Bluetooth Low Energy Beacons Research Articles

Smartphone-based localization for blind navigation in building-scale indoor environments

Continuous, accurate, and real-time smartphone-based localization is a promising technology for supporting independent mobility of people with visual impairments. However, despite extensive research on indoor localization techniques, localization technologies are still not ready for deployment in large and complex environments such as shopping malls and hospitals, where navigation assistance is needed most. We identify six key challenges for accurate smartphone localization related to the large-scale nature of the navigation environments and the user’s mobility. To address these challenges, we present a series of techniques that enhance a probabilistic localization algorithm. The algorithm utilizes mobile device inertial sensors and Received Signal Strength (RSS) from Bluetooth Low Energy (BLE) beacons. We evaluate the proposed system in a 21,000 m2 shopping mall that includes three multi-story buildings and a large open underground passageway. Experiments conducted in this environment demonstrate the effectiveness of the proposed technologies to improve localization accuracy. Field experiments with visually impaired participants confirm the practical performance of the proposed system in realistic use cases.

Aggregation of GPS, WLAN, and BLE Localization Measurements for Mobile Devices in Simulated Environments.

There are multiple available technologies to find the location of a mobile device, such as the Global Positioning System (GPS), Bluetooth Low-Energy beacons (BLE), and Wireless LAN (WLAN) localization. We propose a novel method to estimate the location of a moving device by aggregating information from multiple positioning systems into a single, more precise location estimation. The aggregated location is calculated as the place in which the product of the probability density functions (PDF) of individual methods has the maximum value. The experimental probability density functions of the three analyzed technologies are fitted by gamma distributions based on error histograms found in the literature and measurement data. The location measurements of the individual technologies are provided at different time instants, so the weighted product of the PDFs is used to improve aggregation accuracy. The discrete event-simulation model was used to evaluate the aggregation method with the Gauss–Markov mobility model. Simulations demonstrated that the calculated aggregated location was more accurate than any of the methods taken as the input, and average error was decreased by almost 13% compared to an arithmetic mean of the three considered localization methods, and by more than 36% compared to the single method with the highest accuracy.

A regression model-based method for indoor positioning with compound location fingerprints

ABSTRACT This paper proposed and evaluated an estimation method for indoor positioning. The method combines location fingerprinting and dead reckoning differently from the conventional combinations. It uses compound location fingerprints, which are composed of radio fingerprints at multiple points of time, that is, at multiple positions, and displacements between them estimated by dead reckoning. To avoid errors accumulated from dead reckoning, the method uses short-range dead reckoning. The method was evaluated using 16 Bluetooth beacons installed in a student room with the dimensions of 11 × 5 m with furniture inside. The Received Signal Strength Indicator (RSSI) values of the beacons were collected at 30 measuring points, which were points at the intersections on a 1 × 1 m grid with no obstacles. A compound location fingerprint is composed of RSSI vectors at two points and a displacement vector between them. Random Forests (RF) was used to build regression models to estimate positions from location fingerprints. The root mean square error of position estimation was 0.87 m using 16 Bluetooth beacons. This error is lower than that received with a single-point baseline model, where a feature vector is composed of only RSSI values at one location. The results suggest that the proposed method is effective for indoor positioning.

HuMAn: Complex Activity Recognition with Multi-Modal Multi-Positional Body Sensing

Current state-of-the-art systems in the literature using wearables are not capable of distinguishing a large number of fine-grained and/or complex human activities, which may appear similar but with vital differences in context, such as lying on floor versus lying on bed versus lying on sofa. This paper fills the gap by proposing a novel system, called HuMAn , that recognizes and classifies complex at-home activities of humans with wearable sensing. Specifically, HuMAn makes such classifications feasible by leveraging selective multi-modal sensor suites from wearable devices, and enhances the richness of sensed information for activity classification by carefully leveraging placement of the wearable devices across multiple positions on the human body. The HuMAn system consists of the following components: (a) a practical feature set extraction method from selected multi-modal sensor suites; and (b) a novel two-level structured classification algorithm that improves accuracy by leveraging sensors in multiple body positions; and (c) improved refinement in classification of complex activities with minimal external infrastructure support (e.g., only a few Bluetooth beacons used for location context). The proposed system is evaluated with 10 users in real home environments. Experimental results demonstrate that the HuMAn system can detect 21 complex at-home activities with high degree of accuracy. For same-user evaluation strategy, the average activity classification accuracy is as high as 95 percent over all of the 21 activities. For the case of 10-fold cross-validation evaluation strategy, the average classification accuracy is 92 percent, and for the case of leave-one-out cross-validation strategy, the average classification accuracy is 75 percent.

