Low-power Sensor Research Articles

High-entropy oxide (FeCoNiCrMn)3O4 for room-temperature NO2 sensors

High-entropy oxides (HEOs) with a multi-cation structure have attracted significant attention in the fields of electrochemistry owing to their high entropy stability and cocktail effect. However, there has been very limited research on the use of HEO in the field of sensing. In this work, we utilized FeCoNiCrMn high-entropy alloys as the precursor to synthesize (FeCoNiCrMn)3O4 HEO and investigated their crystal structure, microscopic morphology, elemental valence state information, and gas sensing performance. The sensor exhibited decent response to NO2 at room temperature (RT) without any modification or sensitization methods. To verify the gas sensing mechanism, we simulated the interaction between five metal elements in HEO and the NO2 molecules by density functional theory, which reveals a crucial synergistic effect from the multiple cations to enhance the adsorption and charge transfer of NO2 molecules. This work explores the application potential of (FeCoNiCrMn)3O4 in low-power gas sensors and enriches the material selection for RT sensors.

Sustainable Sea of Internet of Things: Wind Energy Harvesting System for Unmanned Surface Vehicles.

Harvesting wind energy from the environment and integrating it with the internet of things and artificial intelligence to enable intelligent ocean environment monitoring are effective approach. There are some challenges that limit the performance of wind energy harvesters, such as the larger start-up torque and the narrow operational wind speed range. To address these issues, this paper proposes a wind energy harvesting system with a self-regulation strategy based on piezoelectric and electromagnetic effects to achieve state monitoring for unmanned surface vehicles (USVs). The proposed energy harvesting system comprises eight rotation units with centrifugal adaptation and four piezoelectric units with a magnetic coupling mechanism, which can further reduce the start-up torque and expand the wind speed range. The dynamic model of the energy harvester with the centrifugal effect is explored, and the corresponding structural parameters are analyzed. The simulation and experimental results show that it can obtain a maximum average power of 23.25 mW at a wind speed of 8 m/s. Furthermore, three different magnet configurations are investigated, and the optimal configuration can effectively decrease the resistance torque by 91.25% compared with the traditional mode. A prototype is manufactured, and the test result shows that it can charge a 2200 μF supercapacitor to 6.2 V within 120 s, which indicates that it has a great potential to achieve the self-powered low-power sensors. Finally, a deep learning algorithm is applied to detect the stability of the operation, and the average accuracy reached 95.33%, which validates the feasibility of the state monitoring of USVs.

XplAInable: Explainable AI Smoke Detection at the Edge

Wild and forest fires pose a threat to forests and thereby, in extension, to wild life and humanity. Recent history shows an increase in devastating damages caused by fires. Traditional fire detection systems, such as video surveillance, fail in the early stages of a rural forest fire. Such systems would see the fire only when the damage is immense. Novel low-power smoke detection units based on gas sensors can detect smoke fumes in the early development stages of fires. The required proximity is only achieved using a distributed network of sensors interconnected via 5G. In the context of battery-powered sensor nodes, energy efficiency becomes a key metric. Using AI classification combined with XAI enables improved confidence regarding measurements. In this work, we present both a low-power gas sensor for smoke detection and a system elaboration regarding energy-efficient communication schemes and XAI-based evaluation. We show that leveraging edge processing in a smart way combined with buffered data samples in a 5G communication network yields optimal energy efficiency and rating results.

Time Delay Characterization in Wireless Sensor Networks for Distributed Measurement Applications

This paper investigates the critical aspect of synchronization in wireless sensor networks (WSNs) across diverse industrial applications. The low-cost sensor network topologies are analyzed. The communication delay measurements and quantitative jitter analysis are performed under different conditions, and dependencies of the propagation time delay on the data bitrate and modulation type for different hardware implementations of the WSNs are presented. The time delay distribution influence on the time synchronization error propagation over WSN layers was assessed from the experimental probability density functions. The network synchronization based on the controlled propagation delay jitter approach has been proposed. This research contributes quantitative insights into the complexities of synchronization in WSNs, offering a foundation for optimizing network configurations and parameters to extend the operational life of low-power sensor nodes.

