Wireless Smart Research Articles

Cloud-based pipe corrosion monitoring using electromechanical impedance instrumented piezoelectric ring sensor

The corrosion of small-diameter pipes has resulted in numerous significant accidents and substantial economic losses in both industrial and civil sectors. This paper presents a wireless smart corrosion monitoring system based on electromechanical impedance (EMI) technology. This system comprises a smart corrosion sensing node (SCSN) designed using EMI principles and a wireless impedance monitoring system (WIMS) for remote impedance measurement. A finite element simulation was conducted to assess the performance of the SCSN under uniform corrosion conditions. Additionally, a comparative test, with and without fluid inside the pipe, was performed to determine the impact of the fluid on test results. To validate the effectiveness of the proposed wireless smart corrosion monitoring node, corrosion monitoring experiments were conducted on pipe specimens equipped with the sensing node. These findings were complemented by separate corrosion experiments, and it was revealed that as corrosion increased, the peak frequency decreased, indicating a strong linear relationship. The study demonstrates that the wireless smart corrosion monitoring node is highly capable of monitoring pipe corrosion in an experimental setting.

Efficient Strategy to Exchange Road Messages Between Smart Vehicles and Wireless Sensor Networks in Hybrid Sensor and Vehicular Networks

The aim of the intelligent transport system (ITS) is the improvement of road safety. This system is based on intelligent vehicles composing a network called Vehicular Ad hoc NETwork (VANET). This network suffers from a disconnection problem due to its dynamic topology. Therefore, a framework of collaboration between the Wireless Sensor Network (WSN) and VANET called Hybr1id Sensor and Vehicular Networks (HSVN) has been proposed. In HSVN, the WSN can play the role of a relay between disconnected vehicles. This paper aims to propose a strategy allowing the exchange of messages between the two networks. That reduces the number of accidents and improves the management of road traffic. We can summarize our proposition in three essential points. First, an algorithm is proposed to decompose the vehicular network into clusters. This algorithm takes into account the mobile aspect of vehicles and the road model. Second, a data encoding model and a message model have been proposed to improve the quality of messages. That reduces the response time of drivers to a critical situation. Finally, an exchange algorithm is proposed to ensure the transmission of road messages between vehicles and sensors. Its principle of work is based on several scenarios defined relative to the network condition. Obtained results show an improvement in the delivery delays of road messages and the number of exchanged road messages between the vehicles.

Activity Recognition Using Different Sensor Modalities and Deep Learning

In recent years, human activity monitoring and recognition have gained importance in providing valuable information to improve the quality of life. A lack of activity can cause health problems including falling, depression, and decreased mobility. Continuous activity monitoring can be useful to prevent progressive health problems. With this purpose, this study presents a wireless smart insole with four force-sensitive resistors (FSRs) that monitor foot contact states during activities for both indoor and outdoor use. The designed insole is a compact solution and provides walking comfort with a slim and flexible structure. Moreover, the inertial measurement unit (IMU) sensors designed in our previous study were used to collect 3-axis accelerometer and 3-axis gyroscope outputs. Smart insoles were located in the shoe sole for both right and left feet, and two IMU sensors were attached to the thigh area of each leg. The sensor outputs were collected and recorded from forty healthy volunteers for eight different gait-based activities including walking uphill and descending stairs. The obtained datasets were separated into three categories; foot contact states, the combination of acceleration and gyroscope outputs, and a set of all sensor outputs. The dataset for each category was separately fed into deep learning algorithms, namely, convolutional long–short-term memory neural networks. The performance of each neural network for each category type was examined. The results show that the neural network using only foot contact states presents 90.1% accuracy and provides better performance than the combination of acceleration and gyroscope datasets for activity recognition. Moreover, the neural network presents the best results with 93.4% accuracy using a combination of all the data compared with the other two categories.

