Wireless Sensor System Research Articles

Battery-free wireless sensor system with compressed sensing for table grapes cold chain

Cold chain is one of the most important ways to guarantee the quality and safety of table grapes. It is necessary to monitoring the temperature in the whole cold chain process to keep the constant temperature and guarantee the quality and safety of table grapes in cold chain. This paper aims to develop a battery-free wireless sensor system with compressed sensing for the table grapes cold chain. The battery-free wireless sensor system was realized by using the ultra high frequency radio frequency identification (UHF RFID) on-chip temperature sensor with no battery. The temperature fluctuation performance of the table grapes in the cold chain, the compressed sensing (CS) performance of the temperature reconstructed error with the original data and the battery-free wireless sensor system with compressed sensing evaluation were comprehensively analyzed and discuss. The developed battery-free wireless sensor system with compressed sensing could effectively monitor the actual temperature with high reconstruction accuracy to save the data storage space, transmission and operation resources, improve the temperature transparency in cold chain, and finally ensure the quality and safety of table grapes. The developed battery-free wireless sensor system with compressed sensing could be extended for other cold chain applications to improve their transparency and transmission efficiency of the wireless monitoring. It is also possible to integrate the relative humidity, gas sensors into the battery-free wireless sensor system to monitor more environment parameters in cold chain.

Dynamic Analysis to Enhance the Performance of a Rotating-Disk-Based Triboelectric Nanogenerator by Injected Gas.

A rotating-disk-based triboelectric nanogenerator (TENG) generating electrical energy from wind usually includes a propeller. TENGs are widely used because their high frequency of rotation allows them to generate a relatively high output current. Deep analysis of the gas flow in a TENG is essential to improve its energy conversion efficiency. However, previous studies have isolated the propeller and the TENG as separate entities that harvest wind energy and generate electrical energy, respectively. Most studies focused on each entity because considering both the dynamics and the TENG operation together is an intricate process. This paper introduces a dynamic analysis of the gas flow by dividing it into four vertical and horizontal directions and carrying out a COMSOL simulation to verify the pressure on the propeller and the flow of the gas. The electrical outputs are measured while varying the height and angle of the inlet and the number of wings on the propeller. After optimization, the P-TENG generated a high output power density of 283.95 mW/m2, which can light up 205 light-emitting diodes and drive a commercial small electronic appliance. In addition, optimizing the P-TENG through a variety of analyses allowed it to provide sustainable power to a self-powered wireless sensor system.

Application of MEMS‐based accelerometer wireless sensor systems for monitoring of blast‐induced ground vibration and structural health: a review

Ubiquitous wireless sensor network (WSN) enables low-cost monitoring applications such as blast-induced ground vibration (BIGV) and structural health monitoring (SHM). In particular, monitoring and analysing the ambiguous BIGV waves are essential requisite to control and protect surrounding grievous damage structures. Similarly, improving health and longevity of structures using WSN is a new facet that owes to diminish the low-cost installation. Recent advances in WSNs are forging new prospects for sensors. A variety of intelligent sensors are integrated into the wireless system to monitor environmental, and health of civil infrastructures. Considering the current trends in the area of development of wireless monitoring prototypes, Micro-Electro-Mechanical-Systems (MEMS) accelerometer sensors are widely prevalent owing to the small size and inexpensive. In general, BIGV waves are less intensity and low-frequency signals. Hence, it is essential to select an appropriate accelerometer to detect micro-vibration waves. The study exemplifies a summarised review of recently made MEMS-based accelerometer wireless systems for intelligent and reliable monitoring of BIGV and SHM since the last decade. This research effort focuses on the numerous adopted accelerometers and their characteristics such as sensitivity, noise density, measurement range, bandwidth, resolution, network topologies, and performance of designed systems to analyse the micro-vibration levels comprehensively.

Low‐complexity wireless sensor system for partial discharge localisation

This study describes a key element of any modern wireless sensor system: data processing. The authors describe a system consisting of a wireless sensor network and an algorithmic software for condition-based monitoring of electrical plant in a live substation. Specifically, the aim is to monitor for the presence of partial discharge (PD) using a matrix of inexpensive radio sensors with limited processing capability. A low-complexity fingerprinting technique is proposed, given that the sensor nodes to be deployed will be highly constrained in terms of processing power, memory and battery life. Two variants of artificial neural network (ANN) learning models (multilayer perceptron and generalised regression neural network) that use regression as a form of function approximation are developed and their performance compared to K-nearest neighbour and weighted K-nearest neighbour models. The results indicate that the ANN models yield superior performance in terms of robustness against noise and may be particularly suited for PD localisation.

