Monitoring Wireless Sensor Research Articles

An investigation on energy-saving scheduling algorithm of wireless monitoring sensors in oil and gas pipeline networks

With the rapid development of the oil and gas industry, monitoring the safety and efficiency of pipeline networks has become particularly important. In this context, Wireless Sensor Networks (WSNs) are widely used for monitoring oil and gas pipelines due to their flexible deployment and cost-effectiveness. However, since sensor nodes typically rely on limited battery power, extending the network’s lifecycle and improving energy utilization efficiency have become focal points of research. Therefore, this paper proposes an energy-saving scheduling algorithm based on transformer networks, aimed at optimizing energy consumption and data transmission efficiency of wireless monitoring sensors in oil and gas pipelines. Firstly, this study designs a deep learning-based Transformer model that learns from historical data on energy consumption patterns and environmental variables to predict the energy and data transmission needs of each sensor node. Secondly, based on the prediction results, this algorithm employs a dynamic scheduling strategy that automatically adjusts the sensor’s operational mode and communication frequency according to the node’s energy status and task urgency. Additionally, we have validated the effectiveness of the proposed algorithm through field tests and simulation experiments. According to the experimental results, our model has higher efficiency in energy saving. Compared with Convolutional Neural Networks, Recurrent Neural Networks and Graph Neural Networks, the total energy consumption of sensor networks under the model scheduling in this paper was reduced by 6.7%, 33.4% and 26.3%, respectively. Our algorithms improve the energy efficiency and stability of the monitoring system and provide important technical support for future intelligent pipeline monitoring systems. We hope this paper will inspire future scientific research in this field.

A Survey on Error Exponents in Distributed Hypothesis Testing: Connections with Information Theory, Interpretations, and Applications.

A central challenge in hypothesis testing (HT) lies in determining the optimal balance between Type I (false positive) and Type II (non-detection or false negative) error probabilities. Analyzing these errors' exponential rate of convergence, known as error exponents, provides crucial insights into system performance. Error exponents offer a lens through which we can understand how operational restrictions, such as resource constraints and impairments in communications, affect the accuracy of distributed inference in networked systems. This survey presents a comprehensive review of key results in HT, from the foundational Stein's Lemma to recent advancements in distributed HT, all unified through the framework of error exponents. We explore asymptotic and non-asymptotic results, highlighting their implications for designing robust and efficient networked systems, such as event detection through lossy wireless sensor monitoring networks, collective perception-based object detection in vehicular environments, and clock synchronization in distributed environments, among others. We show that understanding the role of error exponents provides a valuable tool for optimizing decision-making and improving the reliability of networked systems.

Injectable ultrasonic sensor for wireless monitoring of intracranial signals.

Direct and precise monitoring of intracranial physiology holds immense importance in delineating injuries, prognostication and averting disease1. Wired clinical instruments thatuse percutaneous leads are accurate but aresusceptible to infection, patient mobility constraints and potential surgical complications during removal2. Wireless implantable devices provide greater operational freedom but include issues such as limited detection range, poor degradation and difficulty in size reduction in the human body3. Here we present an injectable, bioresorbable and wireless metastructured hydrogel (metagel) sensor for ultrasonic monitoring of intracranial signals. The metagel sensors are cubes 2 × 2 × 2 mm3in size that encompass both biodegradable and stimulus-responsive hydrogels and periodically aligned air columns with a specific acoustic reflection spectrum. Implanted into intracranial space with a puncture needle, the metagel deforms in response to physiological environmental changes, causing peak frequency shifts of reflected ultrasound waves that can be wirelessly measured by an external ultrasound probe. The metagel sensor can independently detect intracranial pressure, temperature, pH and flow rate, realize a detection depth of 10 cm and almost fully degrade within 18 weeks. Animal experiments on rats and pigs indicate promising multiparametric sensing performances on a par with conventional non-resorbable wired clinical benchmarks.

