Indoor Positioning: A Comparison of WiFi and Bluetooth Low Energy

for Region Monitoring

Alexander Lindemann, Bettina Schnor, Jan Sohre and Petra Vogel

Department of Computer Science, University of Potsdam, August-Bebel Str. 89, Potsdam, Germany

Keywords:

Assistive Technology, Indoor Positioning, Bluetooth Low Energy.

Abstract:

Mobile devices like smartphones equipped with several sensors make indoor positioning possible at low costs.

This enables location based services, like mobile marketing, navigation, and assistive technologies in health-

care. In case of supporting disoriented people, the exact position of the person has not to be known, but it is

sufﬁcient to inform a caretaker when the attended person enters a critical region. This is the so-called region

monitoring approach. The paper presents results from region monitoring implemented as an app for Android

smartphones using WiFi and the low power protocol Bluetooth Low Energy respectively. Both networks are

compared regarding accuracy and the power consumption on the mobile device.

1 INTRODUCTION

With their widespread use, smartphones get interest-

ing for indoor positioning in the context of assistive

technologies. Thereby, disoriented people like de-

mented people or people suffering from amnesia can

be supported in their daily living. In case of demented

people living in a house for elderlies, the patient, or a

device like a wheelchair, is equipped with a smart-

phone. The smartphone periodically checks whether

the person is still in a safe environment. In case the

person leaves the home environment and seems to get

lost, the smartphone will send an alarm message to

the mobile of the caretaker.

At Potsdam University, the Kompass system is de-

veloped which investigates Ambient Assistive Living

(AAL) technologies for the support of elderlies (Fu-

dickar et al., 2011). The Kompass system uses smart-

phones for fall detection and adds localization infor-

mation to the alarm message when a fall with uncon-

sciousness is detected (Gimpel et al., 2015).

Further, indoor positioning is used for mobile

marketing application and indoor navigation within

complex buildings, like airports.

In our use case the need for accuracy is low com-

pared with mobile marketing applications, where the

application needs to know exactly in front of which

product the user is currently standing, or compared

with indoor navigation. To support disoriented peo-

ple, it is sufﬁcient to notify the caretakers in case the

user leaves the safe environment and enters a critical

region, like the corridor in front of the door.

Therefore, we proposed region-based position-

ing where the given environment is divided into re-

gions (Fudickar et al., 2011). A so-called region map

lists the beacons/routers which have to be received in

each region.

Since most buildings today have a WiFi infrastruc-

ture, this protocol was an obvious ﬁrst choice for po-

sitioning and was successfully tested in the Kompass

project (Schindler, 2011; Schefﬂer et al., 2013; Kap-

pel, 2014).

This paper compares region-based indoor posi-

tioning using WiFi versus Bluetooth Low Energy

(BLE). The evaluation metrics are positioning cor-

rectness (percentage of correct localizations) and

power consumption of the mobile device. BLE is

available on most of modern smartphones and oper-

ates like WiFi in the 2.4 GHz license-free band. BLE

may be an interesting alternative to WiFi, since mod-

ern WiFi-routers have a feature called cell breathing

which makes accurate positioning much harder. A

WiFi-router which uses cell breathing may change its

transmit power dynamically. In case of a high num-

ber of users, the radio reduces its transmit power and

hence its cell radius is decreased. Thereby, heavily

loaded cells may hand over load to neighboring cells.

While this approach is beneﬁcial for load distribution

and a good service quality, the dynamically changing

transmit power makes the deﬁnition of region maps

more difﬁcult. Further, it is a serious problem for po-

sitioning algorithms which rely on Received Signal

314

Lindemann, A., Schnor, B., Sohre, J. and Vogel, P.

Indoor Positioning: A Comparison of WiFi and Bluetooth Low Energy for Region Monitoring.

DOI: 10.5220/0005704603140321

In Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2016) - Volume 5: HEALTHINF, pages 314-321

ISBN: 978-989-758-170-0

Strength values.

The remainder of this article is structured as fol-

lows: The next section discusses different approaches

for wireless indoor positioning. In Section 3, related

work is discussed. The region-based indoor position-

ing algorithm and its parameters are presented in Sec-

tion 4. The results of our evaluation are given in Sec-

tion 5. Finally, the article ends with a conclusion.

2 WIRELESS INDOOR

POSITIONING

Due to the attenuation by walls and other obstacles,

a Global Navigation Satellite System (GNSS) like

GPS or Galileo is not usable for indoor positioning.