Wireless water consumption sensing system for building energy efficiency: A visual-based approach with self-powered operation

The use of smart wireless remote water meters has become popular due to their efficiency in avoiding manual labor and automate meter reading. The current implementation of these measurement instruments often requires expensive external power sources and separate data aggregators, posing challenges for circuit safety and the goal of reducing carbon emissions in aging communities. In response, a novel wireless system has been developed to accurately measure water consumption in buildings. This system incorporates advanced machine learning techniques and utilizes the potential of water-based energy in a seamless manner. This novel methodology utilizes optical sensors to acquire visual data of water gauges and exploits machine learning techniques, specifically the AVSM model also called Advanced Visual Sensing Model (AVSM). This methodology employs a rigorous approach to achieve accurate metric determinations, thereby eliminating the necessity for additional hardware or excessive power configurations. Moreover, an integrated micro water generator has been seamlessly implemented, demonstrating the capability to effectively transfer energy to a lithium battery for the purpose of storage. This implementation guarantees consistent functionality and a significant decrease in reliance on external power supplies. The data augmentation procedure involves the stochastic manipulation of image luminance, resulting in encouraging results when used in conjunction with the AVSM model. Significantly, the dimensions of the model have been significantly decreased, while maintaining a commendable mean average precision (mAP). By leveraging this method and visual-based features, the system can contribute to overall building energy efficiency and reduce the burdens of manual meter reading while supporting CO2 emissions reduction as a feasible and cost-effective solution for building management, promoting sustainability, and advancing energy-efficient practices in old communities. This new wireless system for measuring water usage provides a practical and affordable way to enhance energy efficiency in buildings, lessen the workload of manual meter reading, and help meet carbon emissions reduction goals. As well as optimizing resource and energy use, this method also aligns with eco-friendly solutions to combat global warming and climate change.

Preface

The 2023 5th International Conference on Wireless Communications and Smart Grid (ICWCSG 2023) supported by South China University of Technology was successfully held in Sipsongpanna, China from June 30th to July 2nd, 2023 (hybrid event). This Conference was the fifth in a series of topical meetings, which began with the Guilin meeting in 2019. ICWCSG 2023 was attended by about 60 participants from different countries and regions.For this fifth meeting we chose Sipsongpanna as our Conference venue and met with our old and new friends. While the location of the conferences has been changed, what remained invariant is the aim of these conferences, which is to discuss recent advances and new perspectives in wireless communications and smart grid in a pleasant and friendly atmosphere. At this meeting we had a comprehensive overview of this fascinating field and of future scenarios thanks to the participation of leaders of the most important projects.The Conference presented an outstanding paper volume covering the most recent advances in wireless communications and smart grid, including Computer Vision and Audition, Optical Modulation and Signal Processing, Antenna Modeling and Measurements, Smart Grid Interoperability and Standards, etc. The papers in this Proceedings published by Journal of Physics: Conference Series represent a collection of plenary talks, invited keynote speeches and other contributions.The Conference program consisted of four keynote speeches presented in plenary sessions, including four “hot topic” speeches highlighting the most recent advances in the fields, and various oral and poster presentations. Among them, Professor Wenjie Feng from South China University of Technology, China performed a keynote speech on Research on Millimeter-wave Circuits Based on Gap Waveguide (GWG). In this research, the microstrip transitions were introduced here for system integration between the antenna and the passive/active components. The theory background and the evolution of gap waveguide proposed by Prof. P. S. Kildal, were also described simply. Finally, the future development of the gap waveguide based on the Micro-Electromechanical System (MEMS) technology was discussed. This overview study aimed to provide a solution for the millimeter-wave circuits, especially in the W-band.We would like to thank the participants, especially those who contributed speeches, posters and manuscripts, for making ICWCSG 2023 such an exciting and memorable Conference. We thank the International Technical Program Committee for putting together the outstanding program and the Organizing Committee for their tireless efforts in the organization of the Conference. Finally, we extend our gratitude to the members of Journal of Physics: Conference Series whose friendly and efficient work contributed much to the successful publication of this volume.The Committee of ICWCSG 2023List of Committee Member is available in this pdf.

