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There is a lack of cost-effective and easily exchangeable system solutions for position determination in indoor areas. The article describes a solution system for indoor position determination. The approach is based on beacons and inertial sensors of smartphones. An accuracy of 50 cm is achieved by a clever combination of different methods. The combined methods use the methods of angulation, map matching, fingerprinting, cell-ID approach and a particle filter. The investment costs for a 120 m 2 apartment are around 150 euros, excluding the necessary Android smartphone.
Content may be subject to copyright.
© The Author(s) 2018
D. Uckelmann (Ed.): Proceedings of the 1st Conference on Smart Public Buildings, pp. 6170,
19-20 October 2018, University of Applied Sciences Stuttgart, Germany.
ISBN: 978-3-940670-66-3
J.Bauer a, *, J. Bakakeu a, M. Hopfengärtner a, J. Bürner a, T. Braun a, F. Schäfer a, M. Wittmann a, A. Fehrle a,
B.Maußner a, T. Lechler a, M. Meiners a, C. Konrad a, J. Franke a
a Institute for Factory Automation and Production Systems, Technical Faculty, Friedrich-Alexander University
Erlangen-Nuremberg, Egerlandstr. 7-9, D-91058 Erlangen (Germany),
KEY WORDS: Ambient Assisted Living, Android, Beacon, Localization, IoT, Smart Home
There is a lack of cost-effective and easily exchangeable system solutions for position determination in indoor
areas. The article describes a solution system for indoor position determination. The approach is based on
beacons and inertial sensors of smartphones. An accuracy of 50 cm is achieved by a clever combination of
different methods. The combined methods use the methods of angulation, map matching, fingerprinting, cell-ID
approach and a particle filter. The investment costs for a 120 m2 apartment are around 150 euros, excluding the
necessary Android smartphone.
7.1 Introduction
Numerous applications in the smart home sector require localization of objects or persons. Often, once a person
has been localized, you want to identify the person as well or at least make the person distinguishable from other
people in the environment. Through localization and connected identification, existing systems often only
become applicable for multi-person households or benefit significantly from this functionality, e.g. in cross-room
sound or surface heating systems (Hein et al. 2017, T. Braun et al. 2016). In addition, localization in the field of
Ambient Assisted Living (AAL) plays an important role: fall detection software or applications that generate
health-related information (Bauer et al. 2016, Lutze & Waldhör 2015) and want to display this information
exactly at the place where the user is currently located. Researching groups are focusing on such applications in
the context of the Internet of Things (IoT) due to the increased number of connected devices (Bauer et al., 2014).
Current fall detection systems face the challenge of reliably avoiding false alarms. Causes for such false alarms
are, for example, when the mobile phone is exposed to excessive movement within a pocket or simply falls to the
ground (GFDK Gesellschaft für digitale Kaufberatung mbH, 2017). If it is possible to detect that a fall has
occurred in the sofa area, this information can be included in the result evaluation. This shows that the challenge
so far has been to differentiate between normal movements such as bending, lying down or kneeling and actual
falls (Parikh, 2010). The challenge of fall detection must therefore not be reduced to the localization topic,
although this is of course helpful in this context. Another imaginable application for person recognition is the
intelligent control of surface heating segments in the room. On the one hand, these provide a pleasant feeling of
warmth, as they heat around the people and not the air in the room. On the other hand, energy can be saved, as
less energy is lost through windows during ventilation. Energy can also be saved by regulating the individual
segments accordingly, as heating is only provided in the immediate vicinity of people.
The Global Positioning System (GPS) is established for outdoor positioning numerous mobile phones offer
GPS-based navigation systems, such as the Google Maps app. Under good conditions, the achievable accuracy
without correction is about 5 to 20 meters (GPS Genauigkeit & Einschränkungen, 2017). The fact that most
common GPS receivers also receive Wide Area Augmentation (WAAS) or European Geostationary Navigation
Overlay Service (EGNOS) correction signals improves the actual achievable accuracy to 1 to 3 meters. However,
optimum reception conditions are often not achieved: the GPS radio waves propagating quasi-optically due to
the very high frequency should ideally have visual contact with as many satellites as possible. Errors in the
position calculation are caused by excessive attenuation, reflection of individual or all GPS signals and by the
reception of too few satellites.