Therefore, other approaches are investigated like lo-

calization via the Received Signal Strengths (RSS)

in a wireless network. Typically, the environment

is equipped with so-called beacons which send wire-

less advertisement messages with a beacon identiﬁer.

The smartphones are conﬁgured with the information

about the beacons and their location within the envi-

ronment. Further, the Received Signal Strength Indi-

cator (RSSI) is used to estimate the distance of the

smartphone from the beacon. This is done by com-

paring the currently received signal strengths with a

radio-propagation map which contains the expected

RSSI values. The location with the smallest error dis-

tance is estimated as current location.

A radio-propagation map is generated either by

ﬁngerprinting or by a model-based algorithm. In case

of ﬁngerprinting, time consuming measurements are

done in advance, since the RSSI values have to be col-

lected at every grid point. For example, in (Fudickar

and Valentin, 2014b) the inﬂuence of the grid granu-

larity between 0.5-5 m is investigated for the model-

based approach using WiFi. The ﬁnest grid size of

0.5 m shows the best results in this study. Model-

based algorithms calculate the radio-propagation map

from a formula using different parameters like sig-

nal frequency, the distance from the beacons, and the

number of walls between beacon and the position of

interest. Typically, the inﬂuence of multi-path fading

is not considered in these models. For example, the

International Telecommunication Union (ITU) pro-

posed a path-loss function for the radio frequencies

from 900 MHz up to 5.2 GHz (ITU, 2012). Free

delectable parameters in this model are the distance

power loss coefﬁcient and the ﬂoor penetration loss

factor. The accuracy of the modeling depends on the

tuning of these parameters for the given building.

In the region-based approach, regions are deﬁned

by a list of beacons which have to be received (posi-

tive list) or which may not be seen in the speciﬁed re-

gion (negative list). Hence RSSI values are not used

and therefore no costly calculations have to be done

to determine the position with the least error. This

approach can be combined with a threshold for the

RSSI values to exclude beacons which are only seen

sometimes due to multi-path fading for example. The

beneﬁt of this approach compared to ﬁngerprinting or

model-based positioning is the higher usability, since

it has the lowest setup-time - no time consuming ﬁn-

gerprinting is necessary, nor parameter tuning like in

the case of the model-based algorithms.

The region based approach is already supported

by the iOS API since iOS 4.0 and later

. iOS supports

two kinds of BLE positioning, geographical region

monitoring and beacon region monitoring, where a

beacon region is an area deﬁned by the device’s prox-

imity to Bluetooth low-energy beacons. This ﬁts per-

fectly to our region concept. iOS also supports a

ranging API to determine the relative distance be-

tween a beacon and a device, but the programming

guide also informs about the well-known difﬁculties:

“Beacon ranging depends on detecting the strength of

Bluetooth low-energy radio signals, and the accuracy

of those signals is attenuated (or lessened) by walls,

doors, and other physical objects. The signals are also

affected by water, which means the human body it-

self will affect the signals. It is important to be aware

of these factors when planning your iBeacon deploy-

ment because they will impact the proximity value re-

ported by each beacon.”

3 RELATED WORK

A comparison of different indoor location systems

is given in (Fudickar and Valentin, 2014b). The

majority of the algorithms use either WiFi (Bahl

and Padmanabhan, 2000; Gansemer et al., 2010), or

the 868 MHz network typically used in sensor net-

works (Behnke and Timmermann, 2008; Fink et al.,

2010; Fudickar and Valentin, 2014a). While the

different algorithms show a good accuracy in their

speciﬁc test environments, neither the inﬂuence of

humans nor the inﬂuence of concurrent networks,

like the campus-wide WiFi, on the Received Signal

Strength (RSS) is tested.

iOS Developer Library, Location and Maps Program-

ming Guide

Indoor Positioning: A Comparison of WiFi and Bluetooth Low Energy for Region Monitoring

315

3.1 Fingerprinting and Model-based

Positioning

Different model-based algorithms have been inves-

tigated by Fudickar and Valentin (Fudickar and

Valentin, 2014b; Fudickar and Valentin, 2014a). The

authors investigated the inﬂuence of different parame-

ters on the accuracy of the positioning algorithm. The

parameter tuning process is described in (Fudickar

and Valentin, 2014b), while (Fudickar and Valentin,

2014a) compares the tuned algorithm for WiFi with

a model-based algorithm tuned for the radio frequen-

cies of 868 MHz which are typically used within low

power sensor networks. The authors show that the

868 MHz network is well suited for indoor position-

ing in their test environment since it was more accu-

rate than using WiFi, and extended the device run-

times from 3.39 h (WiFi) to 7.25 h (868 MHz). Since

the energy saving features of current operating sys-

tems like Android may result in even longer runtimes,

the results show the beneﬁt of the low power network.

