz) is achieved, with a peak radiation efficiency of 90% and an average efficiency of 76% within the operating band. To realize wideband impedance matching with the receiving antenna, the rectifying circuit adopts a single-shunt diode half-wave topology, combining L-type and T-type matching networks to significantly extend the operating bandwidth. Experimental results demonstrate that at input power levels of 7 dBm, 7 dBm, and 9 dBm, the rectifier achieves peak conversion efficiencies of 56.7%, 59.8%, and 56.3% at the three target frequencies (2.45 GHz, 2.6 GHz, and 3.5 GHz), respectively. Furthermore, the rectifier exhibits stable rectification performance across a wide input power dynamic range from −15 dBm to 7 dBm. Consequently, the proposed rectenna holds significant application value for passive IoT nodes, low-power sensors, and self-sustainable electronic devices. Full article

This study proposes the Hydroponic Plant Growth Analysis System (HPGAS), a public-data-based preliminary framework for multimodal plant growth state analysis toward future filter-free aquaponic validation. The HPGAS integrates plant images, water quality signals, and environmental signals to estimate an image-centered growth index, growth stage, and proxy abnormal state probability. Because no public dataset jointly provides plant images, direct growth labels, fish metabolic variables, suspended solids, and nitrification-related measurements from a real filter-free aquaponic system, this study is not a direct operational validation. A two-stage evaluation was conducted using the Autonomous Greenhouse Challenge (AGC), HydroGrowNet, and two aquaponic Internet of Things (IoT) water quality datasets. Stage 1 implemented dataset loaders, image–sensor alignment, proxy label generation, and unimodal and fusion baselines. Stage 2 expanded handcrafted image and sensor-context features and adopted month-wise hold-out evaluation. The image-only model achieved the best growth index regression performance, with a root mean square error (RMSE) of 0.0492 ± 0.0187, whereas the fusion model showed a RMSE of 0.0837 ± 0.0196. Conversely, the fusion model achieved the best proxy abnormal state classification performance, with a F1 score of 0.9695 ± 0.0057 under the clean condition, decreasing to 0.9232 ± 0.0263 under sensor dropout and 0.9132 ± 0.0169 under image noise. Under sensor dropout, the fusion model was more stable than the sensor-only model, whereas under image noise it degraded more than the image-only model. These results indicate that multimodal fusion is most useful for proxy abnormal state classification and robust state interpretation, rather than universally superior scalar growth regression. The HPGAS provides a reproducible baseline for future real filter-free aquaponic experiments, while its operational validity remains to be tested using real filter-free aquaponic data. Full article

With the widespread adoption of cryptocurrencies, the ability to conduct continuous offline payments has increasingly become a critical technological requirement. In network-constrained scenarios, current dual-offline payment technologies are useful for single transactions. However, their limitations in continuous payment scenarios have become increasingly evident, making them unable to meet real-world application needs. This has prompted the industry to demand more urgent innovations in research on continuous offline payment capabilities. To address these challenges, this paper proposes a continuous dual-offline payment system capable of supporting multiple continuous payments. The system integrates elliptic curve cryptography (ECC) and zero-knowledge proof (ZKP) technology to generate secure asset credentials, ensuring both immutability and privacy credentials throughout the offline payment lifecycle. A dynamic credential decomposition mechanism enables the splitting of input credentials into change credentials and receipt credentials, facilitating uninterrupted dual-offline payments between hardware wallets. Additionally, it incorporates a batch verification scheme based on smart contracts, utilizing zero-balance verification and chained hash tracing to ensure payment uniqueness and prevent double-spending attacks, thereby guaranteeing the verifiability and validity of payment settlements. Experimental evaluations demonstrate that the proposed system reduces gas consumption per payment and improves execution efficiency during batch processing, combining high security with strong performance. This research provides a feasible solution for the application of digital currencies in offline scenarios, carrying significant theoretical value and practical significance for driving technological innovation and application expansion in the cryptocurrency field. In addition to cryptocurrency payments, the proposed system is also applicable to IoT and sensor network environments. Many IoT devices operate in disconnected or network-limited areas and require secure micro-transactions. Our dual-offline payment mechanism supports such scenarios, as the main cryptographic operations are lightweight enough for typical IoT hardware. This further extends the practical value of our system beyond traditional cryptocurrency payments. Full article