Wireless Sensor System Research Articles

Smart walking cane based on triboelectric nanogenerators for assisting the visually impaired

The increasing number of visually impaired individuals due to global population growth and aging poses a significant societal challenge, leading to a substantial demand for devices that can assist the visually impaired. The emergence of triboelectric nanogenerators (TENGs) has provided a novel approach to achieve self-powered sensing, making them ideal candidates for addressing this issue. In this study, a TENG-based smart cane and related sensor system with a super-easy structure and extremely low cost were developed to assist the visually impaired. The proposed smart cane is based on a single-electrode-mode TENG attached to the bottom of the cane to detect potential dangers. The electrical outputs between the bottom friction layers and common road materials were systematically investigated, and the most appropriate sensing material was selected. Furthermore, a four-wall square cage structure and related wireless sensor system were designed for fall-down detection. The as-designed system can collect fall-down signals and transmit them through Bluetooth, which can automatically place emergency phone calls to caregivers or hospitals when the detected signals exceed the threshold. Additionally, the proposed smart cane can achieve long-term durability and function under water-spraying conditions, which shows possible applicability in wetting environment. This study demonstrated a novel approach for blind navigation, danger detection, and fall-down detection, achieving high sensitivity and accuracy at an extremely low cost. Thus, the proposed system has remarkable real-life applicability.

Design of smart home environment based on wireless sensor system and artificial speech recognition

In the context of the rapid development of intelligent technology, smart home has become the focus of attention. However, the traditional smart home system has some problems such as complicated operation and inconvenient information interaction. In order to solve these problems and improve the user experience of smart home environment, this study establishes a fast and efficient smart home environment design scheme by introducing wireless sensor system and artificial speech recognition technology. By arranging multiple wireless sensor nodes, the monitoring and data acquisition of environmental parameters are realized, and then the voice control and command interaction of smart home devices are realized by using artificial voice recognition technology. Through the effective combination of wireless sensor system and artificial voice recognition technology, users can realize the control of smart home environment through voice commands, and improve the convenience and intelligence of the system. The experimental results show that the design scheme can accurately and stably realize the recognition of user voice commands and the control of smart home devices, and users' evaluation of the degree of intelligence and user experience has also been significantly improved.

Optimal AI Model Splitting and Resource Allocation for Device-Edge Co-Inference in Multi-User Wireless Sensing Systems

Optimal AI Model Splitting and Resource Allocation for Device-Edge Co-Inference in Multi-User Wireless Sensing Systems

Modelling Cutting Temperature and Tool Thermal Error in Dry Cutting under Different Cutting Parameters

This work introduces a non-destructive, wireless sensor system for real-time monitoring of cutting temperatures to predict thermal errors in tools during dry cutting, reducing environmental pollution compared to coolant-based methods. Specifically, the system employs a thermocouple sensor positioned between the disposable insert and the tool shank, secured by the clamping force of the screw, and transmits the temperature data through Bluetooth to achieve real-time measurement. The volume of the cutting slot per tooth is the main factor influencing cutting temperature, and the cutting temperature directly affects the magnitude of tool center point (TCP) thermal error. Therefore, this sensor system offers a solution to the thermal error issue by collecting temperature and thermal deformation data to train a model capable of predicting thermal errors in the cutting tool. This model comprises temperature and thermal error models, both utilizing four predictive models—Random Forest (RF), Gradient Boosting (GB), Extra Trees (ET), and eXtreme Gradient Boosting (XGB)—to identify the most accurate thermal error model. The results show that the ET model performs best for temperature prediction, with a root mean square error (RMSE) of only 1.05°C, while the RF model achieves the lowest error for thermal error prediction, with an RMSE of 1.07 µm. Consequently, this sensor system provides a method for manufacturers to predict thermal errors in real-time during the machining process.

Novel Sandwich-Structured Flexible Composite Films with Enhanced Piezoelectric Performance.