But on the other hand, the lack of mobile devices

equipped with this network is a hurdle.

3.2 Positioning with BLE

Bluetooth Low Energy was already evaluated for in-

door positioning in (Faragher and Harle, 2014; Jiany-

ong et al., 2014).

Fingerprinting: Faragher and Harle compare

WiFi and BLE ﬁngerprinting (Faragher and Harle,

2014). They use 10 Hz beaconing and a sample inter-

val of 1 second on the mobile device. First, they inves-

tigated the impact of a human body on the RSS value.

The experiment shows an inﬂuence of about 10dB. In

a distance of 1 m between beacon and receiver, this

results in a poor range estimate of 5-10 m. Next, the

authors compare the two networks in a 45m by 12m

section of a building which is covered by 3 WiFi ac-

cess points and 19 BLE beacons transmitting at power

level around - 20 dBm. They report that the error dur-

ing the WiFi tracking was less than 8.5 m in 95% of

the measurements, and less than 2.6 m in 95% of the

measurements using BLE. They state that the WiFi

performance was limited by the poor signal geometry

afforded by the existing WiFi infrastructure available

in the laboratory, and the higher BLE beacon density

on the other hand. While these results are promising,

the inﬂuence of human bodies on the positioning ac-

curacy is not further investigated in the tracking test.

Further, the power consumption was not in their fo-

cus.

Model-Based: Jianyong et al. present results from

a model-based positioning system using BLE (Jiany-

ong et al., 2014). They evaluated their algorithms in

a very simple scenario where one room was equipped

with one beacon in each corner without any obstacles.

Under this laboratory conditions, they achieved an er-

ror less than 2 meters in about 96% of their test cases.

Again, the power consumption was not investigated.

3.3 Power Consumption

In (Dementyev et al., 2013), the power consump-

tion of the three low-power protocols, Bluetooth Low

Energy, ZigBee and the proprietary ANT network,

is compared in a cyclic sleep scenario. All three

wireless networks operate in the 2.4-GHz-ISM-band.

In the cyclic sleep scenario, Bluetooth Low Energy

achieves the lowest power consumption (10.1 µA),

compared with ZigBee (15.7 µA) and ANT (18.2 µA)

on the mobile device. Since the energy consumption

of sending and receiving messages is more or less the

same, we expect a similar behavior for the location

scenario, where the mobile device periodically gets

into the listening mode. Therefore, BLE seems to be

the most suited test candidate from these low-power

protocols.

4 USING BLUETOOTH LOW

ENERGY FOR INDOOR

POSITIONING

Several BLE beacons have to be deployed within the

environment. A tracked person is equipped with a

smartphone which scans periodically for advertising

messages from the beacons and identiﬁes each beacon

by its signature (Major/Minor/UUID). The Scan Time

is also a parameter which has to be optimized. Since

sending and receiving messages is a dominant factor

in the power consumption, the Scan Time should be

set as short as possible.

Typically, a beacon can operate in different

modes:

• Advertising Interval: time between 2 beacon ad-

vertising broadcasts. Depending on the hardware,

this time can be conﬁgured in the range from 50-

2000 ms.

A short Advertising Interval results in a higher

power consumption of the beacon, but increases

the probability to receive advertising messages

during the Scan Time of the device.

• Transmit Power: is the physical power of the

transmitted signals.

Again, the Transmit Power is relevant for the

power consumption of the beacon. A higher

HEALTHINF 2016 - 9th International Conference on Health Informatics

316

Transmit Power results in a higher energy con-

sumption of the beacon, but also in a higher re-

ceive probability during the Scan Time of the de-

vice.

As introduced in Section 2, a region map has to be

conﬁgured with a Beacon Positive and a Beacon Neg-

ative List. An example is shown in Table 3. The lo-

calization algorithm is illustrated in detail in Figure 1.

Periodically, the smartphone enables the Bluetooth

adapter to scan for advertising messages and sleeps

between. Since the Bluetooth radio needs some time

to get ready, the smartphone gets for Setup Time sec-

onds again in the sleep mode after starting the BLE

radio. After localization, the smartphone sleeps for

Sleep Interval seconds. Hence, localization is done

about every Sleep Interval plus Sleep Time seconds.

