Wireless Sensor System Research Articles

Stencil-printed Lithium-ion micro batteries for IoT applications

A battery design and fabrication process is demonstrated to make Lithium-ion (Li-ion) microbatteries with high capacity to power IoT devices. The battery consists of printed anode and cathode layers based on graphite and lithium cobalt oxide (LCO) respectively. The active area of the electrodes is scaled down to 1 mm2 and the resulting electrochemical performance is evaluated. These miniature batteries demonstrate a significantly higher discharge capacity (6.4 mAh/cm2) and energy density (23.6 mWh/cm2) than thin-film and thick-film, and 3D microbatteries. This work shows a miniaturized Li-ion battery capable of powering a MEMS-based wireless sensor system with peak current requirements as high as 4 mA, demonstrating its effectiveness as a power source for integrated electronics.

Localisation of wireless nodes with partial connectivity in wireless sensor systems

Owing to their short communication range, wireless nodes in wireless sensor networks (WSNs) can exchange information with devices in their vicinity only. Thus, in sparse networks, the full connectivity of the network is rarely achieved. This renders a centralised approach towards localisation in WSNs useless. Moreover, the exploitation of a centralised algorithm for localisation compromises the scalability in dense networks. Thus, a decentralised, location-aware network with partial connectivity and hybrid (range and direction) measurements obtained between known sensors (reference sensors) and sensors at unknown locations (target sensors) is under focus. The decentralised location estimation is obtained using a linear least squares (LLS) approach and performance enhancements are achieved by introducing a weighing strategy to produce weighted least squares (WLS) estimates. This distributed estimation is made possible by designing a map stitching technique that forms the global map of the network from individual local maps of the wireless nodes without compromising the distributed nature of the network. In the analytical section of the study, theoretical mean squares error expression is derived for LLS estimation, and a Cramer–Rao lower bound is derived to bind the performance of the WLS solution. The algorithm's performance validation is conducted both theoretically and via simulations.

Deployment of Multiple Sink in Different Monitoring Areas Based on the Optimization Technique

Background: Wireless Sensor Networks are widely used in different applications like environmental monitoring, health monitoring, wildlife monitoring, etc. The monitored area may be of any shape, such as circular, rectangular, and square. Finding an ideal node deployment technique in Wireless Sensor Systems Networks (WSNs) that would diminish cost, be powerful to node failure, shorten calculation, and communication overhead, and guarantee full coverage alongside network connectivity is a troublesome issue. Sensing coverage and system connectivity are two of the most basic issues in WSNs as they can straightforwardly affect the network lifetime and activity. In traditional WSNs, deployment of a single sink results in more traffic load on that sink causes higher energy consumption. Thus, it is necessary to deploy multiple sinks. Methods: The efficient deployment of sensors and multiple sinks is a challenging task as the performance of the network depends on it. This paper proposes “Sensor Sink Deployment Optimization Algorithm (SSDOA)” sensors and multiple sinks deployment technique in different monitoring area. The deployment strategy is based on the optimization technique. We have simulated it in Matlab simulator. The impact of sensors and sinks on various network performance parameters like coverage, network lifetime and energy consumption has been analyzed. Results: Compared to existing methods, our method performs better in any monitoring area. Reported numerical results show that the proposed approach SSDOA outperforms PSO, GA and Random deployment in the square monitoring area with 9% better network lifetime, 4% full coverage and 7.3% lesser energy consumption respectively. Furthermore, our proposed approach also performs better in circular and rectangular monitoring area.

Radical low-slung influence 12-Bit SAR ADC

This paper presents a 12-bit SA-ADC for portable low power wireless sensor systems. The proposed SA-ADC operates for rail-to-rail input range and achieves low power consumption. Split capacitor array-based DAC and a novel charge-integration based dynamic comparator are used for low power consumption of the ADC. Measured DNL and INL are −0.59/0.67 LSB and −1.2/1.33 LSB respectively. At sampling rate of 100-kS/s with 1.8-V supply, the ADC consumes only 2-Μw power and achieves a SNDR of 64.42-dB, SFDR of 71.2-dB resulting in an FoM of 14-fJ/Conversion-step. The ADC core occupies an area of 0.238-mm2 and is fabricated in AMS 0.35-μm CMOS technology.

