BLE Signals Research Articles

BLE Signal Reception and Localization Performance with Varying Receiver and Beacon Setups

This paper examines the performance of Bluetooth Low Energy signal reception for indoor localization by analyzing the interactions between gateways, beacons, and receiver placements. The study investigates the effect of different BLE beacon placements on signal strength and localization accuracy. It evaluates ten receiver ceiling-mounted and wall-mounted configurations, as well as five beacon body positions: shoulder, front pocket, back pocket, and wrist. A dataset comprising 2700 data points was collected and localization accuracy was assessed using a Radial Basis Function-based methodology. The results demonstrate that ceiling-mounted gateways offer more stable signal strength and enhanced localization accuracy compared to wall-mounted gateways. The findings highlight the significance of optimizing both gateway positioning and body placement to improve the performance of BLE-based indoor positioning systems.

Deep Learning-Based Emergency Rescue Positioning Technology Using Matching-Map Images

Smartphone-based location estimation technology is becoming increasingly important across various fields. Accurate location estimation plays a critical role in life-saving efforts during emergency rescue situations, where rapid response is essential. Traditional methods such as GPS often face limitations in indoors or in densely built environments, where signals may be obstructed or reflected, leading to inaccuracies. Similarly, fingerprinting-based methods rely heavily on existing infrastructure and exhibit signal variability, making them less reliable in dynamic, real-world conditions. In this study, we analyzed the strengths and weaknesses of different types of wireless signal data and proposed a new deep learning-based method for location estimation that comprehensively integrates these data sources. The core of our research is the introduction of a ‘matching-map image’ conversion technique that efficiently integrates LTE, WiFi, and BLE signals. These generated matching-map images were applied to a deep learning model, enabling highly accurate and stable location estimates even in challenging emergency rescue situations. In real-world experiments, our method, utilizing multi-source data, achieved a positioning success rate of 85.27%, which meets the US FCC’s E911 standards for location accuracy and reliability across various conditions and environments. This makes the proposed approach particularly well-suited for emergency applications, where both accuracy and speed are critical.

Indoor 3D Localization Scheme Based on BLE Signal Fingerprinting and 1D Convolutional Neural Network

Indoor localization schemes have significant potential for use in location-based services in areas such as smart factories, mixed reality, and indoor navigation. In particular, received signal strength (RSS)-based fingerprinting is used widely, given its simplicity and low hardware requirements. However, most studies tend to focus on estimating the 2D position of the target. Moreover, it is known that the fingerprinting scheme is computationally costly, and its positioning accuracy is readily affected by random fluctuations in the RSS values caused by fading and the multipath effect. We propose an indoor 3D localization scheme based on both fingerprinting and a 1D convolutional neural network (CNN). Instead of using the conventional fingerprint matching method, we transform the 3D positioning problem into a classification problem and use the 1D CNN model with the RSS time-series data from Bluetooth low-energy beacons for classification. By using the 1D CNN with the time-series data from multiple beacons, the inherent drawback of RSS-based fingerprinting, namely, its susceptibility to noise and randomness, is overcome, resulting in enhanced positioning accuracy. To evaluate the proposed scheme, we developed a 3D positioning system and performed comprehensive tests, whose results confirmed that the scheme significantly outperforms the conventional common spatial pattern classification algorithm.

Optimization BLE Power Beacon for Indoor Locations Static Smart Device with Gaussian Filter

BLE beacon in an indoor location, battery efficiency usage must be right. Power beacons as one of the keys that need to be optimized. The decrease in power beacons will decrease the estimated distance from an indoor location based on the RSSI value. Therefore, an additional method is needed to recover the estimated distance's accuracy value due to the reduced power. In this paper, the method for recovery accuracy is by using a gaussian filter. Measurements were made at the same position on 3 BLE signals from multipower transmitters, which differed in their transmit power (TX power -1 dBm, -9 dBm and -20 dBm). The first six points are selected with the position of the line of sight as environment 1, and the second six points are chosen with the position of non-line of sight as environment 2 (obstructed). The first point of each environment is used as a reference. In environment 1, transmit power reduces the 24 dB effect to a decrease in accuracy distance estimation. The Gaussian effectiveness filter for improvement accuracy at all measurement points or 100%. In environment 2, reduce power transmit 12 dB is not followed by a decrease in accuracy distance estimation. The effectiveness of the Gaussian filter for improving accuracy is 60% of the number of measurement points. Finally, the Gaussian filter in the power optimization can provide recovery accuracy distance estimation is 80% from measurement sample for environment 1 and environment 2.