During localization, the received beacon signatures

are compared with the region map entries and the ﬁrst

match is returned.

Figure 1: Localization algorithm.

In case a critical region is detected, the device

sends a notiﬁcation to a conﬁgured address (Kompass

server or telephone number) via WiFi or SMS.

Privacy Concerns: Our location system uses 3

different reporting modes to support the privacy con-

cerns of the user which have to be conﬁgured:

1. alarm: The localization information is only re-

ported to the Kompass server in case of an emer-

gency event, for example a detected fall with un-

consciousness.

2. tracking-alarm: The localization is reported to the

Kompass server when the user leaves the safe en-

vironment and enters a critical region.

3. tracking: The device reports periodically the lo-

calization to the server. This helps to ﬁnd lost de-

vices. Further, the localization is sent in case of

the alarm messages.

5 RESULTS

5.1 Hardware and Software Parameters

In our test environment we used BLE iBeacons of

BEACONinside, Model No. B0001-A

which is pow-

ered by two batteries from type AAA. Due to the

manufacturer’s data sheet, the beacons should op-

erate about 1-year once deployed using the default

(highest) Transmit Power of 0 dBm. The mobile de-

vice was the HTC ONE mini2 smartphone with An-

droid 4.4.2 (KitKat) installed.

Figure 2: BEACONinside: Picture of the used Beacon

First, we experimented with the parameters to set

up a robust system. We measured a Setup interval of

2.5 s. Hence, we conﬁgured a Sleep Interval of 4.5 s

to scan about every 10 s. The Advertising Interval was

set to 200 ms and the Scan Time to 3 s. Theoretically,

the device should receive up to 15 advertising broad-

casts during one scan process. The chosen parameter

values are summarized in Table 1.

5.2 Comparison WiFi and BLE

First Experiment: In our ﬁrst test scenario we com-

pared the results from (Kappel, 2014) for WiFi with

a BLE setup. Kappel deployed three WLAN access

points and deﬁned four regions as shown in Table 2

and illustrated in Figure 3. The regions were de-

ﬁned according to the coverage of the WLAN access

points. The access points did not support cell breath-

ing. During a walk along the corridor, Kappel made

65 measurements and reports only one false localiza-

tion (98.4 % correct localizations).

We repeated this experiment using three BLE bea-

cons at the same positions, each sending with the

http://www.beaconinside.com

Picture from press kit of Beaconinside (Copyright

Indoor Positioning: A Comparison of WiFi and Bluetooth Low Energy for Region Monitoring

317

Table 1: Parameter list of the localization algorithm.

Beacon Parameter Value description

Advertising Interval 200 ms Time between two advertisements

Beacon transmit power -23/-6/0 dBm

Smartphone Parameter Value description

Setup time 2.5 s Time until the Bluetooth radio is ready

Sleep Interval 4.5 s Time between localizations

Scan Time 3 s Time the Bluetooth radio is listening

Figure 3: Regions with WLAN access points (Kappel,

2014).

Table 2: Region map for WiFi from (Kappel, 2014).

Region Positive List Negative List

Lab :9f; :98 :69

Floor :69; :9f; :98

Floor 2 :98; :69 :9f

Classroom :69 :98; :9f

highest transmitting power of 0 dBm (three beacons

instead of WLAN access points :98, :9F and :69 in

Figure 3). Similar to the test from Kappel, 70 mea-

surements were made during a walk along the corri-

dor with the smartphone in the hand, but in only 28

measurements the correct region was detected. WiFi

has a further range than BLE and does not fade out as

fast as BLE. Obstacles as walls, doors and reﬂecting

surfaces affect the signal strength of BLE more than

WiFi. Hence, there have been areas in which no ad-

vertisement messages could be received and therefore

no localization was possible.

Table 3: Region map for BLE test.

Region Positive List Negative List

Lab BLE 1 or BLE5 -

Floor BLE3 -

Floor2 BLE4 or BLE2 BLE1, BLE3, BLE 5

Second Experiment: In the next experiment, we

Figure 4: Regions with BLE beacons.

increased the number of beacons and used different

transmit powers. The beacon placement is shown in

Figure 4 and corresponding region map in Table 3.

