Format Template

Vol. 4, No. 2 | Jul – Dec 2020

An Indoor Tracking System using iBeacon and Android

Mohammed Moneer1 , Mahmoud Mahmoud Aljawarneh2 , Lachman Das

Dhomeja1, Gulsher Laghari1, Bisharat Rasool Memon1

Abstract:

Context-aware applications use context to provide relevant information and services to users

with minimal user intervention with the system. User location is such a context for location-

aware services. While in the outdoor location-aware systems Global Positioning System can

provide the user location within the accuracy of 20 meters, it lacks to precisely determine user

location for indoor tracking systems. Thus, different other techniques and mechanisms have been

proposed for indoor tracking systems, such as trilateration, triangulation, fingerprinting, etc.

However, they have added disadvantages including high cost and high-power consumption. To

overcome such problems, iBeacon is a low-cost, low-power solution for such indoor tracking

systems. In this paper, we explore the use of iBeacon for user tracking in an indoor user tracking

system with a prototype implementation and evaluate the system on some general use cases. The

prototype serves as a prelude towards the goal of developing context-aware (in particular,

location-aware) applications.

Keywords: location-aware systems; Bluetooth low energy; iBeacon; Android

1. Introduction

Context-awareness provides the
applications with the ability to adapt their
services for each individual user. Thus, a
context-aware application uses context to
provide relevant information to users [1], [2].
As such, the user interaction with the
application is minimized, and the current
context of the user determines the information
or service the application needs to provide to
the user [3]. One such context is the user
location, which is the key enabler for location-
aware services [3].

While in the outdoor location-aware
systems Global Positioning System (GPS)
suffices to acquire the user location within the
accuracy of 20 meters, it cannot precisely
determine user location for indoor context-
aware systems [4]. In an indoor context-aware

1 University of Sindh, Jamshoro Pakistan
2 SZABIST, Larkana Pakistan

Corresponding Author: gulsher.laghari@usindh.edu.pk

system, to determine the users’ precise location
in a particular building (e.g. room, floor, etc.),
the location information for such an indoor
system needs to be updated regularly, which
also captures and reflects the movement of
users inside the building on the map. Such
indoor context-aware systems provide
customized indoor location-based services
necessary for numerous environments. Some
of these environments include universities,
hospitals, airports, shopping centers, and
schools, etc.

There exist various techniques for indoor
tracking systems, such as trilateration,
triangulation, fingerprinting, etc. Yet there is
no one-size-fits-all solution that works well in
every setting, due to the complexity and
requirements in designing such systems [5],
[6], [7], [8], [9], [10], [11], [12]. Various
technologies used in developing indoor

Mohammed Moneer (et al.), An Indoor Tracking System using iBeacon and Android (pp. 61 - 68)

Sukkur IBA Journal of Computing and Mathematical Science - SJCMS | Vol. 4 No. 2 July - December 2020 © Sukkur IBA University

tracking system such as Wi-Fi, ultrasound,
Bluetooth, and RFID have their inherent
limitations and disadvantages. Wi-Fi based
systems are inexpensive yet have lower
precision values. Despite being expensive, the
ultrasound provides reasonable precision
value. On the other hand, Bluetooth
technology being inexpensive with good
precision involves more power consumption.
RFID based system need a user intervention in
order to work.

Bluetooth Low Energy (BLE) is an
enhancement in the Bluetooth standard which
remarkably reduces the power consumption
besides being less costly [13], [14]. BLE
device with even a coin cell battery can operate
for a couple of years [15]. This makes BLE
ideal medical, industrial, or consumer
applications that require infrequent or periodic
transfers of short messages [14]. Thus, it is
likely that BLE to be used by billions of
smartphones in the near future [16]. iBeacon, a
proximity-based framework proposed by
Apple that uses BLE, allows mobile devices to
approximate their location by calculating how
close they are to an iBeacon— a low-cost BLE
transmitter [14]. Since an iBeacon is a low-
cost, low-power BLE device, many indoor
tracking systems have been introduced based
iBeacon technology. Such systems serve many
purposes including, advertising [20],
crowdsourced sensing [21], and recently
enabling the social distancing in wake of
COVID19 pandemic [22].