VariLoc: Path Loss Exponent Estimation and Localization Using Multi-Range Beaconing

The achievable accuracy of localization based on received signal strength measurements depends on the path loss exponent (PLE), which describes the multipath fading and shadowing characteristics of a channel. An inaccurate estimation of the PLE leads to inaccurate estimation of positions. In this letter, we introduce a new ranging technique, called VariLoc, which uses a multi-range beaconing approach to perform localization and PLE estimation. The main novelty of VariLoc is that it leverages the sensitivity level of a receiver device for sensing the channel and the transmission power, instead of the more commonly used RSSI measurements, which can be very noisy. This letter shows that VariLoc is robust against multipath fading and can estimate the PLE accurately, even in the presence of very deep fading. This letter further introduces a centroid localization algorithm that utilizes VariLoc to obtain estimates for both the PLE and the position of a target node. The performance of the proposed approach is compared with the state-of-the-art localization algorithms and Cramer–Rao bound using simulation and real-world experimentation using Bluetooth beacons.

Confidence-Interval-Fuzzy-Model-Based Indoor Localization

In this paper, an efficient indoor localization algorithm based on the confidence-interval fuzzy model is presented. The width of the confidence interval is essential within the proposed fingerprinting method for calculating weights, which are then taken into account while searching for the $K$ nearest neighbors in the database of fingerprints. For each beacon in the test room, a new confidence-interval fuzzy path-loss model composed of several local linear models is constructed. The map of fingerprints is then constructed of a set of confidence-interval fuzzy models. By their consideration, the localization accuracy is significantly improved in comparison with other commonly used path-loss models. The most important novelty of this paper is the introduction of the confidence interval within the fingerprinting method, which additionally improves localization results. The platform of the localization system is developed on the basis of a smartphone and Bluetooth beacons. Therefore, the localization algorithm has to be optimized in order to be computationally efficient, which is essential for real-time processing and low energy consumption on a smartphone.

Semantic enrichment of spatio-temporal trajectories for worker safety on construction sites

Thousands of fatalities are reported from the construction industry every year and a high percentage of them are due to the unsafe worker movements which resulted in falling from heights, transportation accidents, exposure to harmful environments, and striking against or being struck by the moving equipment. To reduce such fatalities, a system is proposed to monitor worker movements on a construction site by collecting their raw spatio-temporal trajectory data and enriching it with the relevant semantic information. To acquire the trajectories, the use of an indoor positioning system (IPS) is considered. Bluetooth beacons are used for collecting spatio-temporal information of the building users. By means of an Android-based mobile application, neighboring beacons’ signals are selected, and a geo-localization technique is performed to get the unique pairs of users’ location coordinates. After pre-processing this collected data, three semantic enrichment techniques are used to construct semantically enriched trajectories which are as follows: (1) enrichment with the semantic points which maps site location identification to the trajectory points; (2) enrichment with the semantic lines which relies on the speed-based segmentation approach to infer user modes of transportation; (3) enrichment with the semantic region for mapping a complete trajectory on an actual building or a construction site zone. The proposed system will help in extracting multifaceted trajectory characteristics and generates semantic trajectories to enable the desired semantic insights for better understanding of the underlying meaningful worker movements using the contextual data related to the building environment. Generated semantic trajectories will help health and safety (H&S) managers in making improved decisions for monitoring and controlling site activities by visualizing site-zones’ density to avoid congestion, proximity analysis to prevent workers collisions, identifying unauthorized access to hazardous areas, and monitoring movements of workers and machinery to reduce transportation accidents.

A Low-Cost Indoor Activity Monitoring System for Detecting Frailty in Older Adults.

Indoor localization systems have already wide applications mainly for providing localized information and directions. The majority of them focus on commercial applications providing information such us advertisements, guidance and asset tracking. Medical oriented localization systems are uncommon. Given the fact that an individual’s indoor movements can be indicative of his/her clinical status, in this paper we present a low-cost indoor localization system with room-level accuracy used to assess the frailty of older people. We focused on designing a system with easy installation and low cost to be used by non technical staff. The system was installed in older people houses in order to collect data about their indoor localization habits. The collected data were examined in combination with their frailty status, showing a correlation between them. The indoor localization system is based on the processing of Received Signal Strength Indicator (RSSI) measurements by a tracking device, from Bluetooth Beacons, using a fingerprint-based procedure. The system has been tested in realistic settings achieving accuracy above 93% in room estimation. The proposed system was used in 271 houses collecting data for 1–7-day sessions. The evaluation of the collected data using ten-fold cross-validation showed an accuracy of 83% in the classification of a monitored person regarding his/her frailty status (Frail, Pre-frail, Non-frail).

