Low-power Bluetooth Research Articles

The Application of Bluetooth Technology in Indoor Navigation

In the era of rapid technological advancement, as human activities increasingly extend into indoor spaces, navigation needs have expanded from outdoors to indoors. Due to the limitations of traditional outdoor navigation technology in indoor environments, the demand for indoor navigation technology is increasing. The advantages of Bluetooth technology, such as low power consumption, security, and ease of integration, make it widely applicable in indoor positioning and navigation. Based on current research, common indoor wireless positioning methods were discussed, including ranging-based and distance-free algorithms, and low-power Bluetooth indoor positioning methods such as the geometric method and fingerprint library method were elaborated. In addition, the technical support of Bluetooth systems for indoor navigation, such as the Kalman filtering algorithm in signal processing and the Dijkstra algorithm in path planning, was discussed. On this basis, the article proposes the advantages of Bluetooth positioning in device popularization and deployment, as well as the trend of integrating Bluetooth with other devices or technologies to improve positioning and navigation capabilities.

A Wireless Bi-Directional Brain-Computer Interface Supporting Both Bluetooth and Wi-Fi Transmission.

Wireless neural signal transmission is essential for both neuroscience research and neural disorder therapies. However, conventional wireless systems are often constrained by low sampling rates, limited channel counts, and their support of only a single transmission mode. Here, we developed a wireless bi-directional brain-computer interface system featuring dual transmission modes. This system supports both low-power Bluetooth transmission and high-sampling-rate Wi-Fi transmission, providing flexibility for various application scenarios. The Bluetooth mode, with a maximum sampling rate of 14.4 kS/s, is well suited for detecting low-frequency signals, as demonstrated by both in vitro recordings of signals from 10 to 50 Hz and in vivo recordings of 16-channel local field potentials in mice. More importantly, the Wi-Fi mode, offering a maximum sampling rate of 56.8 kS/s, is optimized for recording high-frequency signals. This capability was validated through in vitro recordings of signals from 500 to 2000 Hz and in vivo recordings of single-neuron spike firings with amplitudes reaching hundreds of microvolts and high signal-to-noise ratios. Additionally, the system incorporates a wireless stimulation function capable of delivering current pulses up to 2.55 mA, with adjustable pulse width and polarity. Overall, this dual-mode system provides an efficient and flexible solution for both neural recording and stimulation applications.

A 2.4 GHz Wide-Range CMOS Current-Mode Class-D PA with HD2 Suppression for Internet of Things Applications.

Short-range Internet of Things (IoT) sensor nodes operating at 2.4 GHz must provide ubiquitous wireless sensor networks (WSNs) with energy-efficient, wide-range output power (POUT). They must also be fully integrated on a single chip for wireless body area networks (WBANs) and wireless personal area networks (WPANs) using low-power Bluetooth (BLE) and Zigbee standards. The proposed fully integrated transmitter (TX) utilizes a digitally controllable current-mode class-D (CMCD) power amplifier (PA) with a second harmonic distortion (HD2) suppression to reduce VCO pulling in an integrated system while meeting harmonic limit regulations. The CMCD PA is divided into 7-bit slices that can be reconfigured between differential and single-ended topologies. Duty cycle distortion compensation is performed for HD2 suppression, and an HD2 rejection filter and a modified C-L-C low-pass filter (LPF) reduce HD2 further. Implemented in a 28 nm CMOS process, the TX achieves a wide POUT range of from 12.1 to -31 dBm and provides a maximum efficiency of 39.8% while consuming 41.1 mW at 12.1 dBm POUT. The calibrated HD2 level is -82.2 dBc at 9.93 dBm POUT, resulting in a transmitter figure of merit (TX_FoM) of -97.52 dB. Higher-order harmonic levels remain below -41.2 dBm even at 12.1 dBm POUT, meeting regulatory requirements.

Intelligent Control System for Wood Drying: Scalable Architecture, Predictive Analytics, and Future Enhancements

