Proposed Wireless Sensor Research Articles

Intelligent Dust Monitoring System Based on IoT

Dust is a frequent contributor to health risks and changes in the climate, one of the most dangerous issues facing people today. Desertification, drought, agricultural practices, and sand and dust storms from neighboring regions bring on this issue. Deep learning (DL) long short-term memory (LSTM) based regression was a proposed solution to increase the forecasting accuracy of dust and monitoring. The proposed system has two parts to detect and monitor the dust; at the first step, the LSTM and dense layers are used to build a system using to detect the dust, while at the second step, the proposed Wireless Sensor Networks (WSN) and Internet of Things (IoT) model is used as a forecasting and monitoring model. The experiment DL system train and test part was applied to dust phenomena historical data. Its data has been collected through the Iraqi Meteorological Organization and Seismology (IMOS) raw dataset with 170237 of 17023 rows and 10 columns. The LSTM model achieved small time, computationally complexity of, and layers number while being effective and accurate for dust prediction. The simulation results reveal that the model's mean square error test reaches 0.12877 and Mean Absolute Error (MAE) test is 0.07411 at the same rates of learning and exact features values of vector in the dense layer, representing a neural network layer deeply is connected to the LSTM training proposed model. Finally, the model suggested enhances monitoring performance.

3D printed sub-terahertz photonic crystal for wireless passive biosensing

Monitoring pathogens has become a major challenge for society and research in recent years. Of great interest are refractive index sensors, which are based on the interaction between analytes and electromagnetic waves and allow label-free and fast detection. In addition, the electromagnetic waves can be exploited for wireless communication. However, current refractive index biosensors can only be read from a few centimeters. Here, we demonstrate an innovative concept of a passive wireless sensor based on a sub-terahertz photonic crystal resonator. The fabricated sensors have a reading range of up to 0.9 m and elevation and azimuth acceptance angles of around 90°. We demonstrate the stand-off detection of sub-µm thin-film proteins as test analytes. The proposed wireless sensor opens the door to a non-electronic, compact, and low-cost solution and can be extended to a wireless sensor network monitoring airborne pathogen, which may provide a pre-infection detection to prevent their spread efficiently.

Design and Implementation of an Event-Driven Smart Sensor Node for Wireless Monitoring Systems.

In this paper, an event-driven wireless sensor node is proposed and demonstrated. The primary design objective is to devise a wireless sensor node with miniaturization, integration, and high-accuracy recognition ability. The proposed wireless sensor node integrates two vibration-threshold-triggered energy harvesters that sense and power a threshold voltage control circuit for power management, a microcontroller unit (MCU) for system control, a one-dimensional convolutional neural network (1D-CNN) environment data analysis and vibration events distribution, and a radio frequency (RF) digital baseband transmitter with IEEE 802.15.4-/.6 protocols. The dimensions of the wireless sensor node are 4 × 2 × 1 cm3. Finally, the proposed wireless sensor node was fabricated and tested. The alarming time for detecting the vibration event is less than 6 s. The measured recognition accuracy of three events (knock, shake, and heat) is over 97.5%. The experimental results showed that the proposed integrated wireless sensor node is very suitable for wireless environmental monitoring systems.

Research on Efficiency Optimization of Logistics Vehicle Monitoring Model Based on Wireless Sensor Network

For enhance running effectiveness efficiency of logistics carriage supervisory depended on wireless sensor network (WSN), a novel wireless sensor network optimized by improved bat algorithm is established. Firstly, working theory and system framework of WSN depended on logistics monitoring system are analyzed. Secondly, B-MAC protocol is used in proposed wireless sensor network, and corresponding models are established, and recovery ratio of wireless sensor network is defined, and node deployment optimization of wireless sensor network is constructed. Thirdly, the optimization algorithm of node deployment of WSN is designed based on amended bat algorithm, and analysis procedure of this algorithm is established. Finally, a simulation analysis is carried out, analysis results show that performance of proposed WSN logistics carriage supervisory based on improved bat algorithm is better, which has quicker convergence speed and higher convergence precision, and reliability of proposed WSN logistics carriage supervisory is improved, the energy consumption of sensor node data transmission is reduced, and the life of WSN is improved. Proposed WSN based on logistics carriage supervisory based on improved BA has higher coverage ratio and higher efficiency. Therefore proposed wireless sensor network based on logistics carriage supervisory based on improved bat algorithm can obtain better monitoring efficiency, which has prospect application view.