A Hybrid Method to Improve the BLE-Based Indoor Positioning in a Dense Bluetooth Environment.

Indoor positioning using Bluetooth Low Energy (BLE) beacons has attracted considerable attention after the release of the BLE protocol. A number of efforts have been exerted to improve the performance of BLE-based indoor positioning. However, few studies pay attention to the BLE-based indoor positioning in a dense Bluetooth environment, where the propagation of BLE signals become more complex and more fluctuant. In this paper, we draw attention to the problems resulting from the dense Bluetooth environment, and it turns out that the dense Bluetooth environment would result in a high received signal strength indication (RSSI) variation and a longtime interval collection of BLE. Hence, to mitigate the effects of the dense Bluetooth environment, we propose a hybrid method fusing sliding-window filtering, trilateration, dead reckoning and the Kalman filtering method to improve the performance of the BLE indoor positioning. The Kalman filter is exploited to merge the trilateration and dead reckoning. Extensive experiments in a real implementation are conducted to examine the performance of three approaches: trilateration, dead reckoning and the fusion method. The implementation results proved that the fusion method was the most effective method to improve the positioning accuracy and timeliness in a dense Bluetooth environment. The positioning root-mean-square error (RMSE) calculation results have showed that the hybrid method can achieve a real-time positioning and reduce error of indoor positioning.

Procedures to Select Digital Sensing Technologies for Passive Data Collection With Children and Their Caregivers: Qualitative Cultural Assessment in South Africa and Nepal.

BackgroundPopulations in low-resource settings with high childhood morbidity and mortality increasingly are being selected as beneficiaries for interventions using passive sensing data collection through digital technologies. However, these populations often have limited familiarity with the processes and implications of passive data collection. Therefore, methods are needed to identify cultural norms and family preferences influencing the uptake of new technologies.ObjectiveBefore introducing a new device or a passive data collection approach, it is important to determine what will be culturally acceptable and feasible. The objective of this study was to develop a systematic approach to determine acceptability and perceived utility of potential passive data collection technologies to inform selection and piloting of a device. To achieve this, we developed the Qualitative Cultural Assessment of Passive Data collection Technology (QualCAPDT). This approach is built upon structured elicitation tasks used in cultural anthropology.MethodsWe piloted QualCAPDT using focus group discussions (FGDs), video demonstrations of simulated technology use, attribute rating with anchoring vignettes, and card ranking procedures. The procedure was used to select passive sensing technologies to evaluate child development and caregiver mental health in KwaZulu-Natal, South Africa, and Kathmandu, Nepal. Videos were produced in South Africa and Nepal to demonstrate the technologies and their potential local application. Structured elicitation tasks were administered in FGDs after showing the videos. Using QualCAPDT, we evaluated the following 5 technologies: home-based video recording, mobile device capture of audio, a wearable time-lapse camera attached to the child, proximity detection through a wearable passive Bluetooth beacon attached to the child, and an indoor environmental sensor measuring air quality.ResultsIn South Africa, 38 community health workers, health organization leaders, and caregivers participated in interviews and FGDs with structured elicitation tasks. We refined the procedure after South Africa to make the process more accessible for low-literacy populations in Nepal. In addition, the refined procedure reduced misconceptions about the tools being evaluated. In Nepal, 69 community health workers and caregivers participated in a refined QualCAPDT. In both countries, the child’s wearable time-lapse camera achieved many of the target attributes. Participants in Nepal also highly ranked a home-based environmental sensor and a proximity beacon worn by the child.ConclusionsThe QualCAPDT procedure can be used to identify community norms and preferences to facilitate the selection of potential passive data collection strategies and devices. QualCAPDT is an important first step before selecting devices and piloting passive data collection in a community. It is especially important for work with caregivers and young children for whom cultural beliefs and shared family environments strongly determine behavior and potential uptake of new technology.