This article explores the research and development undertaken as part of a Master’s degree in Computer Engineering, with a primary focus on enhancing control mechanisms for natural wood drying. While this method is known for its cost-effectiveness in terms of labor and energy, it suffers from slower and unstable drying cycles. The project’s objective is to implement an intelligent control system that significantly improves monitoring and recording of humidity levels in each wooden stack. Additionally, the system incorporates the capability to predict humidity based on data sourced from a weather forecasting API. The proposed solution entails a three-layer system: data collection, relay, and analysis. In the data collection layer, low-computing devices, utilizing a Raspberry Pi, measure humidity levels in individual wood stacks. These devices then transmit the data via Low Power Bluetooth to the subsequent layer. The data relay layer incorporates an Android application designed to aggregate, normalize, and transmit collected data. Furthermore, it provides users with visualization tools for comprehensive data understanding. The data storage and analysis layer, developed with Django, serves as the back-end, offering management functionalities for stacks, sensors, overall data, and analysis capabilities. This layer can generate humidity forecasts based on real-time weather information. The implementation of this intelligent control system enables accurate insights into humidity levels, triggering alerts for any anomalies during the drying process. This reduces the necessity for constant on-site supervision, optimizes work efficiency, lowers costs, and eliminates repetitive tasks.

A Wireless, High-Quality, Soft and Portable Wrist-Worn System for sEMG Signal Detection.

The study of wearable systems based on surface electromyography (sEMG) signals has attracted widespread attention and plays an important role in human-computer interaction, physiological state monitoring, and other fields. Traditional sEMG signal acquisition systems are primarily targeted at body parts that are not in line with daily wearing habits, such as the arms, legs, and face. In addition, some systems rely on wired connections, which impacts their flexibility and user-friendliness. This paper presents a novel wrist-worn system with four sEMG acquisition channels and a high common-mode rejection ratio (CMRR) greater than 120 dB. The circuit has an overall gain of 2492 V/V and a bandwidth of 15~500 Hz. It is fabricated using flexible circuit technologies and is encapsulated in a soft skin-friendly silicone gel. The system acquires sEMG signals at a sampling rate of over 2000 Hz with a 16-bit resolution and transmits data to a smart device via low-power Bluetooth. Muscle fatigue detection and four-class gesture recognition experiments (accuracy greater than 95%) were conducted to validate its practicality. The system has potential applications in natural and intuitive human-computer interaction and physiological state monitoring.

High-Performance Surface Electromyography Armband Design for Gesture Recognition.

Wearable surface electromyography (sEMG) signal-acquisition devices have considerable potential for medical applications. Signals obtained from sEMG armbands can be used to identify a person's intentions using machine learning. However, the performance and recognition capabilities of commercially available sEMG armbands are generally limited. This paper presents the design of a wireless high-performance sEMG armband (hereinafter referred to as the α Armband), which has 16 channels and a 16-bit analog-to-digital converter and can reach 2000 samples per second per channel (adjustable) with a bandwidth of 0.1-20 kHz (adjustable). The α Armband can configure parameters and interact with sEMG data through low-power Bluetooth. We collected sEMG data from the forearms of 30 subjects using the α Armband and extracted three different image samples from the time-frequency domain for training and testing convolutional neural networks. The average recognition accuracy for 10 hand gestures was as high as 98.6%, indicating that the α Armband is highly practical and robust, with excellent development potential.

Intelligent Microsystem for Sound Event Recognition in Edge Computing Using End-to-End Mesh Networking

Wireless acoustic sensor networks (WASNs) and intelligent microsystems are crucial components of the Internet of Things (IoT) ecosystem. In various IoT applications, small, lightweight, and low-power microsystems are essential to enable autonomous edge computing and networked cooperative work. This study presents an innovative intelligent microsystem with wireless networking capabilities, sound sensing, and sound event recognition. The microsystem is designed with optimized sensing, energy supply, processing, and transceiver modules to achieve small size and low power consumption. Additionally, a low-computational sound event recognition algorithm based on a Convolutional Neural Network has been designed and integrated into the microsystem. Multiple microsystems are connected using low-power Bluetooth Mesh wireless networking technology to form a meshed WASN, which is easily accessible, flexible to expand, and straightforward to manage with smartphones. The microsystem is 7.36 cm3 in size and weighs 8 g without housing. The microsystem can accurately recognize sound events in both trained and untrained data tests, achieving an average accuracy of over 92.50% for alarm sounds above 70 dB and water flow sounds above 55 dB. The microsystems can communicate wirelessly with a direct range of 5 m. It can be applied in the field of home IoT and border security.

