Probabilistic Grid System for Indoor Mobile Localization Using Multi-Power Bluetooth Beacon Emulator

In this paper, we propose an indoor positioning system based on Bluetooth low energy (BLE), and the localization tag is with characteristics of low power, low cost, and high portability. The proposed system consists of BLE tags, BLE/WiFi repeaters, and a fusion server. The BLE tag is a device which broadcasts BLE beacons. The BLE/WiFi repeaters collect the beacons transmitted from the tag and extract the received signal strength (RSS). The RSS values are then transmitted to the fusion server through WiFi, and the server will estimate the position of the BLE tag with RSS-based positioning algorithm. We propose an indoor positioning algorithm which is a hybrid from received signal strength indication (RSSI)-fingerprint and cell of origin (CoO) to estimate the BLE tag's location. Some modifications are made to typical RSSI-fingerprint and CoO algorithm to get better accuracy. The test results show 1.2 m to 1.4 m positioning accuracy. The size of the BLE tag is 1.7 cm in radius and 0.5 cm thick. Each BLE tag costs 3 US dollars. The current consumption of the tag is 50 μA which can be used without charge for 136 days with a CR2025 battery.

This article presents a preliminary study in order to explore the possibilities for robot localization using measured received signal strength indicator (RSSI) from Bluetooth low energy (BLE) beacons. BLE is a new brand technology focused on information transmission using very low energy consumption. It is being included in mobile devices from year 2011, nowadays almost every new mobile phone is shipped with this technology. Robot localization using particles filter has been developed in recent years using wireless technologies with a significant success. BLE beacons measures are rather noisier than measures from similar wireless devices. In this work we make an initial model of BLE measures and their noise. The model is used to generate data to be processed by a particle filter designed for localization using only ultra-wide band (UWB) beacons ranges. Data are generated with different noise level in order to explore localization errors behavior, these levels cover real noise levels founded in RSSI measure characterization.

This paper describes how the location of a test object or person can be determined at room resolution using Bluetooth Low Energy (BLE). Therefore, the test object or person is equipped with a BLE node and approximately central in each room a low-cost and low-power BLE sensor is placed. No stringent installation requirements or battery replacement is needed for these sensors. They send advertising packets to the mobile node, where the received data is processed quickly, based on the signal strength of the received messages, characterized by the Received Signal Strength Indicator (RSSI). No fingerprinting, propagation characteristics or trilateral statistics are needed to determine in which room the test object or person is located by use of the measured RSSI values. In this paper, the localization is done by a BLE development kit of Bluegiga. Measuring in a real-life environment, we can locate at room resolution a device during more than 90% of the time, without the need for a difficult installation procedure or battery replacements on a regular basis.

Distance estimation and proximity classification techniques are essential for numerous IoT applications and in providing efficient services in smart cities. Bluetooth Low Energy (BLE) is designed for IoT devices, and its received signal strength indicator (RSSI) has been used in distance and proximity estimation, though they are noisy and unreliable. In this study, we leverage the BLE TX power level in BLE models.We adopt a comparative analysis framework that utilizes our extensive data library of measurements. It considers commonly used state-of-the-art model, in addition to our data-driven proposed approach. The RSSI and TX power are integrated into several parametric models such as log shadowing and Android Beacon library models, and machine learning models such as linear regression, decision trees, random forests and neural networks. Specific mobile apps are developed for the study experiment. We have collected more than 1.8 millions of BLE records between encounters with various distances that range from 0.5 to 22 meters in an indoor environment. Interestingly, considering TX power when estimating the distance reduced the mean errors by up to 46% in parametric models and by up to 35% in machine learning models. Also, the proximity classification accuracy increased by up to 103% and 70% in parametric and machine learning models, respectively. This work is one of the first studies (if not the first) that analyze in depth the TX power variations in improving the distance estimation and classification.

Since GPS (Global Positioning System) cannot meet accuracy requirements indoors, indoor Location-Based Services (LBSs) have become increasingly important. BLE (Bluetooth Low Energy) offers cost and accuracy advantages. Typically, the position fingerprinting method is used for indoor positioning. However, due to irregular reflection and absorption, the indoor environment introduces various offsets in Bluetooth RSSI (Received Signal Strength Indicator). This study analyzed the RSSI space and proposed a pre-processing workflow to improve position estimation accuracy by correcting offsets in RSSI space for BLE fingerprinting methods using machine learning. Experiments performed using different position estimation methods showed that the corrected data achieved a 6% improvement over the filter-only result. This study also evaluated the effects of different pre-processing and post-processing filters on positioning accuracy. Experiments were also conducted using a published dataset and showed similar results.