Comparative Effect of a Balance Training Program and Core Stabilization Program on Factors Related to the Prevention of Falling in Healthy Middle-Aged Individuals: A Double-Blind Randomized Controlled Clinical Trial

Objectives: The aim of this study to compare the effectiveness of the balance training program (BTP), and the corestabilization program (CSP), on dynamic balance in healthy middle-aged individuals.Material and Methods: This study was a single-blind randomized controlled trial design. Forty-two healthy middle-aged participants were randomly assigned to the BTP group (n=21), or the CSP group (n=21). Participants in both groups received an intervention program (balance training or core stabilization) 3 times a week, for 60 minutes, over 6 weeks. The primary outcome was the dynamic balance measured by the timed up and go test. The other outcomes were: core muscle endurance, muscle strength of the lower extremities, the flexibility of the lower back and hamstring muscles, and gait variables. The measurements included: the prone bridge endurance test, 5 times sit to stand test, sit and reach test and a wireless movement monitoring inertial sensor system, respectively. All outcomes were measured at baseline, and then after 6 weeks. The data were analyzed by the Independent Sample t-test between groups, and the paired t-test within either group.Results: After 6 weeks, there was no statistically significant difference between the groups in either dynamic balance,or other variables, however, a statistically significant difference was found in core muscle endurance (p-value=0.003). In so saying, the BTP group, statistically significant improvements were found only in core muscle endurance, the flexibility of the lower back and hamstring muscles, and gait variables from pre- to post-test. Moreover, in the CSP group, there was a statistically significant difference from pre- to post-test in all measured outcomes. Conclusion: The Core stabilization program is not superior to a balance training program, for the improvement of dynamic balance. Although, after 6 weeks of training, the study found that the Core stabilization program was effective for improving dynamic balance. This finding may point out that the Core stabilization program helps improve balance in a middle-aged person

(Invited) 3D Conformal Additive Printing of Semiconducting Nanocrystals for Energy Harvesting and Biosensing

Semiconducting nanocrystals have been widely studied in the past two decades with unique and superior energy conversion and sensing properties compared to their bulk form. However, there is still a prominent need to transform the nanocrystals into functional devices in a scalable and low-cost manufacturing process and translate their unique properties from nanoscale to meso- or macro- scale. This work explores a novel 3D conformal aerosol jet printing approach to convert a wide range of nanocrystals into flexible and functional devices for energy harvesting and biosensing. Solution-processed chalcogenide nanoplate inks have been synthesized and printed into flexible thermoelectric energy harvesters, which yield competitive figure of merit ZT values comparable with their rigid bulk counterparts fabricated by conventional method. A flexible thermoelectric device is demonstrated with high power density of 19 mW/cm2 and 1 mW/cm2 with 80 K and 20 K temperature difference respectively. In addition, we have utilized biocompatible 2D nanomaterial inks and printed complex device patterns on 3D curved substrates and soft materials (e.g. hydrogel) for biosensing. The printed sensors can be used as bioelectrodes, temperature sensing, strain sensing, etc. The printed energy harvester and bio-sensors can be integrated into a self-powered wireless sensor system, which can completely eliminate the need of external power supply and greatly facilitate the implementation of these sensors. The 3D conformal aerosol jet printing of colloidal nanoparticle inks demonstrated here opens up exciting opportunities to transform semiconducting nanocrystals into functional and flexible devices for broad energy harvesting and sensing applications.

Dual circularly polarized loop antenna using a pair of resonant even‐modes

Conceptual design of dual circularly polarized (CP) square loop antennas using a pair of resonant even-modes for wireless sensor application is presented. A pair of even-modes is simultaneously excited within a single, nonuniform square loop element and employed to realize a dual circular polarization characteristic. When the natural boundary conditions of the even-modes and the feed lines are matched, respectively, a bidirectional dual CP loop antenna can be attained at first. Then, a unidirectional dual CP loop antenna is designed by introducing a simple metallic reflector. Both bidirectional and unidirectional designs exhibit dual CP performance with available operation bandwidth of 10.0% and 8.5%. The design approach is expected to get applications in dual CP antenna designs for wireless sensor systems.

