Search Results (2,737)

Accidental crushing by sows is the primary cause of pre-weaning piglet mortality in intensive production, often due to the spatiotemporal lag of manual inspection. While Internet of Things (IoT) solutions exist, they frequently face challenges such as vision occlusion, high hardware costs, and latency. To address these, this study developed a low-cost multi-modal edge computing system based on TinyML. Using an ESP32-S3 microcontroller, the system employs a “Motion-Gated Acoustic Detection” strategy, activating a lightweight 1D-CNN model to identify piglet screams only when an IMU detects high-risk postural transitions of the sow. Results show the quantized model (5.1 KB) achieves 95.56% accuracy and 2 ms inference latency. The total end-to-end response latency is within 179 ms, ensuring intervention within the early “golden rescue window.” The low-power design enables the battery life to cover the entire lactation period. Field tests demonstrated that the system intercepted identified crushing risks within the monitored cohort, supporting its potential for significantly improving piglet survival probability. This research overcomes the limitations of single-modal monitoring and provides a scalable, cost-effective engineering intervention for enhancing animal welfare and achieving intelligent, unattended supervision in precision livestock farming. Full article

The increasing demand for autonomous wireless sensors in Internet of Things (IoT) applications has intensified research on vibration energy harvesting, particularly in the low-frequency range where ambient vibrations are most prevalent. However, most vibration energy harvesters operate efficiently only at a single resonance mode, resulting in a narrow operational bandwidth and pronounced performance degradation under frequency detuning. To address this limitation, this paper proposes a multimodal hybrid piezoelectric–electromagnetic vibration energy harvester that exploits both the first and second resonance modes of a cantilever-based structure to achieve broadband low-frequency operation. The design is guided by the complementary utilization of strain-dominated and velocity-dominated regions associated with different vibration modes. Numerical modeling and finite element simulations are employed to investigate the influence of mass distribution, deformation characteristics, and relative velocity on energy conversion performance. A secondary cantilever carrying the electromagnetic coil is introduced to enhance the relative motion between the coil and the magnetic field, thereby extending the effective operational bandwidth. The experimental results demonstrate increased harvested power, improved energy conversion efficiency, and a significantly broadened effective frequency range compared to conventional single-mode piezoelectric and electromagnetic energy harvesters. Full article

With the advancement of 5G and the Internet of Things (IoT), traditional upper-layer authentication mechanisms are vulnerable to attacks, while quantum computing threatens cryptographic security. Radio frequency fingerprint identification (RFFI) offers a physical-layer solution by exploiting inherent hardware imperfections. However, in complex electromagnetic environments, narrowband and especially agile interference (characterized by low power and narrow bandwidth) can severely distort fingerprint features, rendering conventional detection algorithms ineffective. To address this challenge, this paper proposes a novel interference detection framework tailored for Orthogonal Frequency Division Multiplexing (OFDM) systems. First, a signal transmission model incorporating non-ideal hardware characteristics (e.g., DC offset, I/Q imbalance) is established. Based on this model, we design an agile interference detection algorithm comprising two key components: (1) a time-series anomaly detection method that fuses multi-domain expert features (fractal, complexity, and high-order statistics) with machine learning, demonstrating superior performance over the traditional CME algorithm under narrowband interference, and (2) a progressive search segmental detection algorithm that, combined with reconstruction error features extracted by an autoencoder, effectively identifies low-power agile interference by appropriately trading-off computation time for detection sensitivity. Finally, an OFDM simulation platform is developed to validate the proposed methods. The results show that the segmental detection algorithm achieves reliable detection at a jammer-to-signal ratio (JSR) as low as −10 dB, significantly outperforming existing approaches and enhancing the robustness of RFFI in challenging interference environments. Full article

As mobile networks increasingly support sustainable and green Internet of Things (IoT) applications, energy-efficient solutions that address coverage constraints have become paramount. Although backscatter communication (BackCom) offers a low-power option for IoT devices, particularly battery-less IoT nodes, it can suffer from limited coverage. To overcome this, we exploit aerial platforms (UAVs) integrated with non-orthogonal multiple access (NOMA) to enhance both coverage and spectral efficiency. In this paper, we propose a UAV-supported NOMA-enabled BackCom system to serve massive backscatter node (BN) networks. We aim to maximize system throughput by jointly optimizing the power allocation and reflection coefficients of the BNs, along with the trajectory and data collection locations of the UAV. We derive closed-form solutions for the reflection coefficients and the optimal collection locations of the UAV and achieve global optimality in power allocation by utilizing the Karush–Kuhn–Tucker (KKT) optimality conditions in conjunction with the golden-section search (GSS). In addition, we formulate the UAV trajectory optimization problem as a Traveling Salesman Problem (TSP) and propose an efficient low-complexity genetic algorithm (GA)-based solution. The numerical results demonstrate that the proposed scheme outperforms the benchmark schemes in terms of sum-throughput rate and achieves an overall performance enhancement of $8.983$ dB, underscoring the potential of our approach for large-scale battery-less IoT deployments. Full article