COVID-19 has had a devastating impact worldwide, including in care homes where there have been substantial numbers of cases among a very vulnerable population. A key mechanism for managing exposure to the virus and targeting interventions is contact tracing. Unfortunately, environments such as care homes that were most catastrophically impacted by COVID-19 are also those least amenable to traditional contact tracing. A promising alternative to recall and smartphone-based contact tracing approaches is the use of discrete wearable devices that exploit Bluetooth Low Energy (BLE) and Long-Range Wide Area Network (LoRaWAN) technologies. However, the real-world performance of these devices in the context of contact tracing is uncertain. A series of experiments were conducted to evaluate the performance of a wearables system that is based on BLE and LoRaWAN technologies. In each experiment, the number of successful contacts was recorded and the physical distance between two contacts was compared to a calculated distance using the Received Signal Strength Indication (RSSI) to determine the precision, error rate, and duration of proximity. The overall average system contact detection success rate was measured as 75.5%; when wearables were used as per the manufacturer’s guidelines the contact detection success rate increased to 81.5%, but when obstructed by everyday objects such as clothing or inside a bag the contact detection success rate was only 64.2%. The calculated distance using RSSI was close to the physical distance in the absence of obstacles. However, in the presence of typical obstacles found in care home settings, the reliability of detection decreased, and the calculated distance usually appeared far from the actual contact point. The results suggest that under real-world conditions there may be a large proportion of contacts that are underestimated or undetected.

Tracking a moving user in indoor environments using Bluetooth low energy beacons.

In this study, the repeated covering and uncovering by the hands of a beacon before one's eyes is performed, and the identification of the corresponding beacon is obtained based on the changes in the received signal strength indicator at the control terminal. Recently, beacons that employ Bluetooth Low Energy (BLE) have spread, and are used for a variety of purposes. However, when one wishes to control several beacons, there are no clear means for readily identifying each beacon. In the proposed method, the changes in the received signal strength indicator of BLE are used to identify the BLE beacon. The output radio signals of BLE are weak, and almost all BLE beacons are small enough to fit in one's hand. If the beacon is covered with both hands, the signals of BLE are attenuated. In this study, the algorithms for beacon identification were examined, and control applications were prepared. In addition, it was possible to identify within 13 s the target beacon among several beacons.

Received Signal Strength Indicator (RSSI) is used in indoor positioning for measuring object distance to the base station. However, acquiring accurate RSSI values is challenging because wireless interference factors, such as multipath decline interference, make RSSI values of the same object fluctuate over time. Therefore, instead of a single RSSI, RSSI acquisition will collect a set of RSSI values from which the most moderate RSSI is derived. For this purpose, we propose an Enhanced Gaussian Mixture Model (EGMM) to derive a more precise RSSI for improving indoor positioning accuracy. EGMM enhances Gaussian Mixture Model (GMM) by applying Akaike information criterion (AIC) to determine the best K value for GMM to divide RSSI values into K sets representing signals from different paths. Then, EGMM identifies the most appropriate set of RSSI values to derive a more precise RSSI and thus improves the accuracy of indoor positioning. Our EGMM solution performs well in an open indoor space. The experiment is conducted with iBeacon devices, and the average error distance of EGMM is about 64% of those generated by existing Gaussian filtering. The average positioning error of EGMM is about 0.48 meter, which is adequate to indoor positioning accuracy.

Fingerprinting approach based on a radio map is one of the well-received methods for indoor localization. However, owing to the radio devices reconfigured or deteriorated, the radio map will be degraded, rendering the positioning outcomes unreliable. We introduce an unsupervised anomaly detection model, termed Radio Map Anomaly Detection for Fingerprint Indoor Positioning with GAN (RAD-GAN), which trains only the normal RSSI (Received Signal Strength Indication) samples to learn the normality distribution of the radio signal in the spatial indoor domain. We exploit an encoder-decoder-encoder neural network for latent feature extraction. Afterward, an adversarial training scheme is employed to minimize the reconstruction error both within RSSI space and latent feature space. The higher reconstruction deviation of the RSSI vector is indicative of an anomaly from a normal distribution. We evaluate RAD-GAN in a BLE (Bluetooth Low Energy) signal environment with the power gain actively modified and the public UJI long-term Wi-Fi fingerprinting dataset. Experimental results show that RAD-GAN is more sensitive and robust than the former state-of-the-art models.