Advancing Cycling Safety: On-Bike Alert System Utilizing Multi-Layer Radar Point Cloud Clustering for Coarse Object Classification.

Cyclists are considered to be vulnerable road users (VRUs) and need protection from potential collisions with cars and other vehicles induced by unsafe driving, dangerous road conditions, or weak cycling infrastructure. Integrating mmWave radars into cycling safety measures presents an efficient solution to this problem given their compact size, low power consumption, and low cost compared to other sensors. This paper introduces an mmWave radar-based bike safety system designed to offer real-time alerts to cyclists. The system consists of a low-power radar sensor affixed to the bicycle, connected to a micro-controller, and delivering a preliminary classification of detected obstacles. An efficient two-level clustering based on the accumulation of radar point clouds from multiple frames with a temporal projection from previous frames into the current frame is proposed. The clustering is followed by a coarse classification algorithm in which we use relevant features extracted from the resulting clusters. An annotated RadBike dataset composed of radar point cloud data synchronized with RGB camera images is developed to evaluate our system. The two-level clustering outperforms the DBSCAN algorithm, achieving a v-measure score of 0.91, compared to 0.88 with classical DBSCAN. Different classifiers, including decision trees, random forests, support vector machines (SVMs), and AdaBoost, have been assessed, with an overall accuracy of 87% for the three main object classes: four-wheeled, two-wheeled, and others. The system has the ability to improve rider safety on the road and substantially reduce the frequency of incidents involving cyclists.

Response analysis of asymmetric monostable energy harvester with an uncertain parameter

The nonlinear piezoelectric vibration energy harvesting system offers a solution to the power supply challenge for low-power sensors. However, In practical engineering, structural parameter uncertainties arise from manufacturing errors, environmental temperature fluctuations, and changes in material properties. Besides, due to inherent flaws in the structure and materials, asymmetry may occur in the potential well, which leads to the complexity of the dynamic behavior exhibited in energy harvesting system. In this paper, in order to investigate the dynamics of the asymmetric monostable piezoelectric energy harvesting system under an uncertain parameter, the orthogonal polynomial approximation method is applied to transform the stochastic system into an equivalent deterministic system. Then, the responses of the equivalent deterministic system are used to reveal the stochastic behavior of the uncertainty system. The results indicate that, when the uncertainty intensity of the cubic nonlinear coefficient is 0, an increase in the asymmetry level of the potential well leads to an increase in the output voltage. Increasing the other parameters, the output voltage is reduced. In addition, as the uncertainty intensity increases, the parameter intervals of chaotic response become wider and the energy harvesting efficiency decreases. Moreover, as the potential well asymmetry increases, the sensitivity of energy harvesting system to uncertain parameter increases.

Theoretical analysis and potential engineering application of an energy harvester

The technique of energy harvesting, which captures the energy from ambient vibrations, has received increasing attention due to its potential to power low-powered wireless sensors. Therefore, it is important to reveal the nonlinear dynamic mechanism to design a high-performance energy harvester. This paper describes an energy harvester with the decoupled bistable potential (DBEH), in which the potential function can be controlled independently by the barrier height or the barrier width. The harmonic balance method (HBM) is used to obtain the theoretical solutions, and the Jacobian matrix is provided to determine their stability. Thirdly, this paper thoroughly discusses the influence of barrier height and width on output responses and the multi-solution range. Finally, numerical simulations observe the stochastic resonance (SR) phenomenon and the inverse SR phenomenon of the DBEH with Gaussian white noise injection. The SR phenomenon of the DBEH is applied to bearing fault diagnosis, effectively enhancing fault characteristics.

Fast-Response and Low-Power Self-Heating Gas Sensor Using Metal/Metal Oxide/Metal (MMOM) Structured Nanowires.

