Low-power Sensor Research Articles

A Priority based Self-Organised MAC Protocol for Real Time Wireless Sensor Network Applications

Wireless Sensor Networks (WSNs) are expressively utilized in various real-time control and monitoring applications. WSNs have been expanded considering the necessities in industrial time-bounded applications to support the dependable and time-bound delivery of data. Recently, Machine Learning (ML) algorithms have been used to address various WSN-related issues. The use of ML techniques supports dynamically modifying MAC settings based on traffic patterns and network conditions. In WSNs to control the communication between a large numbers of tiny, low-power sensor nodes while preserving energy and reducing latency, effective MAC protocols are essential. This paper addresses the ML centered priority-based self-organized MAC (ML-MAC) protocol to provide a priority-based transmission system to ensure the timely delivery of critical data packets. In this research, depending upon the predictions of the ML model, the MAC parameters are dynamically adjusted to find priority-based channel access and the optimal routing path to meet the deadline of critical data packets. From the result analysis, the average throughput and delay of the proposed ML-MAC algorithm outperforms the existing I-MAC protocol.

Sub-1 GHz RF-based Energy-efficient Sensor Node for Secure Communication in Low-power IoT and Embedded Applications

Background: The Internet of Things (IoT) devices consist of a microcontroller unit for data processing, a low-power wireless radio module for data transmission, and various sensors for data collection. The sensor nodes and processing devices used in the Internet of Things are resource-constrained, with power consumption and security being the two most critical parameters. Objective: This paper addresses the challenges of power consumption and security in IoT scenarios. It presents a low-power and secure heterogeneous multicore sensing architecture designed for low-power IoT and wireless sensor networks. The architecture comprises a sensing and control subsystem, an information processing unit, and a wireless communication module. Methods: The architecture uses a microcontroller unit based on ARM Cortex M4, a low-power sub-1 GHz RF-compliant communication radio, and a few sensors. The proposed architecture has been implemented and tested using the Contiki Operating System. Results: The implemented sensor node architecture demonstrated performance efficiency, lower energy consumption, and higher security. Conclusion: By leveraging efficient power management, data transmission strategies, and cryptographic security, the architecture contributes to developing energy-efficient and secure IoT devices.

Experimental Design: Implementation of IoT-based drip irrigation to enhance the productivity of Cilembu sweet potato (Ipomoea batatas) cultivation

Effective irrigation methods are crucial for meeting plant water needs while preventing excessive evaporation, soil dryness, and water shortages. Conventional irrigation methods often result in inefficient water distribution, leading to a reduced agricultural productivity. This study aimed to enhance water use efficiency and improve crop yield by employing Internet of Things (IoT)-based drip irrigation systems. The system regulates water supply and monitor environmental conditions using low-power sensor nodes that measure soil moisture, air temperature, humidity, and light intensity. Field experiments were conducted to ascertain optimal water requirements by adjusting irrigation durations to 15, 30, 60, and 120 minutes and modifying water application based on soil moisture levels below 70%. Irrigation scheduling was performed at 6 am and 6 pm. The implementation of IoT-based drip irrigation in Cilembu sweet potato field resulted in a production yield of 44 tons/ha, marking a 45% productivity compared to conventional irrigation methods. Furthermore, water savings of 65% were achieved through the application of IoT-controlled drip irrigation. These results demonstrate the potential of IoT-based systems in optimizing water use and enhancing agricultural productivity, making it a valuable approach for sustainable farming practices.

Compact and ultra-wideband high-efficiency rectifier using asymmetric coupled-line impedance transformer

Abstract This paper presents a compact and ultra-wideband high-efficiency microwave rectifier for wireless power transmission (WPT) applications. The input-matching-network utilizes a compact asymmetric coupled transmission line structure, contributing to wideband performance. The rectifier adopts a voltage-doubler topology, resulting in a smooth input impedance across a wide bandwidth. The working principle of the asymmetric coupled transmission line matching network is analyzed. Simulation and measurement are conducted on the proposed rectifier. The fabricated prototype demonstrates a wide bandwidth of 162.5% (0.3–2.9 GHz with the power conversion efficiency (PCE) exceeding 60% at an input power of 18 dBm. Even at an input power of 10 dBm, the measured PCE remains above 50% over the working band. The proposed ultra-wideband rectifier shows promising potential for WPT applications including wireless powering of low-power electronic devices and sensors.

