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

The ability to precisely locate and navigate a partially impaired or a blind person within a building is increasingly important for a wide variety of public safety and localization services. In this paper, we explore indoor localization algorithms using Bluetooth Low Energy (BLE) beacons. We propose using the BLE beacon’s received signal strength indication (RSSI) and the geometric distance from the current beacon to the fingerprint point in the framework of fuzzy logic for calculating the Euclidean distance for the subsequent determination of location. According to our results, the fingerprinting algorithm with fuzzy logic type-2 (hesitant fuzzy sets) is fit for use as an indoor localization method with BLE beacons. The average error of localization is only 0.43 m, and the algorithm obtains a navigation precision of 98.2 ± 1%. This precision confirms that the algorithms provide great aid to a visually impaired person in unknown spaces, especially those designed without physical tactile guides, as confirmed by low Fréchet and Hausdorff distance values and high navigation efficiency index (NEI) scores.

Nowadays, indoor positioning services of mobile phone with Bluetooth low energy (BLE) beacons are increasingly used. However, due to the indoor environment has dense multipath fading, the positioning system has low accuracy. In this paper, the accuracy study of trilateration method for indoor positioning with BLE beacons is presented. The indoor environment room with 9.0 m width, 10.2 m length and 3 BLE beacons is studied. The received signal strength (RSS) of each BLE beacon is measured by using mobile phone. The distance between each BLE beacon and mobile phone is evaluated using channel model. After that, the trilateration method is used to approximate the position of mobile phone. The accuracies, which are obtained from one, average five and average ten measurements, are studied. The distance errors of all positions and the cumulative distribution function (CDF) of the distance error are calculated and shown. The results show that the average multiple measurements tend to decrease the distance error.

Mobile and pervasive computing enabled a new realm of possibilities into the indoor positioning domain. Although many candidate technologies have been proposed, no one can still adapt to every use case. A case centered design and the implementation of the solution within the specific domain is the current research trend. With the rise of Bluetooth Low Energy (BLE) Beacons, i.e., platforms used to interact digitally with the real world, more standard positioning solutions are emerging in different contexts. However the reachable positioning accuracy with this technology is still unacceptable for some real applications (e.g., in the healthcare sector or the emergency management). In this paper, an hybrid localization application coupling a real time model based Pedestrian Dead Reckoning (PDR) technique and the analysis of the Received Signal Strength Indicator (RSSI) of BLE beacons is proposed. In particular, the smartphone application is composed by three main real time threads: a model based step length estimation, heading determination and the fusion of beacon information to reset the position and the drift error of the PDR. In order to give soundness to our approach we firstly validated the step length smartphone app with a stereo-photogrammetric system. The whole proposed solution was then tested on fifteen healthy subjects.

We present a smartphone-based application, called LocBLE, for enabling users to estimate the location of nearby Bluetooth low energy (BLE) beacons. In contrast to existing BLE beacon-based proximity applications that can only show coarse-grained (immediate, near, and far) distance estimation, LocBLE's fine-grained estimation can enhance human-environment interactions. LocBLE has three salient features in estimating location from BLE beacon signals. First, it is adaptive to dynamic signal propagation environments by learning the environmental changes directly from the received signal strength (RSS). Second, it performs sensor-fusion for location estimation by utilizing motion sensor data and RSS readings from a smartphone. Finally, LocBLE improves location tracking accuracy with novel on-line calibration on a set of beacons nearby. We have built a prototype of LocBLE on smartphones and evaluated it on commodity proximity-enabled beacons. Our experimental results demonstrate that LocBLE achieves an average of 1.8m and 1.2m accuracies in locating indoor and outdoor BLE beacons, respectively.

EyeRemember is a mobile app designed to run on wearable electronic devices and serve as an external memory aid for people with brain injury (BI). EyeRemember allows users to voice-record information about family, friends and caregivers (referred to as ‘contacts’ in the app). The technology detects the presence of Bluetooth low energy (BLE) beacons carried by the user’s contacts and reminds users of the recorded information when contacts are present. EyeRemember was first developed for Google Glass and later for Android Wear smartwatches. Both versions were developed in accordance with user-centered design principles, by including end-users and identifying their needs, wants and limitations throughout the design process. This paper describes the features and functionality of EyeRemember, its interaction with BLE beacons and the implementation of the user-centered design process used to collect ongoing feedback from people with brain injury, their caregivers and clinicians at Shepherd Center, a rehabilitation hospital in Atlanta, Georgia, USA. The research design and results of clinical testing are also described. Results indicate EyeRemember is a clinically effective tool for memory compensation for some people with BI and suggests combining wearable computers with BLE beacons may be effective assistive technology. These results support previous research summarized in systematic guidelines recommending the use of external memory aids for memory compensation after BI and indicate it is likely these guidelines are applicable to novel and emerging technologies. The results also further validate previous research demonstrating systematic instruction as an effective method for training the use of technology for people with memory problems.

