Wearable Wireless Sensor Network Research Articles

IOT Based Remote Patient Monitoring System

Abstract: A remote monitoring technique that can accurately track human physiological indicators has applications in virtual reality, sports science, medicine, rehabilitation, and surveillance. The majority of systems now in use for tracking bodily parameters in humans need wiring that limits natural movement. In order to get over this restriction, a wearable wireless sensor network that monitors physiological human body characteristics has been built using an accelerometer, a pulse oximeter, a heart-rate sensor, a temperature sensor, and a galvanic skin response sensor. The individual is being tracked wirelessly using his own location. It would be simple to modify this system to watch athletes and young children. Unlike a tethered monitoring equipment, the wireless feature allows the human body to move freely, making the system genuinely portable, quick, and dependable. The portable and the designed sensor node's small size makes it simple to attach to the body.

A novel SCNN-LSTM model for predicting the SNR confidence interval in wearable wireless sensor network

Accurate real-time prediction of link quality is crucial for enhancing the reliable responsiveness of wearable devices within Wireless Wearable Sensor Networks (WWSNs). Specifically, the Signal-to-Noise Ratio (SNR), a pivotal parameter for predicting link quality, exhibits complex temporal characteristics influenced by stochastic and non-stochastic factors. To improve the accuracy of link quality prediction in WWSNs, we aim to explore a novel predictive model, introducing a filtering layer that seeks to enhance the precision of predicting upper and lower boundaries of link reliability confidence intervals. First, we adopt the SNR time series as the evaluation metric and decompose the SNR sequences into time-varying and stochastic standard deviation sequences by wavelet decomposition. Subsequently, we propose an innovative SCNN-LSTM model, incorporating the SincNet filtering layer to extract specific frequency components from the input SNR sequences. Afterward, integrating standard deviation sequences, the model predicts upper and lower boundaries of link reliability confidence intervals. Finally, we conduct the validation experiments on the public dataset LightGBM-LQP and our WWSN dataset Basketball shot. Compared to BPNN, ARIMA, and WNN, the evaluation matrices of MAE, RMSE, R2 in SCNN-LSTM have been improved, and the deviation between the predicted standard deviation and the actual standard deviation has reached the minimum of 0.1. The results demonstrate that SCNN-LSTM outperforms classical prediction models in predicting upper and lower limits of link reliability confidence intervals in WWSNs.

Security and Privacy of Wearable Wireless Sensors in Healthcare: A Systematic Review

Wearable Wireless Sensor Network (WWSN) devices are widely used in healthcare to monitor health data. However, when WWSN users transmit their data to healthcare professionals or third parties over wireless connections, they face privacy and security vulnerabilities. This paper aims to identify the unsolved privacy and security challenges in wearable sensor devices in healthcare, especially the aspects overlooked by previous research. The main research question is: What are the unsolved privacy and security challenges in wearable sensor devices in healthcare, and what are their implications for users and healthcare professionals? This systematic review employs specific keywords to search for relevant publications on bibliographic databases, including Google Scholar, Scopus, IEEE Xplore, and Web of Science. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) charts helped in screening and summarising the selected papers. The results highlight the critical areas that can make WWSNs vulnerable to security attacks. The findings examine the security and privacy issues of wearable sensor devices in cloud computing, fog computing, the Internet of Things (IoT) and the like. Many studies examine IoT due to its privacy and security challenges, especially regarding handling extensive data, using public channels, deploying advanced technologies, managing sharing policies alongside the growing number of wireless devices, and protecting data from hackers. These challenges seriously threaten the confidentiality, integrity, and availability of health data transmitted by WWSN users to healthcare professionals or third parties in cloud-based environments and IoT and are exacerbated by limited resources. The significant findings thereby focus on unresolved areas in IoT. This paper aims to safeguard against cyber-attacks on healthcare and increase users' adoption rate of WWSN devices.

Recent Advances in Organic Dyes for Application in Dye-Sensitized Solar Cells under Indoor Lighting Conditions