A self-regulation blades wind energy harvester system for self-powered wireless monitoring sensors

Using wind energy in the natural environment provides a promising solution for wireless sensor power supply for ecological, meteorological, environmental, and infrastructure monitoring. However, the uncertainty and disorder of natural wind restrict the further development of wind energy harvester systems and self-powered wireless sensor technology. Hence, this paper proposes a self-regulation blade wind energy harvester system (SBWEHS) for self-powered wireless monitoring sensors in remote field areas with power shortages. The system is mainly composed of three parts: wind harvesting mechanism, generator module, and energy storage module. The device can control the blade overlap ratio according to the wind speed while generating electricity to maximize the power coefficient. The system can control the blade’s closure in bad weather to protect the device. Based on the computational fluid dynamics technology of Ansys Fluent software, this study evaluated the impact of wind speed and blade overlap ratio on the two-stage blades. Experiments revealed that when the overlap ratio of the blades is fixed at 0.2 and the wind speed is set at 16 m/s, the maximum average power will reach 0.79 W, which fulfills the power requirements of wireless sensors. These results illustrate that the SBWEHS can effectively supply power for wireless monitoring sensors, especially in remote natural environments.

Design and Testing of Miniaturized Dual-Band Microstrip Antenna Sensor for Wireless Monitoring of High Temperatures

Design and Testing of Miniaturized Dual-Band Microstrip Antenna Sensor for Wireless Monitoring of High Temperatures

Recent Progress in Application‐Oriented Self‐Powered Microelectronics

AbstractWith the rapid development of the Internet of Things (IoTs), numerous distributed wide‐area low‐power electronic devices have been utilized in various fields, such as wireless monitoring sensors and wearable electronics. Due to the dispersion and mobility of microelectronic devices, their energy supply faces serious challenges. The inconvenience and non‐environmental friendliness of using traditional centralized low entropy energy and chemical batteries to power distributed microelectronic devices are becoming increasingly prominent. Environmental energy harvesting technology with high entropy characteristics is considered an effective solution for low‐power electronic devices. This paper comprehensively reviews the recent progress in microelectronic technologies based on energy harvesting and signal sensing. First, state‐of‐the‐art micro‐power electronic devices in humans, animals, and the environment are introduced. Secondly, the available micro‐energy sources in the environmentare elaborated and summarized. Then, the principles and characteristics of ambient microenergy harvesting technologies based on different mechanisms are classified, summarized, and analyzed. In addition, this work comprehensively summarizes the applications of self‐powered micro‐electronics technology in 11 different fields, including human, animal, and environment. Finally, research challenges, technical difficulties, and research gaps in self‐powered microelectronics based on micro‐energy harvesting technology are discussed and summarized.

Coenzyme a-responsive hydrogel sensor for wireless monitoring and treatment of osteoarthritis

A coenzyme A (CoA-SH)-responsive electronic hydrogel sensor that can relieve osteoarthritis based on carbonized polydopamine-integrated disulfide bonds-contained polymer dots (cPDA@PDA hydrogel) is conceived in this study. The cleavage of disulfide bonds occurred due to the accumulation of CoA-SH, controlling the release of carbonized polydopamine (cPDA) from the integrated polymer dots (PD). The hydrogel mechanical properties are altered, increasing the maximum stretching from 199.7 % to 352.9 % after 12 h and the shift in the surface morphology. The exposure to CoA-SH controls the release of the cPDA, decreasing the electronic resistivity from 147.1 kΩ to 56.99 kΩ after 12 h. In vitro analysis using the articular chondrocytes of wild type (WT), Nudt7-/-, and Acot12-/- shows a sensitivity toward CoA-SH, which is depicted by the decrease in electronic resistance after treatment. In vivo analysis shows a resistance of 131.1 kΩ for the Nudt7-/- mice, which is lower than the resistance of the NUDT7-restored mice (225.1 kΩ). Additionally, cartilage degradation could be prevented for mice-treated PD-containing hydrogel, showing tibia thickness of 111.5 μm compared to that without PD hydrogel (74.84 μm). This result indicates that the cPDA@PD hydrogel can diminish the accumulation of CoA-SH and facilitate the treatment of osteoarthritis.