This paper presents a high-precision angle demodulation method for magnetic encoders by integrating orthogonal-signal correction with space–time modulation (STM). The proposed approach specifically addresses a critical vulnerability of STM-based high-frequency pulse interpolation: its interpolation accuracy is highly sensitive to zero-crossing timing jitter of the quadrature signals. In practical magnetic encoders, non-idealities such as DC offsets, amplitude mismatch, and phase non-orthogonality in the sine/cosine outputs induce jitter and shift in the zero-crossing points. This directly leads to fluctuations in high-frequency counts and amplifies the final angle error. To mitigate this issue, an online orthogonal-signal correction module is first developed. This module sequentially performs offset estimation, amplitude normalization, and real-time phase orthogonalization, thereby enhancing the orthogonality and zero-crossing stability of the quadrature signals at the source. This preprocessing significantly reduces the sensitivity of the subsequent interpolation counting to noise and signal imperfections. Based on the corrected signals, an STM pulse-counting interpolator is adopted to convert angle information into a time-domain phase (time) difference, and high-frequency counting is used for fine subdivision. A Kalman-filter-based predictor is employed to estimate angular velocity and compensate the intrinsic latency of counting-based demodulation in dynamic conditions. Experimental results demonstrate that the proposed phase orthogonalization correction markedly suppresses zero-crossing timing jitter and enhances the stability of high-frequency pulse interpolation. Consequently, the overall demodulation error is reduced by more than 30 percent compared with existing methods, and the final angle error is maintained within 0.033°. Full article

Wind energy harvesting based on triboelectric nanogenerators (TENGs) is a promising solution for powering distributed Internet of Things (IoT) nodes, yet its practical efficiency and stability are often hindered by the fluctuating and unpredictable nature of wind. Here, we propose a self-regulating TENG (SR-TENG) that leverages the synergistic effects of centrifugal, elastic, and frictional forces to automatically switch between non-contact and contact modes based on wind speed. This configuration achieves an ultra-low start-up wind speed of 0.86 m/s, ensures sustainable high-performance output across a broad wind speed range, and exhibits excellent durability with no observable performance degradation during 23,000 s of continuous operation at 375 rpm. Systematic structural optimization enables the SR-TENG to reach a peak open-circuit voltage of 140 V, a short-circuit current of 12.5 μA, and a transferred charge of 300 nC at 375 rpm. When integrated with a customized power management circuit, the system delivers a 30.39-fold increase in effective output power at a 1 MΩ load and a 4-fold faster charging rate for a 10 μF capacitor. For practical validation, the harvested ambient wind energy successfully powers a wireless temperature-humidity sensor for real-time cloud data transmission. These results highlight that the SR-TENG holds great potential for advanced wind energy harvesting and self-powered sensing applications in distributed IoT systems. Full article

Energy harvesting plays a pivotal role in enabling sustainable power supply for the Internet of Things and distributed sensor networks, particularly for low-power devices. Piezoelectric energy harvesters based on vortex-induced vibrations offer a promising solution for low-wind-speed applications, yet their performance is constrained by limited bandwidth and sensitivity to wind speed variations. This study addresses these limitations by proposing a novel multi-parameter adjustable piezoelectric energy harvester featuring an inclined cylindrical bluff body. By systematically tuning the inclination angle and installation position, the device achieves substantial performance improvements. Experimental results indicate that the optimized configuration yields a wider operational frequency band and enhanced energy conversion efficiency. Through the experimental results, we discovered the existence of the double-peak phenomenon and the plateau phenomenon. The voltage value of the second peak can reach up to 122.4% of the maximum voltage of the first peak. The duration of the maximum plateau phase can maintain between the wind speed of 2.3 m/s and 5.7 m/s. Full article