Age of Information-Aware Networks for Low-Power IoT Sensor Applications

The Internet of Things (IoT) is a fast-growing field that has found a variety of applications, such as smart agriculture and industrial processing. In these applications, it is important for nodes to maximize the amount of useful information transmitted over a limited channel. This work seeks to improve the performance of low-powered sensor networks by developing an architecture that leverages existing techniques such as lossy compression and different queuing strategies in order to minimize their drawbacks and meet the performance needs of backend applications. The Age of Information (AoI) provides a useful metric for quantifying Quality of Service (QoS) in low-powered sensor networks and provides a method for measuring the freshness of data in the network. In this paper, we investigate QoS requirements and the effects of lossy compression and queue strategies on AoI. Furthermore, two important use cases for low-powered IoT sensor networks are studied, namely, real-time feedback control and image classification. The results highlight the relative importance of QoS metrics for applications with different needs. To this end, we introduce a QoS-aware architecture to optimize network performance for the QoS requirements of the studied applications. The proposed network architecture was tested with a mixture of application traffic settings and was shown to greatly improve network QoS compared to commonly used transmission architectures such as Slotted ALOHA.

Low Power Gas Sensors: From Structure to Application.

Gas sensors are pivotal across industries, encompassing environmental monitoring, industrial safety, and healthcare. Recently, a surge in demand for low power gas sensors has emerged, driven by the huge need for applications in portable devices, wireless sensor networks, and the Internet of things (IoT). The practical realization of a densely interconnected sensor network demands gas sensors to have low power consumption for energy-efficient operation. This Perspective offers a comprehensive overview of the progress of low-power sensors for gas and volatile organic compound detection, with a keen focus on the interplay between sensing materials (including metal oxide semiconductors, metal-organic frameworks, and two-dimensional materials), sensor structures, and power consumption. The main gas sensing mechanisms are discussed, and we delve into the mechanisms for achieving low power consumption including material properties and sensor design. Furthermore, typical applications of low power gas sensors are also presented, including wearable technology, food safety, and environmental monitoring. The review will end by discussing some open questions and ongoing needs.

Pulse-Driven MEMS NO2 Sensors Based on Hierarchical In2O3 Nanostructures for Sensitive and Ultra-Low Power Detection.

As the mainstream type of gas sensors, metal oxide semiconductor (MOS) gas sensors have garnered widespread attention due to their high sensitivity, fast response time, broad detection spectrum, long lifetime, low cost, and simple structure. However, the high power consumption due to the high operating temperature limits its application in some application scenarios such as mobile and wearable devices. At the same time, highly sensitive and low-power gas sensors are becoming more necessary and indispensable in response to the growth of the environmental problems and development of miniaturized sensing technologies. In this work, hierarchical indium oxide (In2O3) sensing materials were designed and the pulse-driven microelectromechanical system (MEMS) gas sensors were also fabricated. The hierarchical In2O3 assembled with the mass of nanosheets possess abundant accessible active sites. In addition, compared with the traditional direct current (DC) heating mode, the pulse-driven MEMS sensor appears to have the higher sensitivity for the detection of low-concentrations of nitrogen dioxide (NO2). The limit of detection (LOD) is as low as 100 ppb. It is worth mentioning that the average power consumption of the sensor is as low as 0.075 mW which is one three-hundredth of that in the DC heating mode. The enhanced sensing performances are attributed to loose and porous structures and the reducing desorption of the target gas driven by pulse heating. The combination of morphology design and pulse-driven strategy makes the MEMS sensors highly attractive for portable equipment and wearable devices.