Reliable and Practical Bluetooth Backscatter With Commodity Devices

Recently backscatter communication with commodity radios has received significant attention since specialized hardware is no longer needed. The state-of-the-art BLE backscatter system, FreeRider, realizes ultra-low-power BLE backscatter communication entirely using commodity devices. It, however, suffers from several key reliability issues, including unreliable two-step modulation, productive-data dependency, and lack of interference countermeasures. To address these problems, we propose RBLE, a robust BLE backscatter system that works with an excitation BLE device and a single BLE receiver. First, it uses BLE signals with partial single tones as excitations, making single-bit modulation much more robust. Then it designs dynamic channel configuration that enables channel hopping to avoid interfered channels. Moreover, it presents BLE packet regeneration that uses adaptive encoding to further enhance reliability for various channel conditions. The prototype is implemented using TI BLE radios, iPhones, Android phones, and customized tags with FPGAs. Empirical results demonstrate that RBLE achieves more than 17x uplink goodput gains over FreeRider under indoor LoS, NLoS, and outdoor environments. We also show that RBLE can realize uplink ranges of up to 25 m for indoors and 56 m for outdoors.

Automatic radio map creation in a fingerprinting‐based BLE/UWB localisation system

One of the most popular localisation algorithms used in radio localisation systems is fingerprinting. The method has significant advantages: its implementation is easy and since it does not require any information on system infrastructure placement, it can be implemented in already deployed networks. However, it has a significant drawback, which is a high effort needed to create the radio map used for localisation. This study describes a low-effort method for automatic radio map creation intended for use in hybrid Bluetooth low energy/ultra-wideband (BLE/UWB) positioning systems. The proposed method adopts fingerprint crowdsourcing and radio map interpolation approaches. It consists in collecting signatures comprising user location calculated using an UWB subsystem and measured BLE signal levels. The recorded data is used to identify parameters of a log-distance path loss model and a Gaussian process regressor and interpolate a complete radio map. The presented concept was experimentally tested. The results have shown that the method allows for more accurate localisation than conventional received signal strength ranging based solution.

Study of Activity Tracking through Bluetooth Low Energy-Based Network

This paper proposes a proof-of-concept, low-cost, and easily deployable Bluetooth low energy- (BLE-) based localization system which actively scans and localizes BLE beacons attached to mobile subjects in a room. Using the received signal strength (RSS) of a BLE signal and the uniqueness of BLE hardware addresses, mobile subjects can be identified and localized within the hospital room. The RSS measurement of the BLE signal from a wearable BLE beacon varies with distance to the wall-anchored BLE scanner. In order to understand and demonstrate the practicality of the relationship between RSS of a BLE beacon and the distance of a beacon from a scanner, the first part of the paper presents the analysis of the experiments conducted in a low-noise and nonreflective environment. Based on the analysis conducted in an ideal environment, the second half of the paper proposes a data-driven localization process for pinpointing the movements of the subject within the experimental room. In order to ensure higher accuracy like fingerprinting techniques and handle the increased number of BLE-anchored scanners like geometric techniques, the proposed algorithm was designed to combine the best aspects of these two techniques for better localization. The paper evaluates the effects of the number of BLE wall-mounted scanners and the number of packets on the performance of the proposed algorithm. The proposed algorithm locates the patient within the room with error less than 1.8 m. It also performs better than other classical approaches used in localization.

Beacon-Based Time-Spatial Recognition toward Automatic Daily Care Reporting for Nursing Homes

As the world’s population of senior citizens continues to grow, the burden on the professionals who care for them (carers) is also increasing. In nursing homes, carers often write daily reports to improve the resident’s quality of life. However, since each carer needs to simultaneously care for multiple residents, they have difficulty thoroughly recording the activities of residents. In this paper, we address this problem by proposing an automatic daily report generation system that monitors the activities of nursing home residents. The proposed system estimates the multiple locations (areas) at which residents are situated with a BLE beacon, using a variety of methods to analyze the RSSI values of BLE signals, and recognizes the activity of each resident from the estimated area information. The information of the estimated activity of residents is stored in a server with timestamps, and the server automatically generates daily reports based on them. To show the effectiveness of the proposed system, we conducted an experiment for five days with four participants in cooperation with an actual nursing home. We determined the proposed system’s effectiveness with the following four evaluations: (1) comparison of performance of different machine-learning algorithms, (2) comparison of smoothing methods, (3) comparison of time windows, and (4) evaluation of generated daily reports. Our evaluations show the most effective combination pattern among 156 patterns to accurately generate daily reports. We conclude that the proposed system has high effectiveness, high usability, and high flexibility.