Piezoelectric poly(vinylidene fluoride) (PVDF) and its copolymers have been widely investigated for applications in wearable electric devices and sensing systems, owing to their intrinsic piezoelectricity and superior flexibility. However, their weak piezoelectricity poses major challenges for practical applications. To overcome these challenges, we propose a two-step synthesis approach to fabricate sandwich-structured piezoelectric films (BaTiO3@PDA/PVDF/BaTiO3@PDA) with significantly enhanced ferroelectric and piezoelectric properties. As compared to pristine PVDF films or conventional 0-3 composite films, a maximum polarization (Pmax) of 11.24 μC/cm2, a remanent polarization (Pr) of 5.83 μC/cm2, and an enhanced piezoelectric coefficient (d33 ∼ 14.6 pC/N) were achieved. Simulation and experimental results have demonstrated that the sandwich structure enhances the ability of composite films to withstand higher poling electric fields in comparison with 0-3 composites. The sandwich-structured piezoelectric films are further integrated into a wireless sensor system with a high force sensitivity of 288 mV/N, demonstrating great potential for movement monitoring applications. This facile approach shows great promise for the large-scale production of composite films with remarkable flexibility, ferroelectricity, and piezoelectricity for wearable sensing devices.

A Sock-Embedded Wireless Sensing System Employing Paper-Substrate-Based MEMS Tactile Sensors Array and Low-Power ASIC for Accurate and Comfortable AFib Monitoring

A Sock-Embedded Wireless Sensing System Employing Paper-Substrate-Based MEMS Tactile Sensors Array and Low-Power ASIC for Accurate and Comfortable AFib Monitoring

Node fault diagnosis algorithm for wireless sensor networks based on BN and WSN

Wireless sensor networks, as an emerging information exchange technology, have been widely applied in many fields. However, nodes tend to become damaged in harsh and complex environmental conditions. In order to effectively diagnose node faults, a Bayesian model-based node fault diagnosis model was proposed. Firstly, a comprehensive analysis was conducted into the operative principles of wireless sensor systems, whereby fault-related features were then extrapolated. A Bayesian diagnostic model was constructed using the maximum likelihood method with sufficient sample features, and a joint tree model was introduced for node diagnosis. Due to the insufficient accuracy of Bayesian models in processing small sample data, a constrained maximum entropy method was proposed as the prediction module of the model. The use of small sample data to obtain the initial model parameters leads to improved performance and accuracy of the model. During parameter learning tests, the limited maximum entropy model outperformed the other two learning models on a smaller dataset of 35 with a distance value of 2.65. In node fault diagnosis, the diagnostic time of the three models was compared, and the average diagnostic time of the proposed diagnostic model was 41.2 seconds. In the node diagnosis accuracy test, the proposed model has the highest node fault diagnosis accuracy, with an average diagnosis accuracy of 0.946, which is superior to the other two models. In summary, the node fault diagnosis model based on Bayesian model proposed in this study has important research significance and practical application value in wireless sensor networks. By improving the reliability and maintenance efficiency of the network, this model provides strong support for the development and application of wireless sensor networks.

Immobilizing Polyiodides with Expanded Zn2+ Channels for High‐Rate Practical Zinc‐Iodine Battery

AbstractAqueous zinc‐iodine battery (AZIB) has the advantage of low cost and high specific capacity but suffer from the soluble polyiodides shuttling and sluggish redox kinetics. Herein, these two limitations are addressed by employing a cathode additive (zirconium hydrogen phosphate, denoted as EI‐ZrP) which provides dual functions: an agent for polyiodide confinement, and abundant channels for zinc ion transport. An enlarged crystalline interlayer (from typical 7.5 to 18.3 Å) of the EI‐ZrP significantly enhances the ionic conductivity and simultaneously immobilizes polyiodides, leading to an accelerated conversion process. AZIB with EI‐ZrP in the iodine cathode exhibits a high capacity retention over 10,000 cycles (with a 0.02‰ decay rate per cycle). Quasi‐solid‐state Zn‐I2 pouch cell has been constructed using hydrogel‐paper as separator and EI‐ZrP additive, which delivers a high areal capacity under repeated bending. As a proof‐of‐concept demonstration, the paper battery is integrated to power a wireless flexible pressure sensor system (WFPSS). This strategy may shed light on the rational design of conversion‐type cathode materials for both energy storage and flexible portable electronics.

