Bluetooth Low Energy Protocol Research Articles

Learning minimal automata with recurrent neural networks

AbstractIn this article, we present a novel approach to learning finite automata with the help of recurrent neural networks. Our goal is not only to train a neural network that predicts the observable behavior of an automaton but also to learn its structure, including the set of states and transitions. In contrast to previous work, we constrain the training with a specific regularization term. We iteratively adapt the architecture to learn the minimal automaton, in the case where the number of states is unknown. We evaluate our approach with standard examples from the automata learning literature, but also include a case study of learning the finite-state models of real Bluetooth Low Energy protocol implementations. The results show that we can find an appropriate architecture to learn the correct minimal automata in all considered cases.

Design of Long-Life Wireless Near-Field Hydrogen Gas Sensor.

A compact wireless near-field hydrogen gas sensor is proposed, which detects leaking hydrogen near its source to achieve fast responses and high reliability. A semiconductor-type sensing element is implemented in the sensor, which can provide a significant response in 100 ms when stimulated by pure hydrogen. The overall response time is shortened by orders of magnitude compared to conventional sensors according to simulation results, which will be within 200 ms, compared with over 25 s for spatial concentration sensors under the worst conditions. Over 1 year maintenance intervals are enabled by wireless design based on the Bluetooth low energy protocol. The average energy consumption during a single alarm process is 153 μJ/s. The whole sensor is integrated on a 20 × 26 mm circuit board for compact use.

A COMPUTERIZED ENERGY MANAGEMENT SYSTEM FOR A SMART HOME

This work is devoted to the study of Smart Home systems based on the development of an energy optimization system for a smart home and based on various wireless communication protocols. The paper considers the construction of a home monitoring and control system based on the latest Bluetooth Low-Energy protocol using modern technologies. A device for monitoring and controlling electrical appliances, which is an element of this system, is proposed. The proposed architecture has advantages over other existing systems: reliability, performance, ease of deployment, and management. The system is flexible due to the possibility of selecting operating modes (automatic or manual) and changing various settings that affect the operation of the optimization algorithm. The paper presents examples of the system operation in different modes and at different values of the algorithm settings, consisting of a server deployed on a personal computer and two developed monitoring and control device prototypes. Keywords: smart home, energy consumption optimization, monitoring and control of electrical appliances, smart sockets, Bluetooth low energy.

Performance Evaluation of a Quadcopter by an Optimized Proportional–Integral–Derivative Controller

This paper presents an analysis of a quadcopter’s stability and its sensors’ data reading using an IMU (inertial measurement unit). Firstly, the angular velocity and acceleration data are read by an Stm® development board embedded with an Stm32f401ccu6 microcontroller. The altitude of the measurement instrument platform was determined using an MS5611 barometric pressure sensor combined with a temperature sensor. The quadcopter’s control was achieved by connecting a brushless DC motor to the Stm® board, which received four PWM (pulse-width modulation) signals via the output port. An electronic governor was utilized to control the brushless DC motor, while a pre-existing remote control was designated as the transmitter. The quadcopter receiver received a 2.4 GHz signal from the transmitter using the BLE (Bluetooth low-energy) protocol, which was used to ensure the simultaneous operation of the four brushless DC motors. Finally, a PID (proportional–integral–derivative) controller was employed for parameter adjustment. The collected PID parameter program was developed in the Simulink software as a simulation platform, allowing for the execution of the Simulink model on the Stm® MCU. The Stm® module facilitates monitoring of the performance of the UAV (unmanned aerial vehicle) and enables immediate parameter adjustments to ensure flight stability. This research aims to reduce calculation errors in sensor and controller usage and improve the efficacy of the remote machine module for future industrial applications.

Comparison between Two Time Synchronization and Data Alignment Methods for Multi-Channel Wearable Biosensor Systems Using BLE Protocol