Implementation and experimental evaluation of dynamic capabilities in wireless body area networks: different setting parameters and environments

It is well known that a wireless body area network (WBAN) is a special proposed wireless sensor network (WSN) that can assist in monitoring physiological signals for the evaluation and planning of patient treatment. One of the most challenging issues for WBANs is communication reliability, with acceptable communication efficiency and packet loss. To obtain such network characteristics, collision-free data transmission in networks of wireless sensor nodes is an interesting research problem. In this paper, the experiments of dynamic capabilities in several WBAN scenarios are focused, where the novelty and major contribution of our tests is that the effects of packet inter-arrival times, packet sizes, and the number of nodes deployed in the network, including human movements, indoor and outdoor environments, and transmitter and receiver positions, are all taken into consideration and evaluated. This is achieved by implementing the WBAN using IEEE 802.15.4 low-power sensor nodes. Experimental results illustrate the significant factors that impact the communication reliability of WBANs as measured by the packet delivery ratio (PDR). The experimental results show that the diverse environment testbed can affect network performance for WBAN data transmission. Our findings also show that the best network reliability needs to be set at more than 15 ms in packet interval time to achieve over 90% PDR for every test scenario. More details of the experimental results related to WBAN reliability obtained from all test cases are also discussed and summarized in the paper. To the best of our knowledge, our findings can be useful for users and researchers to consider the optimal point for WBAN setting and configuration to achieve the communication reliability requirements and also to deploy and develop a more reliable WBAN system.

SmartCrawler: A Size-Adaptable In-Pipe Wireless Robotic System with Two-Phase Motion Control Algorithm in Water Distribution Systems.

Incidents to pipes cause damage in water distribution systems (WDS) and access to all parts of the WDS is a challenging task. In this paper, we propose an integrated wireless robotic system for in-pipe missions that includes an agile, maneuverable, and size-adaptable (9-in to 22-in) in-pipe robot, "SmartCrawler", with 1.56 m/s maximum speed. We develop a two-phase motion control algorithm that enables reliable motion in straight and rotation in non-straight configurations of in-service WDS. We also propose a bi-directional wireless sensor module based on active radio frequency identification (RFID) working in 434 MHz carrier frequency and 120 kbps for up to 5 sensor measurements to enable wireless underground communication with the burial depth of 1.5 m. The integration of the proposed wireless sensor module and the two-phase motion controller demonstrates promising results for wireless control of the in-pipe robot and multi-parameter sensor transmission for in-pipe missions.

Wearable Bluetooth Triage Healthcare Monitoring System.

Triage is the first interaction between a patient and a nurse/paramedic. This assessment, usually performed at Emergency departments, is a highly dynamic process and there are international grading systems that according to the patient condition initiate the patient journey. Triage requires an initial rapid assessment followed by routine checks of the patients’ vitals, including respiratory rate, temperature, and pulse rate. Ideally, these checks should be performed continuously and remotely to reduce the workload on triage nurses; optimizing tools and monitoring systems can be introduced and include a wearable patient monitoring system that is not at the expense of the patient’s comfort and can be remotely monitored through wireless connectivity. In this study, we assessed the suitability of a small ceramic piezoelectric disk submerged in a skin-safe silicone dome that enhances contact with skin, to detect wirelessly both respiration and cardiac events at several positions on the human body. For the purposes of this evaluation, we fitted the sensor with a respiratory belt as well as a single lead ECG, all acquired simultaneously. To complete Triage parameter collection, we also included a medical-grade contact thermometer. Performances of cardiac and respiratory events detection were assessed. The instantaneous heart and respiratory rates provided by the proposed sensor, the ECG and the respiratory belt were compared via statistical analyses. In all considered sensor positions, very high performances were achieved for the detection of both cardiac and respiratory events, except for the wrist, which provided lower performances for respiratory rates. These promising yet preliminary results suggest the proposed wireless sensor could be used as a wearable, hands-free monitoring device for triage assessment within emergency departments. Further tests are foreseen to assess sensor performances in real operating environments.