A Double-Needle Gold-Silver Electrodes Continuous Glucose Monitoring Device

ObjectivesDiabetes is a serious, long-term disease and the use of continuous glucose monitoring sensors can reduce reliance on other painful invasive blood testing methods such as the finger blood glucose test. According to our work, a low-cost continuous glucose sensor has been developed based on electrochemical measurement techniques. MaterialsThe sensor is based on a two needles system; a gold and a silver electrode are integrated into a circular shaped electronic printed circuit board (PCB). The sensing part is based on biological electrochemical measurements. Glucose oxidase (Gox) was used as the active sensing element and ferrocene (Fc) as a mediator. Simple and low-cost coating methods were used; these methods are self-assembled monolayers and deep coating. This will reduce the final cost of the sensor as no expensive technique was used. The electrical subsystem contains a low-noise and low-power trans-impedance front-end as well as a single-chip low-power Bluetooth microcontroller with a 12-bit Analog-to-Digital Converter (ADC). ResultsThe sensor was tested in various concentrations of glucose. As a result of initial in vitro experiments, detailed analytical performance metrics are presented. The device has consistently shown a sensitivity of 3.059 mV/(mg/dl) reading with a linear range of 0-400 mg/dl. ConclusionThe proposed study shows promising results for glucose detection. Thus, this type of sensor can be used for different analyzes targeting biological applications after further investigations and analysis.

Experimental Analysis and Monitoring of Photovoltaic Panel Parameters

In this article, we establish a technique based on the internet of things to simultaneously monitor the main values that characterize a photovoltaic solar panel. This technique allows to discover the problems and the monstrosities during the operation. This study also allows to collect the parameters and quantities measured for analysis. This method is based on exploiting the advantages of IoT technology. For this it will be a good choice to use and exploit the Esp32 microcontroller, because the two WIFI and Bluetooth modules are integrated. The design process began by creating a system to measure the intensity of the electric current delivered by the photovoltaic panel. A current sensor was implemented for this purpose. To prevent damage to the microcontroller, a voltage divider was proposed to decrease the voltage at the pin level of the Esp32 for measurement. Next, the power and energy values were calculated to estimate the production capacity. In the final stage, a low-power Bluetooth link was created to transmit the four quantities to a smartphone or other compatible device. Real-time values were presented as graphs on the free ThingSpeak platform and displayed on both, an LCD screen and the serial monitor of the Esp32 microcontroller. The system was tested without any problems or errors.

Intelligent Detection and Analysis of Wearable Devices in Wushu Training

With the integration and development of artificial intelligence and medical technology, wearable intelligent technology has become an important health testing equipment in people’s lives, constantly testing physical function and health. However, according to the movement standards, body indicators, and constantly changing detection indexes of athletes in the exercise process, it can effectively guide athletes to use them correctly and efficiently, which has become an important task of wearable intelligent technology. In the design mode of wearable smart devices, wearable bracelets are designed with acceleration sensors, martial arts training data are measured, and machine learning technology is used to analyze and evaluate the data. When a user uses the method, a sensor is used for collecting the data, the data are transmitted to a processing platform through low-power Bluetooth, the data are analyzed through a program, the accuracy of each action is output, and finally, a standard measurement result of a section of the boxing method is combined. This paper collects and analyzes the data of body characteristics and movement characteristics of wearable intelligent devices in Wushu training. Sensor technology and filtering technology are used to collect and filter the collected information, and better analysis data are obtained. Finally, the filtered data of Wushu are analyzed, and then, the efficiency and performance of different algorithms in Wushu training are compared. Wearable intelligent equipment collects Wushu action training data and then uses fixed threshold classification to recognize Wushu action. The results show that the method used has high accuracy.

Research on Intelligent Control Engineering of Green Low-Carbon Buildings Based on Improved Trilateral Positioning Algorithm

Industrial production, military applications, and environmental monitoring all require corresponding positioning functions as science and technology advance. The location information service has gradually grown in popularity. However, large-scale applications will consume a lot of energy and cause environmental issues. Our further development will therefore focus on how to maintain low-carbon development and advance green technology. The improvement of triangular location algorithm is used in this paper to realize the research of low-power Bluetooth indoor location using the intelligent control project of green low-carbon buildings as the research object. The results show that the improved algorithm significantly improves the accuracy. The daily monitoring of the elderly and the alarm processing in an emergency are experimentally practiced and analyzed through the research of low-power Bluetooth indoor location algorithm. The research on intelligent control engineering of green low-carbon buildings is realized.

Design of intelligent wireless monitoring platform

Taking STC89C52 single chip microcomputer as the controller, the communication carrier adopts low-power Bluetooth 4.0, LCD12864 display screen as the human-computer interaction interface, open-loop stepping motor and closed-loop steering gear as the action actuator, reflective infrared sensor and reflective photoelectric sensor as the detection sensing system, integrating electronics, machinery This set of intelligent wireless monitoring platform developed by *software and other aspects realizes three functions: wireless remote control monitoring, automatic panoramic detection and automatic intrusion alarm. The design has strong practicability. The whole system has the characteristics of low cost and low power consumption. It can play an important role in the field of security.