Semisupervised Deep Reinforcement Learning in Support of IoT and Smart City Services

Smart services are an important element of the smart cities and the Internet of Things (IoT) ecosystems where the intelligence behind the services is obtained and improved through the sensory data. Providing a large amount of training data is not always feasible; therefore, we need to consider alternative ways that incorporate unlabeled data as well. In recent years, Deep reinforcement learning (DRL) has gained great success in several application domains. It is an applicable method for IoT and smart city scenarios where auto-generated data can be partially labeled by users' feedback for training purposes. In this paper, we propose a semi-supervised deep reinforcement learning model that fits smart city applications as it consumes both labeled and unlabeled data to improve the performance and accuracy of the learning agent. The model utilizes Variational Autoencoders (VAE) as the inference engine for generalizing optimal policies. To the best of our knowledge, the proposed model is the first investigation that extends deep reinforcement learning to the semi-supervised paradigm. As a case study of smart city applications, we focus on smart buildings and apply the proposed model to the problem of indoor localization based on BLE signal strength. Indoor localization is the main component of smart city services since people spend significant time in indoor environments. Our model learns the best action policies that lead to a close estimation of the target locations with an improvement of 23% in terms of distance to the target and at least 67% more received rewards compared to the supervised DRL model.

Análisis fish en Pisum sativum y P. fulvum con secuencia telomérica de arabidopsis y ribosómica de soya.

La secuencia 18/25S rDNA de la soja y la TTTAGGG de A.thaliana fueron utilizadas como sondas con el objetivo deanalizar la organización de los genomas de Pisum sativum, P.fulvum y las líneas F4 (P. sativum X P. fulvum) con la técnicade la hibridación in situ fluorescente (FISH). El experimentose realizó en la Universidad de Córdoba, España, en el veranodel 2006. La sonda 18/25S produjo señales de intensidadvaria ble en las tres líneas. Estas señales corresponden con lospares de organizadores nucleolares (NOR ) localizados en loscromosomas 4 y 7 de P. sativum y 4, 7 y 5 de P. fulvum. Laintensidad de las señales varió de muy fuerte a media namentefuerte en P. sativum, a pequeñas y discretas en P. fulvum, loque sugiere que existen diferencias en el número de veces quela secuencia se repi te en los genomas de estas dos especie s.Las señales que se observaron en la línea F4 con la sonda derDNA se asemejaron en tamaño e intensidad a las que se observaronen P. sativum y P. fulvum. Las muestras de las líneasF4 evaluadas presentaron dos NOR , si bie n en una de ellas seobservó una señal muy baja en un tercer par de cromosomas.La secuencia TTTAGGG hibridó en los telómeros de las treslíneas estudia das, lo que mostró que la secuencia teloméricade A. thaliana está también presente en el género Pisum.

Continuously Variable Signal Delays with Ultrasound

A device to introduce continuously variable delays in elec- tric signals with frequencies in the megahertz range is proposed. The basis of the device is the generation of replicas of ultrasonic wave pack- ets by conduction electrons in metals. The construction, advantages, and limitations of the device are discussed. S I. INTRODUCTION URFACE acoustic wave devices are frequently used to delay signals by a fixed amount. We propose an acoustic wave device that produces a continuously varia- ble signal delay. The device uses the generation of repli- cas of ultrasonic wave packets in pure metal single crys- tals at low temperatures in applied magnetic fields. The generation of replica wave packets was first observed in ultrapure gallium (l) and later correctly attributed to the interaction of conduction electrons with ultrasound (2). The proposed device converts an electric pulse into a sound wave packet. An external magnetic field is used to produce a replica of the wave packet that has the same shape as the original or packet, but a smaller amplitude. The replica precedes the primary packet by an amount that depends inversely on the strength of the mag- netic field. Thus the delay produced by the device (the time between the conversion of an electric signal into an ultrasonic wave packet and the subsequent conversion of the replica into an electric signal) is also field-dependent. One varies the delay by varying the strength of the applied field. Although both sound packets are converted back into electric pulses, the pulse that corresponds to the primary wave packet is diverted by a SPDT switch.