Advanced Driver Assists and Safety Management System

Electronics and communication is the most important field. In this paper, we can describe how much safety is in the Automobile industry. In this paper, we are using uno-Arduino. The different types of sensors facilities are also provided using key points. The different sensors are provided to check visitor count. In this system, we can monitor and control all the safety precautions their one IoT web platform. This helps in the proper utilization of drivers and helps in avoiding accidents. This paper can be implemented in any two-wheelers, heavily loaded trucks, small SUVs, compact cars. In our paper, the electronics machine/components will be automatically working with using of Arduino program. The proposed wireless sensor platform is an attempt to develop more safety devices that can be used in multiple areas such as homes, schools, and public utilities to reduce accidents. This Advanced Driver Assists system will provide real-time accident detections and monitoring usage information that helps in real-time by using GSM, GPS, and sensors.

Carbon‐nanotube‐loaded planar gas and humidity sensor

AbstractThis paper reports a gas and humidity sensor design with improved sensitivity. The design is based on a planar antenna loaded with carbon nanotubes (CNTs). Sensing is achieved by the conductivity change of the CNT film when exposed to various gases or humidity levels. The sensitivity improvement was achieved by replacing a portion of the patch conductor at the dominant edge with a strip of CNT film, and accordingly having the CNT film exercise the maximum effect on the antenna's radiation. The fabricated sensor was tested in a sealed chamber by filling the chamber with several different gases and then with water vapor. The experimental results were excellent, and the gas sensing capability of the CNT‐loaded patch antenna was better than previously reported designs. The sensor was designed for 2.4 GHz so that it can easily be compatible with Wi‐Fi and Bluetooth devices, and the sensor geometry can be easily scaled to other frequencies, which shows great promise for applications of the proposed wireless sensor in environmental monitoring and weather forecasting.

IoT-enabled Low Power Environment Monitoring System for prediction of PM2.5

Air pollution is a major concern worldwide due to its significant impacts on the global environment and human health. The conventional instruments used by the air quality monitoring stations are costly, bulkier, time-consuming, and power-hungry. Furthermore, due to limited data availability and non-scalability, these stations cannot provide high spatial and temporal resolution in real-time. Although energy-efficient, wireless sensor network with the high spatio-temporal resolution is one of the potential solutions, real-time remote monitoring of all significant air quality parameters with low power consumption is challenging. To address this challenge, we propose internet of things-enabled low power environment monitoring system for real-time monitoring of ten significant air quality parameters. Moreover, the proposed system enables remote monitoring and storage of data for future analysis. Unlike earlier research work, further expansion of the proposed system is easily possible, as the proposed Wireless Sensor Node (WSN) can interface a higher number of sensors with the same number of interfacing pins. We did an in-depth analysis through calibration, experiments, and deployment which confirms the power efficiency, flexibility, reliability and accuracy of the proposed system. Results illustrate the low power consumption of 25.67mW, data transmission reliability of 97.4%, and battery life of approximately 31 months for a sampling time of 60 min. The study of the correlation between Particulate Matter 2.5 (PM2.5) and other pollutants is performed using Central Pollution Control Board data of 41 months. The initial study related to correlation is performed for the future work of developing a prediction model of PM2.5 using highly correlated pollutants. The future approach for developing a prediction model in the form of analytical equations with the help of artificial neural network is demonstrated. This approach can be implemented using the proposed WSN or low-cost processing tool for evaluating PM2.5 from precursor gases. Therefore, this approach can be one of the promising approaches in the future for monitoring PM2.5 without power-hungry gas sensors and bulkier analyzers.