With the escalating global awareness of air quality management, the need for continuous and reliable monitoring of toxic gases by using low-power operating systems has become increasingly important. One of which, semiconductor metal oxide gas sensors have received great attention due to their high/fast response and simple working mechanism. More specifically, self-heating metal oxide gas sensors, wherein direct thermal activation in the sensing material, have been sought for their low power-consuming characteristics. However, previous works have neglected to address the temperature distribution within the sensing material, resulting in inefficient gas response and prolonged response/recovery times, particularly due to the low-temperature regions. Here, we present a unique metal/metal oxide/metal (MMOM) nanowire architecture that conductively confines heat to the sensing material, achieving high uniformity in the temperature distribution. The proposed structure enables uniform thermal activation within the sensing material, allowing the sensor to efficiently react with the toxic gas. As a result, the proposed MMOM gas sensor showed significantly enhanced gas response (from 6.7 to 20.1% at 30 ppm), response time (from 195 to 17 s at 30 ppm), and limit of detection (∼1 ppm) when compared to those of conventional single-material structures upon exposure to carbon monoxide. Furthermore, the proposed work demonstrated low power consumption (2.36 mW) and high thermal durability (1500 on/off cycles), demonstrating its potential for practical applications in reliable and low-power operating gas sensor systems. These results propose a new paradigm for power-efficient and robust self-heating metal oxide gas sensors with potential implications for other fields requiring thermal engineering.

Global estimation of range resolved thermodynamic profiles from micropulse differential absorption lidar

We demonstrate thermodynamic profile estimation with data obtained using the MicroPulse DIAL such that the retrieval is entirely self contained. The only external input is surface meteorological variables obtained from a weather station installed on the instrument. The estimator provides products of temperature, absolute humidity and backscatter ratio such that cross dependencies between the lidar data products and raw observations are accounted for and the final products are self consistent. The method described here is applied to a combined oxygen DIAL, potassium HSRL, water vapor DIAL system operating at two pairs of wavelengths (nominally centered at 770 and 828 nm). We perform regularized maximum likelihood estimation through the Poisson Total Variation technique to suppress noise and improve the range of the observations. A comparison to 119 radiosondes indicates that this new processing method produces improved temperature retrievals, reducing total errors to less than 2 K below 3 km altitude and extending the maximum altitude of temperature retrievals to 5 km with less than 3 K error. The results of this work definitively demonstrates the potential for measuring temperature through the oxygen DIAL technique and furthermore that this can be accomplished with low-power semiconductor-based lidar sensors.

A Low-Power Optical Sensor With Dynamically Adjustable Field of View for Photosynthetically Active Radiation (PAR) Measurement

A Low-Power Optical Sensor With Dynamically Adjustable Field of View for Photosynthetically Active Radiation (PAR) Measurement

Exploring IoT Integration for Innovative Advancements in Civil Engineering

Civil engineering developments have allocation with the recent trends of integration with IoT technologies and devices. This technology significantly improves automation and remote monitoring for various tasks in the construction process, building maintenance and safety, allowing operators to remain remote free ever than before. IoT is simply the connection of several devices to the internet as per the need. The real implication of IoT is beyond and complex than this. The IoT is meant to signify the establishment of entire networks of devices which are connected to the internet and with each other. Various sensors and monitors recognize the conditions, communicate them to online network or app, and transmit relevant information to your phone automatically. Internet of Things (IoT) is the technology that transforming construction industries, automate processes, and improves construction area and other relevant fields in civil like Global Positioning System (GPS), Water Resources Management and Water Supply, Smart Construction, Water & Waste Water Treatment, Traffic Management and Control, Waste Management, Concrete Technology, Environmental Impact Assessment & Monitoring and Smart Lighting System. This research paper covers implementation of IoT in all these fields of civil engineering and updating the current scenario. IoT is potentially used to increase the productivity, on-site safety, and operational efficiency. Through the placement of low-power sensors, managers can improve worksite visibility at every stage of a project in realtime, from planning to construction, and even task post-construction. The construction industry is bringing real-time information into processes that are long-standing. Internet of Things (IoT) devices and sensors are collecting job site data in a more affordable, efficient and effective way than can ever previously imagined. This latest technology represents exciting hikes forward in the civil engineering world. Foremost principles among these are integrated in this study for Internet of Things with the world of civil engineering.