Autonomous Wireless Sensor System for Emergency Monitoring Roads with Low Communication Coverage

Rural areas often face communication challenges due to limited mobile coverage on remote roads, posing significant difficulties in reporting emergencies and accidents. This study presented an autonomous vehicle tracking system using low-cost radar sensors to detect possible emergencies in the sections of roads with low cell coverage. The radar sensor system could determine the number of vehicles that passed through the nodes and classify them based on the vehicle type. Each node within the system is equipped with ten radars, a processor unit, and a radio transmitter to communicate with the network in real-time, achieving a rapid response time of just 0.2 s. To ensure seamless connectivity, two distinct wireless communication networks are employed, one for the connection between the towers in the same node and the other for the connection between nodes and a center with cellular coverage. The results of this study can be useful in conveying emergency messages, as well as traffic management.

Wireless sensor system for real-time performance monitoring in sports

In Paralympic sports, investigating seating ergonomics and optimizing for performance is crucial due to individual impairments. Usually, experiments are conducted in laboratory environments and for skiing, usually on a treadmill. In this paper, we are moving experiments out of the laboratory setting to in-slope performance monitoring of kinetics and kinematics. A wireless sensor system is developed and validated in terms of delay. The results show a median delay of 52 ms for the wired main system and 53 ms for the wireless sub-system. The sensor system was implemented on a highly adjustable Paralympic sit-ski, and an experiment was conducted to pinpoint optimal equipment settings for an individual athlete. In addition, the system provided force data from both knees, seat, belt, and both poles. The data collected can also be used to analyze the technique, in addition to assisting in the classification process in the LW10–12 class. The proposed system design also allows for adding a vast amount of different sensor types, and by testing for delay, synchronized with well-known GNSS and IMU sensors already used in many sports to analyze athlete performance.

Applications of Sustainable Hybrid Energy Harvesting: A Review

This paper provides a short review of sustainable hybrid energy harvesting and its applications. The potential usage of self-powered wireless sensor (WSN) systems has recently drawn a lot of attention to sustainable energy harvesting. The objective of this research is to determine the potential of hybrid energy harvesters to help single energy harvesters overcome their energy deficiency problems. The major findings of the study demonstrate how hybrid energy harvesting, which integrates various energy conversion technologies, may increase power outputs, and improve space utilization efficiency. Hybrid energy harvesting involves collecting energy from multiple sources and converting it into electrical energy using various transduction mechanisms. By properly integrating different energy conversion technologies, hybridization can significantly increase power outputs and improve space utilization efficiency. Here, we present a review of recent progress in hybrid energy-harvesting systems for sustainable green energy harvesting and their applications in different fields. This paper starts with an introduction to hybrid energy harvesting, showing different hybrid energy harvester configurations, i.e., the integration of piezoelectric and electromagnetic energy harvesters; the integration of piezoelectric and triboelectric energy harvesters; the integration of piezoelectric, triboelectric, and electromagnetic energy harvesters; and others. The output performance of common hybrid systems that are reported in the literature is also outlined in this review. Afterwards, various potential applications of hybrid energy harvesting are discussed, showing the practical attainability of the technology. Finally, this paper concludes by making recommendations for future research to overcome the difficulties in developing hybrid energy harvesters. The recommendations revolve around improving energy conversion efficiency, developing advanced integration techniques, and investigating new hybrid configurations. Overall, this study offers insightful information on sustainable hybrid energy harvesting together with quantitative information, numerical findings, and useful research recommendations that progress and promote the use of this technology.

Construction of rehabilitation training simulation model based on energy monitoring and transmission by wireless sensor networks

In today's world, with the advent of information technology, the speed of social development has also been greatly improved, and the national demand is also increasing. Among them, national health issues have always been the focus of countries. In addition to basic medical testing, the rehabilitation training for special groups in the medical industry is also gradually becoming more mature. In traditional wireless sensor networks, the energy consumption of nodes is often high, because each node needs to continuously send and receive data, leading to energy waste. In addition, many nodes remain active even when data transmission is not required, further increasing energy consumption. The coverage range of traditional wireless sensor networks is influenced by the number and distribution of nodes, often resulting in coverage vulnerabilities and blind spots. This makes it impossible for the network to fully cover the target area, reducing the reliability and accuracy of monitoring. Based on the above situation, this paper studies the construction of rehabilitation training simulation model by using wireless sensor network monitoring coverage and transmission technology on the basis of energy acquisition. Firstly, it discusses the development trend of rehabilitation training technology in various countries and the social status of rehabilitation training. Through the use of wireless sensor network monitors to monitor the target data collection and model construction. The target coverage and transmission speed are optimized by combining reinforcement learning algorithm SARSA and multilayer feedforward BP neural network. The sensor coverage and transmission of monitoring targets are also studied. The results show that the optimized wireless sensor system has a good effect in rehabilitation training.