A Reinforcement Learning Approach to Service Based User Admission in a Multi-Tier 5G Wireless Networks

The expected massive connectivity in 5G wireless network is bound to become a challenge to service providers. Many services over the 5G network will be aligned to a particular radio access network (RAN). As a result admitting a service based user to a particular RAN will depend on the most efficient radio access technology selection (RAT). This is because 5G network will adopt multi-tier radio access networks ranging from high power macro base stations to extremely low power Bluetooth connectivity. Selection of a service oriented RAT is critical because some wireless services have superior quality of service under certain RATs. Maintaining efficient RAT selection by network operators will improve power allocation efficiency, bandwidth allocation efficiency and operation expenditure. The complexity of associating a RAT to service based user while considering network state such as service packet size, the turnaround time, the power allocation has not been fully explored. In this paper we propose a reinforcement learning approach to user admission based on efficient RAT selection considering wireless services in a cross tier wireless radio access network domain. The proposed algorithm is expected to improve RAT selection efficiency while minimizing the computation complexity. We perform extensive simulation using Python dynamic libraries and present our results alongside existing approaches

Research progress of smart phone positioning and navigation sensors

Smart phone has gradually become an indispensable mobile device in people’s life, and its built-in sensors develop rapidly with the popularity of smart phones. A variety of sensors built in smart phones can provide different positioning sources for indoor positioning, so as to provide convenience for people’s activities in large public places. Nowadays, multisensor indoor positioning technology based on smart phones has become a research hot spot, such as indoor visual positioning technology developed by Google and ibeacon indoor positioning technology based on low-power Bluetooth launched by apple. This paper summarizes the development status of smart phone built-in sensors for indoor positioning, sorts out the new smart phone built-in sensors of several popular mobile phone brands in the current market, expounds the sensors for indoor positioning in smart phone sensors and related positioning technologies, and finally looks forward to the future development of smart phone positioning and navigation sensors in the field of positioning.

Sensor Signal-based Learning Improvement Framework for Intellectually Disabled (ID) Child Education

According to World Health Organization (WHO), persons with cognitive impairments such as dementia, aphasia and development disorders account around 1.25% of world population. Specifically, there are 31 million individuals including 35.29% children with Intellectual Disabilities (ID) in India. Only 1% of these children have access to school and majority of them fail to express their needs and thoughts in classrooms. Their learning skills can be enhanced by incorporating wireless sensors in Assistive Technologies (AT). Rapid growing signal-based wireless sensors with smart devices motivate the development of many learning methodologies for ID school Education. This paper aims to design, implement and evaluate an Alternative and Augmentative Communicate (AAC) system for ID school education by validating, utilizing and manifesting signal-based wireless sensors. This context-based AAC employs the Low Power Bluetooth (BLE) technology for the nonverbal interaction in class room teaching. The system utilizes the high indoor tracking accuracy of signal-based BLE beacons to acquire the effective context from backend application server and presents the symbols in smart devices. The children communicate with visual objects like apple at an appropriate time i.e. during lunch time by utilizing smart phone or tablet. The system incorporates the involvement of user management such as login and profile maintenance for teachers and caregivers through smart devices.

Machine Learning in Wireless Sensor Networks for Smart Cities: A Survey

Artificial intelligence (AI) and machine learning (ML) techniques have huge potential to efficiently manage the automated operation of the internet of things (IoT) nodes deployed in smart cities. In smart cities, the major IoT applications are smart traffic monitoring, smart waste management, smart buildings and patient healthcare monitoring. The small size IoT nodes based on low power Bluetooth (IEEE 802.15.1) standard and wireless sensor networks (WSN) (IEEE 802.15.4) standard are generally used for transmission of data to a remote location using gateways. The WSN based IoT (WSN-IoT) design problems include network coverage and connectivity issues, energy consumption, bandwidth requirement, network lifetime maximization, communication protocols and state of the art infrastructure. In this paper, the authors propose machine learning methods as an optimization tool for regular WSN-IoT nodes deployed in smart city applications. As per the author’s knowledge, this is the first in-depth literature survey of all ML techniques in the field of low power consumption WSN-IoT for smart cities. The results of this unique survey article show that the supervised learning algorithms have been most widely used (61%) as compared to reinforcement learning (27%) and unsupervised learning (12%) for smart city applications.