BLE RSS Measurements Dataset for Research on Accurate Indoor Positioning

RSS-based indoor positioning is a consolidated research field for which several techniques have been proposed. Among them, Bluetooth Low Energy (BLE) beacons are a popular option for practical applications. This paper presents a new BLE RSS database that was created to aid in the development of new BLE RSS-based positioning methods and to encourage their reproducibility and comparability. The measurements were collected in two university zones: an area among bookshelves in a library and an area of an office space. Each zone had its own batch of deployed iBKS 105 beacons, configured to broadcast advertisements every 200 ms. The collection in the library zone was performed using three Android smartphones of different brands and models, with beacons broadcasting at −12 dBm transmission power, while in the other zone the collection was performed using of one those smartphones with beacons configured to advertise at the −4 dBm, −12 dBm and −20 dBm transmission powers. Supporting materials and scripts are provided along with the database, which annotate the BLE readings, provide details on the collection, the environment, and the BLE beacon deployments, ease the database usage, and introduce the reader to BLE RSS-based positioning and its challenges. The BLE RSS database and its supporting materials are available at the Zenodo repository under the open-source MIT license.

Indoor Location Learning Over Wireless Fingerprinting System With Particle Markov Chain Model

This paper describes research toward a tracking system for locating persons indoor based on low-cost Bluetooth Low Energy (BLE) beacons. Wireless fingerprinting based on BLE beacons has emerged as an increasingly popular solution for fine-grained indoor localization. Inspired by the idea of mobility tracking used in the cellular network, this paper proposes a BLE-based tracking system, designated as BTrack, to learn the location area (LA) of an indoor user based on the reported wireless fingerprinting combined with statistical analysis. We propose a new particle Markov chain model to evaluate the LA-level performance regarding the visibility area in an environment with large obstacles. In the presence of sight obstructions, BTrack is evaluated using a real-world test bed built in a library with tall bookshelves. The performance of the proposed system is evaluated in terms of the mean distance error and the LA prediction accuracy considering the direct line-of-sight. Compared with the existing methods, BTrack reduces the average localization error by 25% and improves the average prediction accuracy by more than 16% given a random mobility pattern.

Evaluation of a technology platform utilizing Bluetooth low energy (BLE) beacons to measure movement of animals, assets and personnel throughout a pig production network

Evaluation of a technology platform utilizing Bluetooth low energy (BLE) beacons to measure movement of animals, assets and personnel throughout a pig production network

Trusted K Nearest Bayesian Estimation for Indoor Positioning System

Indoor positioning systems have received increasing attention because of their wide range of indoor applications. However, the positioning system generally suffers from a large error in localization and has low solidity. The main approaches widely used for indoor localization are based on the inertial measurement unit (IMU), Bluetooth, Wi-Fi, and ultra-wideband. The major problem with Bluetooth-based fingerprinting is the inconsistency of the radio signal strength, and the IMU-based localization has a drift error that increases with time. To compensate for these drawbacks, in the present study, a novel positioning system with IMU sensors and Bluetooth low energy (BLE) beacon for a smartphone are introduced. The proposed trusted K nearest Bayesian estimation (TKBE) integrates BLE beacon and pedestrian dead reckoning positionings. The BLE-based positioning, using both the K-nearest neighbor (KNN) and Bayesian estimation, increases the accuracy by 25% compared with the existing KNN-based positioning, and the proposed fuzzy logic-based Kalman filter increases the accuracy by an additional 15%. The overall performance of TKBE has an error of <;1 m in our experimental environments.

Improving Indoor Fingerprinting Positioning With Affinity Propagation Clustering and Weighted Centroid Fingerprint

Nowadays, research and development of various indoor positioning systems (IPS) have been increasing owing to flourishing social and commercial interest in location-based services (LBSs). Among LBS technologies, we used the Bluetooth low energy beacon in our system, which consumes less energy and is embedded in many current smartphones and tablets. In particular, the fingerprinting method has become a prime choice in the design of IPS owing to its good location estimation and the fact that a line-of-sight from access points is not required. We propose an improved two-step fingerprinting localization using multiple fingerprint features to enhance the localization accuracy. The proposed system uses a propagation model to convert RSS of beacons to distance and estimate the weighted centroid (WC) of nearby beacons. The estimated WCs along with signal strength and rank of the nearby beacons are stored in the server database for localization instead of RSS from all the deployed beacons. First, the proposed system makes use of diverse fingerprinting features to increase localization accuracy that also reduces both the physical size of the database and the amount of data communication with the server in the execution phase; second, affinity propagation clustering minimizes the searching space of RPs and reduces the computational cost; third, exponential averaging is introduced to smooth the noisy RSS. The experimental results obtained by real field deployment show that the proposed method significantly improves the performance of the positioning system in both the positioning accuracy and radio-map database size.