Comparison of Thermally Grown Carbon Nanofiber‐Based and Reduced Graphene Oxide‐Based CMOS‐Compatible Microsupercapacitors

Microsupercapacitors as miniature energy storage devices require complementary metal–oxide–semiconductor (CMOS) compatible techniques for electrode deposition to be integrated in wireless sensor network sensor systems. Among several processing techniques, chemical vapor deposition (CVD) and spin coating, present in CMOS manufacturing facilities, are the two most viable processes for electrode growth and deposition, respectively. To make an argument for choosing either of these techniques to fabricate MSCs utilizable for an on‐chip power supply, we need a comparative assessment of their electrochemical performance. Herein, the evaluation of MSCs with CVD‐grown carbon nanofiber (CNF)‐based and spin‐coated reduced graphene oxide (rGO)‐based electrodes is reported. The devices are compared for their capacitance, energy and power density, charge retention, characteristic frequencies, and ease of fabrication over a large sweep of scan rates, current densities, and frequencies. The rGO‐based MSCs demonstrate 112 μF cm−2 at 100 mV s−1 and a power density of 12.8 mW cm−2. The CNF‐based MSCs show 269.7 μF cm−2 and 30.8 mW cm−2. CVD‐grown CNF outperforms spin‐coated rGO in capacitive storage at low frequencies, whereas the latter is better in terms of charge retention and high‐frequency capacitance response.

Very High Bit Rate Near-Field Communication with Low-Interference Coils and Digital Single-Bit Sampling Transceivers for Biomedical Sensor Systems.

The evolution of microelectronics increased the information acquired by today’s biomedical sensor systems to an extent where the capacity of low-power communication interfaces becomes one of the central bottlenecks. Hence, this paper mathematically analyzes and experimentally verifies novel coil and transceiver topologies for near-field communication interfaces, which simultaneously allow for high data transfer rates, low power consumption, and reduced interference to nearby wireless power transfer interfaces. Data coil design is focused on presenting two particular topologies which provide sufficient coupling between a reader and a wireless sensor system, but do not couple to an energy coil situated on the same substrate, severely reducing interference between wireless data and energy transfer interfaces. A novel transceiver design combines the approaches of a minimalistic analog front-end with a fully digital single-bit sampling demodulator, in which rectangular binary signals are processed by simple digital circuits instead of sinusoidal signals being conditioned by complex analog mixers and subsequent multi-bit analog-to-digital converters. The concepts are implemented using an analog interface in discrete circuit technology and a commercial low-power field-programmable gate array, yielding a transceiver which supports data rates of up to 6.78 MBit/s with an energy consumption of just 646 pJ/bit in transmitting mode and of 364 pJ/bit in receiving mode at a bit error rate of , being 10 times more energy efficient than any commercial NFC interface and fully implementable without any custom CMOS technology.

A Self-powered Wearable Wireless Sensor System Powered by a Hybrid Energy Harvester for Healthcare Applications

In this paper, a wearable medical sensor system is designed for long-term healthcare applications. This system is used for monitoring temperature, heartbeat, blood oxygen saturation (SpO2), and the acceleration of a human body in real-time. This system consists of a temperature sensor, a pulse oximeter sensor, an accelerometer sensor, a microcontroller unit, and a Bluetooth low energy module. Batteries are needed for supplying energy to this sensor system, but batteries have a limited lifetime. Therefore, a photovoltaic–thermoelectric hybrid energy harvester is developed to power a wearable medical sensor system. This harvester provides sufficient energy and increases the lifetime of the sensor system. The proposed hybrid energy harvester is composed of a flexible photovoltaic panel, a thermoelectric generator module, a DC–DC boost converter, and two super-capacitors. Experimentally, in active-sleep mode, the sensor system consumes an average power of 2.13 mW over 1 h and works without the energy harvester for 46 h. Finally, the experimental results illustrate the sustainable and long-term monitoring operation for the medical sensor system.

Inductive wireless power transfer system using multiple coils for rotating propulsive shaft applications

AbstractAn induction wireless power transmission (I‐WPT) system using multiple coils has been proposed to drive a wireless sensor system (WSS) that monitors the status of the propulsion shaft. While maintaining a constant transfer voltage, the minimum number of coils and the minimum air gap between coils were extracted through several experiments. The optimized air gap between transmitter (Tx) and receiver (Rx) coils was 3 mm and the number of the Rx and Tx coils was four and six, respectively. The optimized I‐WPT system was installed on the shaft with a diameter of 20 cm and obtained the stable transferred voltage characteristics and output power of 1.75 W with above 75% efficiency in spite of different rotation speed of the shaft.