Application of piezoelectric self powered wireless sensor in CCAL vibration environment monitoring

With the growth of the economy and social progress, people's requirements for mechanical equipment are becoming higher and higher, and the complexity of mechanical equipment is also increasing. To improve the safety and reliability of mechanical equipment, the establishment of Wireless Sensor Network (WSN) with the function of monitoring and self energy supply in vibration environment is an effective way to solve this problem. This study presents a piezoelectric self-powered wireless sensor monitoring system with a piezoelectric self-powered circuit model, and utilizes the FFT algorithm to reduce power consumption and achieve a sleep-wake-sleep cycle intermittent periodic operation mechanism for low-power devices. The results show that the charging speed of the PISS structure is 2.4 %, 65.8 %, and 80 % faster than the standard, SISS, and SCE structures, respectively. Compared with the triangular cantilever, the output voltage and output power of the rectangular cantilever have increased by 59.5 % and 80.9 %, respectively. When the acceleration is 1 g, the output power and output voltage are 0.5 mW and 6 V, respectively. When the acceleration is 2 g, the output power and output voltage are 2.2 mW and 12 V, respectively. The above results indicate that the piezoelectric self-powered wireless sensor monitoring system in this study has a certain reference value in the field of self-powered wireless sensors.

Asymmetric-elastic-structure fabric-based triboelectric nanogenerators for wearable energy harvesting and human motion sensing

With rapid advancement in wearable electronics, triboelectric nanogenerators (TENGs) have attracted great attention for energy harvesting and bio-motion sensing because of low cost, environmentally friendly, safe, sustainable and easy availability. However, the existing structure is not well suitable and desirable for ordinary apparel. There are great challenges in balancing structural and motion adaptability with high-performance output for practical wearable applications. Therefore, herein we ingeniously combine human motions and the difference in elasticity between fabrics to develop an asymmetric-elastic-structure fabric-based triboelectric nanogenerator (AesF-TENG) which can realize rapid contact and separation under small deformation or force, and can efficiently harvest the mechanical energy under the natural motion of the human body to obtain better wearable potential. The AesF-TENG is composed of nylon and doped polydimethylsiloxane (PDMS) used as positive and negative triboelectric materials respectively, and stretchable elastic fabric and conventional cotton fabric as the base. The developed AesF-TENG can efficiently harvest biomechanical energy and maintain stable electrical performance after 20 washes and 120,000 durability tests and also drive commercial electronic watch, calculator and LEDs. Additionally, it could be also utilized as a self-powered wearable sensor for wireless monitoring of human body motions. The prominent output power performance of AesF-TENG together with human motion and textiles show their great potentials for viable applications in wearable electronics and smart textiles in the near future.

Visualization of Environmental Sensing Data in the Lake-Oriented Digital Twin World: Poyang Lake as an Example

Access to real-time environmental sensing data is key to representing real-time environmental changes in the digital twin of lakes. The visualization of environmental sensing data is an important part of establishing a digital twin of lakes. In the past, environmental sensing data display methods were either charts or two-dimensional map-based visualization methods. Breaking through the traditional visualization methods of environmental sensing data and realizing a multi-dimensional and multi-view display of environmental sensing data in a digital twin of lakes is something that this particular paper tries to resolve. This study proposes a visualization framework to integrate, manage, analyze, and visualize the environmental sensing data in the digital twin of lakes. In addition, this study also seeks to realize the coupling expression of geospatial data and long-term monitoring sequence data. Different visualization methods are used to realize the visualization of environmental sensing data in the digital twin of lakes. Using vector and scalar visualization methods to display ambient wireless sensor monitoring data in a digital twin of lakes provides researchers with richer visualization methods and means for deeper analysis. Using video fusion technology to display environmental sensing video surveillance data strengthens the integration of the virtual environment and real space and saves time for position identification using video surveillance. These findings may also help realize the integration and management of real-time environmental sensing data in a digital twin of lakes. The visualization framework uses various visualization methods to express the monitoring data of environmental wireless sensors and increases the means of visualizing environmental sensing data in the world of a lake digital twin. This visualization framework is also a general approach that can be applied to all similar lakes, or other geographical scenarios where environmental sensing devices are deployed. The establishment of a digital twin of Poyang Lake has certain practical significance for improving the digital management level of Poyang Lake and monitoring its ecological changes. Poyang Lake is used as an example to verify the proposed framework and method, which shows that the framework can be applied to the construction of a lake-oriented digital twin.