Low-noise and low-power pixel sensor chip for gas pixel detectors

Low-noise and low-power pixel sensor chip for gas pixel detectors

Buckling-driven piezoelectric defect-induced energy localization and harvesting using a Rubik’s cube-inspired phononic crystal structure

Wireless sensor networks that enable advanced internet of things (IoT) applications have experienced significant development. However, low-power electronics are limited by battery lifetime. Energy harvesting presents a solution for self-powered technologies. Vibration-based energy harvesting technology is one of the effective approaches to convert ambient mechanical energy into electrical energy. Various dynamic oscillating systems have been proposed to investigate the effectiveness of energizing low-power electronic sensor devices for supporting various IoT applications across engineering disciplines. Phononic crystal structures have been implemented in vibrational energy harvesters due to their unique bandgap and wave propagation properties. This work proposes a Rubik’s cube-inspired defective-state locally resonant three-dimensional (3D) phononic crystal with a 5 × 5 × 5 perfect supercell that contains 3D piezoelectric energy harvesting units. The advantage of defect-induced energy localization is utilized to harness vibrational energy. The 3D piezoelectric energy harvesting units are constructed by the buckling-driven assembling principle. Adapting to the low-frequency and broadband characteristics of ambient vibration sources, soft silicone gel is used to encapsulate the buckled 3D piezoelectric units, which are embedded in the 3D cubic phononic crystal to assemble an entire system. The energy harvesting performance of various defective layouts and their defect modes is discussed. The results demonstrate that the harvester functions well under multidirectional, multimodal, and low-frequency conditions. The proposed methodology also offers a new perspective on vibrational energy harvesters for defective phononic crystals with superior working performance.

Research on piezoelectric vibration energy harvester for cutting part of roadheader

At present, coal mine tunneling technology is developing in the direction of intelligence and unmanned. The piezoelectric vibration energy harvester can convert the vibration energy generated by the cutting part of roadheader into electrical energy and realize the energy self-sufficiency of the low-power wireless sensor. In practical applications, the vibration frequency of the cutting part of roadheader is different, and the working condition is complex, resulting in the energy recovery efficiency of the vibration energy harvester is not ideal. In order to improve its adaptability and reliability, a separated excitation broadband piezoelectric vibration energy harvester is proposed in this paper.

Self-standing perylene diimide covalent organic framework membranes for trace TMA sensing at room temperature

The unprecedented demand for highly selective, real-time monitoring and low-power gas sensors used in food quality control has been driven by the increasing popularity of the Internet of Things (IoT). Herein, the self-standing perylene diimide based covalent organic framework membranes (COFMPDI-THSTZ) were prepared via liquid–liquid interfacial synthesis method. By incorporating the perylene diimide monomer into the COFM through molecular engineering, COFMPDI-THSTZ based sensor demonstrated an outstanding trimethylamine (TMA)-sensing performance at room temperature. Benefited from the TMA-accessible self-standing membrane morphology, π-electron delocalization effect, and extensive surface area with continuous nanochannels, the specific and highly sensitive TMA measurement has been achieved within the range of 0.03–400 ppm, with an exceptional theoretical detection limit as low as 10 ppb. Moreover, the primary internal mechanism of COFMPDI-THSTZ for this efficient TMA detection was investigated through in-situ FT-IR spectra, thereby directly elucidating that the chemisorption interaction of oxygen modulated the depletion layers on sensing material surface, resulting in alterations in sensor resistance upon exposure to the target gas. For practical usage, COFMPDI-THSTZ based sensor exhibited exceptional real-time in-situ sensing capabilities, further confirmed their potential for application in dynamic prediction evaluation of marine fish products and quality monitoring in IoT.

Low-Wind-Speed Galloping Wind Energy Harvester Based on a W-Shaped Bluff Body

Galloping-based piezoelectric energy harvesting systems are being used to supply renewable electricity for low-power wireless sensor network nodes. In this paper, a W-shaped bluff body is proposed as the core component of a piezoelectric wind energy harvester. Experiments and simulations have shown that the W-shaped bluff body can improve harvesting efficiency at low wind speeds. For the W-shaped structure, the finite element simulation results indicate that the structure can help improve the aerodynamic performance to obtain high aerodynamic force. The experimental results demonstrate that compared with the traditional bluff bodies, the piezoelectric wind energy harvester with the W-shaped bluff body (WEHW) can generate higher output voltages and has a lower cut-in speed. When the length L is 30 mm and the rear groove angle β is 30°, the W-shaped structure can induce the best harvesting performance. When an external load resistance of 820 KΩ is connected and the wind speed is 5 m/s, the WEHW generates an average output power of 0.28 mW.