Beacons positioning detection, a novel approach

Recent Bluetooth Low Energy (BLE) beacons provide new opportunities to explore positioning. Beacon positioning determination using current approaches is supported by pre-calculated formulas, for generic beacons, whereas the position can be accurately estimated with a low error up to a small distance; or based on fingerprinting the signal for the given space. In both cases, the accuracy variate depending on hardware specifications and other conditions such as beacon brand, wrap material, temperature, wind, location, surrounding interference, battery strength, among others.This paper introduces a method for beacon-based positioning, based on signal strength measurements at key distances for each beacon. This method allows for different beacon types, brands, and conditions. Depending on each situation (i.e., hardware and location) it is possible to adapt the distance measuring curve to minimize errors and support higher distances, while at the same time keeping good precision. Moreover, this paper also presents a comparison with traditional positioning method, using formulas for distance estimation, and then position triangulation. Performed tests took place at the library of the campus of the Polytechnic Institute of Viseu.Experimental results show that the proposed position technique has 13.2% better precision than triangulation, for distances up to 10 meters.

Intelligent beacon location and fingerprinting

The complex way radio waves propagate indoors, leads to the derivation of location using fingerprinting techniques. In this cases, location is computed relying on WiFi signals strength mapping. Recent Bluetooth Low Energy (BLE) provides new opportunities to explore positioning. Indoor location identification plays a fundamental role as a business and personal level. At a business level, indoor location pinpointing where GPS signal is nonexistent is used to advise users and send push notifications (e.g., stores publicity, guide persons with special needs, or even for emergency evacuation).In this work is studied how BLE beacons radio signals can be used for indoor location scenarios, as well as their precision. The proposed study is performed inside the campus of Viseu Polytechnic Institute, using hundreds of students, each with his smart-phone, as proof of concept. Experimental results show that BLE allows having less than 1.5 meters error approximately 90% of the times.

BLE Bluetooth Beacon based Solution to Monitor Egress of Alzheimer’s Disease Sufferers from Indoors

With the ageing people community, Alzheimer’s disease (AD) is the most common disease in prevalence. It affects the memory of the affected individual by causing neuron degeneration. Such cognitive disability leads people to roam around places since they are ignorant about location. They may go afar from the safe area to traffic prone areas which cause serious, life threatening issues to them. Proposed study introduces a technology that involves a wearable device for Alzheimer disease affected elderly patients and listening device given to care takers or doctors. This system detects and notifies caregivers about the departure of people to risk locations from their residence zone. This system uses Bluetooth Low energy (BLE) communication technology that utilizes transmitter and receiver fixed to elderly individual as wearable device and in residence respectively. Based on the signal strength between the transmitter and receiver, the distance estimation between the elderly and the receiver is calculated with the help of a Raspberry Pi(Rpi) and an alert notification is sent to doctors/caretakers when the distance exceeds safety limit.

A qualitative analysis of context-aware ubiquitous learning environments using Bluetooth beacons

ABSTRACTThe use of Bluetooth Low Energy (BLE) beacons is becoming widespread for context-aware learning environments. This technology can be used in indoor and outdoor settings such as museums, shops, home and schools in order to identify the location of learners and their contexts. Specifically, in the constructivist view of learning, learners can use such sensing technology for constructing knowledge and experiencing learning at any time, anywhere. However, exploration of such technology has been limited in the constructivist context-aware ubiquitous learning (U-learning) literature. For this purpose, the authors evaluated two android-based U-learning applications: one was an outdoor learning environment in a garden, the other was about locating books in a library. The applications were implemented and tested with school and university students. The qualitative results are presented in this article, and show how such sensing technology is promising for both pedagogical environments.

The trilateration-based BLE Beacon system for analyzing user-identified space usage of New Ways of Working offices

For successful implementation of the New Ways of Working (NWW) office, it is necessary to plan on the basis of user-identified space usage information. Of the different methods, a trilateration-based Bluetooth low-energy (BLE) Beacon system has a greater potential as it provides specific location information. However, the feasibility of implementing a trilateration-based BLE Beacon system has not yet been validated in space-usage analysis in the NWW office. The present study thus aimed to establish whether such a system would be suitable for the purpose. Two sets of experiments were performed in an open-plan office in order to evaluate system accuracy. The present study used two indexes: Distance Difference (DD) and Accuracy Rate of User-Identified Location Estimation (AR). The best average DD was 0.7 m at desk 5 in the first experiment, which is promising. However, the overall experimental DDs were 1.63 m and 1.93 m, while both DD and AR showed significant variance between desks. These findings indicate that system accuracy and consistency are not yet sufficient for space usage analysis in the NWW office. Accordingly, to enhance system accuracy and consistency it will be essential to develop strategies for optimal system deployment and to combine them with other methods.