In this paper, we propose an iBeacon based
indoor user tracking system and implement a
proof-of-the-concept prototype. We evaluate
the system on some general use cases. The
prototype serves as a prelude towards the goal
of developing context-aware applications.

We organize the paper as follows. In
Section 2, we provide review of some related
literature and then propose our approach and
the proof-of-the-concept implementation of
indoor user tracking system in Section 3. Then,
we evaluate our prototype in Section 4. Finally,
in Section 5, we provide the concluding
remarks and future directions.

2. Related Work

In this section, we provide the review of the
related literature on indoor tracking systems,
which use different mediums.

2.1. WiFi

Jiang has proposed an indoor positioning
system exploiting WiFi Received Signal
Strength (RSS) in mobile phones along with
the record of previously tracked locations [5].
The system improved accuracy over five
percent compared to other positioning systems
that used WiFi.

Similarly, Au et. al. developed an indoor
tracking system with WiFi Received Signal
Strength (RSS) [6]. The system obtains indoor
location by providing users with wireless
internet access using IEEE 802.11. They use
theory of Compressive Sensing (CS) on the
devices. For experimental test setup, they use
windows mobile. Resultantly, the system
overperformed to the existing fingerprinting
methods.

WiFiPoz system uses a combination of
propagation and zoning method (i.e. dividing
building into geographical information zones)
to position through the WiFi [7]. The
fingerprint method comprises two phases (1)
offline training phase that is a record of all the
received signal strengths and (2) online phase
that uses the result obtained from the offline
phase. Experimental results showed better
results compared to traditional fingerprinting
algorithms with the improved accuracy of the
location estimation.

The common problem with the WiFi based
indoor positioning system is that the accuracy
is not absolute as the attenuation with these
signals is the main cause of their less accuracy,
in many cases multi-WiFi access point are
needed to compute the position of a specific
device.

2.2. Bluetooth

Bekkelien used Bluetooth fingerprinting
technique where the Bluetooth device works
together as beacons to estimate the location of
the mobile device [8]. The work is divided into

Mohammed Moneer (et al.), An Indoor Tracking System using iBeacon and Android (pp. 61 - 68)

Sukkur IBA Journal of Computing and Mathematical Science - SJCMS | Vol. 4 No. 2 July - December 2020 © Sukkur IBA University

two phases, the first phase is offline phase
where all Bluetooth devices start emitting the
signals to form a map, and the second phase is
used to estimate the location of that Bluetooth
device based on the RSSI and the number of
beacons visible to that mobile device. This
method works well when the device is
stationary, however, soon the device starts
moving the accuracy of the results decreases
drastically.

Gu and Ren performed an empirical study
to elicit the impact of various factors including
the distance, orientation, and obstacles on the
Bluetooth signals in a setting of real-world
scenarios [9]. Then built a localization model
characterizing the relationship between
changes of RSSI values and the target location.
Pursuant to this, exploiting the user motion,
they propose a scheme that can localize the
target device.

The fingerprint based indoor location
systems are hard to implement owing to the
quality of measurement of RSSI, even devices
of the same brand have varied recorded values
[10].

2.3. Camera

Mulloni et. al. used camera phones to
determine user location [11]. They used the
camera to assist navigation and localization of
the users with marker-based tracking
techniques. The inherent limitation of the
proposed system is that for improved detection
accuracy, it requires users’ training.

2.4. RFID

Seco et. al. used Received Signal Strength
(RSS) of radio frequency signal coupled with
Bayesian method. The gaussian processes, an
observation model, is used in Bayesian method
[12]. The results demonstrated that the
gaussian processes enhances the positioning
accuracy.

Daly modified the RFID tag with the
electromagnetic and dielectric properties of the
concrete [17]. The modified new passive RFID
tags when embedded in concrete, could be
easily read one meter above the surface.