Communication performance optimization of coal mine goaf LoRa AD hoc network sensor system based on tree topology

Real-time accurate monitoring of goaf gas, temperature, and other parameters is of key significance for the prevention and control of spontaneous coal combustion in the goaf. In this paper, a tree network and communication protocol for LoRa technology are proposed to solve the problems of small communication coverage and short endurance of the traditional goaf wireless monitoring system. In this paper, a geometric topology structure is designed for wireless sensor nodes to find relay nodes through the path loss model, and an energy-saving optimization algorithm routing scheme is designed. In addition, LoRa AD hoc network experiment platform and upper computer system are developed and designed. Through numerical analysis and experimental results in coal mines, it is proved that the proposed method significantly reduces the energy consumption of the whole network. The coverage radius is increased by 300% compared with the traditional star wireless sensor system, which makes the endurance time and communication quality have higher reliability.

Temperature and Humidity Wireless Sensor System based on Bluetooth Low Energy and Solar Self-power

According to the actual needs of the modern greenhouse, this paper designs a low-energy solar self-powered temperature and humidity wireless sensor system based on Bluetooth low energy. The system uses a Panasonic AM-1801 solar panel and S6AE103A single-chip microcomputer as the energy management module to realize the self-power supply of the system, and uses Si7202-A20-IM1 as the sensor module to collect the temperature and humidity in the environment. The monitoring data are transmitted through the Bluetooth wireless network. The receiving end of the upper computer uses the CY8C5868LTI-LP039 module USB Dongle to receive data. The system realizes the wireless data transmission of the temperature and humidity sensor in the greenhouse environment and the real-time network monitoring of the ambient temperature and humidity through the Bluetooth network. This system can be applied to various greenhouse systems.

Development of a PCB-based passive capacitive sensor for fluidic flow detection

A passive wireless sensor system integrated with capacitive fluidic flow detection is proposed and developed based on the printed circuit board (PCB) technique. The capacitive sensing structure consists of PCB-based electrodes enclosing an insulating pipe that contains the fluidic flow of interest. The conductivity of the fluidic flow and the appearance of foreign objects within the flow can be determined by analysing the resonant frequency of the detection path in the proposed system. Experimental results demonstrate that the resonant frequency increases according to the increase in electrical conductivity of the fluidic flow. In ad-dition, the sensing performance is also confirmed by the detection of sizes and electrical conductivities of NaCl droplets passing through the detection zone. Furthermore, this work indirectly verifies the effectiveness and feasibility of the integration of passive wireless sensing technique into the fluidic flow detector by using the PCB fabrication technique and demonstrates great potential for use in various applications in biomedical and chemical fields, especially in biomedical applications.

Reliability Analysis of Selective Transmission Enabled Wireless Sensor System in IIoT

Reliability Analysis of Selective Transmission Enabled Wireless Sensor System in IIoT

A novel inductively coupled capacitor wireless sensor system for rapid antibiotic susceptibility testing

BackgroundThe increasing prevalence and severity of antimicrobial resistance (AMR) present a major challenge to our healthcare system. Rapid detection of AMR is essential for lifesaving under emergent conditions such as sepsis. The current gold standard phenotypic antibiotic susceptibility testing (AST) takes more than a day to obtain results. Genotypic ASTs are faster (hours) in detecting the presence of resistance genes but require specific probes/knowledge of each AMR gene and do not provide specific information at the phenotype level. To address this unmet challenge, we developed a new rapid phenotypic AST.ResultWe designed a new electrochemical biosensor based on the concept of magnetically coupled LC sensors. The engineered LC sensors can be placed in 96-well plates and communicate the reading remotely with a receiver coil for signal analysis. The sensors were validated by monitoring the growth of Escherichia coli, Staphylococcus aureus, and Pseudomonas aeruginosa in the presence and absence of different antibiotics. Drug-resistant strains were used as controls. Bacterial growth was detected within 30 min after inoculation, allowing rapid determination of antibiotic susceptibility at the phenotype level. The sensor also functions in the presence of host proteins when tested with 2% FBS in growth media.ConclusionsWith the compatibility with 96-well plates, this label-free rapid 30-min AST has the potential for low-cost applications with simple integration into the existing workflow in clinical settings.