Bluetooth Low Energy (BLE) beacons are useful to estimate a user’s location in indoor situations. Because our university has numerous BLE beacons, they are easy to use for indoor route navigation. For this study, we propose a position estimation method using BLE beacons. Using this method, we eliminate the existing difficulty of error accumulation of earlier systems and improve indoor location estimation accuracy. We designed an indoor position estimation method and conducted an estimation accuracy evaluation experiment comparing the proposed method and existing methods. The experiment results underscore the effectiveness of the proposed method.

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.

Bluetooth low energy (BLE)-based indoor localization has been extensively researched due to its cost-effectiveness, low power consumption, and ubiquity. Despite these advantages, the variability of received signal strength indicator (RSSI) measurements, influenced by physical obstacles, human presence, and electronic interference, poses a significant challenge to accurate localization. In this work, we present an optimised method to enhance indoor localization accuracy by utilising multiple BLE beacons in a radio frequency (RF)-dense modern building environment. Through a proof-of-concept study, we demonstrate that using three BLE beacons reduces localization error from a worst-case distance of 9.09–2.94 m, whereas additional beacons offer minimal incremental benefit in such settings. Furthermore, our framework for BLE-based localization, implemented on an edge network of Raspberry Pies, has been released under an open-source license, enabling broader application and further research.

Indoor positioning opens up opportunities for a wide range of applications, including active marketing, accessibility and security. Although GPS (Global Positioning System) is widely used for outdoor location, it is inaccurate and in some cases unavailable indoors. One of the solutions is to use Bluetooth Beacons to determine the distance between the device and the beacon indoors using the Received Signal Strength Indicator (RSSI). The location of the object in the environment can be determined using at least three beacons and methods such as trilateration. This work aims to evaluate the use of artificial neural networks (ANN) to determine the distance and location of the laptop in an indoor environment. A first experiment compares the Log Distance Path Loss (LDPL) model and the ANN to determine the distance between the beacon and a laptop. A second experiment compares which method is best to determine the position of the laptop in a room. The following methods were evaluated: a) trilateration with distance calculation using the LDPL method; b) trilateration with distance calculation using an ANN; and c) position determination using an ANN. The results show that RSSI values can vary due to obstacles and the position of the antenna between the beacon and the laptop.

The Global Positioning System (GPS) uses signals from satellites to specialised devices to detect an entity’s location on Earth’s surface. However, GPS is ineffective for indoor navigation due to high power consumption and signal obstruction by walls. This study proposes a low-cost, energy-efficient alternative using Bluetooth 4.0-enabled Bluetooth Low Energy (BLE) beacons to enable Indoor Positioning and guide users in confined spaces, such as libraries. Tiny, low-energy BLE beacons transmit Advertising Packets at regular intervals. In contrast, Anchor Nodes send and receive signals to construct a Received Signal Strength Indication (RSSI)-based signal-to-distance model, enabling triangulation-based localisation. The system monitors behaviour and environmental interaction through RSSI analysis and user interaction with Beacons. Proven in a library setting, the BLE-based system proved extremely accurate in guiding users to destinations, bridging GPS shortcomings at minimal energy expenditure and cost.

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.

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 shows an implementation of a smart office energy management system that use Bluetooth Low Energy (BLE) beacons, smart plugs, and a mobile app, which provides the location-aware energy-saving service without user intervention. Moreover, this paper provides a reasonable realtime energy-saving estimation method by which more effective feedback for motivation can be conveyed to the occupant of an office. According to the experimental results obtained over three months, the proposed system produced meaningful energy savings for PCs, monitors, and lights.

An attendance management system is developed such that the students present in the class can register their attendance by scanning their student ID cards over not only the teacher's terminal device but also their own personal Android devices. This system solves the problem with the conventional system that it took a long time for all the students to scan their cards by a single terminal device. In order to spoil cheating students who try to resister their attendance illegally from outside the classroom, we employ a BLE (Bluetooth Low Energy) beacon device to transmit a magic number necessary for proper registration within the classroom.

Despite recent advances in technology, providing personalized experiences to customers in physical environments like retail shops, restaurants and cafes remains challenging. This paper proposes a pervasive environment that utilizes Bluetooth Low Energy (BLE) beacons in conjunction with unsupervised machine learning to personalize a customer's visit to a coffee shop. Some key aspects of the proposed solution include personalized content delivery, providing customers with an automatic table reservation based on their preferences, using a barista interface to provide personalized interaction with customers, and allowing customers to monitor real-time coffee shop conditions. An architecture utilizing MQTT and an NSQL database was implemented. Historical traces of customer's physical behaviour acquired using beacons in the coffee shops were used to create clusters of similar customers. The best performing clustering algorithms were K-Medoids and Hierarchical clustering using the Optimal Matching (OM) distance resulting in a Purity of 0.910 and 0.941 respectively.