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Improving Indoor Localization Using BluetoothLow Energy Beacons

Rakib Shahriar

Objectives

Describes basic principles of a radio-based indoor localization.

Implemented a distributed system for collecting radio fingerprints by mobile devices with the Android operating system.

Bluetooth Low Energy (BLE) technology is used to improve indoor localization performance.

The localization of stationary objects based on WiFi, Bluetooth Low Energy, and their combination has been evaluated using the data measured during the experiment in a building.

Authors

• Pavel Kriz• Filip Maly• Tomas Kozel

• Faculties of Informatics and Management, University of Hradec Kralove

Problem statement

Locating objects outside is usually not a problem to locate a person or a mobile device.

Localization becomes extremely complicated inside buildings in high-density urban areas because of rare line-of-sight to the tracking systems (GPS, GLONASS, Galileo).

Traditional localization techniques are based on radio networks (WiFi) and fingerprints of signal strengths of WiFi devices.

Localization accuracy is influenced by a number of circumstances, for example, by characteristics of transmitters and receivers and characteristics of the environment which influence the radio signal propagation.

How to improve accuracy?

Solution

Bluetooth Low Energy (BLE) technology is used as an alternative supplementing WiFi access points.

Combination of BLE and WiFi access points provide more accurate localization.

Because of power consumption, where WiFi access points cannot be put, BLE enabled devices are installed to strengthen the coverage.

Basic localization approaches

Triangulation

• This is a object location detection technique• Methods based on triangulation can be further divided into lateration and

angulation• Method of estimations:

• Distance from several transmitters based on signal attenuation• Time characteristics of the signal propagation (TOA: Time Of Arrival, TDOA:

Time Difference of Arrival)• Direction of the received signal (AOA: Angle of Arrival)

• Works good for open spaces

Fingerprinting

• This method localizes objects by machine learning algorithms• Has two phases:

• Learning vectors of Received Signal Strength Indicator (RSSI) • Localization itself—the device to be localized measures the RSSI values and compares

them with the data in the fingerprint database using a suitable method• Few Methods of estimation:

• K-NN (Used in this work)• SVM• Neural Network

Bluetooth-Based Localization

• Nokia invented Bluetooth Low Energy (BLE) in 2010.

• Proximity estimation based on signal strength.

• Previous Bluetooth protocol was not feasible because of high cost and high power assumption, BLE changed the possibilities.

iBeacon Technology

How it works?

• iBeacon is a protocol developed by Apple.

• It uses the Bluetooth Low Energy standard.

• It broadcasts its identifier to nearby portable electronic devices.

• Can be run by a coin battery up to two years.

How utilized in this work?

• Used beacons are made by Estimote.

• Beacons transmit its identification data to BLE enabled Android smartphones/tablets.

• Advertisement data = MAC Address + UUID + Major Number + Minor Number

Methods and Architecture

Positioning Method

• Weighted 𝑘𝑘-Nearest Neighbors (𝑘𝑘-NN) in Signal Space method

• Compare untagged fingerprints with tagged fingerprints in the database using Euclidean distance

Where,m is vector for untagged fingerprints is vector for tagged fingerprintD is the distance

Where,P is the position of measured untagged fingerprint

System ArchitectureNotes:

• Data acquisition in JSON format

• Couchbase NoSQL database

• User subscription using Google accounts

Test Site

The Campus Building

Notes on Test Site• 52m × 43m area

• Several Cisco made WiFi transmitters

• 17 Estimote made BLE beacons

• Evenly placed in corridors and classrooms

• Putting behind dropped ceilings reduced localization performance

• Performance improved by putting beacons on the bottom side of the mineral fiber ceiling tile

Evaluation & Discussion

Comparison of localization accuracy

Localization accuracy

depending on scanning duration (scanning started

at time 0)

Which technique gives faster localization?

• BLE promises faster initial localization than WiFi does.

• This effect becomes even stronger in combination with WiFi.

For example, in the 2nd

second of the scanning, the authors were unable to

localize the mobile device in 168 positions using WiFi, in 36 positions using BLE, and in 20 positions using combination of BLE and

WiFi.

Localization accuracy depending

on scanning duration

(scanning started 4 s before time 0)

BLE Beacons Density

• Group A in red

• Group B in Green

Comparison of localization

accuracy among different BLE deployment configurations

Any abnormalities during experimentation?• Yes

• BLE beacons 13-17 were put completely hidden inside the wooden tables with metal sides.

• But still compared to the ceiling setup of BLE beacons, beacons 13-17 covered wider areas.

Localization results summary

Follow up work-1

• Time: 2017• Title: Optimization of Algorithms in Relation to iBeacon• Authors: Jan Budina, Martin Zmítko, Pavel Kříž• Institution: University of Hradec Kralove, Czech Republic

• Objective: A short paper on the description of algorithms in relation to the iBeacon. Implementation of kNN algorithm was described with mathematical details.

Follow up work-2

• Time: September 2017• Conference: ICCCI 2017 (Springer Link)• Title: Different Approaches to Indoor Localization Based on Bluetooth Low Energy

Beacons and Wi-Fi• Authors: Radek Bruha, Pavel Kriz• Institution: University of Hradec Kralove, Czech Republic

• Objective: Similar to this paper but they added a comparison of K-NN algorithm and modified Particle Filter Algorithms.

A newer technology

• Li-Fi is a visible light communications system that is capable of transmitting data at high speeds over the visible light spectrum, ultraviolet and infrared radiation.

• The term was first introduced by Harald Haas during a 2011 TEDGlobal talk in Edinburgh.

My Opinion

Positives

• The authors used few state-of-the-art technologies to detect object positions in an indoor environment.

• Use of Bluetooth Low Energy (BLE) allows multiple advantages • Improvement on localization accuracy• Improvement on human smartphone/tablet interaction

• Experimentation was shrewdly performed considering practical application challenges.

Negatives

• The authors presented object localization on corridors in a indoor environment but skipped denser areas like the rooms or closets.

• Sticking to only Android compatible devices may mislead the experimentation results. iOS based devices should have been used.

• Manuscript is well written, however, a few graphs are very hard to understand.

• Cost of installation should also have included in the discussion.

Questions?