This paper presents the design and evaluation of a Bluetooth Low Energy (BLE)-based beamforming measurement system for enhanced terminal differentiation in indoor environments. The conventional sum-beam method using array antennas often fails to provide sufficient Received Signal Strength Indicator (RSSI) contrast between adjacent Smart Blocks, limiting the precision of proximity sensing. To overcome this, we propose a novel beamforming technique that employs microcontroller-driven RF switching to compare the sum and delta beams. This hardware-implemented approach ensures that the received power at the terminal is determined exclusively by the antenna radiation pattern, thereby improving spatial selectivity. Experimental measurements conducted on a Gate-Free fare system testbed demonstrate that the proposed method achieves up to 9.3 dB improvement in RSSI separation compared to conventional configurations. The system’s measurement repeatability and robustness were validated through repeated trials under varying distances and terminal heights. The results confirm the feasibility of deploying the proposed system in BLE-based real-time localization and sensing applications requiring high differentiation accuracy.

With the increasing demand for location-based services, indoor localization technology based on wireless sensor networks (WSNs) has recently received significant attention. Bluetooth low energy (BLE) has become a research hotspot among the numerous WSNs due to its ubiquitous deployment, low-power consumption, and low cost. Many previous BLE-based indoor localization technologies can only use received signal strength indication (RSSI) for localization in the 2-D plane and achieve coarse accuracy. However, high-accuracy location information is required in an increasing number of applications, e.g., elderly fall detection for medical applications, which urgently requires a new BLE-based localization technology in 3-D space. To this end, we propose Spotlight, an angle-based fine-grained indoor localization system in 3-D space using BLE sensors. The critical idea of the Spotlight is to measure the angle-of-arrival (AoA) and elevation-of-angle (EoA) of the signal from the target to the BLE receivers and utilize them to achieve accurate 3-D localization. Specifically, we develop an offset elimination algorithm and data stitching algorithm, utilizing the second-order cone (SoC) programming to obtain accurate angle estimates. Then, the simulated annealing algorithm (SAA) is used to search for the target location in 3-D space. Finally, we develop the prototype system with commercial off-the-shelf (COTS) BLE sensors and conduct extensive experiment measurements in real-world scenarios. The results demonstrate that the proposed system can achieve a median localization error of 36 cm in the 2-D plane and 48 cm in the 3-D space.

As WiFi technology becomes more widespread and integrated, precise location tracking within complex indoor environments is gaining significance in contemporary public spaces.Nevertheless, challenges persist in indoor scenarios, including issues such as noise and multi-path effects stemming from the degradation of the Received Signal Strength Indicator (RSSI). Additionally, obstacles causing signal shading contribute to a decrease in the accuracy of indoor location tracking. Within this paper, we introduce an innovative indoor localization algorithm termed Vision Transformer Indoor Localization (VTIL). This algorithmleverages RSSI and Vision-Transformer (ViT) technologies to enhance indoor positioning accuracy. Initially, the RSSI fingerprints undergo normalization by scaling them to the maximum and minimum values. To mitigate the impact of noise and irrelevant features, the Principal Component Analysis (PCA) algorithmis then employed for effective feature extraction.Secondly, the RSSI fingerprint library is converted into an RSSI gray image library. Then the RSSI gray image is divided into several small blocks, and the position-coding input is performed in the form of a sequence. The ViT model divides the weight ratio of each block in the RSSI image to alleviate the impact of multi-path effects. According to the experimental results using public datasets, our approach achieves a noteworthy 37.26% reduction in the average distance estimation error when compared to existing indoor fingerprint localization algorithms.

D-Log: A WiFi Log-based differential scheme for enhanced indoor localization with single RSSI source and infrequent sampling rate

In daily life, mobile and wearable devices with high computing power, together with anchors deployed in indoor en-vironments, form a common solution for the increasing demands for indoor location-based services. Within the technologies and methods currently in use for indoor localization, the approaches that rely on Bluetooth Low Energy (BLE) anchors, Received Signal Strength (RSS), and lateration are among the most popular, mainly because of their cheap and easy deployment and accessible infrastructure by a variety of devices. Never-theless, such BLE- and RSS-based indoor positioning systems are prone to inaccuracies, mostly due to signal fluctuations, poor quantity of anchors deployed in the environment, and/or inappropriate anchor distributions, as well as mobile device hardware variability. In this paper, we address these issues by using a collaborative indoor positioning approach, which exploits neighboring devices as additional anchors in an extended positioning network. The collaborating devices' information (i.e., estimated positions and BLE- RSS) is processed using a multilayer perceptron (MLP) neural network by taking into account the device specificity in order to estimate the relative distances. After this, the lateration is applied to collaboratively estimate the device position. Finally, the stand-alone and collaborative position estimates are combined, providing the final position estimate for each device. The experimental results demonstrate that the proposed collaborative approach outperforms the stand-alone lateration method in terms of positioning accuracy.