Vibration energy harvesting is a promising approach to support and supplement power, thereby extending the lifetime of low-power sensor nodes under suitable vibration conditions, i.e., in environments where sufficient ambient vibrations are available. It is not a universal autonomous power-supply solution, particularly when generalized across the Internet of Things (IoT), because the harvested power is typically limited to the µW–mW range and depends strongly on the vibration frequency content, amplitude, and operating point relative to resonance. Furthermore, many practical harvesters rely on resonant mechanisms, which are inherently narrowband, and therefore their performance can degrade significantly under detuning or broadband/variable-frequency excitations. In addition, energy-management and power-conditioning electronics (rectification, storage, and regulation) are required to convert the generated electrical energy into a stable and usable DC supply for practical loads. In this work, we develop a nonlinear state-space model of a three-phase electromagnetic vibration energy harvester with spatially displaced coils and evaluate its electrical output characteristics and DC power behavior using numerical simulations. Full article

This paper presents a Systematic Literature Review (SLR) on green scheduling and task offloading strategies for energy optimization in edge computing environments. The evolution of low-latency, high-performance applications has driven the widespread adoption of distributed computing paradigms such as Edge Computing, Fog-Cloud architectures, and the Internet of Things (IoT). In this context, Mobile Edge Computing (MEC) is often combined with Unmanned Aerial Vehicles (UAVs) to extend computational capabilities to areas with limited infrastructure, bringing processing closer to the data source to reduce latency and improve scalability. Nevertheless, these systems encounter substantial energy-related challenges, particularly in battery-powered or resource-constrained environments. To address these concerns, green computing strategies—especially energy-efficient scheduling and task offloading—have emerged as promising approaches to optimize energy usage in edge environments. Green scheduling optimizes task allocation to minimize energy consumption, whereas offloading redistributes workloads from resource-constrained devices to edge or cloud servers. Increasingly, these techniques are enhanced through artificial intelligence (AI) and machine learning (ML), enabling adaptive and context-aware decision-making in dynamic environments. This paper conducts a systematic literature review (SLR) to synthesize the most widely adopted strategies for energy-efficient scheduling and task offloading in edge computing, highlighting their impact on sustainability and performance. The analysis provides a comprehensive view of the state of the art, examines how architectural contexts influence energy-aware decisions, and highlights the role of AI/ML in enabling intelligent and sustainable edge systems. The findings reveal current research gaps and outline future directions to advance the development of robust, scalable, and environmentally responsible computing infrastructures. Full article

Here, we develop a digital community management application (APP) and an energy prediction and analysis system for smart communities. The system integrates the internet of things (IoT) technology and multiple prediction models to improve the intelligence and automation of community energy management. The developed APP has the following functions: user classification, announcement notification, express delivery management, GPS positioning navigation, calendar, and energy forecast. The hardware architecture of the system consists of a voltage/current sensing module, a Wireless Fidelity (Wi-Fi) module, and an Arduino platform, allowing real-time feedback and display of power consumption data. The energy forecasting part proposes a two-layer hybrid model architecture. This architecture combines Seasonal Trend decomposition using Loess (STL) time series decomposition, extreme gradient boosting (XGBoost), and Seasonal Autoregressive Integrated Moving Average (SARIMA) models to predict residential electricity consumption trends over the next 3 years. The results of the model prediction are verified using the data on Taiwan’s electricity consumption. The model accurately predicts the average monthly residential electricity consumption with a relative error of 5.8%, an acceptable energy management accuracy. This system integrates APP applications and efficient prediction models, demonstrating its great potential in smart community energy management and enhanced resident interaction. Full article

Long-term home-based sleep monitoring requires wearable sensing devices that strictly balance signal precision with power constraints. This study presents the design and implementation of a low-noise, low-power wearable single-channel electroencephalography (EEG) system for automatic sleep staging. The hardware architecture integrates a TI ADS1298 analog front-end with an STM32F4 microcontroller, utilizing differential sampling and hardware-based filtering to effectively suppress power-line interference and baseline drift. System-level testing demonstrates an average power consumption of approximately 150.85 mW, enabling over 24.6 h of continuous operation on a 1000 mAh battery, which meets the requirements for overnight monitoring. To achieve accurate staging without draining the wearable’s battery, we adopted and deployed a lightweight deep learning model, SleePyCo, on the cloud backend. This architecture was specifically optimized for our edge–cloud collaborative execution, which combines contrastive representation learning with temporal dependency modeling. Validation on the ISRUC dataset yielded an overall accuracy of 79.3% ± 3.0%, with a notable F1-score of 88.3% for Deep Sleep (N3). Furthermore, practical field trials involving 10 healthy subjects verified the system’s engineering stability, achieving a valid data rate exceeding 97% and a Bluetooth packet loss rate of only 0.8%. These results confirm that the proposed hardware–software co-designed system provides a robust, energy-efficient IoMT sensing solution for daily sleep health management. Full article

