Environment-Aware Tracking Scheme for Smartphones Based on BLE Beacons

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.

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.

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.

Bluetooth Low Energy (BLE) is one of the key technologies empowering the Internet of Things (IoT) for indoor positioning. In this regard, Angle of Arrival (AoA) localization is one of the most reliable techniques because of its low estimation error. BLE-based AoA localization, however, is in its infancy as only recently direction-finding feature is introduced to the BLE specification. Furthermore, AoA-based approaches are prone to noise, multi-path, and path-loss effects. The paper proposes an efficient Convolutional Neural Network (CNN)-based indoor localization framework to tackle these issues specific to BLE-based settings. We consider indoor environments without presence of Line of Sight (LoS) links affected by Additive White Gaussian Noise (AWGN) with different Signal to Noise Ratios (SNRs) and Rayleigh fading channel. Moreover, by assuming a 3-D indoor environment, the destructive effect of the elevation angle of the incident signal is considered on the position estimation. The effectiveness of the proposed CNN-AoA framework is evaluated via an experimental testbed, where In-phase/Quadrature (I/Q) samples, modulated by Gaussian Frequency Shift Keying (GFSK), are collected by four BLE beacons. Simulation results corroborate effectiveness of the proposed CNN-based AoA technique to track mobile agents with high accuracy in the presence of noise and Rayleigh fading channel.

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.

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Optimizing AP and Beacon Placement in WiFi and BLE hybrid localization

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.

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.

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.

As communication technology and smartphones develop, many indoor positioning applications based on Bluetooth low energy (BLE) beacons have emerged. However, in a complex BLE network, it can be challenging to select the optimal reference beacon, and accurate positioning becomes difficult. Fortunately, if the BLE network is displayed on a map, we can intuitively grasp the structure and density of the beacons in each area, which is important information for accurate positioning. Therefore, in this study we developed a map-aided indoor positioning algorithm to model the relationship between beacons in the positioning area in a parking lot. Specifically, the algorithm split all beacons into multiple cell areas to find the optimal reference beacon in that area. Then, the optimal reference beacon is used to find the preferred reference beacons among the real-time beacons. Finally, the positioning results were calculated and evaluated according to the preferred beacons. According to the results, our method can optimize the selection of reference beacons in different areas. The average positioning accuracy was 2.09 m and the results can be scored accurately. The results verify that our algorithm can effectively use map information to guide the selection of reference beacons in complex environments.

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.