2.5. BLE and iBeacon

Since BLE provides low-cost, low-power
devices, it has been used in many
contemporary indoor systems. Rida et. al.
proposed an indoor positioning system using
BLE and smart devices, by measuring the
RSSI of the Bluetooth signals using the
trilateration technique [18]. The algorithm is
based on Trilateration technique that needs
availability of more than two devices in a
specific room to estimate the location of the
device; soon the smart device enters the
environment, it connects to the nearest three
nodes by measuring the RSSI and determines
the distance between the devices and nodes.

To increase the efficiency in the emergency
room, Lin et. al. proposed a system that can
monitor the patient location using the mobile
application and Bluetooth low energy (BLE)
[19]. They used RSSI based algorithm to
determine the location, based on the signal
advertised from the beacons.

Yang et. al. proposed a three-layered
architecture of an indoor positioning system
for hospitals-based iBeacon [13]. They used
shortest distance algorithm to help patients
find their department or ward.

BlueSentinel, an iBeacon-based indoor
localization system, provides a prototype
system for the use case of a smart home to
solve the occupancy detection problem [16].

To explore the strengths and limitations of
iBeacons and determine a good architectural
model for context-aware applications, Sykes
et. al. developed four applications for different
use cases [4]. They concluded that iBeacons
offer a low energy alternative with more
accuracy compared to wireless access points,
however, to their disadvantage signal strength
is susceptible to fluctuations due to the
surrounding environment hence negatively
affects proximity accuracy.

In this paper, we also propose an iBeacon
based indoor user tracking system and
implement a prototype. The prototype serves
as a prelude towards the goal of developing
context-aware applications.

Mohammed Moneer (et al.), An Indoor Tracking System using iBeacon and Android (pp. 61 - 68)

Sukkur IBA Journal of Computing and Mathematical Science - SJCMS | Vol. 4 No. 2 July - December 2020 © Sukkur IBA University

3. Proposed Approach

At the core of any indoor tracking system
is to localize the objects. Thus, there involves
the choice to choose certain sensing devices to
locate the objects. In our proposed system, we
choose Bluetooth Low Energy (BLE) beacons
or more specifically iBeacons.

iBeacon is a technology introduced by
Apple where a transmitter device, referred to
as a beacon, transmits push notifications to
other receiver devices using Bluetooth Low
Energy (BLE) [16]. BLE standard
comparatively offers low power consumption
as well as lower cost for Bluetooth
communication. Thus, iBeacon has been used
in many contemporary systems [16], [4], [13],
[14]. Essentially, iBeacon technology provides
coarse-grained indoor location positioning
primarily based on the proximity—the
proximity to some nearby object serves as the
proxy to the location. In a typical iBeacon
deployment, the beacons periodically advertise
the information and the app on receiving
devices periodically listens for that
information to know about the surrounding
beacons [13]. The advertised information
includes (i) Universally Unique IDentifier
(UUID), which identifies the beacon region,
(ii) Major value, which is used to group related
beacons when they all have the same UUID,
(iii) Minor value, which is used to distinguish
between the beacons with same UUID and
Major value [16], and (iv) the received signal
strength indicator (RSSI), which is used to
measure the proximity of a mobile device to a
pre-installed iBeacon to approximate the
location of the mobile device [14].

Using iBeacon, we propose a 3-tier
architecture for the indoor tracking system.
First, we propose to install iBeacons in pre-
selected areas in a building (such as rooms) to
monitor the location of the users in the building
relative to those iBeacons. Second, to locate
the users in the building we propose each user
has a BLE enabled mobile device that can
approximate the users’ location relative to
nearby iBeacons and send that information to
the server. Third, we propose a server
application to visualize the presence of the

users in the building. Fig. 1. depicts the
architecture of the proposed approach.

Fig. 1. Architecture of the proposed approach.

