Wireless Sensor System Research Articles

Visual analysis of action using machine learning and distributed expectation-maximization algorithm

We propose the novel visual analysis system Video of Things (VoT) using machine learning and a distributed Expectation-Maximization (EM) method. The VoT-enabled approach is proposed to perform a visual analysis of action in the context of wireless sensor systems. For the technical impact analysis of VoT, we used the EM algorithm. Furthermore, we investigate the notion of merging the clustered grid with the uniform filter in the target tracking of the distributed EM of particle filtering. We grid the network monitoring region with the technical impact analysis of VoT, then clusters the whole sensor nodes, build the system structure using a real scenario of visual actions in the VoT, and thoroughly analyze the system implementation process. Finally, we present investigations to validate the performance of the proposed research approach with a technical impact analysis of VoT. According to the study findings, the proposed VoT-based system can play an essential role in the recognition and technical impact analysis of table tennis activity.

Technical note: a nose ring sensor system to monitor dairy cow cardiovascular and respiratory metrics.

Monitoring cardiovascular and respiratory measurements corresponds to the precision livestock farming (PLF) objective to continuously monitor and assess dairy cows' welfare and health. Changes in heart rate, breathing rate, and oxygen saturation (SpO2) are valuable metrics in human and veterinary medicine to assess stress, pain, illness, and detect critical conditions. The common way to measure heart rate is either manually or with a stethoscope. Under research conditions, heart rate is usually measured with a sports watch chest belt. Breathing rate is obtained by counting the cow's flank movements which is a time-consuming and labor-intensive method that requires training and is prone to human error. No devices are available on the market that enable practical and easy pulse oximetry in farm animals. This study presents a wireless nose ring sensor system (NoRS) composed of thermal and photoplothysmography sensors that attach to the nostrils of four Holstein dairy cows. The NoRS's thermocouple measured the cow's nasal cavity air temperature; an optic sensor measured the IR (660 nm) and RED (660 nm) signals reflected from the cow's nasal septum. Breathing was calculated from the thermocouple signal's center frequency with a fast Fourier transformation or the signal peak count (i.e., oscillations). The breathing rate was compared to breathing observed by concurrently counting the flank movements. Heart rate and SpO2 were measured by integrated pulse oximetry and heart rate monitor module (MAX30101 TinyCircuit) assembled on the NoRS circuit. Heart rate was also measured with FFT and by counting the number of peaks from the optic sensor's raw IR and RED signals. These measures were compared to an off-the-shelf hand-held pulse oximeter's heart rate and SpO2 readings during the same time. The comparisons revealed highly significant correlations for the heart rate readings where the strength of the correlation was sensitive to the method. The correlation between breathing rate and the veterinarian's visual observations was low, albeit significant. Thus, inhale-exhale cycle counting constitutes a more precise approach than flank movement counts. The hand-held device's 96% SpO2 is compatible with near-saturation values expected in healthy cows. The mean NoRS SpO2 reading was 3% less. After further piloting under field conditions, the NoRS will require no animal restraining to automatically and continuously record cows' breathing rate, heart rate, and SpO2.

A Breathable and Stretchable Metastructure for a Versatile Hybrid Electronic Skin Patch with Long‐Term Skin Comfort

AbstractWith increasingly diverse functionalities of electronic skins (E‐skins), components and structures for the E‐skin have also become more diverse and complex. It is extremely challenging to make all the components and devices required for additional functionalities stretchable and breathable to ensure skin comfort. Herein, we report a facile strategy to realize a versatile hybrid E‐skin patch with great skin comfort by developing a breathable and stretchable metastructure to serve as the platform material of the hybrid E‐skin patch. A Kagome‐based mechanical metastructure made of breathable, stretchable medical adhesive integrates and tethers non‐stretchable or stiff components and devices to the skin, allowing for both the breathability and mechanical comfort of skin. A wireless skin sensor system to sense electrocardiogram (ECG) signals and wirelessly transmit ECG signals in an event‐driven manner such as sending R peaks only is developed on a polyimide‐based flexible printed circuit board. The Kagome metastructure‐tethered wireless ECG sensor patch does not cause significant skin discomfort when worn for five days, and successfully enables the event‐driven wireless monitoring of ECG signals. We envision that this facile and versatile approach expands the type of materials and functionalities of E‐skin for digital healthcare, personalized medicine, and smart prosthetics with emerging functionalities.