Practical hardware demonstration of a multi-sensor goal-oriented semantic signal processing and communications network

Recent advancements in machine learning, particularly real-time extraction of rich semantic information, reshape signal processing techniques and related hardware architectures. To address the highly challenging requirements of next-generation signal processing applications in networked platforms, we investigate low-power hardware implementation alternatives for a multi-sensor, goal-oriented semantic communications network. Specifically, we focus on cost-effective Raspberry Pis in a multi-sensor semantic video communication application, showcasing adaptability from traditional CPU/GPU configurations. Additionally, we provide a preliminary investigation on implementing semantic extraction tasks through in-memory computation using memristor arrays to further emphasize the potential future of low-power low-cost semantic signal processing. Hardware demonstrations using Raspberry Pi 4Bs and simulations with in-memory computation architectures offer promising hardware architectures with cost-effective and low-power sensor alternatives to the next-generation semantic signal processing applications and semantic communication systems.

Exceptional Electrical Detection of Trace NO2 via Mixed Metal MOF-on-MOF Film-Based Sensors.

The tunability of metal-organic frameworks (MOFs) makes them exceptional materials for the development of highly selective, low-power sensors for toxic gas detection. Herein, we demonstrate enhanced detection of NO2 gas by a MOF-based electrical impedance sensor made using a unique mixed metal MOF-on-MOF synthesis. A combined experimental and computational study was performed using the exemplar NixMg1-x-MOF-74 to understand the fundamental structure-property relationships behind metal mixing and MOF film synthesis methods on sensor performance. Density functional theory results indicated that the presence of Ni in Mg-MOF-74 increased framework stability and increased the electron density of states at lower energies near the HOMO, as well as enhanced the NO2-Mg adsorption interaction. Impedance data of the NixMg1-x-MOF-74 films with larger Ni contents showed greater impedance change after exposure to 1 ppm of NO2 gas. Furthermore, when synthesized through either a drop-cast or direct solvothermal film growth approach, the monometallic Ni-based sensors had the best performance. However, the mixed metal NixMg1-x-MOF-74 sensors synthesized through a MOF-on-MOF approach resulted in the highest impedance change, outperforming all monometallic Ni-based sensors. In particular, the mixed metal Ni-on-Mg-MOF-74 film was the best-performing sensor with an impedance change of 309 upon trace NO2 exposure. Change in impedance response after NO2 exposure was improved by 52% compared to the best monometallic Ni-on-Ni-MOF-74 sensor. Structural analysis of the Ni-on-Mg film showed that the first Mg-MOF-74 layer acts as a structural template controlling the structural features of the final film after metal exchange with Ni. This led to improved film quality, evidenced by the greater crystallinity and larger MOF grain sizes, and resulted in enhanced sensor performance which was not achievable through other metal mixing methods. Altogether, this study identifies structure-property relationships and synthetic templating methods that inform MOF-based sensor design, allowing for improved detection of toxic compounds.

Design of 2.45 GHz rectenna based on high-gain 2-element array antenna and 7-stage voltage-doubler rectifier for energy harvesting applications

Abstract This paper proposes a new design of a 2.45 GHz compact rectenna for ambient RF energy harvesting, presenting a complete energy harvesting system. The proposed rectenna design features a two-element antenna array with high gain and a novel voltage-doubler configuration. The two-element antenna array is fabricated on Rogers RT/Duroid-5880 substrate, utilizing a rectangular microstrip patch antenna fed by a symmetric 50 Ω coplanar line. The antenna’s performance is tested numerically and experimentally in terms of return loss (S11) and radiation patterns. The results show excellent matching bandwidth at 2.45 GHz with a high gain of 7.03 dBi, providing a reasonable agreement between measurement and simulation results. The rectenna design employs a 7-stage voltage doubler rectifier that generates a dynamic DC voltage and improves the circuit’s reliability and performance. The receiving antenna and the rectifier are connected via a single L-section, comprising a shunt capacitor and a series inductor, for impedance matching, resulting in size reduction. The rectifier exhibits a peak simulated power conversion efficiency (PCE) of 75% and a measured efficiency of 60% at an input power of 20 dBm. The rectenna yields a high output DC voltage of 4.9 V at an input power of 13 dBm with a resistive load of 1 kΩ. These results reveal that the proposed 2.45 GHz rectenna is a suitable candidate for RF energy harvesting applications in low-power sensors and electronic devices.