3.1. Implementation

We implement a proof-of-the-concept
system to demonstrate and evaluate our
approach. We deploy our system at the
Institute of Information and Communication
Technology (IICT), University of Sindh,
Jamshoro. Below, we describe how each step
is handled for our 3-tier architecture.

1. Installing the iBeacons. We install one
iBeacon device in each room of the IICT
building in a specific position so that it covers
the room space and can precisely indicate the
users’ position relative to the room. Thus, each
room maps to the UUID of the iBeacon.

2. Android app on the users’ mobile
devices. To localize the users in the building,
each user is expected to carry the BLE enabled
Android phone to receive the advertised
information from the iBeacon to approximate
their location relative to the iBeacon. Since
knowing the advertised information such as the
UUID of the iBeacon and RSSI alone is not
helpful to monitor the users in the building, we
develop an Android app that uses this
information to calculate the users’ location and
send it to the server.

The app is developed for Android devices
with Bluetooth 4.0 support and runs as a
service so that it keeps running even when the
mobile is locked. The app essentially performs
following main tasks:

Mohammed Moneer (et al.), An Indoor Tracking System using iBeacon and Android (pp. 61 - 68)

Sukkur IBA Journal of Computing and Mathematical Science - SJCMS | Vol. 4 No. 2 July - December 2020 © Sukkur IBA University

- Monitoring the iBeacons. This
allows the app to monitor the entry
and/or exit to a specific room.

- Ranging. The app periodically listens
to the advertised information from
the iBeacon and measures the
distance between the mobile device
and iBeacon using RSSI.

- Send the location to the server.
During the ranging, the app has
already calculated the user’s location
(i.e., the room where the user
currently is inside) that is sent to the
server via SMS.

The app starts monitoring when the user
enters the building. When the mobile device
reaches in range of a certain iBeacon, the app
starts ranging to measure the proximity to the
iBeacon. Soon this proximity distance
becomes less than a set threshold, the app
sends an SMS text to inform the server app
about the user’s location. We set the proximity
distance threshold to 0.5 meter. To avoid the

battery, drain, the app then stops ranging,
yet it keeps monitoring.

3. Visualizing the users on the Server. To
visualize the physical location of the users on
the building map, we design a server app. The
app consists a GUI representing architectural
layout of IICT building as a map as shown in
Fig. 2.

The app has an SMS listener to receive the
SMS text that embodies the users’ location
information sent by the app running on the
users’ mobile devices. When the SMS listener
determines that the received SMS text is from
the registered user, it parses the text to extract
the user location. Once the location (i.e., the
room which is mapped to a specific UUID of a
beacon) is extracted, the server fetches the
picture of the user from the database and places
it on that specific room in the map. Here the
individual user is distinguished from other
users based on their mobile number.

Similarly, when the users move around the
building, leave a room and enter in another
room, the server app updates the visual map
correspondingly.

Fig. 2. Map of the IICT building on the server
app.

4. Evaluation and Results

In this section, we evaluate the proof-of-
the-concept system with two simple use cases
to demonstrate that the proposed approach is
effective.

The first use case is to demonstrate how the
implemented system tackles it when a single
user moves around the building. While the
second use case tests the system when two
users simultaneously move around the
building.

4.1. Single User Scenario

In this scenario, a single user is assigned
the task to first enter the research lab, then
leave the research lab and enter in data
communication lab, and finally leave the data
communication lab and enter computer lab II.
Meanwhile, the server app is monitored to
check that it correctly tracks and visualizes the
user activity on the map.

This whole exercise is depicted in Fig. 3.
The user enters the research lab (a), which is
reflected on the map on server app (b). Then,
the user leaves the research lab and moves to
data communication lab (c), the map on the
server app is updated correspondingly; the
picture of user is reflected on the data
communication lab (d) and removed from the
research lab (e). Finally, the user leaves data
communication lab and enters computer lab II
(f), which is also reflected on server app; the
picture of user is placed on computer lab II (g)
and removed from the data communication lab
(h).