A Wireless Multi-Channel Capacitive Sensor System for Efficient Glove-Based Gesture Recognition With AI at the Edge

This brief presents a wireless smart glove based on multi-channel capacitive pressure sensors that is able to recognize 10 American Sign Language gestures at the edge. In this system, 16 capacitive sensors are fabricated on a glove to capture the hand gestures. The sensor data is captured by a 16-channel CDMA-like capacitance-to-digital converter for training/inference at the edge device. Unlike the conventional approach where the capacitive information is recovered before further signal processing, our proposed system approach takes advantage of the capability of the machine learning (ML) algorithms and directly processes the code-modulated signals without demodulation. As a result, it reduces the input data throughput fed into the ML algorithms by $20\times $ . The on-site ML implementation significantly reduces decision-making latency and lowers the required data throughput for wireless transmission by at least $4\times $ . The highest testing classification accuracy of our system achieved is 99.7%, with a <0.1% difference from the conventional demodulated sensing scheme.

A Noncontact Microwave Sensor Based on Cylindrical Resonator for Detecting Concentration of Liquid Solutions

This article presents a microwave sensor composed of a cylindrical cavity resonator and thin plastic liquid container for noncontactual detection of the liquid solution concentration. The resonant frequency is tuned to around 11 GHz, resulting in miniaturized dimensions and easy integration with a wireless sensor system. The sensor is with a small volume of 21 cm3 and sensitivity of 0.4 kHz/(mg/L). Simulated results show a good linear relationship between the concentration of the sample solution and resonant frequency shift with the high sensitivity, which is consistent with the experimental result. Compared with other designs, the sensor also has a simple structure, low cost and easy implementation to in-situ and in-vitro detection scenarios.

Wireless Ti3C2Tx MXene Strain Sensor with Ultrahigh Sensitivity and Designated Working Windows for Soft Exoskeletons.

Emerging soft exoskeletons pose urgent needs for high-performance strain sensors with tunable linear working windows to achieve a high-precision control loop. Still, the state-of-the-art strain sensors require further advances to simultaneously satisfy multiple sensing parameters, including high sensitivity, reliable linearity, and tunable strain ranges. Besides, a wireless sensing system is highly desired to enable facile monitoring of soft exoskeleton in real time, but is rarely investigated. Herein, wireless Ti3C2Tx MXene strain sensing systems were fabricated by developing hierarchical morphologies on piezoresistive layers and incorporating regulatory resistors into circuit designs as well as integrating the sensing circuit with near-field communication (NFC) technology. The wireless MXene sensor system can simultaneously achieve an ultrahigh sensitivity (gauge factor ≥ 14,000) and reliable linearity (R2 ≈ 0.99) within multiple user-designated high-strain working windows (130% to ≥900%). Additionally, the wireless sensing system can collectively monitor the multisegment exoskeleton actuations through a single database channel, largely reducing the data processing loading. We finally integrate the wireless, battery-free MXene e-skin with various soft exoskeletons to monitor the complex actuations that assist hand/leg rehabilitation.

Comparison between Flat and Hierarchical Routing Protocols in Wireless Sensor Network

Wireless Sensor Network (WSN) has delivered the accessibility of small, tiny and low cost sensor nodes which are capable to sense various kinds of physical and environmental conditions, data processing, wireless communication and data gathering. In wireless sensor network routing protocols can be divided into two categories first is flat routing protocol and another is hierarchical routing protocol. In this paper flat and hierarchical routing protocols are evaluated and compared based on various performance parameters. In the last decade we have seen expanded enthusiasm for the potential utilization of remote wireless sensor systems (WSNs) in an extensive change of uses and it has turned into a unique research zone. So finally, in this research paper we are focusing on two different classes of routing protocols in WSN: flat routing and hierarchical or clustering routing.

High-Resolution Dielectric Constant Measurement Using a Sensor Antenna With an Allocated Link for Data Transmission

A dual-band low-profile patch antenna, which operates based on aperture-coupling mechanism, is exploited to measure the permittivity of a sample under test (SUT) in the microwave frequencies and send the measured data simultaneously. The upper resonant frequency, which is considerably affected by changing the permittivity of the SUT, can be utilized for sensing purpose. In contrast, the lower resonant frequency is almost independent of the samples hence can be used as the communication link. The samples are placed on top of the feeding side of the structure. The variation in the sample’s permittivity leads to changes in the input impedance of the sensor antenna. While this change results in having different feedline-dependent resonant frequencies, the cavity-dependent resonant frequency is almost preserved intact for different samples. The sensor antenna is designed using electromagnetic solver and an equivalent circuit is proposed to model its behavior. The effect of varying parameters in the equivalent model is investigated. Moreover, by fabricating the sensor, the measurement results show that by changing the permittivity of samples from 1 to 12.85, the upper band resonant frequency (feedline-dependent) shifts from 12.30 to 11.37 GHz (78 MHz per unit dielectric constant). Nevertheless, the small variation in resonant frequency and radiation characteristics of the antenna in the lower band (cavity-dependent) can provide a stable communication channel with respect to the SUT. This link with a reasonable 2.8% (9.54-9.81 GHz) 10-dB impedance bandwidth can be reserved for sending the measured and pilot data in a wireless sensor system.