A high-performance all-day vertical thermoelectric generator based on a double-sided reflective structure

Harvesting environmental energy sources is crucial for achieving renewable energy generation and net-zero emissions. However, ensuring uninterrupted electricity generation from environmental energy sources remains challenging. In this study, we present a simple, compact, and expandable all-day vertical passive thermoelectric generator (V-TEG) with a double-sided reflective structure that simultaneously harnesses solar and space cold energy. Outdoor experimental measurements, combined with finite element simulation analysis, revealed that the optimal angles of the solar reflector relative to the TEG range from 30° to 50°. Additionally, the TEG placed in the north–south orientation was found to enhance its daytime performance. The use of low-cost black paint combined with the solar reflector achieved equivalent spectral selectivity, effectively minimizing the infrared heat loss of the hot end of the TEG. During a 48-h outdoor test, the V-TEG achieved an average daytime power of 112.00 mW/m2 and a peak of 363.42 mW/m2. Comparative experiments demonstrated that the V-TEG outperformed horizontally placed TEG during the daytime. Furthermore, scaling the system by connecting three TEGs in series significantly increased the daily power output. This compact V- TEG system offers a promising solution for long-term power generation in low-power sensors and off-grid communities, supporting renewable energy and carbon neutrality goals.

Sensor-Aware Data Imputation for Time-Series Machine Learning on Low-Power Wearable Devices

Wearable devices that have low-power sensors, processors, and communication capabilities are gaining wide adoption in several health applications. The machine learning algorithms on these devices assume that data from all sensors are available during runtime. However, data from one or more sensors may be unavailable due to energy or communication challenges. This loss of sensor data can result in accuracy degradation of the application. Prior approaches to handle missing data, such as generative models or training multiple classifiers for each combination of missing sensors are not suitable for low-energy wearable devices due to their high overhead at runtime. In contrast to prior approaches, we present an energy-efficient approach, referred to as Sensor-Aware iMputation (SAM), to accurately impute missing data at runtime and recover application accuracy. SAM first uses unsupervised clustering to obtain clusters of similar sensor data patterns. Next, it learns inter-relationship between clusters to obtain imputation patterns for each combination of clusters using a principled sensor-aware search algorithm. Using sensor data for clustering before choosing imputation patterns ensures that the imputation is aware of sensor data observations. Experiments on seven diverse wearable sensor based time-series datasets demonstrate that SAM is able to maintain accuracy within 5% of the baseline with no missing data when one sensor is missing. We also compare SAM against generative adversarial imputation networks (GAIN), transformers, and k-nearest neighbor methods. Results show that SAM outperforms all three approaches on average by more than 25% when two sensors are missing with negligible overhead compared to the baseline.

One-third octave spectrum calculation to meet the attenuation limits of a Class 1 analyzer (IEC 61260-1:2014) for low-power noise sensors

Thanks to the advances in technology and the cost reductions of electronic components, nowadays it is possible to produce low-cost and low-power acoustic sensors with extended capabilities such as the one-third octave band spectrum calculation. However, there are some doubts about the accuracy of these devices. Thus, to meet the attenuation limits of a Class 1 analyzer for low frequencies as stated in IEC 61260-1:2014 normative, this research proposes two algorithms to calculate the one-third octave band spectrum, both algorithms are based on the Power Spectral Density (PSD). The first one consists of a subdivision of frequency bins, where a PSD bin is divided into smaller parts, gain adjusted, and assigned to its corresponding one third-octave band. The other one is based on signal decimation-accumulation, where samples, that are processed every 125 ms due to the sensor design, are first decimated and then accumulated to increase the frequency bin resolution. Both algorithms are programmed into the sensor and the power consumption and acoustical performance are evaluated and compared to a typical multi-rate filter bank. It is observed that both proposed algorithms improve the performance of the low-cost sensor in terms of energy consumption and accuracy.