Multiphase coexisted perovskite/PVDF composite derived wearable piezo-tribo hybrid energy harvester for wireless smart applications

Multiphase coexisted perovskite/PVDF composite derived wearable piezo-tribo hybrid energy harvester for wireless smart applications

Research on Hybrid Data Clustering Algorithm for Wireless Communication Intelligent Bracelets

Wireless communication smart bracelet data include motion data, sleep time data, heart rate and blood pressure data and positioning data, etc. These data have diversity and high complexity, and there are interconnections or interactions between the data, which have high clustering difficulty. To this end, a new data clustering algorithm is studied for wireless communication smart bracelets. The K-medoids algorithm is used to calculate the intra-cluster, inter-cluster, or overall similarity to complete the initial clustering of the bracelet data. Setting the clustering evaluation index can determine the optimal number of clusters. The data objects that are closely surrounded and relatively dispersed are selected as the initial clustering centers and combined with the new index IXB to complete the improvement of the data clustering algorithm. The test results show that the accuracy, recall, and F1 of the research algorithm for clustering the heart rate monitoring dataset, temperature monitoring dataset, energy consumption dataset, and sleep monitoring dataset are higher than 97%, which indicates that the data clustering effect of the algorithm is good.

Advanced multifunctional IGBT packing materials with enhanced thermal conductivity and electromagnetic wave absorption properties

Insulated-Gate Bipolar Transistors (IGBT) face limitations in high-frequency electronic applications due to heat accumulation and electromagnetic interference issues. To address these challenges, it is crucial to develop packing materials with excellent electromagnetic interference immunity and heat dissipation properties. In this research, a novel epoxy-based packing material (MDCF@C-ZrO2/EP) with high electromagnetic wave absorption and exceptional thermal transport properties was produced by employing a unique three-dimensional carbon structure-induced nanomaterial dispersion strategy. In particular, the three-dimensional MDCF structure effectively prevents packing agglomeration and fosters the formation of an abundant hetero-interface between MDCF and C-ZrO2, leading to improved impedance matching and enhanced electromagnetic wave dissipation capabilities. Remarkably, even at a mere 5 wt% filling level, the material demonstrates an impressive reflection loss value of −58.92 dB and a wide effective absorption bandwidth of 6.68 GHz, effectively covering the entire Ku-band. Additionally, the 3D MDCF@C-ZrO2 significantly enhances the phonon transport path and elevates the thermal conductivity of pure epoxy resin by an impressive ∼ 150%. As a result, this innovative research holds tremendous potential in enabling the application of IGBTs in high-power and high-frequency electronic components, while also contributing to the advancement of next-generation wireless communications and smart devices.

Unconstrained Piezoelectric Vascular Electronics for Wireless Monitoring of Hemodynamics and Cardiovascular Health.

The patient-centered healthcare requires timely disease diagnosis and prognostic assessment, calling for individualized physiological monitoring. To assess the postoperative hemodynamic status of patients, implantable blood flow monitoring devices are highly expected to deliver real time, long-term, sensitive, and reliable hemodynamic signals, which can accurately reflect multiple physiological conditions. Herein, an implantable and unconstrained vascular electronic system based on a piezoelectric sensor immobilized is presented by a "growable" sheath around continuously growing arterial vessels for real-timely and wirelessly monitoring of hemodynamics. The piezoelectric sensor made of circumferentially aligned polyvinylidene fluoride nanofibers around pulsating artery can sensitively perceive mechanical signals, and the growable sheath bioinspired by the structure and function of leaf sheath has elasticity and conformal shape adaptive to the dynamically growing arterial vessels to avoid growth constriction. With this integrated and smart design, long-term, wireless, and sensitive monitoring of hemodynamics are achieved and demonstrated in rats and rabbits. It provides a simple and versatile strategy for designing implantable sensors in a less invasive way.