A Multimodal Sensing Platform for Interdisciplinary Research in Agrarian Environments.

Agricultural and environmental monitoring programs often require labor-intensive inputs and substantial costs to manually gather data from remote field locations. Recent advances in the Internet of Things enable the construction of wireless sensor systems to automate these remote monitoring efforts. This paper presents the design of a modular system to serve as a research platform for outdoor sensor development and deployment. The advantages of this system include low power consumption (enabling solar charging), the use of commercially available electronic parts for lower-cost and scaled up deployments, and the flexibility to include internal electronics and external sensors, allowing novel applications. In addition to tracking environmental parameters, the modularity of this system brings the capability to measure other non-traditional elements. This capability is demonstrated with two different agri- and aquacultural field applications: tracking moth phenology and monitoring bivalve gaping. Collection of these signals in conjunction with environmental parameters could provide a holistic and context-aware data analysis. Preliminary experiments generated promising results, demonstrating the reliability of the system. Idle power consumption of 27.2 mW and 16.6 mW for the moth- and bivalve-tracking systems, respectively, coupled with 2.5 W solar cells allows for indefinite deployment in remote locations.

Medical Big Data and Postoperative Nursing of Fracture Patients Based on Cloud Computing.

Based on the standards for wireless sensor system identification, the sensor node identity OID identification and the management object OID identification in the SNMP MIB are merged, and a management object OID identification coding mechanism for the SNMP-based wireless sensor system is proposed to make the node management system only. The identity, attributes, and multiple entities of the target sensor node in the wireless sensor network can be identified and managed by the node management object OID. The source of abnormal medical big data generally uses two models of multidimensional data and sliding window for detection and verification. First, the sliding window can be used to detect abnormalities. The result is that under this condition, the detection rate of medical big data is more than 95%; the effect is very good, but in different dimensions, the detection rate of four-dimensional data is 2.9% higher than that of a single-dimensional one. On the basis of the ZigBee wireless network, the terminal signal transmission of fracture treatment can be realized. On this basis, combined with the actual needs of fracture treatment, it can be built with its wireless module. The wireless network has a certain basic function. The reform of the nursing system was carried out on the basis of the safety and reliability of the nursing system, the efficiency of the nursing system was improved, and timely and safe nursing services were achieved.

Battery-free, tuning circuit-inspired wireless sensor systems for detection of multiple biomarkers in bodily fluids.

Tracking the concentration of biomarkers in biofluids can provide crucial information about health status. However, the complexity and nonideal form factors of conventional digital wireless schemes impose challenges in realizing biointegrated, lightweight, and miniaturized sensors. Inspired by the working principle of tuning circuits in radio frequency electronics, this study reports a class of battery-free wireless biochemical sensors: In a resonance circuit, the coupling between a sensing interface and an inductor-capacitor oscillator through a pair of varactor diodes converts a change in electric potential into a modulation in capacitance, resulting in a quantifiable shift of the resonance circuit. Proper design of sensing interfaces with biorecognition elements enables the detection of various biomarkers, including ions, neurotransmitters, and metabolites. Demonstrations of “smart accessories” and miniaturized probes suggest the broad utility of this circuit model. The design concepts and sensing strategies provide a realistic pathway to building biointegrated electronics for wireless biochemical sensing.

Real-Time Wireless Pressure Sensing System for Stall and Loading Measurements in the Rotating Frame of Reference for a Low Speed Compressor

In this paper the fabrication and characterization of a novel wireless pressure sensor system that can monitor and report real-time changes in pressure on the rotor blade of a low speed axial compressor is demonstrated. The pressure sensor was positioned on the second-stage rotor at approximately the mid-span location on the pressure surface of the blade near the leading edge and hard wired to a circuit box located on the center line of the compressor drum. The output of the pressure sensor is converted from a voltage to a frequency and transmitted wirelessly via circularly polarized antenna to a stationary antenna that was approximately 1 meter away, mounted at the centerline location on the inlet cart. The output signal was recorded on a spectrum analyzer with a LabVIEW program for data acquisition. Compressor rotation was ramped from 0 RPM up to its design speed of 1000 RPM and the airflow was decreased by closure of downstream throttle valve until the compressor stalled. The measurements from the wireless pressure system show increasing pressure as compressor flow rate is decreased, until a precipitous drop in pressure occurs which signals that the compressor has stalled.