Among the emerging photovoltaic (PV) technologies, Dye-Sensitized Solar Cells (DSSCs) appear especially interesting in view of their potential for unconventional PV applications. In particular, DSSCs have been proven to provide excellent performances under indoor illumination, opening the way to their use in the field of low-power devices, such as wearable electronics and wireless sensor networks, including those relevant for application to the rapidly growing Internet of Things technology. Considering the low intensity of indoor light sources, efficient light capture constitutes a pivotal factor in optimizing cell efficiency. Consequently, the development of novel dyes exhibiting intense absorption within the visible range and light-harvesting properties well-matched with the emission spectra of the various light sources becomes indispensable. In this review, we will discuss the current state-of-the-art in the design, synthesis, and application of organic dyes as sensitizers for indoor DSSCs, focusing on the most recent results. We will start by examining the various classes of individual dyes reported to date for this application, organized by their structural features, highlighting their strengths and weaknesses. On the basis of this discussion, we will then draft some potential guidelines in an effort to help the design of this kind of sensitizer. Subsequently, we will describe some alternative approaches investigated to improve the light-harvesting properties of the cells, such as the co-sensitization strategy and the use of concerted companion dyes. Finally, the issue of measurement standardization will be introduced, and some considerations regarding the proper characterization methods of indoor PV systems and their differences compared to (simulated) outdoor conditions will be provided.

Wearable Wireless Sensor Network for Mitigating COVID-19 Transmission Through Physical Distancing

The global community is currently facing a critical situation with the widespread transmission of the Covid-19 virus. The rapid increase in Covid-19 cases has necessitated immediate action and treatment. As the World Health Organization (WHO) recommended, maintaining a minimum distance of one meter between individuals is an effective preventive measure to avoid transmission through respiratory droplets. To address this issue, this paper introduces a cost-effective design and implementation of a wearable wireless sensor network using the ESP32 microcontroller and Bluetooth Low-Energy (BLE) technology that promotes physical distancing and contact tracing to mitigate the spread of COVID-19. In this research, we have determined that the safe distance between individuals is 1.5 meters. Proximity testing has indicated that an RSSI value of -62 dBm corresponds to a distance below 1.5 meters. Hence, we set -62 dBm as the distance threshold between wearable devices to ensure safe physical distancing. These wearable wireless sensor network devices are intended to assist individuals in maintaining a safe distance from others, thereby reducing the risk of COVID-19 transmission.

Maintenance 5.0: Towards a Worker-in-the-Loop Framework for Resilient Smart Manufacturing

Due to the global uncertainty caused by social problems such as COVID-19 and the war in Ukraine, companies have opted for the use of emerging technologies, to produce more with fewer resources and thus maintain their productivity; that is why the market for wearable artificial intelligence (AI) and wireless sensor networks (WSNs) has grown exponentially. In the last decade, maintenance 4.0 has achieved best practices due to the appearance of emerging technologies that improve productivity. However, some social trends seek to explore the interaction of AI with human beings to solve these problems, such as Society 5.0 and Industry 5.0. The research question is: could a human-in-the-loop-based maintenance framework improve the resilience of physical assets? This work helps to answer this question through the following contributions: first, a search for research gaps in maintenance; second, a scoping literature review of the research question; third, the definition, characteristics, and the control cycle of Maintenance 5.0 framework; fourth, the maintenance worker 5.0 definition and characteristics; fifth, two proposals for the calculation of resilient maintenance; and finally, Maintenance 5.0 is validated through a simulation in which the use of the worker in the loop improves the resilience of an Industrial Wireless Sensor Network (IWSN).

Robotic and sensor technology for frozen shoulder Rehabilitation

A common clinical shoulder problem is frozen shoulder. The degree of shoulder joint movement must be measured as part of the rehabilitation procedure. These assessments can be used to assess the severity of a patient's disease, set rehabilitation goals, and determine the appropriate level of activity difficulty, as well as comprehend the rehabilitation's impacts. In the healthcare industry, robots are rapidly being used; as they are in many other industries, Specifically for hospital logistics support, surgery, and rehabilitation. Because rehabilitation affects millions of individuals all around the world, there has been steady growth in the rehabilitation robotics sector over the last decade, with the advent of new technology aimed at overcoming the different challenges that this industry faces. A model is proposed in this study for identifying movements produced during frozen shoulder rehabilitation activities. The model is made up of wearable wireless sensor network (WSN) inertial sensor nodes that were built particularly for this investigation and enable for the identification of inertial sensor nodes of physiological movements anywhere. We describe current robotic systems for shoulder rehabilitation in this work, with an emphasis on indicators and further emerging technologies that, when used with robots, can enhance the advantages of rehabilitation for regaining shoulder function.