Quad-Band Rectifier Circuit Design for IoT Applications

In this work, a novel quad-band rectifier circuit is introduced for RF energy harvesting and Internet of Things (IoT) applications. The proposed rectifier operates in the Wi-Fi frequency band and can supply low-power sensors and systems used in IoT services. The circuit operates at 2.4, 3.5, 5, and 5.8 GHz. The proposed RF-to-DC rectifier is designed based on Delon theory and Greinacher topology on an RT/Duroid 5880 substrate. The results show that our proposed circuit can harvest RF energy from the environment, providing maximum power conversion efficiency (PCE) greater than 81% when the output load is 0.511 kΩ and the input power is 12 dBm. In this work, we provide a comprehensive design framework for an affordable RF-to-DC rectifier. Our circuit performs better than similar designs in the literature. This rectifier could be integrated into an IoT node to harvest RF energy, thereby proving a green energy source. The IoT node can operate at various frequencies.

Compact vertical emitting ring interband cascade lasers for isotope-resolved CO2 sensing

We present a compact vertically emitting ring interband cascade laser (ICL) with low power consumption and the possibility for seamless integration into various CO2 sensing applications. Our devices exhibit desirable performance characteristics in battery-driven handheld devices, including room temperature (20 °C) threshold currents as low as 15 mA, small footprints, and stable single-mode emission, suitable for rapid isotope-resolved CO2 detection. Through epi-down bonding with sub-micron accuracy, we achieved robust integration of substrate-emitting ring ICLs, ensuring reliability and scalability that would be required for mass production. We present comprehensive experimental results validating the efficacy of our approach, including spectral analysis and CO2 sensing capabilities with limits of detection of 24 and 13 ppmv utilizing the 12CO2 P(60) and 13CO2 R(10) transitions in the ν3 fundamental band, respectively. The demonstrated devices hold great promise for a wide range of industrial applications, including environmental monitoring, process control, and atmospheric research, where compact low-power sensors are essential.

Low-Power Chemiresistive Gas Sensors for Transformer Fault Diagnosis.

Dissolved gas analysis (DGA) is considered to be the most convenient and effective approach for transformer fault diagnosis. Due to their excellent performance and development potential, chemiresistive gas sensors are anticipated to supersede the traditional gas chromatography analysis in the dissolved gas analysis of transformers. However, their high operating temperature and high power consumption restrict their deployment in battery-powered devices. This review examines the underlying principles of chemiresistive gas sensors. It comprehensively summarizes recent advances in low-power gas sensors for the detection of dissolved fault characteristic gases (H2, C2H2, CH4, C2H6, C2H4, CO, and CO2). Emphasis is placed on the synthesis methods of sensitive materials and their properties. The investigations have yielded substantial experimental data, indicating that adjusting the particle size and morphology structure of the sensitive materials and combining them with noble metal doping are the principal methods for enhancing the sensitivity performance and reducing the power consumption of chemiresistive gas sensors. Additionally, strategies to overcome the significant challenge of cross-sensitivity encountered in applications are provided. Finally, the future development direction of chemiresistive gas sensors for DGA is envisioned, offering guidance for developing and applying novel gas-sensitive sensors in transformer fault diagnosis.

Bio-inspired 3D-Printed supercapacitors for sustainable energy storage

This study presents a significant enhancement of the electrochemical properties of commercially available high-surface-area bare activated carbon (BAC) through the grafting of catechin hydrate (CH) redox active molecules. The grafting of CH onto the BAC was confirmed by the results from the Thermogravimetric analysis, X-ray photoelectron spectroscopy and Brunauer-Emmett-Teller. Moreover, this study introduces 3D printed deep eutectic solvent electrolytes, composed of choline chloride and urea, highlighting the potential of sustainable and greener materials in energy storage. A 3D-printed fully bio-inspired supercapacitor achieved a maximum specific capacitance of 75 F g−1 at a scan rate of 1 mV s−1 (37 F g−1 at a current density of 0.2 A g−1), demonstrating performance comparable to that of previously reported state-of-the-art fully 3D-printed and disposable paper supercapacitors (25.6 F g−1 at a scan rate of 1 mV s−1 for 1.2 V). Furthermore, the device exhibited electrochemical performance within an extended potential window of 2.0 V, coupled with cycling stability of approximately 90.2 % after 10,000 cycles, offering a specific energy of 20.6 W h kg−1 at a specific power of 560 W kg−1. The developed bio-inspired, eco-friendly, sustainable, 3D-printed supercapacitors can replace the harmful Li-ion batteries currently used in low-power wireless sensor applications.