Energy Efficient Solutions in Wireless Sensor Systems for Water Quality Monitoring: A Review

This paper presents energy-efficient solutions for wireless sensor systems intended for the monitoring of water quality at water stations. Energy problems, such as energy scarcity and energy consumption in wireless sensor networks, are critical issues that raise concerns when it comes to defining the lifetime of a network. This can be attributed to the finite energy budget of water quality sensor nodes and also the lack of energy-efficient schemes. To address these energy problems, a detailed study has been carried out in this paper on some of the major approaches developed to address energy issues in wireless sensor systems, and dedicated to the monitoring of water quality applications in a way that contextualizes the considered solution methods. Furthermore, the solution methods are classified, while their strengths and weaknesses are also identified. Also, several suggestions are made on solution methods toward further improvement. Therefore, this paper suggests a reasonable direction for future studies on developing energy-efficient solutions in wireless sensor systems for monitoring water quality applications.

On Rapid Development of Reactive Wireless Sensor Systems

Abstract We present a software platform for designing and testing wireless networks of sensors and actuators (WSNs). The platform consists of three components: an operating system for small-footprint microcontrollers (dubbed PicOS), a software development kit (SDK) amounting to a C-based, event-oriented (reactive) programming language, and a virtual execution platform (VUE2) capable of emulating complete deployment environments for WSNs and thus facilitating their rapid development.1 Its most recent incarnation introduced in the present paper is a component of the WSN lab being currently set up at Vistula in collaboration with Olsonet Communications Corporation.2 We highlight the platform’s most interesting features within the context of a production WSN installed at independent-living facilities.

Security using Location Based Key LBK System for Effective Multipath Attack and Protection for Location Discrimination in Wireless Sensor Systems

Security using Location Based Key LBK System for Effective Multipath Attack and Protection for Location Discrimination in Wireless Sensor Systems

Private and Secure Healthcare Data Transmission and Analysis for Medical Wireless Sensing System

Private and Secure Healthcare Data Transmission and Analysis for Medical Wireless Sensing System

Development of soil moisture measurement with wireless sensor web-based concept

<span lang="EN-US">Measurement of soil moisture commonly by applying the soil moisture sensors is to measure the condition of the ground around which is relatively not wide. Therefore if applied for the large-scale, repeated measurements are required in accordance with the determined point. As a result it takes time to get the whole results. With the existence of wireless sensor technology then this problem can be overcome. This wireless sensor system will create a network consisting of nodes and server. In this study the server part is a server computer that requires a web server application together with its script to display and store data, while the node part is the data reader system. In the data system reader module, the sensor device is required as the input that is SEN0114, the processor is a microcontroller, while the wireless uses Wi-Fi module that is ESP8266. Wi-Fi topology used later is infrastructure (using access points). In this research, it begins by testing the sensor and then testing the data validation between the node and the server. SEN0114 sensor has different value from the American Standard Method (ASM) that is 0.922%. While the data validation test of the measurement result is Wi-Fi ESP8266 module which has a maximum distance of 14 meters toward the access points.</span>

A radio frequency‐based antenna sensor as a protractor

AbstractAn antenna sensor is proposed to play dual role of a functional antenna and a sensor in a wireless sensor system, in an effort to reduce data loss and to increase transmission rate by eliminating a certain interface. A function of protractor was implemented on the coil antenna, working at radio frequency, to demonstrate the antenna sensor. The coil antenna was made of a copper wire with a wire diameter of 1 mm and had a coil diameter of 60 mm. With the impedance matching for the antenna designed at a resonant frequency of 43 MHz, a signal composed of multiple waves at shifted frequencies was transmitted from a mixer to the bent coil sensor, which acts as the emitting antenna. Upon received at the receiver coil, the signal was analyzed using the fast Fourier transform (FFT) algorithm. The received frequency‐domain signals were used to establish the characteristic matrix for the antenna sensor during a calibration process. The characteristic matrix becomes the fingerprint pattern to identify test angle of the bent coil sensor through the calculation of the Euclidean distance based upon the k nearest neighbor (kNN) classifier of the machine learning. The results showed that all the 16 test angles were identified accurately in the correct angle range.