Probabilistic Bigraphs

Bigraphs are a universal computational modelling formalism for the spatial and temporal evolution of a system in which entities can be added and removed. We extend bigraphs to probabilistic bigraphs, and then again to action bigraphs, which include non-determinism and rewards. The extensions are implemented in the BigraphER toolkit and illustrated through examples of virus spread in computer networks and data harvesting in wireless sensor systems. BigraphER also supports the existing stochastic bigraphs extension of Krivine et al. and using BigraphER we give, for the first time, a direct implementation of the membrane budding model used to motivate stochastic bigraphs.

Predicting and Determining the Time Between Metrological Failures of Smart Systems for Precision Farming

Introduction. Solving the problem of forecasting and determining the operating time for metrological failure and conducting the first calibration of smart systems of precision land use is possible by solving the problem of self-calibration of smart sensors that are part of these smart systems (SS). This problem is solved and described in [1]. The purpose of the paper is the methodology for dynamic prediction and determination of the time between metrological failures (MF) and the first verification of SS designed for precision farming. Results. The article describes a method patented in Ukraine for measuring the SS operating time for a MF (dynamic prediction method) based on a synthesized probabilistic-physical model (PP-model) of SS MF described by a multi-parameter Kondratov – Weibull distribution function (DF) with controlled (flexible) parameters. The proposed model describes the relationship between the normalized error and the parameters of the metrological reliability (MR) of the SS. It is shown that the dynamic regression PP-models of MF are a combination of the capabilities of regression models using flexible multi-parameter DF, with the possibility of using dynamic (spatio-temporal) processes covering different trends in the change in the values of normalized errors and their uncertainty bands, confidence level, time frame, acceptable boundary conditions, etc. Dynamic regression models of MF SS make it possible to understand the relationship between DF variables and allow the possibility of studying metrological problems (“scenarios”) of the “what if …” type. The dynamic regression method is a set of techniques for reciprocating approximation of the values of the shift parameter of the dynamic PP-model of MF to the predicted value of the shift parameter of the static PP-model of MF SS, as well as methods for assessing the reliability and accuracy of forecasting and determination. The article describes the essence of a new method for determining the operating time of the SS in the MF using the PP-model of the MF based on the Kondratov – Weibull DF. For the first time, a graphical portrait of the PP-model of SS metrological failures in the combined system of scales (coordinates) has been developed and presented - with the scales "probability of metrological failure Pξ" and "normalized error ξx" and separate or combined scales of "interval time scale tx " and "calendar time scale". The procedure for determining the time of the first verification is described, the advantage of non-periodic verifications is noted in order to save costs for their implementation. The possibility of occurrence of "conditional misses" in determining the error and time of operation on the MF during one or another verification is shown. Their existence is established only after the subsequent verification, analysis of the obtained data, and drawing the curve of the DF on a graphical portrait. It is recommended to choose the time between verifications as a multiple of one year, and to carry out verifications on the same day and month of the year. Conclusions. The dynamic regression method is an effective and versatile method due to the high accuracy of forecasting and determining the operating time in the MF. It can also be implemented using MF PP- models based on the DF of Kondratov - Cauchy, Kondratov - Laplace and others. Keywords: smart sensor, self-calibration, wireless sensor systems, methods of redundant measurements, problems of metrological support.

An Intelligent Controlling and Monitoring of Green House Effect for Indian Farmers

In this we propose wireless sensor based IoT system for greenhouse to control and monitoring parameter. By using this methodology, we will support our farmers to increase the growth of plants in different variety of field. An implementation of this system for greenhouse controlling and monitoring uses parameter such as, light intensity, temperature, soil moisture, relative humidity. This proposed work we will help to control and monitor these parameters which are sensed by different types of sensors like; light sensor, temperature sensor, soil moisture sensor, humidity sensor. The implementation is involving a wireless sensor system (WSS) which is based on IoT in greenhouse controlling and monitoring. In case of wireless communication, we used ESP8266E for create sensor node and actuator node for monitor parameters, sending by a wireless connection to computer via Wi-Fi communication, and give suitable command to the end device to overcome the variation of environmental parameter inside greenhouse. These implementations help farmer to grow plants in non-farmable condition of plants.