Bidirectional Thermoelectric Assisted Synchronous Charge Extraction Circuit Based on Buck Structure for Piezoelectric Energy Harvesting

Energy harvesting technology is currently a research hotspot, which can effectively solve energy supply problems in fields such as wireless sensor networks, wearable devices, and smart homes. An energy harvesting system using a thermoelectric generator (TEG) for energy assistance of a piezoelectric transducer (PZT) is proposed. It employs a synchronous charge extraction circuit based on a buck structure (BUCK-SECE), which uses thermoelectric energy to inject energy into the piezoelectric element in both directions to boost the initial voltage of the BUCK-SECE, increasing its output power. The experimental results demonstrate that the utilization of thermoelectric energy can increase the output power of BUCK-SECE by a factor of 3.3 under low-vibration conditions and by a factor of 0.8 under high-vibration conditions. Therefore, the proposed energy harvesting system in this article can effectively enhance the output power of piezoelectric energy transducers and yield desirable performance in real-world applications.

GPS-based fall detection system for old and specially-abled people

Falls are a serious public health concern for older people across the world. Modern telemedicine now depends heavily on remote monitoring of older patients and the ability to spot threats to human health. If a fall is not assisted in time, it can significantly reduce an older person's mobility, independence, and his/her quality of life. Older people who experience post-traumatic problems or mortality frequently do so because of falls. As a result, preventing falls consequences or providing essential help on time may depend on the early identification of falls. In this article, we propose an internet of things (IoT) based system that makes use of low-power wireless sensor networks, smart devices and cloud computing to detect falls and track positions for older and specially-abled people. The tracking is done by sending links of positions from the proposed system every 15 seconds to a specified google drive. On the other hand, an alert message will be delivered to the caregiver whenever a fall is happened. Thus, a MPU-6050 sensor and NEO-6M global positioning system (GPS) module are used with ESP32 microcontroller for the aforementioned purposes. A pilot study with several protocols was carried out to validate the cost-effective proposed system and achieved good results.

DEVICE FOR AUTOMATED CONTROL OF EXPIRATION DATES OF FOODSTUFFS, STORED IN REFRIGERATOR

One of the main problems of storing food (especially perishable) in refrigerators is compliance with their expiration dates. To solve it, it is necessary to constantly monitor and promptly notify food consumers about the approaching expiration of these deadlines. At present, this problem is solved mainly manually by checking the expiration date information printed on food packaging visually or using a hand-held scanner. At the same time, time is spent on checking, there is a high probability of untimely consumption of the food product or even its spoilage, which leads to certain financial losses. Today, given the development of Internet of Things technologies, it is possible to simply and cheaply automate the process of monitoring the expiration dates of food products stored in refrigerators and having a corresponding label on the packaging. Since 2021, Russia has introduced mandatory labeling of dairy products using the 2D Data Matrix code, which allows you to obtain data on expiration dates from the Chestny Znak state labeling and tracking system. Currently, the industry is already producing smart household refrigerators that can be connected to the Internet to transmit various information to the user. However, the common disadvantages of the known designs of smart refrigerators are their high cost, design complexity and the impossibility of working with 2D Data Matrix code labels applied to food packaging. A solution is proposed in the form of a stand-alone wireless smart device that allows you to simply and economically implement automatic notification of users about the approaching expiration date of food stored in the refrigerator, thereby eliminating the possibility of untimely consumption and spoilage. The proposed device does not require structural changes to the equipment of the refrigerator, has a low cost and ease of installation and use, and allows you to turn an ordinary refrigerator into a smart one.