Wireless wearable sensor systems for biomedical signal acquisition have developed rapidly in recent years. Multiple sensors are often deployed for monitoring common bioelectric signals, such as EEG (electroencephalogram), ECG (electrocardiogram), and EMG (electromyogram). Compared with ZigBee and low-power Wi-Fi, Bluetooth Low Energy (BLE) can be a more suitable wireless protocol for such systems. However, current time synchronization methods for BLE multi-channel systems, via either BLE beacon transmissions or additional hardware, cannot satisfy the requirements of high throughput with low latency, transferability between commercial devices, and low energy consumption. We developed a time synchronization and simple data alignment (SDA) algorithm, which was implemented in the BLE application layer without the need for additional hardware. We further developed a linear interpolation data alignment (LIDA) algorithm to improve upon SDA. We tested our algorithms using sinusoidal input signals at different frequencies (10 to 210 Hz in increments of 20 Hz-frequencies spanning much of the relevant range of EEG, ECG, and EMG signals) on Texas Instruments (TI) CC26XX family devices, with two peripheral nodes communicating with one central node. The analysis was performed offline. The lowest average (±standard deviation) absolute time alignment error between the two peripheral nodes achieved by the SDA algorithm was 384.3 ± 386.5 μs, while that of the LIDA algorithm was 189.9 ± 204.7 μs. For all sinusoidal frequencies tested, the performance of LIDA was always statistically better than that of SDA. These average alignment errors were quite low-well below one sample period for commonly acquired bioelectric signals.

A Novel Model to Quantify the Impact of Transmission Parameters on the Coexistence Between Bluetooth Low Energy Pairs

We noted that communication performance of a Bluetooth Low Energy (BLE) connection is heavily affected by the radio interference from other connections or networks. Reliability is becoming a key requirement in BLE for its use in various Internet of Things applications. Hence, there is a widely recognized need for an in-depth study to reveal the parameters impacting BLE reliability under such radio interference. In this article, we investigate how transmission parameters, e.g., number of packets and packet transmission time, influence reliability of the BLE protocol. Specifically, a mathematical model is presented to explore the impact of the transmission parameters on the reliability of a BLE pair under interference caused by other pairs. This mathematical model is able to show the reliability issues from both the side of the BLE connection under interference and the side of the interference itself. The model is validated and novel insights on the common usage of BLE parameters by a wide range of experimental evaluations are provided. Experimental results highlight the correctness of the mathematical model, thus quantify the interplay between transmission parameters and coexistence, also the influence of other related parameters. This research provides a design-level or system-level insight in BLE usage and deployment.

An Enhanced Piezoelectric-Generated Power Technique for Qi Wireless Charging

This paper aims to design and implement a robust wireless charging system that utilizes affordable materials and the principle of piezoelectricity to generate clean energy to allow the user to store the energy for later use. A wireless charging system that utilizes the piezoelectricity generated as a power source and integrated with Qi-standard wireless transmission would substantially affect the environment and the users. The approach consists of a full-wave-rectified piezoelectric generation, battery storage, Qi-standard wireless transmission, and Bluetooth Low Energy (BLE) as the controller and application monitor. Three main functions are involved in the design of the proposed system: power generation, power storage, and power transmission. A client application is conceived to monitor the transmission and receipt of data. The piezoelectric elements generate the AC electricity from the mechanical movements, which converts the electricity to DC using the full-wave bridge rectifiers. The sensor transmits the data to the application via BLE protocols. The user receives continuous updates regarding the storage level, paired devices, and remaining time for a complete charge. A Qi-standard wireless transmitter transfers the stored electricity to charge the respective devices. The output generates pulses to 60 voltage on each compression of a transducer. The design is based on multiple parallel configurations to solve the issue of charging up to the triggering value VH = 5.2 V when tested with a single piezoelectric transducer. AA-type battery cells are charged in parallel in a series configuration. The system is tested for a number of scenarios. In addition, we simulate the design for 11.11 h for approximately 70,000 joules of input. The system can charge from 5% to 100% and draw from 98%. Using four piezos in the designed module results in an average output voltage of 1.16 V. Increasing the number of piezos results in 17.2 W of power. The system is able to wirelessly transmit and store power with a stable power status after less than 0.01 s.

Research on the behaviour of Bluetooth Low Energy protocol in the heart rate monitoring application

In the paper, the authors check the behaviour of Bluetooth Low Energy protocol in a popular smart wristband and a microcontroller in the heart rate monitoring application. The measurements were collected using a development board with the ESP32 System on Chip. The authors tested measurement period stability and measurement reliability in various conditions.