A Three-Port Zero-Power RFID Sensor Architecture for IoT Applications

A novel zero-power wireless sensor architecture is proposed and demonstrated in this paper. The proposed wireless sensor, which is a passive sensor, combines UHF RFID and a capacitive sensor to enable reading of physical and chemical parameters wirelessly, without compromising much on the read-range, and complexity of conventional RFID tags. The sensor alters the phase of backscattered RFID signal, which is detected at the receiver using a non-coherent IQ demodulator. Due to the universal nature of this architecture, any type of sensor, such as temperature, humidity, water level sensor, can be realized. For the sake of simplicity, a varactor diode was initially used to get a thorough understanding of the system. Following that, a flood sensor was used to evaluate the performance of zero-power wireless sensor in a real-world application.

Centralized MPPT Controller System of PV Modules by a Wireless Sensor Network

An efficient monitoring and control system for solar photovoltaic modules, which combines the use of a non-linear MPPT backstepping controller with a custom wireless sensor network (WSN) has been developed. The infrastructure consists of a wireless smart photovoltaic system (WSPS) and a wireless centralized control system (WCC). The data of sensing, coordination and control is handled by using a WSN based on IEEE 802.15.4 technology in beacon enable mode and with guaranteed time slot. This assures the data transmission and a synchronous acquisition, which are critical elements in a wireless photovoltaic monitoring system. All measured data is gathered by an autonomous, compact and low-cost sensor node installed in each PV module, and it is transferred to the coordinator node. The power consumption of the sensor node represents only 0.25% of the power delivered by the photovoltaic module. A backstepping controller to track the Maximum Power Point (MPP) by means of a buck-boost converter derives the reference parameters to return to each PV module accordingly. The wireless solution uses low latency techniques to achieve a real-time monitoring and a stable performance of the controller. The centralized control identifies all the network nodes and significantly simplifies the maintenance operations. Experimental validation shows the robustness against interference and security in the wireless data transmission and confirms the feasibility of the proposed wireless sensor system in tracking the maximum power transfer under different weather conditions, achieving an efficiency over the 99% in the MPPT.

Fetal heart detection based wide area network technology with wireless sensor transmission

Fetal heart detection technology, especially in remote areas, has so far been manual and lacks facilities for monitoring fetal safety. This results in many pregnant women at risk of labor, so early detection of fetal heart conditions becomes important. Development of fetal heart detector technology based on wide area network technology that is able to provide real-time monitoring results and is connected to the proposed wireless sensor network transmission. Its integration with smartphones makes it easy to use in rural and remote areas. The design of this monitoring system called detector of fetal abnormalities technology has the advantage of sending medical data for pregnant women and fetal heart conditions to families and to health care centers for pregnant women in large cities or obstetricians. Visualization of data in image, graphic and text, can be used as a digital-based maternal and fetal health detector tool. In the initial trial, fetal heart rate data from 4 pregnant subject were evaluated and obtained results with an average curation of 94%.

Performance analysis of cooperative wireless sensor network with index‐based modulation

Spatial modulation (SM) emerges as a potential technique for next generation wireless networks. Enhanced spectral efficiency and equitable system complexity led SM to various innovations. In SM only one radio-frequency chain is active during the transmission of the signal. The information is carried by the index of the active antenna. Throughput loss and reduced spectrum utility of higher-order modulation schemes can be overcome by activating more than one antenna. The shortcoming of SM is overcome by an enhanced SM in which index of an antenna and its constellation conveys the information. In the proposed wireless sensor network nodes are considered with two antennas. Based on the primary constellation the symbols are transmitted from a single active antenna and symbols from secondary constellation are transmitted from the two active antennas. The performance of the proposed network is compared to the network with SM.

