An Ensemble Filter for Indoor Positioning in a Retail Store 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.

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

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.

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.

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)-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.

As Bluetooth Low Energy (BLE) is a low-cost wireless technology, it has emerged to dominate a beacon technology market. BLE advertising is a critical part of the protocol that can broadcast messages to nearby scanners. Based on BLE 5.0 specification promising new functionality for connectionless services, the wide-ranging enhancements implemented in BLE advertising mechanisms open unlimited possibilities for more advanced beacons. For the advertising packets, beacons can increase the payload size or use chained packets so that more content can be provided in an advertising event. However, the channel selection algorithms in BLE do not support the transmission of diverse extended advertisements. The packets of different advertising events that need to be transmitted on specific channels should be considered for better transmission efficiency. In this paper, we give a clear classification of advertising services for BLE beacons, propose simple channel selectivity schemes to improve the service ratio, and show an analytical model to study the blocking probability of the BLE advertising network. In addition, we use this analytical model to identify the blocking performance of the network. Simulation results validated by the analytical model show that the proposed schemes improve the transmission efficiency of advertising services in BLE beacons.

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.

An interactive digital signage system which allow users to fetch detailed information using their smartphone is widely deployed. Bluetooth Low Energy(BLE) broadcast beacon is a one of possible method to broadcast a detailed information to users. However, BLE beacon doesn't provide a way to assay an authenticity of beacon since BLE broadcast is insecure untrusted communication. Out-of-band authentication is used to improve trustworthiness for such untrusted pathway. A new authentication protocol is proposed to assay the authenticity of digital signage when the information is broadcasted via BLE beacon. The authors implement the protocol and evaluate its effectiveness.

Bluetooth Low Energy (BLE)-based beacons are expected to be used to provide various short range services in near future. We consider a system where mobile devices send periodic BLE beacons (consisting of the devices' identity) and the infrastructure nodes are required to track these devices to gather information about the users behavior in order to improve customized services provided to the user. For such systems, we propose the design and present a prototype (on CSR 1010 kits) where location detection and tracking are done via vision-based approach assisted by the RF observations. Features, extensions and optimizations of this system are discussed in detail. Emphasis is provided on in-network processing to disambiguate the multiple objects detected using image processing and assign the right identities as received via BLE beacons to the right objects. Further, the approach provides reduction in the beacon generation rate from the user devices, thereby improving the battery lifetime of the end devices.

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.

One of the problems associated with the implementation of indoor location detection systems is the time-consuming procedure of equipment adjustment, which includes indoor map construction, radio signal map creation and calibrating signal propagation model. Thus, the equipment adjustment is a time-consuming and expensive process to be perform every time when there are changes in equipment configuration and allocation. The developed indoor localization system provides navigation of the user inside a room and allows to building radio map and putting Bluetooth Low Energy (BLE) beacons on the map of a room by the efforts of a number of users walking indoors. The architecture of the system is developed so that the different indoor localization techniques can be used and different services can be requested by the user’s mobile application. The user’s navigation inside the room is a combination of PDR based on the built-in smartphone sensors, multilateration and fingerprinting. The indoor navigation ontology is implemented to make decision which of these methods should be used. The key feature of the system is determining the location of BLE beacon. For this purpose the Random Forest algorithm is used, which uses signal levels, user rotation angles and distance to Bluetooth beacon as a training dataset. The geometric parameters of a room are estimated by the radio map and Bluetooth beacon locations.

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.