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BLE Beacons for Indoor Positioning at an Interactive IoT-Based Smart Museum

Petros Spachos, Konstantinos N. Plataniotis

arXiv:2001.07686v1cs.NIeess.SY

TL;DR

The paper addresses museum visits where generic guides and active information retrieval may not match visitors’ interests or limited time. It presents a BLE-beacon and Android system using RSSI localization, automatic exhibit notifications, analytics, and smartphone Kalman filtering. Experiments report promising BLE performance for interactive museums, with accuracy shaped by environmental conditions and improved by filtering in one corridor-distance condition.

  • Problem

    Museum booklets, audio guides, QR codes, and augmented-reality approaches may not provide personalized, passive guidance for visitors with limited time or differing interests.

  • Method

    The system uses BLE beacons and an Android application to trigger exhibit notifications, estimate location from RSSI, collect visit analytics, and apply a smartphone Kalman filter.

  • Results

    Three experiments found promising BLE performance for distance estimation, localization, and detection, while corridor errors beyond 6 m were reduced by the Kalman filter.

  • Takeaways & Limitations

    BLE beacons can support low-cost museum interaction without interfering with other wireless infrastructures, while beacon placement and deployment conditions remain important.

Abstract

from arXiv · show

The Internet of Things (IoT) can enable smart infrastructures to provide advanced services to the users. New technological advancement can improve our everyday life, even simple tasks as a visit to the museum. In this paper, an indoor localization system is presented, to enhance the user experience in a museum. In particular, the proposed system relies on Bluetooth Low Energy (BLE) beacons proximity and localization capabilities to automatically provide the users with cultural contents related to the observed artworks. At the same time, an RSS-based technique is used to estimate the location of the visitor in the museum. An Android application is developed to estimate the distance from the exhibits and collect useful analytics regarding each visit and provide a recommendation to the users. Moreover, the application implements a simple Kalman filter in the smartphone, without the need of the Cloud, to improve localization precision and accuracy. Experimental results on distance estimation, location, and detection accuracy show that BLE beacon is a promising solution for an interactive smart museum. The proposed system has been designed to be easily extensible to the IoT technologies and its effectiveness has been evaluated through experimentation.

I. INTRODUCTION

Museums need more personalized, passive visitor guidance as collections expand and visitors have limited time. The paper proposes BLE-based interaction and RSSI localization, evaluated across distance, localization, and detection experiments.

  • Traditional booklets and audio guides may not meet individual interests or help visitors discover relevant exhibits within limited visit time.
  • QR codes and augmented reality provide exhibit information but require visitors to scan, download software, or otherwise take action.
  • BLE beacons can trigger exhibit notifications, while an Android application estimates visitor location from nearby beacon RSSI values.
  • The application records retention time and collection interests to support recommendations for future visits.
  • Three experiments examine single-beacon distance estimation, three-beacon localization during movement, and detection accuracy across beacon topologies.
  • Beacon placement is flexible, but acceptable performance requires an accurate path loss model and depends on deployment characteristics such as noise and interference.
  • In complex indoor environments, localization can be sufficient for museum applications tolerating errors of a few meters, while filtering and wider anchor spacing can improve accuracy.
  • Detection accuracy decreases with greater receiver distance and more neighboring beacons, making proximity conditions important.

II. RELATED WORK

Related work positions RSS-based BLE localization as an accessible indoor-positioning approach and describes prior museum systems using QR, RFID, and other interactive technologies. The paper distinguishes its system by using only beacons and a smartphone application for location and visit analytics.

  • UWB offers accurate TOA-based localization and robust operation but requires additional hardware deployment.
  • RSS-based indoor positioning is popular because RSS signals are widely available and low cost, despite susceptibility to noise and interference.
  • Prior work improves RSS localization with Bayesian filters, sigma-point Kalman smoothing, and unscented Kalman filtering with optimization.
  • IoT and smartphone sensing support indoor localization across smart-city, home, healthcare, and other applications.
  • Smart-museum research includes self-aware exhibits, QR-code trails, RFID personalization, and BLE ranging combined with smartphone pedestrian dead reckoning.
  • This work uses beacons as the sole information source for visitor location and visit analytics, avoiding additional sensors or devices.
  • The system combines beacon features, RSS-based ranging, and trilateration for smartphone-based museum localization.

1) BLE wireless technology:

BLE beacons provide low-cost, low-power wireless signaling suitable for indoor museum deployments, but ranging accuracy depends on environmental propagation and beacon configuration. The paper derives distance from RSSI and estimates position through trilateration.