Wireless automated environmental monitoring system

Advancements in compact integrated circuit fabrication have allowed the amalgamation of wireless transceivers, signal processing, and sensors into a unified unit. This breakthrough enables seamless interaction with the physical realm, with applications spanning security, production oversight, and environmental monitoring. Herein, we investigate the design nuances of a distributed sensor network, where each node operates under energy and communication constraints. The integration of existing wireless technologies, computational capabilities, and organic polymers into a new breed of intelligent devices stands as a pivotal aspect. Within this article, we scrutinize the deployment of a wireless sensor network utilizing the Bluetooth Low Energy protocol. Leveraging organic polymers as a sensing stratum enhances energy efficiency and selective sensitivity in monitoring applications. Notably, the expeditious and cost-effective production of electronics rooted in organic polymers constitutes a substantial advantage. Flexible organic electronics, hinging on the semiconductor and flexible traits of organic materials, represent a foundational element of our technology. We delve into key performance prerequisites for operational devices, along with accomplishments and technical hurdles in the realm of designing and fabricating next-generation devices, targeting the optimization of products.

Fingerprinting and analysis of Bluetooth devices with automata learning

Automata learning is a technique to automatically infer behavioral models of black-box systems. Today’s learning algorithms enable the deduction of models that describe complex system properties, e.g., timed or stochastic behavior. Despite recent improvements in the scalability of learning algorithms, their practical applicability is still an open issue. Little work exists that actually learns models of physical black-box systems. To fill this gap in the literature, we present a case study on applying automata learning on the Bluetooth Low Energy (BLE) protocol. It shows that not only the size of the system limits the applicability of automata learning. Also, the interaction with the system under learning creates a major bottleneck that is rarely discussed. In this article, we propose a general automata learning architecture for learning a behavioral model of the BLE protocol implemented by a physical device. With this framework, we can successfully learn the behavior of six investigated BLE devices. Furthermore, we extended the learning technique to learn security critical behavior, e.g., key-exchange procedures for encrypted communication. The learned models depict several behavioral differences and inconsistencies to the BLE specification. This shows that automata learning can be used for fingerprinting black-box devices, i.e., characterizing systems via their specific learned models. Moreover, learning revealed a crashing scenario for one device.

Optimizing BLE-Like Neighbor Discovery

Neighbor discovery (ND) protocols are used for establishing a first contact between multiple wireless devices. The energy consumption and discovery latency of this procedure are determined by the parametrization of the protocol. In most existing protocols, reception and transmission are temporally coupled. Such schemes are referred to as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">slotted</i> , for which the problem of finding optimized parametrizations has been studied thoroughly in the literature. However, slotted approaches are not efficient in applications in which new devices join the network gradually and only the joining devices and a master node need to run the ND protocol simultaneously. For example, this is typically the case in IoT scenarios or bluetooth low energy (BLE) piconets. Here, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">slotless</i> protocols that decouple reception and transmission can achieve significantly lower worst-case latencies than slotted ones. In this paper, we study slotless, BLE-like protocols, which schedule receptions and transmissions independently using periodic intervals (PI). For this class of protocols, optimal parameter values remain unknown. To address this, we propose an optimization framework for PI-based protocols, which translates any specified duty-cycle (and therefore energy budget) into a set of optimized parameter values. We show that the parametrizations resulting from one variant of our proposed scheme are optimal when one receiver discovers one transmitter, and no other parametrization or ND protocol – neither slotted nor slotless – can guarantee lower discovery latencies for a given duty-cycle in this scenario. Since the resulting protocol utilizes the channel more aggressively than other ND protocols, beacons will collide more frequently. Hence, due to collisions, the rate of successful discoveries gracefully decreases for larger numbers of devices discovering each other simultaneously. We also propose a scheme for configuring the BLE protocol (and not just BLE- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">like</i> protocols). Though it is not clear whether the resulting parametrizations minimize the latencies of BLE, reasonably low worst-case latencies can be guaranteed.

Performance Improvement on Reception Confirmation Messages in Bluetooth Mesh Networks

Bluetooth mesh is a recent technology built on the Bluetooth low energy protocol stack architecture, focusing on the Internet of Things. It represents an excellent solution for commercial and industrial lighting applications, though it is still evolving. One of the biggest challenges of the Bluetooth mesh network is the improvement of confirmation messages reception. In a Bluetooth mesh network, determining the Status of the received messages is a critical aspect that can generate unexpected issues when multiple devices respond simultaneously, as it may occur in some lighting applications. This behavior can reduce the probability of message delivery due to collisions, especially when the number of devices in the network increases. This article aims to improve the reliability in receiving confirmation messages in a Bluetooth mesh network by proposing a new technique of spreading Status overtime. To evaluate the proposed technique’s performance, we compare our technique with a Bluetooth mesh network with standard configuration (SC) using real nodes experimental setup. We evaluated our results in terms of packet-loss rate, obtaining 98.84% of the Status received for the network with our optimized configuration and 96.98% for those with the SC. Finally, an in-depth performance evaluation method for the analysis of the lost Status was also conducted.