The integration of Artificial Intelligence (AI) into Internet of Things (IoT) medical devices has revolutionized arrhythmia monitoring. However, the high computational and power demands of traditional Deep Learning (DL) models pose significant challenges for long-term, battery-operated smart electronics. Spiking Neural Networks (SNNs), inspired by the biological efficiency of the human brain, offer a promising solution. This paper reviews the intersection of SNNs, low-power IoT hardware, and biomedical signal processing. I examine the transition from frame-based to event-driven processing, and discuss the hardware–software co-design necessary for next-generation cardiac wearables. Full article

The proliferation of Internet of Things (IoT) devices and embedded processors has recently spurred rapid advances in hardware-level security. This paper systematically reviews developments in securing microcontroller units (MCUs) and constrained embedded platforms from 2020 to 2026, a period marked by the finalization of NIST’s post-quantum cryptography standards and accelerated commercial deployment of hardware security primitives. Through analysis of the peer-reviewed literature, industry implementations, and standardization efforts, we survey five critical areas: post-quantum cryptography (PQC) implementations on resource-constrained hardware, physically unclonable functions (PUFs) for device authentication, hardware Roots of Trust and secure boot mechanisms, side-channel attack mitigations, and Trusted Execution Environments (TEEs) for microcontroller-class devices. For each domain, we analyze technical mechanisms, deployment constraints (power, memory, cost), security guarantees, and commercial maturity. Our review distinguishes itself through its integration perspective, examining how these primitives must be composed to secure real-world embedded systems, and its emphasis on post-standardization PQC developments. We highlight critical gaps including PQC memory overhead challenges, ML-resistant PUF designs, and TEE developer friction, while documenting commercial progress such as PSA Level 3 certified components and 500+ million PUF-enabled devices deployed. This synthesis provides practitioners with practical guidance for securing the next generation of IoT and embedded systems. Full article

The increasing reliance of Internet of Things (IoT) applications on low-power wide-area network technologies, particularly Long Range Wide Area Network (LoRaWAN), has amplified the need for security monitoring approaches that go beyond attack-specific signatures and generic traffic anomalies. Existing solutions are often tailored to individual threat scenarios or rely on statistical indicators, which limits their ability to systematically capture protocol-level misuse in an interpretable manner. This paper addresses this gap by proposing a protocol-aware validation methodology based on a Digital Twin abstraction of LoRaWAN communication behavior. The Over-The-Air Activation (OTAA) procedure is modeled as a finite-state machine that encodes expected message sequences, timing constraints, and specification-driven state transitions. Observed network events are continuously evaluated against this formal state model, enabling the identification of protocol-level deviations indicative of anomalous or non-conformant behavior. Illustrative examples include replay behavior, timing inconsistencies, and integrity-related anomalies, although the framework is not limited to predefined attack categories. The results demonstrate that state machine-based Digital Twin provides a structured and extensible foundation for protocol-aware security validation and Security Operation Center (SOC)-oriented telemetry enrichment. In this sense, the presented approach represents a concrete step toward protocol-aware intrusion detection for LoRaWAN networks by establishing a state-synchronized semantic validation layer upon which higher-level detection mechanisms can be built. Full article

The growing demand for wearable electronics and the Internet of Things (IoT) calls for flexible piezoelectric energy harvesters with substantially improved power output. Polyacrylonitrile (PAN) polymers, with their high polarization and excellent thermal stability, are among the most promising candidates for efficient flexible piezoelectric materials. However, the performance of existing PAN-based harvesters remains limited, and strategies for further enhancing their output are still insufficiently explored. Herein, this study aims to overcome the output bottleneck of PAN-based PENGs by implementing a novel mechanical excitation strategy. Using electrospun flexible PAN-BaTiO₃ nanocomposite films, we systematically compared the electromechanical responses under conventional compression and impact modes. Real-time synchronized force–current measurements in compression mode revealed that the output current increases progressively with drive frequency (2–10 Hz). Specifically, the PENG with PAN-20 wt.% BaTiO₃ achieved a peak current of 0.33 mA at 10 Hz, showing an approximately 7.9-fold enhancement over its pure PAN counterpart. More importantly, under 6 Hz impact excitation, the device exhibited a remarkable output current density of 1.0 mA cm⁻² and a peak power density of 256.5 µW cm⁻². This current density is 95 times higher than that in compression mode at a comparable frequency and surpasses the performance of most recently reported piezoelectric and triboelectric nanogenerators. With an effective area of 16 cm², the PENG could simultaneously illuminate up to 275 commercial LEDs or 100 individual bulbs and maintained stable operation over 63,530 cycles. This work overcomes the output bottleneck in low-frequency energy harvesting and provides an effective pathway toward practical energy-harvesting applications. Full article