Vibration energy harvesting is a promising solution for powering battery-free IoT systems; however, the instability of ambient vibrations presents significant challenges, such as limited harvested energy, intermittent power supply, and poor adaptability to various applications. To address these challenges, this paper proposes ViPSN2.0, a modular and reconfigurable IoT platform that supports multiple vibration energy harvesters (piezoelectric, electromagnetic, and triboelectric) and accommodates sensing tasks with varying application requirements through standardized hot-swappable interfaces. ViPSN 2.0 incorporates an energyindication power management framework tailored to various application demands, including light-duty discrete sampling, heavyduty high-power sensing, and complex-duty streaming tasks, thereby effectively managing fluctuating energy availability. The platform’s versatility and robustness are validated through three representative applications: ViPSN-Beacon, using an ultra-lowcost structural PZT (ϕ35 mm, <0.002 $) to enable a BLE advertisement from a single transient fingertip press with 100 m line of sight; ViPSN-LoRa, supporting wireless communication powered by wave vibrations in actual marine environments (Bohai Bay) with per-uplink task energy compatible with kilometer-scale field links; and ViPSN-Cam, enabling intermittent image capture and wireless transfer, delivering one frame approximately every 15 s under typical conditions. Experimental results demonstrate that ViPSN 2.0 can reliably meet a wide range of requirements in practical battery-free IoT deployments under energy-constrained conditions.

Object counting aims to quantify instances of specific categories within a given image. Traditional methods heavily rely on extensive, category-specific annotated data for model training, resulting in limited generalisation capabilities across diverse categories. To address this problem, category-agnostic object counting has emerged, enabling the direct counting of arbitrary objects with minimal examples or textual prompts. However, existing approaches still face significant challenges in complex scenarios, including subtle inter-instance feature variations, severe occlusion in dense object clusters, and background noise. This paper introduces SCCNet, a novel arbitrary-shot categoryagnostic object counting framework. SCCNet unifies few-shot and zero-shot counting paradigms by seamlessly integrating a vision foundation model (SAM), a vision language model (CLIP), and an innovatively designed Basic Counting Module (BCM). To specifically tackle the intricate problem of feature matching, the BCM network incorporates three core modules: 1) An Edge-Aware Feature Enhancement (EAFE) module leverages the Laplacian operator to extract edge features and generate attention masks, enhancing fine-grained feature modelling; 2) A Channel-Sensitive Feature Fusion (CSFF) module integrates an adaptive 1D convolution mechanism to enhance matching accuracy in densely populated object regions; 3) A Dual-Axis Contextual Attention (DACA) module adaptively extracts contextual information to mitigate background noise interference. Extensive experiments on the FSC-147 benchmark dataset demonstrate that our method significantly outperforms state-of-the-art counting methods, fully validating the superiority of the proposed framework. The code is available at https://github.com/Dev-why1024/SCCNet.

For boosting output power, frequency-up methods like gear train and plucking are introduced into pendulum-based electromagnetic energy harvesters (EEHs). Still, the gear train and plucking methods have the problems of high manufacturing costs and high energy loss, respectively. To address the above issues, inspired by biological grooming behavior, we present a low-cost, high-efficiency EEH utilizing 3D printing. This device is excited by an asymmetric stiffness pendulum, induced by an oblique cantilever beam, converting the ultra-low frequency of human motion into high-speed, unidirectional rotation of the rotor. The energy density comparison between the sandwich and back iron structure of the EEH is made by the simulation, and then parameters are selected. The bench-top swing experiment system is established to test the performance of the EEH, and the established equivalent electromagnetic model discusses the load resistance result. The feasibility of human walking for powering the Internet of Things (IoT) device is explored when wearing the EEH. The bench-top test result shows that the EEH can reach the normalized power of 1.07 · 10-4 W/(Hz ·°) and normalized power density of 6.6 W/(m3·°). Through human testing at a walking of 1 km/h, the IoT device can run normally, showing great potential for achieving self-power and cost-effective IoT devices.

Energy harvesting from human motion has great potential of sustainably powering Internet of Things (IoT) sensors and satisfying their continuous sensing requirement. In this article, we propose a high-performance wrist-worn energy harvester to efficiently capture the biomechanical energy of arm swinging to self-power wearable sensors. Based on coaxial topology, a planetary gear system serves as frequency-up converter to increase energy conversion capacity, and all the functional units are coaxially installed to achieve a highly compact structure. Thanks to improved energy conversion capacity and structure compactness, the energy harvester can efficiently capture the kinetic energy of arm swinging and achieve high average power. We derive an analytical model to predict the system dynamics and power generation performance using the Lagrangian approach and mirror image method. We fabricate a miniature prototype with different proof mass configurations, which is characterized under bench-top excitations and tested under real walking excitations. The results show that in bench-top testing, the prototype generates maximum average power of 2.73 mW and maximum power density of 535.29$\mathbf {\mathrm {\mu }}\text{W}$/cm3 at the excitation frequency of 1.2 Hz among different configurations. In terms of average power and power density, this energy harvester significantly overperforms its counterparts. In real walking testing, the prototype generates maximum average power of 3.13 mW at a walking frequency of 1.2 Hz among different configurations and achieves higher power output than bench-top testing. Finally, the prototype is used to simultaneously power four sensors and demonstrates great potential in self-powered IoT applications.

Inertial energy harvesters have great potential to sustainably power wearable sensors for the Internet of Things. However, the bulky proof mass used to capture kinetic energy limits the power density of such an energy harvester. Targeted for high-power density, we propose an inertial energy harvester without additional proof mass to efficiently scavenge the kinetic energy of human limb swing. By adopting a planetary structure, the power generation unit, comprising the base, coils, rotor, and magnets, serves as an eccentric weight of the system, so no additional proof mass is needed. Excited by limb swing, the power generation unit oscillates along the sun gear so that the rotor is actuated by the sun gear to spin and produce electricity. We build a theoretical model to predict the average output power under limb swing excitations. We fabricate a miniature prototype to experimentally characterize the energy harvester under pseudowalking excitation and evaluate its performance in real walking. The results show that the prototype generates a maximum power of 1.46 mW and power density of $454.82~{\mu }\text{W}$ /cm3in pseudowalking testing, which are over ten times those of its counterparts. In the real walking test, the prototype performed even better, achieving 1.84 and 2.95 mW when worn on the wrist and ankle, respectively. After power regulation, the energy harvester can fully power a pedometer at various walking speeds. Finally, simulations using real walking data demonstrate that the proposed device reaches higher power density compared with conventional structures and that introducing the additional proof mass unnecessarily increases power density.