*Corresponding author.
62 J. Bauer, J. Bakakeu, M. Hopfengärtner et al.
However, GPS is not available for indoor use and there is a lack of sufficiently accurate, cost-effective, easily
upgradable localization options. Here, there are electromagnetic, acoustic and optical methods, which are used to
determine the position. These methods vary in the respective acquisition and installation costs. In 2015, 65% of
Germans used a smartphone and 40% a tablet (Lutter et al., 2015). Thus it is helpful to consider existing
possibilities of such devices when designing an indoor localization system. Android and iOS are common
operating systems for mobile devices, i.e. smartphones, smartwatches and tablets. According to current statistics,
Android’s market share is 85% among smartphone operating systems, while iOS has a share of 14.7% (Statista
GmbH, 2017). Both related vendors, Google and Apple, offer beacons in their product portfolios, too. In the
context of this paper, four identical beacons of the second generation from Beaconinside (Beaconinside GmbH,
2017) are used (see Figure 23).
Figure 23. Beacon of the second generation of the company Beaconinside
These beacons send a Bluetooth signal and complement the standard inertial sensors in the devices. As an
alternative to the battery-intensive Wi-Fi approach, localization in (Chandel et al., 2016) was performed by
combining access to the inertial sensors of a smartphone with the use of beacons. This fusion, which was
realized within a positioning system InLoc by the authors, turned out to be very promising. An analysis of the
accuracy and performance of a comparable methodology is investigated in (Dabove et al., 2015). Indoor
positioning is becoming more and more attractive due to beacons and more accurate inertial sensors in
smartphones from the cost-benefit aspect (Borrmann et al., 2015). Beacons periodically transmit a Bluetooth
signal with adjustable period length and transmission power. The minimum period duration is set to 100
milliseconds (ms), while the transmission power is set to 3 decibel-milliwatts (dBm).
Since the introduction (Android versions comparison, 2017) of Android 4.3 with the name Jelly Bean on
24.07.2013 Android devices support the Bluetooth Low Energy Technology (BLE) for low-energy
communication: Device A sends a BLE signal and an identification number (ID). Device B receives this signal
in a certain signal strength, the received signal strength intensity (RSSI), and assigns it to the beacon belonging
to the ID. On the basis of this signal strength and linked calculations, the distance and thus the position of the
communication participants can be estimated.
Now that the topic of localization has been sufficiently addressed, general and promising strategies for position
determination are examined in detail. Subsequently, a decision is made as to which of these strategies will be
incorporated into a solution concept for a cost-effective and easily retrofittable system for determining the
position of people indoors. The paper concludes with an analysis of the performance of the described approach.
7.2 Approaches for Position Detection
An existing floor plan is helpful as a basis for determining the position. This floor plan can be provided by third
parties, e.g. from existing architectural drawings, or it can be digitally generated and derived by mobile devices,
e.g. a vacuum cleaner robot. In the following, a floor plan of an apartment is presented (see Figure 24), which
played an important role in the implementation of the concept.
SmartLocate Indoor Localization with Bluetooth Beacons and Smartphone Sensors 63
Figure 24. Floor plan of the apartment in which the proof of concept was carried out
Four beacons are placed in the 116 m2 apartment. This is followed by the already described exchange of
information between the transmitter and the receiver of the Bluetooth signal. In the specific application, a mobile
phone with the Android operating system 6.0 Marshmallow (market launch 05.10.2015) (Brodersen, 2017)
serves as a receiver for the Bluetooth signals of the beacons. It thus determines the received signal strength, the
RSSI value of each beacon and, based on these RSSI values, the position of the mobile phone in the room or
The mobile phone contains numerous sensors that can be used for a possible indoor localization. As a basis for
the relevant calculations, different coordinate systems can be used: the space coordinate system (R-KOS), the
earth coordinate system (E-KOS), the body coordinate system (K-KOS) and the device coordinate system (G-
KOS) (see Figure 25).
Figure 25. Coordinate systems relevant for a smartphone and their interaction, here the R-KOS, E-KOS, K-KOS
With the R-KOS, the x and y axes are on the plane of the building floor plan and the z axis is perpendicular to it.