Analysis of incremental LMS adaptive algorithm over wireless sensor networks with delayed-links

Wireless sensor networks (WSN) are used for many delay-sensitive applications, e.g. military surveillance, emergency response, city management, etc. Therefore, modeling of delay is very important. This paper presents a study of the impact of sensors' sensing delay, process delay and transmission delay in the estimation of the desired unknown parameter in the WSN. Recently proposed wireless sensor networks, in the literature, assume perfect nodes and links, in view of delay. It means that no consideration has been made about the delay in the sensing, processing and, transmission procedures. The proposed method in this paper analyzes the behavior of the distributed incremental estimation algorithm in the presence of delay in wireless sensor networks. Weighted spatio-temporal energy conservation method is used to evaluate the transient and steady state behavior of the wireless sensor networks with delay without putting any restriction on regressor's distribution. The equations that illustrate mean square deviation (MSD), excess mean square error (EMSE) and mean square error (MSE) behavior of individual nodes, are driven. Also, simulations show that overall delay could be calculated to turn off nodes in some iterations without affecting the performance of the distributed estimation algorithm or adding extra latency to the network, which can improve power management strategies by modifying sleep-wake scheduling protocols. Eventually, it is shown that simulation results have a good match with derived theoretical expressions.

Self-Powered Wireless Sensor for Fault Diagnosis of Wind Turbine Planetary Gearbox

This paper proposes a wind turbine planetary gearbox (PGB) fault diagnosis method based on a self-powered wireless sensor. The proposed wireless sensor, which consists of a piezoelectric energy harvester, a power management circuit, a microcontroller unit (MCU), a radio-frequency (RF) module, and an accelerometer, can acquire the vibration signals of wind turbine PGB by the accelerometer. The piezoelectric energy harvester utilizing vibration environment is optimized as a power supply for the proposed wireless sensor, including the MCU, RF module, and accelerometer. An ac–dc converter combined with a low-dropout voltage regulator is developed to provide stable dc voltage for the proposed wireless sensor. Stacked denoising autoencoder (SDAE) shows excellent performance in learning robust features from the noised signal. Thus, in this paper, the SDAE method is adopted to learn robust and distinguishable features from measured signals. Then, the least squares support vector machine (LSSVM) is employed to classify features extracted by the SDAE. Both the SDAE and LSSVM are optimized by quantum particle swarm optimization (QPSO). The experimental results show that the presented power supply can generate 3.3-V dc voltage, which ensures regular operation of the rest of the wireless sensor. The proposed wireless sensor can achieve a reliable communication distance of 40.8 m in the test environment. Furthermore, the SDAE approach and LSSVM show excellent performance in feature extraction and fault diagnosis, respectively. The experimental results indicate that the proposed method is effective in terms of fault diagnosis for the wind turbine PGB.

Ultrasonic wireless sensor development for online fatigue crack detection and failure warning

This paper develops a wireless sensor for online fatigue crack detection and failure warning based on crack-induced nonlinear ultrasonic modulation. The wireless sensor consists of packaged piezoelectric (PZT) module, an excitation/sensing module, a data acquisition/processing module, a wireless communication module, and a power supply module. The packaged PZT and the excitation/sensing module generate ultrasonic waves on a structure and capture the response. Based on nonlinear ultrasonic modulation created by a crack, the data acquisition/processing module periodically performs fatigue crack diagnosis and provides failure warning if a component failure is imminent. The outcomes are transmitted to a base through the wireless communication module where two-levels duty cycling media access control (MAC) is implemented. The uniqueness of the paper lies in that 1) the proposed wireless sensor is developed specifically for online fatigue crack detection and failure warning, 2) failure warning as well as crack diagnosis are provided based on crack-induced nonlinear ultrasonic modulation, 3) event-driven operation of the sensor, considering rare extreme events such as earthquakes, is made possible with a power minimization strategy, and 4) the applicability of the wireless sensor to steel welded members is examined through field and laboratory tests. A fatigue crack on a steel welded specimen was successfully detected when the overall width of the crack was around 30 um, and a failure warnings were provided when about 97.6% of the remaining useful fatigue lives were reached. Four wireless sensors were deployed on Yeongjong Grand Bridge in Souht Korea. The wireless sensor consumed 282.95 J for 3 weeks, and the processed results on the sensor were transmitted up to 20 m with over 90% success rate.