  • BLE reduces power consumption and cost while maintaining a communication range similar to classic Bluetooth.
  • BLE advertising uses three widely spaced channels, helping prevent Wi-Fi interference, although RSS measurements can exhibit substantial fading.
  • Beacons are small, battery-operated devices that can be placed flexibly and powered by coin cells, AA batteries, USB, or solar sources.
  • Transmission power trades off range, battery drain, and interference; a 2 to 5 m range is sufficient for the proposed museum application.
  • Advertising interval trades signal stability and battery lifetime against the ability to reach fast-moving receivers.
  • Measured power is the expected RSS at 1 m and can be calibrated to estimate receiver distance.
  • The path loss model uses RSSI, distance, a propagation constant n, and the received signal strength A at 1 m.
  • In noisy environments, Gaussian shadowing with standard deviation σRSSI affects the RSSI-based distance estimate.

C. Trilateration

Trilateration estimates a smartphone’s position from distances to beacons at known locations by finding the intersection of beacon-centered circles. Because RSSI is noisy, mean square error is used to quantify location error and support calibration.

  • C. Trilateration: Lateration estimates a receiver’s location from distances to points with known locations, while trilateration uses three such points.The smartphone is represented by the intersection of three circles centered at beacon locations, with radii determined from estimated distances.
  • C. Trilateration: The smartphone location is calculated from beacon coordinates, known radii, and equations combining the geometric constraints.The setup assumes the smartphone at (x, y), with beacon coordinates and radii specified before combining the equations.
  • C. Trilateration: RSSI noise and interference produce error between the estimated and real locations, which is measured using mean square error.MSE helps assess system accuracy and perform necessary calibrations.

IV. PROPOSED SYSTEM ARCHITECTURE

The proposed IoT-based smart museum combines beacons, a visitor-installed Android application, and a museum-server data collection platform.

  • IV. PROPOSED SYSTEM ARCHITECTURE: The architecture consists of beacons distributed throughout the museum, an Android application on the visitor’s smartphone, and a server-based data collection platform.Together, these components support the proposed IoT-based smart museum.

A. System services

The proposed system provides proximity, localization, and data analytics services through coordinated beacon, smartphone, and server functions.

  • Proximity service: Proximity service notifications provide exhibit information when visitors approach beacons, with more information delivered as they move closer.The application also records visitor interest in the specific exhibit.
  • Localization service: Localization service uses messages from different beacons as visitors move between them to estimate their approximate museum location.The application can track the visitor’s path and support recommendations based on preferences and room distances.
  • Data analytics service: Data analytics service forwards visitor paths, preferences, retention times, and action timestamps to a processing center.These records support analysis of visitor behavior during museum visits.

B. Hardware components

The prototype uses Gimbal Series 21 BLE beacons and an Android smartphone application, with localization and data handling distributed between the phone, network, and museum server.

  • B. Hardware components: Gimbal Series 21 beacons were selected for their low price and extended lifetime, using four AA batteries with a typical 18-month life.They transmit every 100 ms while operating continuously.
  • B. Hardware components: A Bluetooth 4.0 LG Nexus 5 running Android 6.0.1 served as the experimental receiver.Bluetooth 4.0 was required for the developed application.
  • B. Hardware components: The Android application runs in the background, notifies visitors near beacons, and displays exhibit information when they move closer.It records retention time and beacon ID while visitors remain near an exhibit.
  • B. Hardware components: Without Wi-Fi, the application stores collected information locally and forwards it to the server when wireless connectivity returns.With Wi-Fi, the data are forwarded to the control room, where the recommendation system runs.
  • B. Hardware components: Localization uses neighboring-beacon RSSI and IDs on the application, with particle filtering available over Wi-Fi and Kalman filtering without Wi-Fi.The system displays the visitor’s location on the museum map.
  • B. Hardware components: Experiments in a large laboratory room and corridor varied wireless transmissions, people, obstacles, and beacon configurations.Three experiment sets evaluated the system in each environment.

A. Path loss model

The path-loss model is calibrated separately for a laboratory and corridor using RSSI measurements, then combined with Kalman filtering to estimate beacon distance. Environmental noise and obstruction increase RSSI variation and estimation error, while filtering improves cumulative accuracy.