In-situ electrostatic field regulating the recrystallization behavior of P(VDF-TrFE) films with high β-phase content and enhanced piezoelectric properties towards flexible wireless biosensing device applications

Fabrication of flexible piezoelectric polymers is essential for the development of wearable biosensors. Nevertheless, the piezoelectric coefficient of polymers is usually an order of magnitude lower than the inorganic piezoelectric materials, which limits the miniature of the devices. Herein, a simple and cost-effective technique is proposed by applying an electrostatic field to in-situ regulate the recrystallization behavior of the poly(vinylidene fluoride-trifluorethylene) (P(VDF-TrFE)) film during the thermal annealing and recrystallization stage. Significantly enhanced properties are obtained, including a large piezoelectric coefficient (d33~30.7 pC/N) and a high voltage sensitivity of 167.12 mV/kPa. The outstanding performance is mainly attributed to the applied electrostatic field maintained during the film growth stage, which promotes the generation of nucleation sites for the polar β lamellae crystals. A wireless sensor system is further established with the obtained P(VDF-TrFE) film, demonstrating great potential applications in movement monitoring. The proposed facile approach could potentially be applied to the large-scale production of polymer films with excellent piezoelectric properties for flexible wireless biosensing device applications.

Wireless Sensor System of UAV Infrared Image and Visible Light Image Registration Fusion

The application of multisource sensors to drones requires high-quality images to ensure it. In simultaneous interpreting two or more multisensor images based on the same scene or target, the image obtained by the UAV sensor is limited by the imaging time and the shooting angle. The images obtained may not be aligned in the spatial position, thus affecting the fusion effect. Therefore, different sensor images must be registered before image fusion. During the shooting process of the drone imaging sensor, imaging angle, and environmental conditions, the obtained various sensor images will have rotation, translation, and other deformations in the spatial position so that they do not reach the spatial position. Therefore, it is impossible to directly perform image fusion directly. Therefore, before the multisensor image fusion, the image registration process must be completed to ensure that the two images are aligned in space. This paper analyzes the principles; based on the principles of the Powell search algorithm and improved walking algorithm, an algorithm combining Powell and improved walking algorithm is proposed. This paper also studies several traditional image neutrosophic fusions. The algorithm combines the fusion optimization algorithm proposed in this paper greatly reduces the calculation speed and improves the performance of the optimization algorithm and success rate.

System Resource Allocation and Rural Industry Revitalization Based on Max-Min Algorithm.

At this stage, the research hotspots on wireless multimedia sensor networks mainly include MAC protocol, network security, and node positioning. Research on resource allocation is scarce. Due to the lack of an effective wireless resource allocation strategy, even the most advanced transmission technology will not be able to fully utilize its unique advantages due to unreasonable resource allocation. Therefore, in-depth research on wireless resource allocation strategies is of great significance for the better application of wireless transmission technology. The entire network uses OFDMA mechanism to divide time slots. According to this method, resource allocation can be divided into intercluster and intracluster, and then the Max-Min subcarrier allocation algorithm is innovated and perfected to ensure maximum information transmission speed and throughput and promote information data efficient transmission. Based on the research of wireless sensor system resource allocation technology, China has emphasized industrial prosperity as the key point in its rural revitalization strategy, and doing a good job in technological innovation and industrial innovation is of great significance to economic development. At this stage, the capacity of rural industrial economic development is poor, and the economic level of most rural areas is not high. This situation is inseparable from problems such as limited industrial development and insufficient technological innovation capabilities. In order to promote the prosperity of rural industries and achieve the goal of rural revitalization, we must proceed from the fundamentals and give priority to solving the outstanding problems encountered in the process of rural industrial revitalization. Promoting the revitalization of rural industries can effectively solve the current plight of agricultural development and is an inherent requirement for rural prosperity and the country's prosperity. Aiming at the problems in the process of restricting the revitalization of rural industries, this article adheres to the principle of integrating theory with practice and puts forward practical and feasible measures to solve the problem in a targeted manner.