In contrast to the R-KOS, the E-KOS is based on the earth’s surface. Relative to the R-KOS, there is a
translational displacement around the position vector 𝑟 and a rotation of the z-axis around the angle of rotation
. With the K-KOS the origin is at the current position of the person, x- and y-plane are tangential to the earths
surface, with the y-axis pointing in the current direction of motion of the person. Relative to the E-KOS, the z-
axis is rotated by the angle Ψ, the time-dependent yaw angle, which represents a rotation of the direction of
movement to the geomagnetic north direction. The G-KOS is permanently connected to the Android device and
64 J. Bauer, J. Bakakeu, M. Hopfengärtner et al.
therefore follows the movement of the smartphone (see Figure 26). The coordinates can be represented as
vectors and can be transformed via a matrix.
Figure 26. G-KOS is a device-associated coordinate system with a fixed connection to the terminal
After explaining the background to the sensors and physical conditions, the focus is now on the helpful
calculation methods for position determination. The basic localization methods are methods that calculate the
position of the unknown point on the basis of known points, such as angulation or lateration. Triangulation is
widely used in cartography. If the length of one side and the angles of a triangle are known, the remaining side
lengths can be determined by trigonometric formulas. In trilateration (see Figure 27), it is not the angles that are
known, but the actual distances. Both approaches therefore allow the position of the unknown object to be
determined. Angles can be measured much more accurately than distances. However, the calculation procedure
for determining the position of the searched point is more complex. Consequently, angle-based methods are
usually used for long distances and distance-based methods if the position often has to be determined anew.
Multilateration therefore offers a technically simpler implementation option for position determination. For a
determination in two-dimensional space using the multilateration method, three known points are necessary to
determine the position of the fourth point. The solution of the nonlinear equation system leads to the position
calculation of the searched point.
Figure 27. Trilateration principle for position determination based on less known starting points (Geographic
Information Systems)
The distance between the transmitter and receiver object can be calculated from an RSSI value received by the
transmitter i. The distance between the object and the transmitter resulting from the spatial components is
SmartLocate Indoor Localization with Bluetooth Beacons and Smartphone Sensors 65
(x(t),y(t)) = (1)
In order to calculate the position of the object, a cost function can be set up. This takes into account the
calculated distances of all n transmitters. The cost function is then minimized to determine the position based on
the RSSI values:
C(x(t), y(t)) = (2)
- (3)
In the above equation, C (x(t), y(t)) denotes the position-dependent cost function at time t, which has a minimum
at the current position of the object.
Within the framework of lateration, distances between two points have to be determined. There are various
methods for measuring distances. The “time of arrival” method measures the transit time of a transmitted signal
until it reaches its destination. Alternatively, two signals can be sent simultaneously and the time difference on
arrival is determined. This method is called “time difference of arrival”. In addition to Bluetooth, there are other
signals that can be used for distance measurement, such as Wi-Fi signal strength, infrared or ultrasonic signals.
In addition, there are optical and acoustic methods. Each method has advantages and disadvantages, especially
with regard to retrofitting and installation costs. In addition, optical localization systems are viewed critically by
residents. Acceptance problems (Wolfangel, 2017) exist, for example, with cameras in the home. Against the
background of the increasing networking of the residential environment, such systems can result in security gaps
which are deliberately abused by crackers. Possible consequences of an external attack can be that residents are
spied on or disturbances are caused. The protection of privacy in the personal living environment represents a
basic need (Klebsch, 2017) for the user and, together with the topics of information security and user-
friendliness, is essential for a sustainable solution concept to emerge.
In indoor localization, one is confronted with the fact that the position of the tenant changes dynamically. For
this purpose, there are different procedures to take this general condition into account. The Cell ID method, also
known as the proximity method, uses a specific RSSI threshold to assign a circular field to each beacon. The
system therefore recognizes whether the person is in one of the circles or not (see Figure 28). The Cell ID
method, with a target accuracy of 50 cm, usually ignores many areas of the residential environment.