Self-configuring BLE deep sleep network for fault tolerant WSN

This paper is focused on Wireless Sensor Network (WSN) leveraging on Bluetooth Low Energy (BLE) connectivity for low energy applications which is fault tolerant versus communication path failures. The topic is important to create a robust sensorized environment to be applied in industrial context or smart infrastructure to enable scheduled monitoring with low power consumption applications. Currently BLE applications are mainly thought for smart home solutions, health care and positioning systems. In those applications the BLE nodes are continuously supplied by external power suppliers. Our goal is to design a self-configuring network with a synchronized deep sleep behavior, aimed to optimize the energy consumption, with an overall active time interval constraint optimized with a data-driven method. The aim is to find a tradeoff between the on time and the ability to collect all the nodes data, pursuing a low power consumption. Our research is based on BLE protocols, interaction between edge systems for data collection and cloud system for data analysis and software agent optimization system. The paper analyses different configurations and describes the possible optimization algorithm to be used for the software agent design, in order to reach a fine-tuned control to improve the fault tolerance and fault diagnosis of the system. Finally experimental results are compared with the estimates obtained via a software simulation tool implemented for this architectural pattern.

Enhance your smartphone with a Bluetooth arduino nano board

Using smartphones in experimental physics teaching offers many advantages in terms of engagement, pedagogy and flexibility. But it presents drawbacks such as possibly endangering the device and also facing the heterogeneity of available sensors on different smartphones. We present a low-cost alternative that preserves the advantages of smartphones: using a microcontroller equipped with a large variety of sensors that transmits data to a smartphone using a Bluetooth low-energy protocol. This device can be lent to students with little risk and used to perform a wide range of experiments. It opens the way to new types of physics teachings.

Implementation of Oxygen Saturation Sensor Data Acquisition Based on Bluetooth Low Energy Protocol

Sensor data acquisition is used to obtain sensor data from IoT devices that already provide the required sensor data. To acquire sensor data, we can use Bluetooth Low Energy (BLE) protocol. This data acquisition aims to process further data which will later be sent to the server. Bluetooth Low Energy (BLE) has an architecture consisting of sensors, gateways, and data centers, but with this architecture, there are several weaknesses, namely the failure when sending data to the data center due to not being connected to internet network and data redundancy at the time of data delivery is done. The proposed solution to solve this problem is to create a system that can acquire sensor data using the Bluetooth Low Energy (BLE) protocol with use a store and forward mechanism and checking data redundancy. The proposed system will be implemented using sensors from IoT devices, the gateway used is Android devices, and using the Bluetooth Low Energy protocol to acquire data from sensors. Then the data will be sent to the cloud or server. The results of the test give the results of the system being successfully implemented and IoT devices can be connected to the gateway with a maximum distance of 10 meters. Then when the system stores, for every minute there is an increase in data of 4 kb. Then there is no data redundancy in the system.

How detection ranges and usage stops impact digital contact tracing effectiveness for COVID-19

To combat the COVID-19 pandemic, many countries around the globe have adopted digital contact tracing apps. Various technologies exist to trace contacts that are potentially prone to different types of tracing errors. Here, we study the impact of different proximity detection ranges on the effectiveness and efficiency of digital contact tracing apps. Furthermore, we study a usage stop effect induced by a false positive quarantine. Our results reveal that policy makers should adjust digital contact tracing apps to the behavioral characteristics of a society. Based on this, the proximity detection range should at least cover the range of a disease spread, and be much wider in certain cases. The widely used Bluetooth Low Energy protocol may not necessarily be the most effective technology for contact tracing.