(Invited) smart Wearable Electronics for Chronic Disease Management

60% of Americans live with at least one chronic disease. These diseases and associated comorbidities are now the leading causes of death in the United States. Effective management of complex chronic diseases requires body-wide, long-term, accurate, and continuous monitoring of multiple physiological signals from wearable devices to precisely determine the pathological state. These wearable physiological signal monitoring can dramatically reduce the demand of physician visiting and increase patients’ engagement and treatment adherence rate. Although wearable bioelectronic devices have shown huge potential in chronic disease management, existing technology still mostly relies on rigid chip components, where the interface between chips and skin/tissue has been a bottleneck that limits the system performance (noise, robustness etc). To address these challenges, my research has been involved in the exploration of rational system design concepts, material and device fabrication innovation, and tailored algorithms to enable smart wearable electronics that targets next-generation chronic disease management.Here I would like to discuss two of my developed technology platforms to elaborate the concept of smart wearables. First, I will describe a body area sensor network technology platform. In the system design concept side, this technology utilizes a novel wireless hybridization strategy and an unconventional detuned RFID working regime that provide an ideal skin interface. In the material/device side, this system integrates several skin-conforming polymer-based soft devices (ring oscillators, RF diodes, antennas, transistors etc.) as an integrated soft multimode sensing sticker. (1,2) Next, I will describe a wireless closed-loop smart bandage that can accelerate and monitor the wound healing process. (3) Such smart bandage also integrates new design concepts (wireless, battery-free design that combines both sensing and stimulation), novel materials (low-impedance, high-toughness, and tunable adhesion hydrogel as skin interface) and closed-loop control algorithms. Overall, the developed technology platform could assess multiple health outcomes and treatment responses to various chronic diseases. Ultimately, this technology will help to reduce chronic disease burden, lower medical costs, and provide a better quality of life for patients.

A Novel SDWSN-Based Testbed for IoT Smart Applications

Wireless sensor network (WSN) environment monitoring and smart city applications present challenges for maintaining network connectivity when, for example, dynamic events occur. Such applications can benefit from recent technologies such as software-defined networks (SDNs) and network virtualization to support network flexibility and offer validation for a physical network. This paper aims to present a testbed-based, software-defined wireless sensor network (SDWSN) for IoT applications with a focus on promoting the approach of virtual network testing and analysis prior to physical network implementation to monitor and repair any network failures. Herein, physical network implementation employing hardware boards such as Texas Instruments CC2538 (TI CC2538) and TI CC1352R sensor nodes is presented and designed based on virtual WSN- based clustering for stationary and dynamic networks use cases. The key performance indicators such as evaluating node (such as a gateway node to the Internet) connection capability based on packet drop and energy consumption virtually and physically are discussed. According to the test findings, the proposed software-defined physical network benefited from “prior-to-implementation” analysis via virtualization, as the performance of both virtual and physical networks is comparable.

Machine-learning and water energy harvesting based wireless water consumption sensing system in buildings

In recent years, smart wireless remote water meters have become popular, but this kind of smart water meter needs an external power supply, and every 100 to 200 installed this type of water meter, needs to set up an independent collector. This kind of water meter is expensive, and the external power supply will increase the energy consumption of the building, which is not conducive to the circuit safety of the old community. In order to reduce the burden of manual meter reading and reduce the building's "carbon emissions". A wireless water consumption sensing system for buildings based on machine learning and water energy collection is designed. Images are collected and recognized by visual sensors to realize the visual reading of water meter data. The system is powered by a 12V micro water generator and transferred to lithium battery energy storage to realize the self-energy supply of the whole system. The lightweight YOLOv5 was used to implement the model training process. Then, on the premise of the small sample data set, the data set was expanded by a data enhancement method which randomly changed the image brightness and hue. Finally, the training model is deployed on MCU through the TensorFlow Lite framework to realize edge AI detection. Experiments show that the size of the lightweight YOLOv5 is reduced by 87.1% and the mAP reaches 92.3%. The system can complete the water meter wireless meter reading and realize the system self-power stably. In addition, the system can be expanded for other meter reading tasks based on visual features.

Self-Powered Wireless Temperature and Vibration Monitoring System by Weak Vibrational Energy for Industrial Internet of Things.

Developing self-powered smart wireless sensor networks by harvesting industrial environmental weak vibration energy remains a challenge and an impending need for enabling the widespread rollout of the industrial internet of things (IIoT). This work reports a self-powered wireless temperature and vibration monitoring system (WTVMS) based on a vibrational triboelectric nanogenerator (V-TENG) and a piezoelectric nanogenerator (PENG) for weak vibration energy collection and information sensing. Therein, the V-TENG can scavenge weak vibration energy down to 80 μm to power the system through a power management module, while the PENG is able to supply the frequency signal to the system by a comparison circuit. In an industrial vibration environment where the vibration frequency and amplitude are 20 Hz and 100 μm, respectively, the WTVMS can upload temperature and frequency information on the equipment to the cloud in combination with the narrowband IoT technology to realize real-time information monitoring. Furthermore, the WTVMS can work continuously for more than 2 months, during which the V-TENG can operate up to 100 million cycles, achieving ultrahigh stability and durability. By integrating weak vibration energy harvesting and active sensing technology, the WTVMS can be used for real-time online monitoring and early fault diagnosis of vibration equipment, which has great application prospects in industrial production, machinery manufacturing, traffic transportation, and intelligent IIoT.