Efficient energy resource utilization in a wireless sensor system for monitoring water quality

In this paper, a new approach to energy harvesting and data transmission optimization in a heterogeneous-based multi-class and multiple resource wireless transmission wireless sensor network system that focus on monitoring water and its quality is presented. Currently, energy is a scarce resource in wireless sensor networks due to the limited energy budget of batteries, which are typically employed for powering sensors. Once the available energy of a particular sensor node battery is depleted, such sensor node becomes inactive in a network. As a consequence, such node may not be able to participate in the transmission of the application signal in the uplink stage of the network, resulting in a lack of ability to communicate vital signals in a timely manner. Energy scarcity has been a long standing problem in wireless sensor network applications. To address this problem, energy harvesting from intended radiofrequency power source is considered in this work. However, wirelessly powered wireless sensor network systems are confronted by unfairness in resource allocation problem, as well as interference problem in multiple energy resource transmissions. These problems adversely impact the performance of the system in the context of the harvested energy by the sensor nodes, sensors information transmission rates, and the overall system throughput rate. These problems are tackled in this paper by formulating a sum-throughput maximization problem to reduce system energy consumption and enhance the system overall throughput rate. The throughput optimization problem is formulated as a non-convex function. Through the exploitation of the problem structure, it is converted to a convex function. The mathematical models of the optimization problem are validated through numerical simulations. The simulation results reveal that the proposed wireless powered sensor network system outperforms an existing wireless powered sensor network system, by comparison of the numerical simulations of this work to the numerical simulations of the existing WPSN system, regardless of the distances of the sensor nodes to the IPS and the base station. Also, the newly proposed method performs efficiently using parameters that include path-loss exponent impact, performance comparison of systems, convergence based on iteration, comparison based on unequal network distances to the BS, transmission power impact on the attainable throughput and on fraction of energy consumed on information transmissions, and influence of different number of nodes in the network classes.

Preliminary Network Centric Therapy for Machine Learning Classification of Deep Brain Stimulation Status for the Treatment of Parkinson’s Disease with a Conformal Wearable and Wireless Inertial Sensor

The concept of Network Centric Therapy represents an amalgamation of wearable and wireless inertial sensor systems and machine learning with access to a Cloud computing environment. The advent of Network Centric Therapy is highly relevant to the treatment of Parkinson’s disease through deep brain stimulation. Originally wearable and wireless systems for quantifying Parkinson’s disease involved the use a smartphone to quantify hand tremor. Although originally novel, the smartphone has notable issues as a wearable application for quantifying movement disorder tremor. The smartphone has evolved in a pathway that has made the smartphone progressively more cumbersome to mount about the dorsum of the hand. Furthermore, the smartphone utilizes an inertial sensor package that is not certified for medical analysis, and the trial data access a provisional Cloud computing environment through an email account. These concerns are resolved with the recent development of a conformal wearable and wireless inertial sensor system. This conformal wearable and wireless system mounts to the hand with the profile of a bandage by adhesive and accesses a secure Cloud computing environment through a segmented wireless connectivity strategy involving a smartphone and tablet. Additionally, the conformal wearable and wireless system is certified by the FDA of the United States of America for ascertaining medical grade inertial sensor data. These characteristics make the conformal wearable and wireless system uniquely suited for the quantification of Parkinson’s disease treatment through deep brain stimulation. Preliminary evaluation of the conformal wearable and wireless system is demonstrated through the differentiation of deep brain stimulation set to “On” and “Off” status. Based on the robustness of the acceleration signal, this signal was selected to quantify hand tremor for the prescribed deep brain stimulation settings. Machine learning classification using the Waikato Environment for Knowledge Analysis (WEKA) was applied using the multilayer perceptron neural network. The multilayer perceptron neural network achieved considerable classification accuracy for distinguishing between the deep brain stimulation system set to “On” and “Off” status through the quantified acceleration signal data obtained by this recently developed conformal wearable and wireless system. The research achievement establishes a progressive pathway to the future objective of achieving deep brain stimulation capabilities that promote closed-loop acquisition of configuration parameters that are uniquely optimized to the individual through extrinsic means of a highly conformal wearable and wireless inertial sensor system and machine learning with access to Cloud computing resources.

Determination of L-Ascorbic Acid Using MBs-AOX/GO/IGZO/Al by Wireless Sensing System and Microfluidic Framework

In this paper, the L-ascorbic acid (L-AA) biosensor based on magnetic beads-ascorbate oxidase/graphene oxide/indium gallium zinc oxide/aluminum (MBs-AOX/GO/IGZO/Al) membrane was integrated with the potentiometric measurement system with the microfluidic framework in order to investigate the different sensing characteristics under the static condition and the dynamic condition. The L-AA biosensor showed the average sensitivity of 78.9 mV/decade (25 °C) under the static condition, and it showed the average sensitivity of 81.7 mV/decade (25 °C) at the optimal flow rate ( $25~\mu \text{L}$ /min). Besides, the XBee module was used to apply in the remote detection for the L-AA biosensor, and the experimental results were shown the average sensitivity of 78.7 mV/decade (25 °C). Moreover, we also investigated various experiments, such as temperature effect, hysteresis effect, and lifetime. These experimental results would be beneficial to the development of the L-AA biosensor based on electrochemical detection.