Monitoring of Cow Location in a Barn by an Open-Source, Low-Cost, Low-Energy Bluetooth Tag System

Indoor localization of dairy cows is important for cow behavior recognition and effective farm management. In this paper, we propose a low-cost system for low-accuracy cow localization based on the reception of signals sent by an acceleration measurement system using the Bluetooth Low Energy protocol. The system consists of low-cost tags and receiving stations. The tag specifications and the localization accuracy of the system were studied experimentally. The received signal strength propagation model and dependence on the tag orientation was studied in an open-space and a barn environment. Two experiments for the evaluation of localization accuracy were conducted with 35 and 19 cows for two days. The localization reference was achieved from feeding stations, a milking robot and videos of cows decoded manually. The localization accuracy (mean ± standard deviation) was 3.27 ± 2.11 m for the entire barn (10 × 40 m2) and 1.9 ± 0.67 m for a smaller area (4 × 5 m2). The system can be used for recognizing long-distance walking, crowded areas in the barn, e.g., queues to milking robots, and cow’s preferable locations. The estimated system cost was 500 + 20 × (cow number) € for one barn. The system has open-access software and detailed instructions for its installation and usage.

Energy Modeling for the Bluetooth Low Energy Protocol

Bluetooth Low Energy (BLE) is a wireless protocol optimized for low-power communication. To design energy-efficient devices, the protocol provides a number of parameters that need to be optimized within an energy, latency, and throughput design space. Therefore, an energy model that can predict the energy consumption of a BLE-based wireless device for different parameter value settings is needed. As BLE differs from the well-known Bluetooth Basic Rate (BR) significantly, models for Bluetooth BR cannot be easily applied to the BLE protocol. In past years, there have been a couple of proposals on energy models for BLE. However, none of them can model all the operating modes of the protocol. This article presents an energy model of the BLE protocol, which allows the computation of a device’s power consumption in all possible operating modes. To the best of our knowledge, our proposed model is not only one of the most accurate ones known so far (because it accounts for all protocol parameters), but it is also the only one that models all the operating modes of BLE. Based on this model, guidelines for system designers are presented that help choose the right parameters for optimizing the energy consumption. The model is publicly available as a software library for download.

A Comparison Analysis of BLE-Based Algorithms for Localization in Industrial Environments

Proximity beacons are small, low-power devices capable of transmitting information at a limited distance via Bluetooth low energy protocol. These beacons are typically used to broadcast small amounts of location-dependent data (e.g., advertisements) or to detect nearby objects. However, researchers have shown that beacons can also be used for indoor localization converting the received signal strength indication (RSSI) to distance information. In this work, we study the effectiveness of proximity beacons for accurately locating objects within a manufacturing plant by performing extensive experiments in a real industrial environment. To this purpose, we compare localization algorithms based either on trilateration or environment fingerprinting combined with a machine-learning based regressor (k-nearest neighbors, support-vector machines, or multi-layer perceptron). Each algorithm is analyzed in two different types of industrial environments. For each environment, various configurations are explored, where a configuration is characterized by the number of beacons per square meter and the density of fingerprint points. In addition, the fingerprinting approach is based on a preliminary site characterization; it may lead to location errors in the presence of environment variations (e.g., movements of large objects). For this reason, the robustness of fingerprinting algorithms against such variations is also assessed. Our results show that fingerprint solutions outperform trilateration, showing also a good resilience to environmental variations. Given the similar error obtained by all three fingerprint approaches, we conclude that k-NN is the preferable algorithm due to its simple deployment and low number of hyper-parameters.

Extending Bluetooth LE Protocol for Mutual Discovery in Massive and Dynamic Encounters

Bluetooth Low Energy (BLE) is probably the best technological tool that we can harness today for studies on close human interactions. It has a wide deployment base (i.e., on smartphones), has peer discovery as an inherent protocol feature, does not require infrastructure (e.g., satellites or base stations) to operate, and sparingly uses energy that is good for extended monitoring. In this paper, we show that we can use the BLE peer discovery capability on smartphones to detect and monitor massive and dynamic encounters, which would provide valuable insights into many epidemiological or sociological phenomena. However, being designed for more leisurely interactions, BLE needs some stretching in order to be used in large-scale operations. Specifically, the protocol design is not optimal to rapidly discover hundreds of devices in the communication range, whereas dense crowds and mass gatherings are not unrealistic in city life. Moreover, if the crowd is dynamic, discovery becomes even more time-pressed because encounters should be recorded before churn. In this paper, we push the BLE technology with the requirements to discover hundreds of devices before co-presence expires, and to work continually over a typical smartphone charge cycle. Specifically, we investigate how we should modify the BLE protocol and how we should set its protocol parameters for this purpose. We show that with the proposed changes and configurations, we can accelerate the speed of discovery for massive and dynamic crowds by more than an order of magnitude compared to the case that we naively follow the guidance of the current BLE standard.