A Combined Semi-Supervised Deep Learning Method for Oil Leak Detection in Pipelines Using IIoT at the Edge

Pipelines are integral components for storing and transporting liquid and gaseous petroleum products. Despite being durable structures, ruptures can still occur, resulting not only in financial losses and energy waste but, most importantly, in immeasurable environmental disasters and possibly in human casualties. The objective of the ESTHISIS project is the development of a low-cost and efficient wireless sensor system for the instantaneous detection of leaks in metallic pipeline networks transporting liquid and gaseous petroleum products in a noisy industrial environment. The implemented methodology is based on processing the spectrum of vibration signals appearing in the pipeline walls due to a leakage effect and aims to minimize interference in the piping system. It is intended to use low frequencies to detect and characterize leakage to increase the range of sensors and thus reduce cost. In the current work, the smart sensor system developed for signal acquisition and data analysis is briefly described. For this matter, two leakage detection methodologies are implemented. A 2D-Convolutional Neural Network (CNN) model undertakes supervised classification in spectrograms extracted by the signals acquired by the accelerometers mounted on the pipeline wall. This approach allows us to supplant large-signal datasets with a more memory-efficient alternative to storing static images. Second, Long Short-Term Memory Autoencoders (LSTM AE) are employed, receiving signals from the accelerometers, and providing an unsupervised leakage detection solution.

Iron oxide-immobilized porous carbon nanofiber-based radio frequency identification (RFID) tag sensor for detecting hydrogen sulfide

Hydrogen sulfide (H2S) is the richest and stinkiest toxic gas found around coal mines, manholes, and semiconductor industries. However, there are not many studies on H2S detection around various industrial sites using wireless sensors. This study proposes an H2S wireless sensor system that communicates of the reader antenna and the sensor tag using 904 MHz activated radio frequency identification (RFID). The sensor tag is formed through an iron oxide-immobilized multiscale pore contained carbon nanofiber (Fe2O3-MPCNF)-based conductive paste. This wireless sensor tag has a large detection range (0.2 to 100 ppm) at 25 °C, is very sensitive to H2S gas (0.2 ppm or less), and is inexpensive to process. In addition, the sensor tag has a long operating range (5–40 cm) and excellent flexibility, and has excellent cycle stability (up to 35 days), making it practical for wearable devices for wireless detection.

Long-Range Low-Power Multi-Hop Wireless Sensor Network for Monitoring the Vibration Response of Long-Span Bridges.

Recently, vibration-based monitoring technologies have become extremely popular, providing effective tools to assess the health condition and evaluate the structural integrity of civil structures and infrastructures in real-time. In this context, battery-operated wireless sensors allow us to stop using wired sensor networks, providing easy installation processes and low maintenance costs. Nevertheless, wireless transmission of high-rate data such as structural vibration consumes considerable power. Consequently, these wireless networks demand frequent battery replacement, which is problematic for large structures with poor accessibility, such as long-span bridges. This work proposes a low-power multi-hop wireless sensor network suitable for monitoring large-sized civil infrastructures to handle this problem. The proposed network employs low-power wireless devices that act in the sub-GHz band, permitting long-distance data transmission and communication surpassing 1 km. Data collection over vast areas is accomplished via multi-hop communication, in which the sensor data are acquired and re-transmitted by neighboring sensors. The communication and transmission times are synchronized, and time-division communication is executed, which depends on the wireless devices to sleep when the connection is not necessary to consume less power. An experimental field test is performed to evaluate the reliability and accuracy of the designed wireless sensor network to collect and capture the acceleration response of the long-span Manhattan Bridge. Thanks to the high-quality monitoring data collected with the developed low-power wireless sensor network, the natural frequencies and mode shapes were robustly recognized. The monitoring tests also showed the benefits of the presented wireless sensor system concerning the installation and measuring operations.