A High-Sensitivity Wide Input-Power-Range Ultra-Low-Power RF Energy Harvester for IoT Applications

Radio frequency energy harvesting (RFEH) is very attractive for the Internet of things (IoT) and self-powered micro-systems such as wearable biomedical devices and wireless sensor networks. This paper proposes, analyzes, and implements a new RF-DC converter in standard 130 nm CMOS technology. The developed converter is designed and optimized for ultra-low-power IoT and wearable biomedical applications using the 900 MHz ISM band. The proposed 10-stage cross-connected rectifier compensates the transistors threshold voltage by using both dynamic and static bias compensation techniques. An analytical model of the rectifier based on the MOSFET transistor equations is presented, allowing optimization of the rectifier as a function of the number of stages and transistors sizing, improving the sensitivity and the input power range of the converter. The measurement results demonstrate a sensitivity of &#x2212;25.5 dBm for 1 V output across a 5-<inline-formula> <tex-math notation="LaTeX">$\text{M}\Omega $ </tex-math></inline-formula> resistive load and &#x2212;29 dBm for a 100 <inline-formula> <tex-math notation="LaTeX">$\text{M}\Omega $ </tex-math></inline-formula> load, which is better than the best previously reported results. The measured peak end-to-end efficiency of the proposed harvester is 42.4&#x0025; at &#x2212;16 dBm input power, delivering 2.19 V to a 450 <inline-formula> <tex-math notation="LaTeX">$\text{k}\Omega $ </tex-math></inline-formula> load.

A Real-Time Wearable Physiological Monitoring System for Home-Based Healthcare Applications

The acquisition of physiological data are essential to efficiently predict and treat cardiac patients before a heart attack occurs and effectively expedite motor recovery after a stroke. This goal can be achieved by using wearable wireless sensor network platforms for real-time healthcare monitoring. In this paper, we present a wireless physiological signal acquisition device and a smartphone-based software platform for real-time data processing and monitor and cloud server access for everyday ECG/EMG signal monitoring. The device is implemented in a compact size (diameter: 30 mm, thickness: 4.5 mm) where the biopotential is measured and wirelessly transmitted to a smartphone or a laptop for real-time monitoring, data recording and analysis. Adaptive digital filtering is applied to eliminate any interference noise that can occur during a regular at-home environment, while minimizing the data process time. The accuracy of ECG and EMG signal coverage is assessed using Bland–Altman analysis by comparing with a reference physiological signal acquisition instrument (RHS2116 Stim/Recording System, Intan). Signal coverage of R-R peak intervals showed almost identical outcome between this proposed work and the RHS2116, showing a mean difference in heart rate of 0.15 ± 4.65 bpm and a Wilcoxon’s p value of 0.133. A 24 h continuous recording session of ECG and EMG is conducted to demonstrate the robustness and stability of the device based on extended time wearability on a daily routine.

Buffer-loss estimation to address congestion in 6LoWPAN based resource-restricted ‘Internet of Healthcare Things’ network

The Internet of Healthcare Things (IoHT) consists of a wide variety of resource-restricted, heterogeneous, IoT-enabled, wearable/non-wearable medical equipment (things) that connect over the internet to transform traditional healthcare into a smart, connected, proactive, patient-centric healthcare system. The pivotal functions of the 6LoWPAN protocol stack enable comprehensive integration of such networks from wearable wireless sensor networks (W-WSN) to IoHT, as TCP/IP does not suffice the requirements of IoHT networks. As a result, the congestion in the IoHT network increases with a growing number of devices, resulting in loss of critical medical information due to buffer loss and channel loss, which is unacceptable. In this paper, we explored different applications of patient-centric IoHT architectures to draw a realistic resource-limited topological layout of IoHT for congestion estimation. After critically reviewing existing congestion schemes for 6LoWPANs, we proposed an effective buffer-loss estimation model based on the Queuing Theory to determine the number of packets lost at the node’s buffer. The buffer is modeled as an M/M/1/K Markov Chain Queue. The M/M/1/K Queue equilibrium equation is used to establish a relationship between the probabilities of the buffer being empty or completely filled. We derived the expressions for total buffer-loss probability and expected mean packet delay for the resource-constraint IoHT network. Furthermore, to validate the buffer-loss estimation, an analytical model is used to compare buffer-loss probabilities, the number of packets dropped at leaf/intermediate nodes and the number of packets successfully received at the local sink node. The results show a close correlation between both the models on varying values of the number of leaf nodes, buffer size, offered packet load and available channel capacity. Thus, in resource-restrictive IoHT, the proposed model performs better than two related works.

Piezoelectricity and Flexoelectricity from an Energy Harvesting Perspective: Nanogenerators

Energy harvesting is the process by which energy can be obtained from external sources and used for wearable electronics and wireless sensor networks. Piezoelectric nanogenerators are energy harvesting devices that convert mechanical energy into electric energy by using nanostructured materials. This article summarizes work to date on piezoelectric nanogenerators, starting with the basic theory of piezo- and flexo-electricity and moving through reports on nanogenerators using nanostructures, flexible substrates and alternative materials. A sufficient power generated from nanogenerators suggests feasible applications for either power sources or strain sensors of highly integrated nanodevices. Further improvements in nanogenerators holds promise for the development of self-powered implantable and wearable electronics.