Target location service system in microgrid operation based on internet of things

Abstract With the gradual reduction of conventional energy sources, the problem of environmental pollution is becoming more and more serious. Environmentally friendly, efficient and flexible power generation models are widely favored, and microgrids emerge as the times require. Microgrid is a medium and low voltage network including power distribution, storage devices, loads and other equipment. It can operate in grid-connected mode or as a stand-alone system in an island environment, continuing to supply power to users even when the large grid is disrupted. Compared with traditional power grids, microgrids have small capacity and changeable operation modes, making it difficult to locate distribution areas and short-circuit currents. At the same time, it is difficult to trace the faults during operation. So far, microgrids have had a lot of technical issues in operation that need to be resolved quickly. In recent years, IoT technology has developed rapidly. The Internet of Things is a comprehensive network that integrates location services, monitoring services, information identification and monitoring services through information exchange and communication technologies, such as video recognition technology, positioning technology, and wireless sensor technology. In order to monitor the operation mode of the microgrid in real time, find the source of the fault and locate the operation target, based on the Internet of Things technology, this paper installs wireless sensors in the line segment to monitor the line and other applications. This paper uses wireless sensor networks to record positioning variables, processes and data analysis to achieve the accuracy of microgrid operation target positioning. In addition, in order to improve the positioning accuracy of wireless sensor monitoring network and solve the problem of energy limitation, DV-Hop algorithm EB-IDMF algorithm and R-A algorithm are also introduced. Finally, the feasibility of the system is verified through experiments and tests. With the blessing of the positioning algorithm, the positioning accuracy is improved by 4.9%, which has great practical value for the target positioning of microgrid operation.

Design of Nuclear Radiation Monitoring System in Floor Exploration Based on Deep Learning.

Nuclear radiation environmental monitoring has become an important issue in floor surveys. From the perspective of regional environmental nuclear radiation monitoring, it is of great practical significance to establish a scientific and reliable wireless sensor monitoring network for timely and accurately grasping nuclear radiation status and ensuring nuclear safety. In this article, we design a regional environmental nuclear radiation monitoring system based on Zigbee wireless sensor network by using Zigbee wireless technology. First, the network topology suitable for nuclear radiation environment monitoring is designed. Second, the JN5121 module is selected as the core of the Zigbee wireless sensor to build the network hardware platform. Finally, the article focuses on the receiving mechanism, data transmission, coordination between network nodes, network structure, and dynamic network management, and carries on the software development to the Zigbee wireless environment monitoring network. The system can measure the data in real time, display the dose rate of y radiation effectively, and realize the functions of remote control, field control, security alarm, and environmental monitoring. It has good promotion and application value.

Femtosecond Laser Ablation Assisted NFC Antenna Fabrication for Smart Contact Lenses

AbstractSmart contact lenses (SCLs) have drawn substantial interest for continuous health monitoring applications. Even though most of the reported works utilize near‐field communication (NFC) or inductive coupling for wireless powering and data transmission, developing a scalable and rapid fabrication technique for annular ring antennas confined in a small contact lens area is still an unsolved challenge. Here, femtosecond laser ablation is employed for the first time as a simple, single‐step, and highly precise fabrication technique for NFC antennas using conventional flexible printed circuit board materials. Antenna lines with depth and width of 9 and 35 μm are achieved, respectively. The antenna with a footprint of 19.5 mm2 is characterized in biological solution followed by aging, and bending tests, and a frequency deviation of less than %1 is recorded. A real‐life application is demonstrated by fabricating an SCL embedded with the antenna, an NFC chip, and an electrochemical sensor for wireless monitoring of glucose in artificial tear solution by a smartphone. The device could successfully quantify biologically relevant glucose concentrations ranging from 0.2 to 1 mM with a limit‐of‐detection of 66 μM. In addition, device response to interfering molecules is less than ±1 nA, and the spike‐and‐recovery test is successfully demonstrated.

System Design and Validation of a Wireless Sensor Monitoring System in Silage

Silages have become the main feed for ruminants and biogas production and are often stored in large stacks. When a silo is filled, a plastic cover is laid out and fermentation begins. From this moment, the entire silo becomes a black box for farmers: if any spoilage due to air breaches takes place, it often will only be recognized when the stack is opened and massive losses have already occurred. In the present work, a wireless sensor monitoring system for silage stacks is designed that shall detect changes in the silage environment until feedout and can therefore enable the farmer to prevent biomass losses. The nail-shaped node design offers elevated feed safety opportunities and can be easily removed before feedout. For data transmission, LoRaWAN is used in combination with a hardware-based timer. The sensor nodes are able to endure the full extent of a silage stack storage period in a full-scale silage test of approximately 40 weeks without battery shortage. The resulting measurements show that CO2, O2, relative humidity and temperature sensors at the silage surface can detect changes within the silage environment due to air breach. Temperatures in stable regions beneath 40 cm can be detected and give information about long-term stability.