Mohammed Moneer (et al.), An Indoor Tracking System using iBeacon and Android (pp. 61 - 68)

Sukkur IBA Journal of Computing and Mathematical Science - SJCMS | Vol. 4 No. 2 July - December 2020 © Sukkur IBA University

Fig. 3. Demonstration of a single user
scenario.

4.2. Two-user Scenario

In this scenario, the system is evaluated on
whether it can track two users when they are in
different rooms and when they gather in a
single room. Thus, to demonstrate this, two
users (user1 and user2) are assigned the task to
first enter in different rooms, user1 to enter in
the research lab while the user2 to enter in the
computer lab II. Finally, they need to meet in
the data communication lab.

Similarly, this whole exercise is depicted in
Fig. 4. First, the user1 enters the research lab
(a), which is reflected on server map (b) and
user2 enters the computer lab II (c), which is
also reflected on server map (d). Then, user1
leaves the research lab and enters the data
communication lab (e), the server map is
updated correspondingly (f). Finally, user2
also leaves the computer lab II and enters the
data communication lab (g), which the server

app correctly tracks and updates the map
accordingly (h).

5. Conclusion and Future Work

In this paper, we presented an indoor user
tracking system and implemented a proof-of-
the-concept prototype as a prelude towards the
goal of developing context-aware applications.
The system uses iBeacon technology for user
tracking. We evaluated the working of the
system on a couple of general uses cases. It
turns out that iBeacon is a good choice for
indoor tracking as its inexpensive and is a low
power consumption solution. However,
iBeacon has also its limitations. As pointed out
by Paek et. al. [14], iBeacon RSSI values and
the signal propagation model have significant
variations for iBeacon vendors, indoor
environment, and obstacles. Thus, in future
these limitations need to be addressed for
specific location-aware solutions.

Fig 4. Demonstration of two user’s scenario.

Mohammed Moneer (et al.), An Indoor Tracking System using iBeacon and Android (pp. 61 - 68)

Sukkur IBA Journal of Computing and Mathematical Science - SJCMS | Vol. 4 No. 2 July - December 2020 © Sukkur IBA University

References

[1] A. K. Dey, "Understanding and using

context." Personal and ubiquitous

computing 5, no. 1 (2001): 4-7.

[2] P. Rosenberger and D. Gerhard,

"Context-awareness in industrial

applications: definition, classification and

use case." Procedia CIRP 72 (2018):

1172-1177.

[3] E. Kaasinen, "User needs for location-

aware mobile services." Personal and

ubiquitous computing 7, no. 1 (2003): 70-

79.

[4] E. R. Sykes, S. Pentland and S. Nardi,

"Context-Aware Mobile Apps using

iBeacons: Towards Smarter Interactions"

(2015). Faculty Publications and

Scholarship. Paper 3. [Online] Available:

http://source.sheridancollege.ca/fast_app

l_publ/3.

[5] L. Jiang, "A WLAN fingerprinting based

indoor localization technique." M. Sc.

Thesis, The graduate College at the

University of Nebraska (2012).

[6] A. W. S. Au, C. Feng, S. Valaee, S.

Reyes, S. Sorour, S. N. Markowitz, D.

Gold, K. Gordon, and M. Eizenman.

"Indoor tracking and navigation using

received signal strength and compressive

sensing on a mobile device." IEEE

Transactions on Mobile Computing 12,

no. 10 (2013): 2050-2062.

[7] Y. Xiaoyi, “WiFiPoz–an accurate indoor

positioning system” MS thesis, Eastern

Washington University 2012. [Online]

Available: http://dc.ewu.edu/theses/81/

(accessed 20/11/2018).

[8] A. Bekkelien, "Bluetooth indoor

positioning." Master's thesis, University

of Geneva (2012).

[9] Y. Gu and F. Ren. "Energy-efficient

indoor localization of smart hand-held

devices using Bluetooth." IEEE Access 3

(2015): 1450-1461.