Development a Low-Cost Wireless Smart Meter with Power Quality Measurement for Smart Grid Applications.

Developing a low-cost wireless energy meter with power quality measurements for smart grid applications represents a significant advance in efficient and accurate electric energy monitoring. In increasingly complex and interconnected electric systems, this device will be essential for a wide range of applications, such as smart grids, by introducing a real-time energy monitoring system. In light of this, smart meters can offer greater opportunities for sustainable and efficient energy use and improve the utilization of energy sources, especially those that are nonrenewable. According to the 2020 International Energy Agency (IEA) report, nonrenewable energy sources represent 65% of the global supply chain. The smart meter developed in this work is based on the ESP32 microcontroller and easily accessible components since it includes a user-friendly development platform that offers a cost-effective solution while ensuring reliable performance. The main objective of developing the smart meters was to enhance the software and simplify the hardware. Unlike traditional meters that calculate electrical parameters by means of complex circuits in hardware, this project performed the calculations directly on the microcontroller. This procedure reduced the complexity of the hardware by simplifying the meter design. Owing to the high-performance processing capability of the microcontroller, efficient and accurate calculations of electrical parameters could be achieved without the need for additional circuits. This software-driven approach with simplified hardware led to benefits, such as reduced production costs, lower energy consumption, and a meter with improved accuracy, as well as updates on flexibility. Furthermore, the integrated wireless connectivity in the microcontroller enables the collected data to be transmitted to remote monitoring systems for later analysis. The innovative feature of this smart meter lies in the fact that it has readily available components, along with the ESP32 chip, which results in a low-cost smart meter with performance that is comparable to other meters available on the market. Moreover, it is has the capacity to incorporate IoT and artificial intelligence applications. The developed smart meter is cost effective and energy efficient, and offers benefits with regard to flexibility, and thus represents an innovative, efficient, and versatile solution for smart grid applications.

Engineering Challenges of Stationary Wireless Smart Ocean Observation Systems

Engineering Challenges of Stationary Wireless Smart Ocean Observation Systems

A CNFs/MWCNTs aerogel film-based humidity sensor with directionally aligned porous structure for substantially enhancing both sensitivity and response speed

In response to the growing demand for wearable devices and environmental monitoring, this study presents a novel fabrication method for directionally aligned CNFs/MWCNTs aerogel film (AGF), thereby achieving high-performance, flexible humidity sensors with rapid response and excellent mechanical properties. By combining freeze-drying and directional freezing techniques, an ordered porous structure within the AGF was created to promote efficient water molecule exchange and optimized conductivity. The sensor sensitivity was fine-tuned by adjusting the MWCNTs content, reaching a peak response of 71.5% (ΔI/I0) at 75% RH, with response and recovery times of 18 s and 47 s, respectively. This surpasses the performance of traditional sensors based on CNFs/MWCNTs. Upon evaluating the mechanical properties of AGF, it was found that they possessed outstanding flexibility and bending resistance, making them highly suitable for wearable electronic applications. The versatility of AGF sensors was demonstrated in various humidity monitoring scenarios, such as close-range positioning of moist objects and real-time human breathing monitoring. To showcase practical applicability, an STM32-based smart wireless humidity monitoring system suitable for real-time data collection, remote monitoring, and flexible installation was developed. Moreover, this fabrication method holds vast potential in various application fields, including wearable devices, environmental monitoring, and electronic skin.

Metasurface-enabled smart wireless attacks at the physical layer

Metasurface-enabled smart wireless attacks at the physical layer