Design of Wireless Temperature and Humidity Acquisition System

Based on the Z-Stack protocol Stack, with the CC2530 radio frequency transceiver chip and DHT22 digital temperature and humidity sensor as the core, this article puts forward a new implementation designed for the wireless temperature and humidity acquisition system on the basis of the Zigbee wireless communication technology. With the method of upper machine software by GUI, changes in temperature and humidity can be shown on the display terminal intuitively. The article mainly expounds the system structure design, temperature andhumiditydataacquisitionprocessandgraphicalinterfacemonitoringimplementation. Experimentsshowthat the wireless sensor system can make the real-time data acquisition of temperature and humidity, which has not only good efficiency and scalability, but also high reliability and excellent stability.

Objectively measured movement asymmetry in yearling Standardbred trotters

Lameness evaluation of Standardbred trotters can be challenging due to discrepancies in observed movement asymmetry between in-hand and track exercise, and between different trotting speeds. There are few studies on objective measurement of movement in Standardbreds, and little knowledge regarding biological variation and clinical significance of measured movement asymmetry in this breed. To quantify the prevalence and magnitude of objectively measured movement asymmetry in young Standardbred trotters, and identify associations with trainer, sex, height, track type and in-hand measurement prior to or after track trials. Cross-sectional, observational study. A total of 114 Standardbred yearlings were evaluated with a wireless inertial sensor system during trot in-hand and when driven on a track. After exclusions relating to lameness or technical difficulties, 103 horses were included in the study; 77 were evaluated in-hand and on the track, 24 only in-hand and 2 only on the track. Front and/or hindlimb parameters were above asymmetry thresholds previously established for other breeds during in-hand trials for 94 (93%) horses and during track trials for 74 (94%) horses. Most horses showed mild asymmetry. A minority of horses (20%) switched side of the asymmetry for one or more parameters between in-hand and track trials. Mixed model analyses revealed no significant effects of trial mode (in-hand or track trial, in-hand trial pre- or post-track trial, straight or oval track), trainer or horse height. Females had a significant but small reduction in asymmetry in one front limb parameter (HDmax ) compared with males (1.7mm, 95% CI 0.18-3.28, P=.03). High data variability, reflected in large trial standard deviations, relating mainly to a lack of horse compliance. A high proportion of Standardbred yearlings showed movement asymmetries. There was no group-level effect between in-hand and track trials, however, considerable individual variation was observed.

Parametric evaluation of deep brain stimulation parameter configurations for Parkinson's disease using a conformal wearable and wireless inertial sensor system and machine learning.

Deep brain stimulation enables highly specified patient-unique therapeutic intervention ameliorating the symptoms of Parkinson's disease. Inherent to the efficacy of deep brain stimulation is the acquisition of an optimal parameter configuration. Using conventional methods, the optimization process for tuning the deep brain stimulation system parameters can intrinsically induce strain on clinical resources. An advanced means of quantifying Parkinson's hand tremor and distinguishing between parameter settings would be highly beneficial. The conformal wearable and wireless inertial sensor system, such as the BioStamp nPoint, has a volumetric profile on the order of a bandage that readily enables convenient quantification of Parkinson's disease hand tremor. Furthermore, the BioStamp nPoint has been certified by the FDA as a 510(k) medical device for acquisition of medical grade data. Parametric variation of the amplitude parameter for deep brain stimulation can be quantified through the BioStamp nPoint conformal wearable and wireless inertial sensor system mounted to the dorsum of the hand. The acquired inertial sensor signal data can be wirelessly transmitted to a secure Cloud computing environment for post-processing. The quantified inertial sensor data for the parametric study of the effects of varying amplitude can be distinguished through machine learning classification. Software automation through Python can consolidate the inertial sensor data into a suitable feature set format. Using the multilayer perceptron neural network considerable machine learning classification accuracy is attained to distinguish multiple parametric settings of amplitude for deep brain stimulation, such as 4.0 mA, 2.5 mA, 1.0 mA, and 'Off' status representing a baseline. These findings constitute an advance toward the pathway of attaining real-time closed loop automated parameter configuration tuning for treatment of Parkinson's disease using deep brain stimulation.