[10] B. Viel and M. Asplund. "Why is

fingerprint-based indoor localization still

so hard?" In Pervasive Computing and

Communications Workshops (PERCOM

Workshops), 2014 IEEE International

Conference on, pp. 443-448. IEEE, 2014.

[11] A. Mulloni, D. Wagner, I. Barakonyi, and

D. Schmalstieg. "Indoor positioning and

navigation with camera phones." IEEE

Pervasive Computing 2 (2009): 22-31.

[12] F. Seco, C. Plagemann, A. R. Jiménez,

and W. Burgard. "Improving RFID-based

indoor positioning accuracy using

Gaussian processes." In Indoor

Positioning and Indoor Navigation

(IPIN), 2010 International Conference

on, pp. 1-8. IEEE, 2010.

[13] J. Yang, Z. Wang, and X. Zhang. (2015),

"An ibeacon-based indoor positioning

systems for hospitals." International

Journal of Smart Home 9, no. 7, pp. 161-

168.

[14] J. Paek, J. Gil Ko, and H. Shin. "A

measurement study of BLE iBeacon and

geometric adjustment scheme for indoor

location-based mobile applications."

Mobile Information Systems 2016

(2016).

[15] C. Gomez, J. Oller, and J. Paradells,

"Overview and evaluation of bluetooth

low energy: An emerging low-power

wireless technology." Sensors 12, no. 9

(2012): 11734-11753.

[16] G. Conte M. De Marchi, A. A. Nacci, V.

Rana, and D. Sciuto, "BlueSentinel: a first

approach using iBeacon for an energy

efficient occupancy detection system." In

BuildSys@ SenSys, 2014, pp. 11-19.

[17] D. Daly, T. Melia, and G. Baldwin.

"Concrete embedded RFID for way-point

positioning." In Indoor Positioning and

Indoor Navigation (IPIN), 2010

International Conference on, pp. 1-10.

IEEE, 2010.

[18] M. Er Rida, F. Liu, Y. Jadi, A. A. A.

Algawhari, and A. Askourih. "Indoor

location position based on bluetooth

signal strength." In Information Science

and Control Engineering (ICISCE), 2015

Mohammed Moneer (et al.), An Indoor Tracking System using iBeacon and Android (pp. 61 - 68)

Sukkur IBA Journal of Computing and Mathematical Science - SJCMS | Vol. 4 No. 2 July - December 2020 © Sukkur IBA University

2nd International Conference on, pp. 769-

773. IEEE, (2015).

[19] X. Y. Lin, T. W. Ho, C. C. Fang, Z. S.

Yen, B. J. Yang, and F. Lai. "A mobile

indoor positioning system based on

iBeacon technology." In Engineering in

Medicine and Biology Society (EMBC),

2015 37th Annual International

Conference of the IEEE, pp. 4970-4973.

IEEE, (2015).

[20] Ting-Ting Yang and Hsueh-Wen Tseng.

2020. “A service-aware channel partition

and selection for advertising in bluetooth

low energy networks.” In Proceedings of

the 35th Annual ACM Symposium on

Applied Computing (SAC '20).

Association for Computing Machinery,

New York, NY, USA, 2144–2150.

DOI:https://doi.org/10.1145/3341105.33

73950.

[21] L. -W. Chen, J. -X. Liu and W. -C.

Huang, "GuidingAll: A Smart Proactive

iBeacon System with Crowdsourced

Sensing Based on IoT Technologies,"

2020 IEEE International Conference on

Pervasive Computing and

Communications Workshops (PerCom

Workshops), Austin, TX, USA, 2020, pp.

1-3, doi:

10.1109/PerComWorkshops48775.2020.

9156090.

[22] K. R. Tiwari, I. Singhal and A. Mittal,

"Smart Social Distancing Solution Using

Bluetooth® Low Energy," 2020 5th

International Conference on Computing,

Communication and Security (ICCCS),

Patna, 2020, pp. 1-5, doi:

10.1109/ICCCS49678.2020.9277175.