Self-Powered Wireless Sensor Using a Pressure Fluctuation Energy Harvester.

Condition monitoring devices in hydraulic systems that use batteries or require wired infrastructure have drawbacks that affect their installation, maintenance costs, and deployment flexibility. Energy harvesting technologies can serve as an alternative power supply for system loads, eliminating batteries and wiring requirements. Despite the interest in pressure fluctuation energy harvesters, few studies consider end-to-end implementations, especially for cases with low-amplitude pressure fluctuations. This generates a research gap regarding the practical amount of energy available to the load under these conditions, as well as interface circuit requirements and techniques for efficient energy conversion. In this paper, we present a self-powered sensor that integrates an energy harvester and a wireless sensing system. The energy harvester converts pressure fluctuations in hydraulic systems into electrical energy using an acoustic resonator, a piezoelectric stack, and an interface circuit. The prototype wireless sensor consists of an industrial pressure sensor and a low-power Bluetooth System-on-chip that samples and wirelessly transmits pressure data. We present a subsystem analysis and a full system implementation that considers hydraulic systems with pressure fluctuation amplitudes of less than 1 bar and frequencies of less than 300 Hz. The study examines the frequency response of the energy harvester, the performance of the interface circuit, and the advantages of using an active power improvement unit adapted for piezoelectric stacks. We show that the interface circuit used improves the performance of the energy harvester compared to previous similar studies, showing more power generation compared to the standard interface. Experimental measurements show that the self-powered sensor system can start up by harvesting energy from pressure fluctuations with amplitudes starting at 0.2 bar at 200 Hz. It can also sample and transmit sensor data at a rate of 100 Hz at 0.7 bar at 200 Hz. The system is implemented with off-the-shelf circuits.

FITFES: A Wearable Myoelectrically Controlled Functional Electrical Stimulator Designed Using a User-Centered Approach.

Myoelectrically Controlled Functional Electrical Stimulation (MeCFES) has proven to be a useful tool in the rehabilitation of the hemiplegic arm. This paper reports the steps involved in the development of a wearable MeCFES device (FITFES) through a user-centered design. We defined the minimal viable features and functionalities requirements for the device design from a questionnaire-based survey among physiotherapists with experience in functional electrical stimulation. The result was a necklace layout that poses minimal hindrance to task-oriented movement therapy, the context in which it is aimed to be used. FITFES is battery-powered and embeds a standard low power Bluetooth module, enabling wireless control by using PC/Mobile devices vendor specific built-in libraries. It is designed to deliver a biphasic, charge-balanced stimulation current pulses of up to 113 mA with a maximum differential voltage of 300 V. The power consumption for typical clinical usage is 320 mW at 20mA stimulation current and of less than [Formula: see text] in sleep mode, thus ensuring an estimated full day of FITFES therapy on a battery charge. We conclude that a multidisciplinary user-centered approach can be successfully applied to the design of a clinically and ergonomically viable prototype of a wearable myoelectrically controlled functional electrical stimulator to be used in rehabilitation.

Research on Vehicle Positioning Method Based on Underground Garages

With the continuous development of urbanization, the number of vehicles has increased year by year. In order to solve the hard-to-park problem, underground garages have been built on a large scale in many cities. However, due to the fact that GPS signals are kept out in the underground garage, GPS technology does not work. As a consequence, drivers cannot use navigation equipment to search for the parking space. In view of this, vehicle positioning technique for vehicles in underground garages has hold the public attention and relevant research has been done. In this paper, we propose an efficient method of accurate vehicle positioning. First, use the low-power Bluetooth device, then use Gaussian filter to optimize the RSSI ranging algorithm, and then use the maximum likelihood method to improve the three-loop positioning algorithm. Finally, a comparison is made between the algorithm and the traditional algorithm. A large number of experiments have proved that this method can be used to determine the positions of vehicles in underground garages.

Bluetooth 5.2 Technology and Application

Abstract Bluetooth, a wireless short-distance communication technology, realizes data exchange between fixed devices and mobile device. It widely used in wireless devices, including wireless mice, wireless headsets, notebook computers and other devices. With the development of the times, the traditional Bluetooth technology can no longer meet people’s needs, and a new generation of low-power Bluetooth audio technology standards came into being. Bluetooth 5.2 uses LE Audio technology, which uses low-complexity communication coding and decoding technology to realize low-power transmission of high-quality audio. This paper mainly introduces the new features of Bluetooth 5.2 technology, including enhanced ATT protocol, LE Power Control and LE Isochronous Channels.