Eciton: Very Low-power Recurrent Neural Network Accelerator for Real-time Inference at the Edge

This article presents Eciton, a very low-power recurrent neural network accelerator for time series data within low-power edge sensor nodes, achieving real-time inference with a power consumption of 17 mW under load. Eciton reduces memory and chip resource requirements via 8-bit quantization and hard sigmoid activation, allowing the accelerator as well as the recurrent neural network model parameters to fit in a low-cost, low-power Lattice iCE40 UP5K FPGA. We evaluate Eciton on multiple, established time-series classification applications including predictive maintenance of mechanical systems, sound classification, and intrusion detection for IoT nodes. Binary and multi-class classification edge models are explored, demonstrating that Eciton can adapt to a variety of deployable environments and remote use cases. Eciton demonstrates real-time processing at a very low power consumption with minimal loss of accuracy on multiple inference scenarios with differing characteristics, while achieving competitive power efficiency against the state-of-the-art of similar scale. We show that the addition of this accelerator actually reduces the power budget of the sensor node by reducing power-hungry wireless transmission. The resulting power budget of the sensor node is small enough to be powered by a power harvester, potentially allowing it to run indefinitely without a battery or periodic maintenance.

Real-time, noise and drift resilient formaldehyde sensing at room temperature with aerogel filaments

Formaldehyde, a known human carcinogen, is a common indoor air pollutant. However, its real-time and selective recognition from interfering gases remains challenging, especially for low-power sensors suffering from noise and baseline drift. We report a fully 3D-printed quantum dot/graphene-based aerogel sensor for highly sensitive and real-time recognition of formaldehyde at room temperature. By optimizing the morphology and doping of printed structures, we achieve a record-high and stable response of 15.23% for 1 part per million formaldehyde and an ultralow detection limit of 8.02 parts per billion consuming only ∼130-microwatt power. On the basis of measured dynamic response snapshots, we also develop intelligent computational algorithms for robust and accurate detection in real time despite simulated substantial noise and baseline drift, hitherto unachievable for room temperature sensors. Our framework in combining materials engineering, structural design, and computational algorithm to capture dynamic response offers unprecedented real-time identification capabilities of formaldehyde and other volatile organic compounds at room temperature.

Evaluation of Occupancy Lighting Control Based on Low-Power Privacy-Preserving Sensor

Evaluation of Occupancy Lighting Control Based on Low-Power Privacy-Preserving Sensor

Towards Flexible and Low-Power Wireless Smart Sensors: Reconfigurable Analog-to-Feature Conversion for Healthcare Applications.

Analog-to-feature (A2F) conversion based on non-uniform wavelet sampling (NUWS) has demonstrated the ability to reduce energy consumption in wireless sensors while employed for electrocardiogram (ECG) anomaly detection. The technique involves extracting only relevant features for a given task directly from analog signals and conducting classification in the digital domain. Building on this approach, we extended the application of the proposed generic A2F converter to address a human activity recognition (HAR) task. The performed simulations include the training and evaluation of neural network (NN) classifiers built for each application. The corresponding results enabled the definition of valuable features and the hardware specifications for the ongoing complete circuit design. One of the principal elements constituting the developed converter, the integrator brought from the state-of-the-art design, was modified and simulated at the circuit level to meet our requirements. The revised value of its power consumption served to estimate the energy spent by the communication chain with the A2F converter. It consumes at least 20 and 5 times less than the chain employing the Nyquist approach in arrhythmia detection and HAR tasks, respectively. This fact highlights the potential of A2F conversion with NUWS in achieving flexible and energy-efficient sensor systems for diverse applications.