ICT Framework for Supporting Applied Behavior Analysis in the Social Inclusion of Children with Neurodevelopmental Disorders.

The applied behavior analysis (ABA) model emphasizes observable and measurable behaviors by carrying out decision making using experimental data (behavioral observation assessment strategies). In this framework, information and communication technology (ICT) becomes highly suitable for enhancing the efficiency and effectiveness of the methodology. This paper aims to delve into the potential of ICT in providing innovative solutions to support ABA applications. It focuses on how ICT can contribute to fostering social inclusion with respect to children with neurodevelopmental disorders. ICT offers advanced solutions for continuous and context-aware monitoring, as well as automatic real-time behavior assessments. Wireless sensor systems (wearable perceptual, biomedical, motion, location, and environmental sensors) facilitate real-time behavioral monitoring in various contexts, enabling the collection of behavior-related data that may not be readily evident in traditional observational studies. Moreover, the incorporation of artificial intelligence algorithms that are appropriately trained can further assist therapists throughout the different phases of ABA therapy. These algorithms can provide intervention guidelines and deliver an automatic behavioral analysis that is personalized to the child's unique profile. By leveraging the power of ICT, ABA practitioners can benefit from cutting-edge technological advancements to optimize their therapeutic interventions and outcomes for children with neurodevelopmental disorders, ultimately contributing to their social inclusion and overall wellbeing.

A Wearable Multimodal Wireless Sensing System for Respiratory Monitoring and Analysis.

Wireless sensing systems are required for continuous health monitoring and data collection. It allows for patient data collection in real time rather than through time-consuming and expensive hospital or lab visits. This technology employs wearable sensors, signal processing, and wireless data transfer to remotely monitor patients' health. The research offers a novel approach to providing primary diagnostics remotely with a digital health system for monitoring pulmonary health status using a multimodal wireless sensor device. The technology uses a compact wearable with new integration of acoustics and biopotentials sensors to monitor cardiovascular and respiratory activity to provide comprehensive and fast health status monitoring. Furthermore, the small wearable sensor size may stick to human skin and record heart and lung activities to monitor respiratory health. This paper proposes a sensor data fusion method of lung sounds and cardiograms for potential real-time respiration pattern diagnostics, including respiratory episodes like low tidal volume and coughing. With a p-value of 0.003 for sound signals and 0.004 for electrocardiogram (ECG), preliminary tests demonstrated that it was possible to detect shallow breathing and coughing at a meaningful level.

A Novel Gait Phase Recognition Method Based on DPF-LSTM-CNN Using Wearable Inertial Sensors.

Gait phase recognition is of great importance in the development of rehabilitation devices. The advantages of Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) are combined (LSTM-CNN) in this paper, then a gait phase recognition method based on LSTM-CNN neural network model is proposed. In the LSTM-CNN model, the LSTM layer is used to process temporal sequences and the CNN layer is used to extract features A wireless sensor system including six inertial measurement units (IMU) fixed on the six positions of the lower limbs was developed. The difference in the gait recognition performance of the LSTM-CNN model was estimated using different groups of input data collected by seven different IMU grouping methods. Four phases in a complete gait were considered in this paper including the supporting phase with the right hill strike (SU-RHS), left leg swimming phase (SW-L), the supporting phase with the left hill strike (SU-LHS), and right leg swimming phase (SW-R). The results show that the best performance of the model in gait recognition appeared based on the group of data from all the six IMUs, with the recognition precision and macro-F1 unto 95.03% and 95.29%, respectively. At the same time, the best phase recognition accuracy for SU-RHS and SW-R appeared and up to 96.49% and 95.64%, respectively. The results also showed the best phase recognition accuracy (97.22%) for SW-L was acquired based on the group of data from four IMUs located at the left and right thighs and shanks. Comparably, the best phase recognition accuracy (97.86%) for SU-LHS was acquired based on the group of data from four IMUs located at left and right shanks and feet. Ulteriorly, a novel gait recognition method based on Data Pre-Filtering Long Short-Term Memory and Convolutional Neural Network (DPF-LSTM-CNN) model was proposed and its performance for gait phase recognition was evaluated. The experiment results showed that the recognition accuracy reached 97.21%, which was the highest compared to Deep convolutional neural networks (DCNN) and CNN-LSTM.