Development and Implementation of a Portable Cigarette Filter Holes Recognition Device Based on Low-Power Image Sensor and High-Precision Coaxial Rotary Scanning

During the cigarette production process, the detection of holes in the filters presents an urgent challenge for manufacturers. Utilizing image sensors offers an effective solution to tackle this challenge effectively. While current detection devices mitigate this issue through the use of auxiliary light source technology and sophisticated deep learning image processing, they are often large-scale, stationary, and demand substantial computational resources. To overcome this limitation, a low-power battery-powered portable filter holes detection device is designed based on image processing to facilitate portability and sampling. Additionally, backlight transmission lighting components are employed to guarantee aperture definition after imaging. Furthermore, a filter holes recognition algorithm is proposed based on multi-threshold combination of computer vision (CV). The algorithm forms a plan view of the circumference of the cigarette by image stitching, then performs filter holes detection and filters the results. Experimental results show that operators can easily carry the device for sampling and testing, and can accurately perform filter holes detection, and the results provide valuable insights into the adjustment of system parameters in the cigarette production process. This device significantly aids in enhancing efficiency during the manual inspection processes both in the testing room and at the equipment stations.

Combined Sweeping and Jumping Method to Enhance Node Insertion Algorithm for Wi-Fi Sensor Networks

Two dominant driving forces for evolving communication technologies in the current society have been the proliferation of wireless access networks to the Internet and the broadbandization of access and infrastructure networks. Through these evolutions of communication technologies, high-resolution contents are instantly delivered to wireless devices such as mobile phones, wireless tablets, and headsets. Recently, wireless sensor networks, where up to 1000 low-power sensors are wirelessly connected to each other, have been created and connected to the Internet, which presents a new challenge of efficiently coordinating the transmissions of many wireless sensors with minimal transmission overheads. Developing an efficient Medium Access Control (MAC) protocol governing the transmissions of wireless sensor networks is crucial for the success of wireless sensor networks for the realization of the Internet of Things (IoT). In 2023, the node insertion algorithm was proposed to efficiently derive the minimal number of serially connected multipolling sequences of many wireless sensors, by which Access Points (APs) can poll wireless sensors with minimal polling overheads. In this paper, the combined sweeping and jumping method is presented to dramatically enhance the searching performance of the node insertion algorithm. To validate the performance of the combined sweeping and jumping method, simulation results are presented for wireless sensor networks where wireless sensors with varying transmission ranges exist.

Sustainable energy harvesting system for low-power underwater sensing devices

In marine scientific research, ocean monitoring is crucial where the battery powered sensor devices are placed under the water to collect different information like temperature, pressure, and turbidity in underwater sensor networks (UWSNs). Thus, keeping these devices active for longer periods is challenging. In the last decades, the piezoelectric transducer (PZT) material has been used widely for constructing more environmentally friendly energy harvesting systems. The PZT harvester offers a promising solution by eliminating the need for batteries for running devices in the future with less maintenance. The PZT harvester allows the system to generate higher voltage to run low-power devices. This paper designed and developed a new renewable energy harvester system using PZT transducers for running different types of underwater sensor devices like temperature, turbidity, and obstacle sensors. The proposed PZT-based energy harvester employs a two stage amplification model for generating higher voltage and current to run multiple devices. The sensing information collected from these sensors is transmitted to the cloud which is later utilized for analysis and decision making. Experiment results show the proposed PZT-based energy harvester can generate a voltage of 13 volts (V) and a current of 43.3 milliampere (mA) equivalent to 562 milliwatt (mW) which is very good to run multiple low-power underwater sensor devices.