An Intelligent System for Real-Time Condition Monitoring of Tower Cranes

Reliability and safety are major issues in tower crane applications. A new adaptive neurofuzzy system is developed in this work for real-time health condition monitoring of tower cranes, especially for hoist gearboxes. Vibration signals are measured using a wireless smart sensor system. Fault detection is performed gear-by-gear in the gearbox. A new diagnostic classifier is proposed to integrate strengths of several signal processing techniques for fault detection. A hybrid machine learning method is proposed to facilitate implementation and improve training convergence. The effectiveness of the developed monitoring system is verified by experimental tests.

Establishing Modularized Environmental Measurements and Analyzing and Applying Environmental Information for a Windowless Broiler House in Taiwan

Abstract. The purpose of this study was to apply wireless sensor modules to measure the environmental conditions in a windowless broiler house. An environmental index monitoring system was established that automatically estimated the temperature-humidity index (THI) and temperature-humidity-velocity index (THVI) according to the collected database. Finally, prediction of the relationships between the THI or THVI and the feed conversion rate (FCR) was studied. Nine sets of temperature, relative humidity, and wind velocity sensors were used for measuring the inside environment. An additional set was located outside the broiler house. The environmental database used this system of wireless sensors for collecting environmental information, quantifying the environmental indices, and establishing a warning system. The system automatically estimated the THI and THVI according to the database and presented the results in a user interface. Two areas of the broiler house were arranged for daily measurement of feed consumption and individual weight of the broilers for two experiments. These data were applied to calculate the daily weight gain and FCR. The first experiment was conducted from 9 September to 9 October (from summer to autumn) in 2016, and the second experiment was conducted from 3 November to 9 December (from autumn to winter) in 2016. During the experiments, there were no errors in the messages sent by the environmental database and warning system. These results serve as important references for predicting the FCR as well as the THI and THVI. The THVI of the first experiment and the THI of the second experiment predicted the FCR with a good relationship. The results show that the indoor and outdoor THI values could be higher than the standard values. In the FCR statistical analysis, the FCR of the second experiment was lower than the FCR of the first experiment, and they were both lower than the criterion FCR. This implies that opportunities exist for improving the broiler house environment in this subtropical climate. The results of this study could become an important reference for predicting the FCR with the THI and THVI. Finally, prediction of the relationships between the THI or THVI and the FCR were conducted. The results of the second experiment (R2 = 0.8327) were significantly better than the results of the first experiment (R2 = 0.5184). Keywords: Broiler, Feed conversion rate, Temperature-humidity index, Temperature-humidity-velocity index, Warning system, Wireless sensor module.

A low-cost wireless sensor network for long term monitoring of energy performance and sustainability of buildings

This manuscript describes the development of a wireless sensor system for long term monitoring of temperature, humidity and heat flux reading within building structural elements, including places that are hard to reach using wired sensors. The system was tested in cold Latvian climate in 3 different buildings. The main objectives during the development phase were the maximization of network operational lifetime, ensurance of work stability and maintenance cost reduction to make the system feasible for wide use in practical applications. An optimal radio module and microcontroller combination yielded sufficient signal range and data transfer stability, as well as efficient control of energy consumption.

Reliability of Cluster-Based Nodes in Wireless Sensor Networks of Cyber Physical Systems

Sensors are a crucial component of any intelligent control system. Wireless sensor networks (WSNs) are one of the most rapidly developing information technologies and promise to have a variety of applications in the Internet of Things and for mission critical and safety relevant applications. Clustering is one of the important methods for prolonging the network lifetime in WSNs. It involves grouping of sensor nodes into clusters and electing cluster heads for all the clusters. Reliability is one of the most important attribute of such systems. The main drawback of the wireless sensor network is the restricted power sources of the sensing elements. Expanding the lifetime of the wireless sensor systems, energy preservation measures are vital for enhancing the execution of WSNs. This paper proposes method for increasing autonomy time of sensors in cluster-based WSNs prolongation using redundancy of batteries. In the paper Markov model for reliability analyses of sensor node in cluster-based WSN with proposed method is described. On the base of developed model the boundary value of reliability parameters for automatics in different failure modes is analyzed.