Wireless pressure sensor integrated with a 3D printed polymer stent for smart health monitoring

The primary objective of this study was to deploy a promising wireless pressure sensor system capable of monitoring real-time biological signals in an experimental object. MEMS-based micromachining technology was used to fabricate the proposed SU-8 wireless pressure sensor. The sensor utilizes a capacitor-inductor resonant circuit that can operate the sensor without any external power supply. The variable capacitor in the pressure sensor is designed to change the resonance frequency (130, 183 MHz) in response to applied pressure. The fabricated wireless pressure sensor was integrated into a polymer-based smart stent to minimize the discomfort of medication administration and hospital visits. A 3D bio-printing-based manufacturing technique was employed for the production of a smart polymer stent with complicated shapes. The proposed method is considerably more comfortable than the conventional metal stents fabrication process. The polymer smart stent made of the biocompatible polycaprolactone (PCL) material which can be fully absorbed by the body after a medication period. After integrating the fabricated wireless pressure sensor with the polymer smart stent, various basic experiments such as the working distance of the sensor were performed using a simple experimental setup. The biocompatibility of the proposed polymer stent and the wireless pressure sensor was also successfully confirmed using an experimental animal. The preliminary investigation results indicate that the proposed wireless sensor can be used to obtain necessary information in various parts of the human body as well as the stent.

Increasing sensing distance of passive wireless IOP sensor

Continuous intraocular pressure monitoring is very necessary for glaucoma patients. In this paper, a passive wireless intraocular pressure (IOP) sensor using an inductively coupled wireless sensing technique is presented. IOP sensor is consist of a multipath circular spiral coil inductor, variable capacitor and a SU-8 pressure sensitive diaphragm. Capacitor and inductor of sensor are made of high-conductivity and biocompatible metal gold that are encapsulated inside biocompatible SU-8 to prevent damage to eye tissue. By measuring impedance phase dip frequency shift from the external reader coil, the intraocular pressure signal is obtained from the IOP sensor. Small size of the sensor coil due to the biological constrains reduces the sensing distance. The purpose of this paper is increasing the quality factor of sensor coil using the multipath technique in order to enhancement of sensing distance. Simulation has been taken using COMSOL Multiphysics and ADS software. The proposed structure, due to its small size could increase the sensing distance. The dimension of proposed wireless sensor and sensing distance is $$ 2.5\;{\text{mm }} \times 2.5\;{\text{mm}} $$ and 2 cm, respectively, that has a larger sensing distance and smaller size compared with previous works and its sensitivity is 4281 ppm/mmHg.

A WSN-Based Solution for Pollution Detection and Localization in Waterways

To deal with the devastating impact of water pollution, the quality of water distributed within waterways needs to be monitored through a periodic collection and analysis of a set of water quality measurements. Existing water monitoring systems, which are proposed in the literature, exhibit a set of drawbacks including the expensive cost and complexity of deployment, the inaccurate localization of water contamination zones, and the absence of techniques allowing the auto-diagnosis of failures in the monitoring system. We propose in this paper a novel monitoring waterways’ pollution system that combines the use of mobile wireless sensor networks and radio frequency identification (RFID) systems. Thanks to the deployment of a set of fixed RFID tags on the waterways banks, the proposed system allows mobile sensors to accurately locate the positions of the contaminated water zones. It also allows storing pollution, monitoring, and sensor management-related data in the deployed tags. The proposed wireless sensor network implements an energy saving algorithm allowing to reduce the energy consumed by mobile nodes, and to minimize the number of sensors required to monitor a waterway. With respect to WSN-based water monitoring platforms existing in the literature, the proposed system offers a set of enhancements, in terms of design cost, energy consumption, scalability, data accuracy, and tolerance to errors and data loss.