Effect of particle-size distribution and pressure-induced densification on the microstructure and properties of printable thermoelectric composites and high energy density flexible devices

To address the global demand for low-cost, flexible thermoelectric generators (TEGs), this work describes a novel, energy-efficient method of controlling the composite microstructure and resulting thermoelectric (TE) properties of p-type composite films. Combining (1) a small amount of naturally occurring binder that is sufficient to hold TE particles together without significantly decreasing electrical conductivity and (2) a wide distribution of heterogeneous (micro and nano) particles tightly packed on (3) application of mechanical pressure at (4) low-temperature curing for a short duration, this method yields big grains larger than the mean free path for charge carriers (for high electrical conductivity) and nano features smaller than the mean free path for phonons (for low thermal conductivity). The best properties were achieved with 0.05 wt% of chitosan binder, heterogeneous-sized TE particles (100-mesh Bi0.5Sb1.5Te3), 200 MPa applied uniaxial pressure, and curing at 150 °C for 30 min to densify the as-deposited films. The 100-mesh chitosan-BST composite film achieved a ZT of 0.7, comparable to the best reported ZT of printed p-type TE films, but without using high-temperature and long-duration curing. The power output of our best-reported scalable 3-leg-BST TEG was 58 μW. The corresponding power density was 5.72 mW/cm2 at a temperature difference of 38 K, significantly higher than the best reported values for single-leg TEGs. Furthermore, the flexibility of this prototype was performed by successfully bending at a 3-cm radius for 1000 cycles, thus demonstrating a high potential to be used as self-sufficient batteries for next-generation wearable devices and wireless sensor network applications.

High-performance flexible WSe2 flake photodetector with broadband detection capability

In this age of interconnection, photodetectors based on two-dimensional materials have been widely used in wearable physiological monitoring systems and wireless sensor networks. Hence, this paper proposes a novel WSe2 flake photodetector with a metal–semiconductor–metal structure. The photodetector achieved a high responsivity (Ri) of 2.46 A/W under the condition of an external bias voltage at 650 nm as well as an ultra-high detectivity (D*) of 0.76 × 1011 Jones. The photodetector overcomes the shortcomings of traditional photoelectric detectors with large dark currents. A small dark current of ∼1 nA was obtained, which also creates an opportunity for future high-performance photoelectric devices. After 500 bending cycles, the current and dark current of the device decreased only by 1.7%. Additionally, a self-built test platform indicated that the test device has a device response time of up to 6.51 ms, which creates an opportunity for the high-speed corresponding photoelectric devices based on WSe2 flakes in the future.

Hybridized 1T/2H-MoS2/graphene fishnet tube for high-performance on-chip integrated micro-systems comprising supercapacitors and gas sensors

The emerging micro-nano-processing technologies have propelled significant advances in multifunctional systems that can perform multiple functions within a small volume through integration. Herein, we present an on-chip multifunctional system based on a 1T/2H-MoS2/graphene fishnet tube, where a micro-supercapacitor and a gas sensor are integrated. A hybrid three-dimensional stereo nanostructure, including MoS2 nanosheets and graphene fishnet tubes, provides K+ ions with a short diffusion pathway and more active sites. Owing to the large layer spacing of 1T-MoS2 promoting fast reversible diffusion, the on-chip micro-supercapacitor exhibits excellent electrochemical properties, including an areal capacitance of 0.1 F·cm−2 (1 mV·s−1). The variation in the conductivity of 2H-MoS2 when ammonia molecules are adsorbed as derived from the first-principles calculations proves the Fermi level-changes theory. Driven by a micro-supercapacitor, the responsivity of the gas sensor can reach 55.7% at room temperature (27 °C). The multifunctional system demonstrates the possibility of achieving a two-dimensional integrated system for wearable devices and wireless sensor networks in the future.

Small Antennas for Wearable Sensor Networks: Impact of the Electromagnetic Properties of the Textiles on Antenna Performance.