Evaluation of deep learning approaches for oil & gas pipeline leak detection using wireless sensor networks

Pipelines are one of the most common systems for storing and transporting petroleum products, both liquid and gaseous. Despite the durable structures, leakages can occur for many reasons, causing environmental disasters, energy waste, and, in some cases, human losses. The object of the ESTHISIS project is the development of a low-cost and low-energy wireless sensor system for the immediate detection of leaks in metallic piping systems for the transport of liquid and gaseous petroleum products in a noisy industrial environment. In this study, two distinct leakage detection methodologies are presented. First, a 2D-Convolutional Neural Network (CNN) model undertakes supervised classification in spectrograms extracted by the signals acquired by the accelerometers mounted on the pipeline wall. This approach allows us to supplant large-signal datasets with a more memory-efficient alternative to storing static images. The second methodology entails a Long Short-Term Memory Autoencoder (LSTM AE), which directly receives the signals from the accelerometers, providing an unsupervised leakage detection solution. Field tests for the validation of our methods were performed using an experimental pipeline network, while evaluation of their efficiency in a real environment was conducted in the premises of an oil refinery in Greece. Results evince the potency of the LSTM AE to recognize in real-time the emergence of deficiencies and the efficacy of the CNN models to classify accurately spectrograms reflecting the operational condition of the monitored pipelines.

Wireless Volatile Organic Compound Detection for Restricted Internet of Things Environments Based on Cataluminescence Sensors

Cataluminescence-based sensors do not require external light sources and complex circuitry, which enables them to avoid light scattering with high sensitivity, selectivity, and widely linear range. In this study, a wireless sensor system based on hierarchical CuO microspheres assembled from nano-sheets was constructed for Volatile Organic Compound (VOC) online detection. Through sensor characteristics and data process analysis, the results showed that the luminous sensor system has good luminous characteristics, including the intensity of visible light, high signal/noise (S/N) values, and very short response and recovery times. Different VOC concentration values can be detected on multiple wavelength channels and different Cataluminescence signal spectra separations can process multiple sets of Cataluminescence data combinations concurrently. This study also briefly studied the mechanism action of the Cataluminescence sensor, which can specifically be used for VOC detecting.

Piezo/Triboelectric Effect Driven Self‐Powered Gas Sensor for Environmental Sensor Networks

With the growing concern about the safety of the spaces where people work and live, the development of fast and accurate real‐time safety monitoring systems such as gas sensors to detect harmful gas such as H2S, NH3, and NO x is essential. For effective monitoring, a wireless sensor system that can be used anytime, anywhere is required, but there is still a limit to the power supply to operate it. Self‐powered gas sensors with piezoelectric/triboelectric nanogenerators (PENGs/TENGs) are receiving considerable attention because they can implement portable gas sensors that do not require an additional power source. Herein, the latest developments in piezoelectric/triboelectric effect based self‐powered gas sensors are summarized and discussed. The first part of this review discusses the fundamental aspects and strategies of the self‐powered active gas sensor based on the piezoelectric and triboelectric effects. The second part focuses on the self‐powered gas sensors that operate on energy generated by piezoelectric and TENGs.

An Emergy-based Approach to Evaluate the Effectiveness of Integrating IoT-based Sensing Systems into Smart Buildings

Wireless Sensing Systems based on the Internet of Things (IoT) are increasingly being deployed in smart buildings to reduce their energy consumption and environmental impacts. However, the actual environmental costs of integrating IoT-based sensing systems into smart buildings have rarely been considered when reporting on their overall sustainability footprint. This paper presents an Emergy-based methodological approach to ascertain the effectiveness of integrating IoT-based sensing systems into smart buildings. The proposed method employs three novel Emergy Neutrality Indices (ENIs): The Sensing System to Building Emergy (SBEm) ratio, the Emergy Neutrality Score (EmN), and the Emergy Payback Time (EmPBT). The Solar House at Gainesville, Florida, retrofitted with an IoT-based sensing system, is used as an example building for computing these indices. A Building Energy Model (BEM) is created using the EnergyPlusTM simulation engine available in DesignBuilder V7 to determine the operational Emergy of the building. A custom-built CO2 sensor is used to collect the real-time occupancy data for incorporation into the BEM. The Emergy Neutrality computation demonstrates that the integration of the given sensing system is effective in the Solar House, with SBEm, EmN, and EmPBT values of 2.06E-03, 0.28, and 3.61 years, respectively. By varying the sensors’ energy-savings potential and spatio-functional configurations, nine design scenarios are generated and evaluated for their environmental performance using Emergy ratios and the proposed ENIs. Finally, the Solar House is compared with the Rinker Hall to emphasise the significance of reporting these indices. In the future, designers, architects, and other stakeholders could use these ENIs as useful sustainability assessment tools to evaluate the environmental effectiveness of integrating these smart sensing systems into buildings.