A hybrid multimodal energy harvester for self-powered wireless sensors in the railway

The operational safety and reliability of the railway in monitoring systems are constantly increasing. However, the self-powered functioning of sensors utilizing energy harvesting technologies could be a challenge due to wireless sensors in the railway. This paper presents a hybrid multimodal renewable energy harvesting system (HHMRES) to power the wireless monitoring sensors such as accelerometer, humidity sensor, inclinometer, and light sensor alongside the railway in remote areas. The system design consists of a multimodal renewable energy system, power generation, and energy storage system. A hybrid solar photovoltaic system combined with a 3D printed wind turbine coupled with an electromagnetic generator was proposed as HHMRES to harvest solar and wind energy simultaneously. The harvested energy can be stored in super-capacitors and utilized for wireless monitoring sensors alongside the railway. A theoretical model has been developed to characterize the HHMRES. The total pressure and velocity contours were studied through the simulations in ANSYS FLUENT. The experiments were carried out to validate the performance and dynamics of the HHMRES by simulating solar radiations and wind flow with a solar simulator and the wind tunnel, respectively. The results showed that the average output power of the proposed system was 12.31 W at 17.5 m/s wind speed and 1700 W/m2 solar radiations. The techno-economic optimization of HHMRES was performed in HOMER software to find the most optimal design structure, capital cost, operating cost, and replacement cost of the proposed model. The harvester was demonstrated to have significant power output to operate wireless sensors alongside the railway.

Design and Experimental Investigation of a Rotational Piezoelectric Energy Harvester with an Offset Distance from the Rotation Center.

Rotational energy harvesting technology has attracted more and more attention recently. This paper presents a piezoelectric rotational energy harvester that can be mounted with an offset distance from the rotation center. The piezoelectric energy harvester is designed to be dynamically excited by the force due to gravity, which causes the piezoelectric cantilever beams in the harvester to vibrate periodically as the harvester rotates. A novel design of the harvester structure with a hollow mass is proposed and analyzed in this paper. Experiments were performed to investigate the design and analysis. A power output of 106~2308 μW can be achieved at the rotating frequencies of 0.79~14 Hz with a piezoelectric cantilever beam in the prototyped energy harvester. Results showed that the prototyped harvester can be mounted on a rotating wheel hub and output sufficient power in a wide frequency range for wireless monitoring sensors.

Wireless Humidity Sensor for Smart Packaging via One‐step Laser‐Induced Patterning and Nanoparticle Formation on Metallized Paper

AbstractIn this work, a scalable and rapid process is developed for creating a low‐cost humidity sensor for wireless monitoring of moisture levels within packaged goods. The sensor comprises a moisture‐sensitive interdigitated capacitor connected to a planar spiral coil, forming an LC circuit whose resonant frequency is a function of environmental humidity. The sensor is fabricated on a commercially available metallized parchment paper through selective laser ablation of the laminated aluminum (Al) film on the parchment paper substrate. The laser ablation process provides a unique one‐step patterning of the conductive Al layer on the paper while simultaneously creating high surface area Al2O3nanoparticles within the laser‐ablated regions. The intrinsic humidity‐responsive characteristics of the laser‐induced Al2O3nanostructures provide the wireless sensor with a tenfold higher sensitivity to humidity than a similar LC resonant sensor prepared by conventional photolithography‐based processes on FR‐4 substrates. The frequency change of the sensor is observed to be a linear function within the range of 0−85% RH, providing an average sensitivity of −87 kHz RH−1with good repeatability and stable performance. Furthermore, the employment of scalable laser fabrication processes using commercially available inexpensive materials renders these technologies viable for roll‐to‐roll manufacturing of low‐cost wireless sensors for smart packaging applications.