The rapid development of wearable wireless sensor networks (W-WSNs) has created high demand for small and flexible antennas. In this paper, we present small, flexible, low-profile, light-weight all-textile antennas for application in W-WSNs and investigate the impact of the textile materials on the antenna performance. A step-by-step procedure for design, fabrication and measurement of small wearable backed antennas for application in W-WSNs is also suggested. Based on the procedure, an antenna on a denim substrate is designed as a benchmark. It demonstrates very small dimensions and a low-profile, all while achieving a bandwidth (|S11| < −6 dB) of 285 MHz from 2.266 to 2.551 GHz, radiation efficiency more than 12% in free space and more than 6% on the phantom. Also, the peak 10 g average SAR is 0.15 W/kg. The performance of the prototype of the proposed antenna was also evaluated using an active test. To investigate the impact of the textile materials on the antenna performance, the antenna geometry was studied on cotton, polyamide-elastane and polyester substrates. It has been observed that the lower the loss tangent of the substrate material, the narrower the bandwidth. Moreover, the higher the loss tangent of the substrate, the lower the radiation efficiency and SAR.

Large‐scale fire rescue in wearable wireless sensor networks: A hole processing and trust value‐based mobile adaptive routing algorithm

SummaryAdvances in wearable technology and the proliferation of wearable devices have led to the rapid development of wearable wireless sensor networks (WWSNs) in the fields of medical monitoring and disaster relief, which have gradually become a key technology for the Internet of Medical Things. This study focused on the safety of rescuers in the event of a large‐scale fire and applied a WWSN to monitor and transmit their physiological information. User mobility is a huge problem for routing in a WWSN: the topology changes rapidly and routing should be adjusted accordingly, but node movement can easily fracture links that need to be repaired, which takes time and can cause routing holes. Thus, mainstream routing algorithms are presently unsuitable for fire rescue. To solve the routing hole problem, a hole processing and trust value‐based mobile adaptive routing algorithm (MARA) is proposed comprising two parts: location‐aided routing for multiple request zones (LAR‐MRZ) and route maintenance algorithm based on the trust value (RMA‐TV). Simulation results showed that MARA provided an excellent performance in terms of the packet delivery rate and delay and demonstrated good adaptability for a WWSN in a fire scenario.

Dynamical attitude configuration with wearable wireless body sensor networks through beetle antennae search strategy

Wearable wireless body sensor networks (WWBSN) are helpful to the dynamical attitude configuration of human-like robots. And then, we employ the Beetle Antennae Search (BAS) algorithm for a field robot to obtain an optimal solution to both path planning and attitude configuration by using the measurements of WWBSN. In terms of obstacle avoidance further more, the proposed Beetle Antennae Search for Attitude Optimization (BASAO) of manipulator develops two important mechanisms, including both experience-oriented part and inner loop step self-attenuate part, to enhance the efficiency and accuracy. earch efficiency is improved by fusing the target information via guidance strategy, and the optimization of each iteration is coordinated by local self-decaying step size. For manipulators mounted with WWBSN, the proposed method is implemented in three-dimensional attitude planning simulations, some of which consider a challenging obstacle situation. Simulation results show that the proposed BASAO algorithm is effective in the attitude planning with WWBSN, compared with the traditional Rapidly-exploring Random Tree (RRT) algorithm.

A privacy preserve big data analysis system for wearable wireless sensor network

Big data and artificial intelligence develop rapidly. Big data analysis has been applied in many fields of smart healthcare. Once these data are leaked or modified during transmission, it will not only invade the privacy of patients, but also endanger their lives. Many researchers worked on encrypted personal health records (PHR). However, there are still some challenges, such as data leakage during deep learning and leakage of training models, and some users do not want their data to be leaked to the analysis organization. How to protect privacy while leveraging deep learning is a pressing issue. In this paper, we present a system for predicting disease and timely alarms by collecting data from sensors and using deep learning to analyze and monitor patient health data. In order to protect the privacy of health data, we adopt an assured data deletion approach which the data owner can choose to revoke some users’ access to their health data. Extensive analysis and experimental results are presented that demonstrate the security, and efficiency of our proposed approach.

Soft sensors form a network

A wearable wireless sensor network for personalized healthcare can be created through the indirect integration of soft on-skin sensors and rigid in-clothes circuits.

A Wearable Wireless Sensor Network Node for Prevention of Physical Injuries

The economic burden of healthcare provision is continually rising. To combat this, targeted preventative measures have been proposed, together with encouragement of healthier lifestyle, enhanced health data collection and empowerment of patients in disease prevention and monitoring. A major cause of impairment is muscular strain and/or bone damage caused by sports injuries in the young, or falling in the elderly. To address such issues, a wearable wireless inertial sensor network is proposed which monitors body dynamics for probable disruptive incidents. The sensor node consists of a combined accelerometer, gyroscope and magnetometer, linked to a micro controller to gather information from the sensor and a wireless transceiver for communication with the network. A miniaturised wearable prototype was designed and realised in hardware.