  • Model calibration: RSSI values were collected every 20 cm up to 5 m, with 10 minutes of measurements at each location.The measurements established the path-loss model for each experimental environment.
  • Environmental effects: RSSI variation increases with distance and is higher in the corridor than in the laboratory because walls and pedestrian blockage reduce line of sight.The laboratory generally maintains line of sight, whereas the corridor has nearby concrete walls and more frequent signal obstruction.
  • Model calibration: The fitted path-loss components were n = 2.208 with A = −68.99 for the laboratory and n = 2.341 with A = −62.94 for the corridor.The corridor experiences noise and interference from beacon transmissions, construction, and people’s movement.
  • Filtering: A smartphone-side Kalman filter was applied to noisy RSSI measurements to improve distance estimation without relying on the Cloud.The filter helps in both environments and tracks the average more closely in the laboratory, where RSSI variation is smaller.
  • Estimation performance: With Kalman filtering, cumulative distance-estimation error was within 2 m in the laboratory and 2.5 m in the corridor, compared with 3 m and 3.5 m using raw data.These bounds hold for 95% of the time for the raw-data results; the filtered results are reported as improved overall performance.
  • Filtering: For errors above 3 m in the laboratory and 6 m in the corridor, Kalman filtering improves performance by smoothing values using previous RSSI measurements.Within those error ranges, raw data and filtered data have similar performance.

C. Localization performance

Localization was tested with three line-of-sight BLE beacons arranged in two triangular topologies and with the smartphone at four locations. Errors were close to centimeters in the tighter topology and below 1 m in the wider topology, with performance varying by receiver position.

  • Experimental setup: Three BLE beacons formed a triangular, line-of-sight topology, and the smartphone was evaluated at locations A, B, C, and D.Two otherwise identical arrangements used d1 = 1 m, d2 = 2 m and d1 = 3 m, d2 = 4 m.
  • Localization results: In the first topology, with beacons closer together, localization error was close to the real distance and within centimeters without filtering.The experiment used raw measurements rather than a filtering technique.
  • Localization results: In the second topology, where beacon spacing increased, localization error was below 1 m.The wider topology used d1 = 3 m and d2 = 4 m.
  • Position effects: Estimation performed best when the smartphone was between two beacons and worst when it was equally distant from all three, as at location D.The paper attributes the poorer equal-distance result probably to interference.
  • Position effects: Localization becomes more challenging when the receiver moves among all three beacons, potentially because their transmissions interfere with one another.The proposed explanation is that interference weakens RSSI signals and degrades estimation.
  • Scope boundary: The reported sub-meter error may increase in more complex scenarios because the experiments used simple topologies and line of sight.The authors note that advanced filtering could improve the raw-data estimates.

D. Detection accuracy

Detection accuracy depends on receiver proximity, beacon spacing, neighboring-device interference, and indoor topology. Corridor deployments perform worse than laboratory layouts because of higher noise, while increased beacon separation improves estimation.

  • The third experiment evaluated detection accuracy with three beacons arranged in laboratory and corridor topologies at different inter-beacon distances.The corridor arrangement used a 2.3 m distance between one pair of beacons, with results reported in Table IV.
  • Detection accuracy is highest when the receiver is close to a beacon and decreases as the receiver moves farther away.Signals from neighboring beacons can have similar RSSI values, reducing detection accuracy.
  • Accuracy drops when the receiver is near the middle beacon and equidistant from the other two beacons.Signals from the equally distant neighboring beacons become more prominent as the receiver moves away from the middle beacon.
  • When neighboring beacons are within 1 m, detection accuracy drops; increasing beacon separation improves system performance.The experiments attribute the degradation to interference from nearby beacons.
  • Corridor topology produces lower detection accuracy than the laboratory setting because the corridor environment has higher noise.More neighboring devices further worsen detection, while greater spacing helps improve estimation.

E. Discussion

The system is designed as a simple, low-cost, easily installed museum technology that operates with minimal user interaction and without disrupting existing wireless infrastructure. Experiments found promising performance, but environmental changes and closely spaced beacons constrain raw-data accuracy.

  • BLE beacons provide promising proximity and localization performance, but raw data may be insufficient when beacons are placed too close together.Advanced filtering can improve performance, including real-time filtering when museum Wi-Fi is available.
  • Dynamic environmental changes such as noise and interference can rapidly reduce beacon proximity and localization accuracy.The deployment area should be studied in advance to improve estimation and minimize errors.
  • The proposed system is simple, low-cost, easy to install, and requires minimal user interaction without interfering with other wireless infrastructures.Beacons can also be relocated to follow changes in exhibit placement.
  • The BLE smart-museum system provides proximity and localization services through iBeacons and an Android application without requiring an Internet connection.Access to a cloud server can increase accuracy.
  • Three experiments produced promising results, showing that BLE beacons can improve museum interaction at low cost without interfering with other wireless infrastructures.
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