Analysis of the environment influence on the efficiency of the information processing algorithm in wireless sensor networks

In this work, the synthesis and analysis of the algorithm that makes a decision on the presence or absence of the observed object using the spatially distributed sensors of wireless sensor networks are executed. The peculiarities of the influence of the external environment on the functioning of local sensors on the example of sound and vibration sensors are shown. With the purpose of determine the degree of influence of the environment on the efficiency of functioning of the system, theoretical formulas for the decision-making efficiency indicators are obtained. The dependences of the total error probability on the energy parameter taking into account the attenuation in the environment are presented. The main types of propagation environment were considered. Analysis of the results is executed and the degree of influence of the environment on the quality of functioning of the wireless sensor system is evaluated. Herewith it was shown that its efficiency, despite its possible significant deterioration due to strong absorption by the propagation environment, can be increased on account of increasing the number of sensors used.

IEpilepsy monitoring and alerting system using machine learning algorithm and WHMS

Advances in wireless electronics devices have led to easy design and develop of wearable sensor systems for health monitoring. These wireless sensors have been considered as one of the most important technologies that can change the future and has garnered lots of attention in the scientific community and the industry during the last years. These devices consist of small battery with limited computation and radio communication capabilities. These wireless sensor systems has become essential in such domains as industrial operations, health care, environmental infrastructure and research and development. Accelerometer sensor added with these wireless devices value to automatically detect seizures in temporal lobe epilepsy patients. The accelerometer sensor that is used in the wireless device is used to calculate the vibration threshold developed in the body. After calculating the threshold with various thresholds factors that has occurred in the body using seizure detection algorithm the device sends alerts to emergency contacts. In the future, we’ll see the mixing of a huge array of wireless networks into existing specialised medical technology. The aim of this paper is not to criticize, but to serve as a reference for researchers and developers in this scientific area and to provide direction for future research improvements.

Parallelized Wireless Sensing System for Continuous Monitoring of Microtissue Spheroids.

Currently, the use of electrical readout methods for the investigation of microtissue spheroids in combination with lab automation tools is hindered by the cable connections that are required to interrogate the on-chip-integrated electrodes. To overcome this limitation, we developed a wireless sensor scheme, which can detect the size variation of microtissues during long-term culturing and drug exposure assays. The sensor system includes an interrogation board, which is composed of an inductor-capacitor (LC) readout circuit, and the tissue culture platform with integrated split-ring sensors. The magnetic coupling between the LC circuit and the sensors enables the interrogation of the on-chip sensors without any wire connection to the culture platform. By optimizing the sensor dimensions and the LC resonance frequencies, we were able to avoid cross talk between neighboring sensors. We integrated 12 tissue compartments on a standard microscopy slide with a sensor-to-sensor pitch of 9 mm, which is in accordance with standard 96-well plate dimensions. As a proof-of-concept experiment for the developed system, we monitored continuously and during more than four days the growth inhibition of colon cancer microtissue spheroids that had been exposed to different concentrations of doxorubicin, a chemotherapeutic compound. The stability of the measurements during long-term culturing and the compatibility of the sensor scheme with standard lab equipment offer great potential for automated electrical microtissue spheroid characterization.

High-Temperature-Annealed Flexible Carbon Nanotube Network Transistors for High-Frequency Wearable Wireless Electronics.

Semiconducting single-walled carbon nanotubes (SWNTs) are potential active materials for fast-growing flexible/wearable applications with low-power dissipation, especially suitable for increasingly important radio-frequency (RF) wireless biosensor systems. However, the operation frequency of the existing flexible carbon nanotube field-effect transistors (CNT-FETs) is far below the current state-of-the-art GSM spectrum frequency band (typical 850 MHz) for near-field wireless communication applications. In this paper, we successfully conduct a 900 °C annealing process for the flexible CNT-FETs and hence significantly improve their operation frequency up to 2.1 gigahertz (GHz), making it possible to cover the current GSM spectra for integrated wireless sensor systems. The high-temperature annealing process significantly improves the electrical characteristic of the flexible CNT-FETs by removing the surfactant impurities of the SWNT materials. The obtained flexible CNT-FETs exhibit record transconductance (gm) as high as 48 μS/μm. Despite an applied strain level of 2%, a characteristic frequency of over 1 GHz is observed. Further demonstration of GHz performance is also exhibited for flexible RF integrated circuits (ICs) such as frequency multipliers and mixers, which are the fundamental components for wireless applications. This work offers a new pathway for realizing SWNT-based wearable wireless GHz sensor systems with power efficiency.