The transmit power of the beacons is set as follows:

beacon BLE1 and BLE5 are in the low transmit power

mode (-23 dBm), beacon BLE3 in median (-6 dBm),

and beacon BLE2 and BLE4 in high transmit power

mode (0 dBm).

In our experiments, a low transmit power is ben-

eﬁcial for a more accurate localization due to the

smaller cell radius (see the Lab region). On the

other hand, beacon messages sent with a low trans-

mit power are lost more often, since they are shielded

by the body of the smartphone user. Therefore, we

equipped the laboratory with two beacons to increase

the receive probability.

Further, we added an additional beacon BLE4 be-

hind the column which was no problem in case of

WiFi, but shielded the BLE beacon signal of beacon

BLE2.

We repeated the measurements and got 85 correct

localizations out of 88 (96.6 %).

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318

5.3 BLE Accuracy Tests

In the following test scenarios, we investigated

whether it is possible to localize more accurate us-

ing BLE. Therefore, we deﬁned smaller regions in

the north part of the building. The test environment

is about 19 m x 19 m. Again, we experimented with

different beacon densities (3 resp. 5 beacons) and dif-

ferent beacon transmit powers. The beacons are de-

ployed as shown in Figure 5 in a height of approxi-

mately 2,3 m. The 10 test positions where the smart-

phone user made the localization are also shown in

Figure 5. For each test position, the localization was

made 9 times which results in 90 measurements. At

each test position, the test person turns around about

◦

after each measurement. Hence, in some of the

experiments there is a direct line-of-sight, and in the

others, there is the human body as an obstacle.

Figure 5: Three beacons with lowest Transmit Power.

Lowest Transmit Power - In our ﬁrst experiment,

we tested with three beacons (Beacon 1, 2 and 3) in

the lowest transmit power mode of -23 dBm. The ex-

pected beneﬁt of the low transmit power is a longer

beacon life time due to energy savings and a more ac-

curate localization due to smaller cells.

The goal of this test scenario was to determine

whether it is possible to distinct two neighboring

rooms (Laboratory 1 and 2), and further to identify

the critical region with two exits (elevator and stair-

case). The region map is shown in Table 4. The re-

gion map was designed in such a way that position 1

and 2 are in the laboratory 1, position 3 and 4 are in

laboratory 2, 9 and 10 within the critical region, and

all other positions (5-8) are within the safe corridor.

From the 90 localizations, 70 were correct and 20

were incorrect. The results for each position are given

in Table 5. Failures occur when the test person stands

near a beacon and shields the beacon broadcast (Po-

sition 1,2, and 10). At position 9, the elevator shields

the signal.

Medium Transmit Power - In the next step, we

Table 4: Region map for experiment with 3 Beacons.

Region Positive List Negative List

Lab 1 Beacon 1 empty

Lab 2 Beacon 2 empty

crit. region Beacon 3 empty

safe corridor empty empty

Table 5: Low Transmit Power (-23 dBm).

Test place correct false

1 5 4

2 7 2

3 9 0

4 9 0

5 7 2

6 9 0

7 7 2

8 8 1

9 4 5

10 5 4

Total 70 20

Percent 77.8 22.2

increased the transmit power to the medium mode of

-6 dBm. The results are shown in Table 6. Due to the

higher transmit power, the beacon cells are enlarged

and overlap. Hence, lots of false localizations occur

(55.5 %). It is notable that a high error rate occurs

at positions 5, 6, 7, and 8 which belong to the safe

corridor. Due to the higher transmit power, advertise-

ment messages from beacon 1, 2, or 3 were received

at these positions which results in a wrong classiﬁca-

tion.

Table 6: Medium Send Signal strength -6 dBm.

Test place correct false

1 9 0

2 9 0

3 2 7

4 4 5

5 0 9

6 1 8

7 0 9

8 0 9

9 7 2

10 9 0

Total 41 49

Percent 45.5 55.5

Higher Beacon Density - The key problem iden-

tiﬁed in the experiment with low transmit power was

the inability to perform any localizations at all, due

to the limited beacon range. In the experiment with

medium transmit power, the key problem identiﬁed

was the number of incorrect localizations due to the

unexpectedly high range. This particularly affected

positions in the corridor where we could not distin-

guish the safe corridor from other regions including

the critical region.

Indoor Positioning: A Comparison of WiFi and Bluetooth Low Energy for Region Monitoring

319

Figure 6: Five beacons with low transmit power.

Table 7: Region map with 5 beacons.