A Comparative Analysis between Battery- and Solar-Powered Wireless Sensors for Soil Water Monitoring

Wireless sensor networks (WSN) have found wide applications in many fields (such as agriculture) over last few years, and research interest is constantly increasing. However, power supply to the sensor nodes remains an issue to be resolved. Batteries are usually used to power the sensor nodes, but they have a limited lifetime, so solar energy harvesters are a good alternative solution. This study provides a comparative analysis between battery and solar energy harvesters for sensor nodes used for soil water monitoring. Experimental results show that small-sized solar panels with low-power energy harvester circuits and rechargeable batteries distinctly outperform secondary batteries in outdoor and continuous-operation applications. The power level of the energy storage device of sensor node 1, which was powered by a small PV panel, remained constantly close to 90% for all days. The power of the other three nodes, which were powered by a rechargeable battery, was initially at 100% of the charge and gradually started to reduce. Sensor node 1 performed a total of 1288 activations during the experimental period, while sensor nodes 2 and 4 behaved satisfactorily and performed a total of 781 and 803 activations, respectively. In contrast, sensor node 3 did not exhibit the same behavior throughout the experiments.

Visible light-responsive mechanically and electronically controllable conductive carbon dot-hydrogel-based pressure-strain sensor for wireless monitoring of antifouling performance

This work describes the visible-light-induced wireless pressure and strain sensor based on carbon dot (CD) loaded conductive hydrogel for the mechanical force-regulated antifouling behavior. The conductivity, as well as pressure and strain sensitivities of the hydrogel, were modulated by modifying the hydrophobic–hydrophilic transformation of the CD components that controlled the antifouling effect through mild mechanical forces, allowing the cells to be squeezed out from the hydrogel. The photocatalytic activity of the CD containing carbonized polydopamine (cPDA) releases the cPDA which enhances the network formation, resulting in a four-fold higher conductivity, 158% enhanced mechanical strength, and 231% improved stretchability, which enhances the pressure and strain sensing response. Furthermore, antifouling phenomenon involved in the physical deformation associated with the changes in the electrochemical behavior of the hydrogel, which was monitored by wireless sensing, demonstrated the effect of mechanical force on the antifouling behavior for the successful removal of cells from the hydrogel. Additionally, the efficacy of the antifouling effect depending on pressure or strain was evaluated by real-time monitoring of the wireless response. This antifouling property of the hydrogel together with visible-light-controlled hydrophobic–hydrophilic transformation can allow the development of hydrogel-based electronics for electronic skin sensors, prosthetics, and biosensing applications.

Introduction of IoT-Based Surrogate Parameters in the Ex-Post Countermeasure of Industrial Sectors in Integrated Permit Policy

New approaches and techniques are required for environmental management in workplaces that emit pollutants. The best available techniques (BATs) that are suggested by the integrated environmental permit system (IEPS) play a pivotal role in providing state-of-the-art approaches and techniques to workplaces. For the workplaces that install facilities with the BATs, it is necessary to perform real-time monitoring to determine whether pollutant emission facilities and treatment facilities are operating normally and whether such facilities can maintain the optimal condition during the emission of pollutants. The environmental management measures used before the IEPS had limitations for workplaces that are subjected to integrated environmental management because they consider the entire workplace as a single pollution source by combining all media. In addition, only the emission concentrations and amounts of pollutants were monitored because only the points at which most pollutants are discharged, i.e., stacks for air pollutant emission facilities and final effluent outlets for water pollutant emission facilities, are monitored. These monitoring approaches in a variety of facilities involve considerable costs, and most of them (95% for stacks in workplaces under IEPS, Korea) cannot perform real-time observation even though some of them are capable of it (5% for stacks managed by Tele Monitoring System, TMS). In this study, the applicability of a wireless sensor monitoring network was examined as a new approach for environmental management in workplaces. Based on the sensor monitoring network, surrogate parameters that can indirectly monitor the critical operating factors of pollutant emission and prevention facilities were developed and then measured in real time to examine the status of these facilities. Surrogate parameters can be used instead of directly measuring pollutants as the former has high efficiency, such as cost reduction, and can provide reliable data on the emission characteristics of pollutants. The wireless network is based on an Internet of Things (IoT) platform under real-time conditions in the emission and production facilities in a workplace. This study suggested that a wireless network using the IoT platform improves the BAT assessment methodology to understand the efficiency of environmental pollution reduction and the economic contribution to the workplace.