Figure 28. Proximity/Cell ID method for determining position on the basis of a threshold value based on the
received signal strength. Location X is assigned to P1
66 J. Bauer, J. Bakakeu, M. Hopfengärtner et al.
The fingerprinting method can be used in addition to the proximity method. This method is divided into an off-
and an online phase. In the offline phase, the resident generates various measuring points and stores the profile
of the received signal strengths at the respective measuring point. The result is a map of measuring points and an
RSSI value range for each measuring point. In the online phase, the received signal strengths can then be
assigned to a measuring point and thus to a position. The fingerprinting procedure normally delivers very good
results. However, it must be taken into account that the offline phase must be started again if a change is made.
Figure 29. Fingerprinting method with predetermined measurement points for better allocation based on received
signal strength
Also relevant is the dead reckoning method. Here only the inertial sensor technology of the mobile phone is used
more precisely the mobile phone can determine in which direction and with which acceleration and thus with
which speed it is moved. This data can then be merged to follow the path of the occupant on the existing floor
plan. The advantage here is that no additional investment is necessary. However, the procedure also has two
serious disadvantages: firstly, a fixed starting point is required and secondly, an error that occurs once
propagates. For this reason, the procedure is a useful addition to other procedures, for example, so that these
procedures in turn enable dead reckoning to detect a starting point or to provide for the detection of accumulated
Figure 30. Flowchart of the dead-reckoning method
SmartLocate Indoor Localization with Bluetooth Beacons and Smartphone Sensors 67
In order to be able to combine relevant procedures in a meaningful way, the object position supplied by the
respective procedure must be compared and evaluated. The particle filter, also known as the sequential Monte
Carlo method (SMC), is suitable for this purpose. The particle filter scatters particles in the state space and
determines the most probable system state. The SMC method is particularly suitable here because it can be
applied very flexibly.
7.3 SmartLocate Approach
Within the scope of the objective, a cost-effective, easy to retrofit and robust indoor navigation system was
designed: SmartLocate, an Android based app. The developed algorithm for position determination is based on a
combination of beacons, a given floor plan in combination with an Android smartphone, fingerprinting, inertial
sensors and map matching. Based on the descriptions above, it was decided to combine fingerprinting, map
matching, proximity and dead reckoning with a particle filter. With the particle filter, it is helpful to carry out the
propagation step permanently, i.e. whenever there is a change of orientation or a step. The estimation step and
the resampling of the particles are triggered when
a step and thus possibly a wall is crossed,
a new fingerprinting position value (approximately every 3 sec) is available,
a new proximity position value (about every 4 sec) is present.
7.4 Results
SmartLocate achieves the targeted accuracy of around 50 cm by skillfully combining the methods (see Figure
31). In addition to the accuracy, both the time required to determine the position and the robustness of
SmartLocate appear to be sufficient. After just a few meters or seconds, the person can be located in the home, as
can be seen from the point clouds, which are becoming closer and closer (see Figure 32).
Figure 31. Isolated view of the methods used in SmartLocate proximity, fingerprinting, dead reckoning and
particle filter
68 J. Bauer, J. Bakakeu, M. Hopfengärtner et al.
Figure 32. Mode of operation of the particle filter, starting from a holistically scattered particle cloud including
position estimation (A) up to the narrowing close to the position of the target object (X). After a few steps, there
is a congruence between A and X, known as AX
For the test scenario shown (see Figure 33), an accuracy of about 50 cm could be achieved after 10 steps. This
accuracy is based on the evaluation of six test scenarios (see Figure 34). Such accuracy is probably sufficient for
personal localization, especially when considering that beacons are often placed in less visible areas of the living
environment, such as the ceiling, for aesthetic reasons.
Figure 33. The test scenario for determining the average position error
SmartLocate Indoor Localization with Bluetooth Beacons and Smartphone Sensors 69
Figure 34. Mean position error for six selected test scenarios
SmartLocate will be further optimized in the future. In the described use case a mobile phone was used and
served as receiver of the BLE signal. However, the mobile phone is often stored and charged at home. The next
step is to test the use of a smartwatch as a BLE receiver.
It also seems helpful to pass on the determined position of the person or another IoT object to any existing smart
home system and its visualization. In order to take this approach into account, a connection to the proven
middleware “Eclipse SmartHome” or “openHAB” is currently being designed. A derivation of the floor plan
data from a vacuum cleaner robot is also in progress, so that the prerequisite for the application of the solution
system can also be generated automatically.
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