Region Positive List Negative List

Lab 1 Beacon 1 Beacon 4

Lab 2 Beacon 2 Beacon 4

Crit. Region Beacon 3 or 5 Beacon 1, 2, 4

Safe Corridor - Beacon 1, 2

Hence, we increased the beacon density as shown

in Figure 6. All beacons operated in the low transmit

power mode. We added one beacon in the corridor

to distinguish one area of the corridor from the other.

Furthermore, we added a beacon at the elevator, since

the beacon for that region could often not be seen due

to a missing line of sight (elevator shaft). For that rea-

son, point 8 is considered part of the critical region for

this experiment. The region map is shown in Table 7.

As can be seen in Table 8, this improves the results

for some positions only, while worsening the results

of other areas. In particular, point 7 is now showing

poor performance due to the high range of the added

beacon at the elevator shaft. As expected, points 9

and 10 however achieve better results compared to the

experiment with medium transmit power.

Table 8: Higher beacon density with 5 beacons.

Test place correct false

1 5 4

2 6 3

3 4 5

4 7 2

5 8 1

6 9 0

7 1 8

8 6 3

9 8 1

10 7 2

Total 61 29

Percent 67.8 32.3

5.4 Power Consumption

In a further experiment, we compared the power con-

sumption of WiFi and BLE for region monitoring.

Kappel reports experimental results for a HTC Evo

3D using WiFi (Kappel, 2014). He measured the

runtime of the smartphone when the localization is

performed every 10 or 30 seconds and compares this

value with the runtime when no positioning is done. If

the positioning app was not running, the battery was

discharged after 185 hours and 31 min. With the run-

ning app and an interval of 10 s for positioning, the

device runtime was 37 hours and 15 min, for an inter-

val of 30 s, the runtime was 50 hours and 43 min.

Table 9: Power Consumption.

Localization device runtime

WiFi (Kappel, 2014) BLE

Without 185.5 hours 500 hours

every 10 s 37 hours 70.5 hours

every 30 s 80 hours 151 hours

We measured the power consumption of region

monitoring for BLE on the smartphone HTC ONE

mini2. Table 9 shows that BLE improves signiﬁcantly

the device runtime.

6 CONCLUSIONS AND FUTURE

WORK

Low power networks like Bluetooth Low Energy are

attractive for indoor positioning due to the easy in-

stallation, the low power consumption and the long

life cycle of the beacons. Therefore, we compared

Bluetooth Low Energy (BLE) with WiFi in a region

monitoring scenario where the localization is done on

a mobile device like a smartphone. The region-based

positioning was implemented as an app for Android

smartphones.

Regarding power consumption, the beneﬁt of BLE

is impressive. BLE nearly doubled the runtime of the

mobile device from 37 hours up to 70 hours when the

device tries to localize its position every 10 seconds.

Regarding accuracy, the result is not so obvious.

Even with the maximal signal strength, we did not get

a coverage like WiFi. So we had to increase the bea-

con density. Since the costs per beacon is low com-

pared with WiFi, this is no serious drawback. On the

other hand, the higher beacon density combined with

different transmit power modes allows the deﬁnition

of smaller regions and hence a more accurate local-

ization compared with WiFi.

In a coarse grain scenario, we achieved 96.6 %

correct localizations with BLE. In a similar scenario,

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320

Kappel reported 98.4 % correct localizations in case

of WiFi (Kappel, 2014). In a second test series, we

tried to identify smaller regions with BLE. In these

experiments, the best result was 77.7% correct local-

izations.

Another aspect for the usability of the presented

approach is the setup time of the beacon infrastruc-

ture. While it is short compared with ﬁngerprinting

or parameter tuning for model-based algorithms, the

effort for beacon positioning and test measurements

is not neglectable.

In our use case, the caretakers are notiﬁed when a

demented and disoriented person leaves the safe area.

For sending notiﬁcation messages, the BLE infras-

tructure is not suited. Hence, the existing WiFi infras-

tructure of the building or SMS messages via GSM

have to be used.

In (Fudickar et al., 2011) a dynamic localization

interval is motivated. For example, a resident may

have lunch and is not moving. In this situation, the

localization interval may be increased for further en-

ergy savings. Therefore, we will combine sensor data

from the smartphone’s accelerometer which are al-

ready used for fall detection with the localization sys-

tem. Further, we will use thresholds for the RSS val-

ues and investigate their inﬂuence on the positioning

accuracy.

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