International Journal of Informatics Society, VOL.10, NO.3 (2019) 119-128

119

Regular Paper

BLE Beacon’s Defect Detection Method Based on Room Model of BLE and Wi-Fi
Shota Ikeda† , Fumitaka Naruse ‡ , and Katsuhiko Kaji‡†

Graduate School of Business Administration and Computer Science, Aichi Institute of Technology, Japan‡
Department of Information Science, Aichi Institute of Technology, Japan{b17703bb, k14092kk, kaji}@aitech.ac.jp

Abstract - In this research, we propose a defect-detection
method for a Bluetooth Low Energy (BLE) beacon. The typesof defect are breakdown, battery exhaustion, removal and relocation. To detect such defects, we create a BLE model and
Wi-Fi model for each room. The BLE model and Wi-Fi model
consist of probabilities of observation of each beacon s radio
information. By comparing the newly acquired BLE radio information and Wi-Fi radio information with the BLE/Wi-Fi
room model, it is possible to detect the problem occurring in
the beacon. As a proposed method, we compare the roommodel with the radio-wave information of the acquired BLE
and Wi-Fi, and detect the beacon defect automatically. We
conducted indoor experiments and beacon defect-detection
experiments using the proposed method. In the room estimation experiment, the accuracy of estimation at BLE withthe optimal boundary value was 86%, and the estimation accuracy at Wi-Fi was 95%. In beacon defect-detection experiments, the correct answer rate for defect detection was 94%.
However, when the beacon disappeared from a room, the detection accuracy was 35%, which was less than half.
Keywords: BLE beacon, fingerprint, room estimation, defect detection, Wi-Fi

1 INTRODUCTION
BLE beacons (from here on, simply ’beacons’) are increasingly used for services in public facilities such as indoor location estimation, room estimation, attendance management,
distribution of coupons, mountaineer distress prevention, andcheckpoints of electronic stamp rallies [1]–[4]. To estimate
the position of a room in room estimation, one to several beacons are installed in each room. Also, there are many methods of installing beacons at a fixed distance, and they are used
for indoor management and recording of walking routes [5],[6]. They are used to distribute coupons to the smartphonesof people who pass in front of a store [7]. For mountaineeringsafety, climbers can bring beacons that sound an alert if anyone strays too far from the group. In electronic stamp rallies,
beacons are installed at checkpoints. When a smartphone isclose to the checkpoint’s beacon, the user can press the stamp
on the smartphone application [8], [9].
Because beacons are small and easy to carry, it is difficult tomanage many beacons simultaneously. Beacons run on batteries whose lifetime is from about six months to two years.
Thus, when dealing with many beacons, we have to check every one to figure out which has run out of batteries. Also,

beacons are small, have the same shape, and are easy to carry.
Therefore, someone can move them accidentally, or they canbe miss installed.
When the target environment is small, usually the administrator of the environment can easily manage the small numberof beacons because it is easy to grasp what kind of defect isoccurring in which beacon and respond quickly.
In a large-scale environment with many beacons such as auniversity or electronic stamp rally at a large park, the administrator must collectively manage the system, and it is unrealistic to check beacons for failure one by one.
In this research, we generate a BLE/Wi-Fi-based room model
to estimate defects such as battery outage, malfunction, removal, and relocation. The BLE model and the Wi-Fi model
consist of probabilities of observation of signal informationfor each room. When a new BLE/Wi-Fi radio wave observation is acquired, the system compares the obtained radio
wave information with each room model, and estimates theroom from which the radio waves were acquired. After that,the system compares the estimated room model with the acquired radio wave information and finds the beacon defect.
Battery-powered beacons are mostly used, so the batteries
run out during long-term operation. Also, since beacons are
small and have a shape that is difficult to fix to a wall, thereis the possibility of them being moved. Additionally, in order to manage many beacons, there are cases where beacons
are mistakenly installed in the wrong room. Because beacons only transmit BLE radio waves, they cannot communicate with each other. Therefore, it is impossible to confirm
problems between beacons.
Using the proposed method, the administrator does not haveto undertake the impractical task of checking each beacon individually.
The rest of this paper is structured as follows. In Section
2, we explain related research. Section 3 describes our beacon defect detection method. Section 4 describes experiments
using the proposed method. Section 5 summarizes and discusses future issues.

2

RELATED RESEARCH

Wireless LAN, beacons, built-in sensors of smart phones,
etc. are often used for indoor position estimation and room
estimation research. However, there are few studies on detecting activity or defects of beacons for terminal management.
There is research on the behavior monitoring of BLE beacons using participatory sensing. In Asahi’s method [8], theISSN1883-4566 © 2019 - Informatics Society and the authors. All rights reserved.

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beacon is used as a check point of an electronic stamp rally.
When the smartphone terminal that introduced the application receives radio waves, beacon information and time data
are sent to the server. When monitoring the information transmitted to the server, the data continuously transmitted may beinterrupted in some cases. Since the transmission of data suddenly stops, it is a method that grasps the activity state of the
beacon. This method is considered to be effective as beacon
management during service operation. However, if the beacon installed as a checkpoint is moved, or if it is installed at aplace where people do not always go, it cannot be determined
whether the beacon is running or not. Also, there is a possibility that some time lag may occur between a beacon sending
information, and that information being confirmed. Additionally, even if a beacon is moved to an unexpected place, whensomeone brings a smartphone within range of that beacon, themethod confirms that the beacon is normal.
When estimating position using campus LAN, it is necessary to measure the radio wave intensity of Wi-Fi for eachposition. When using the wireless access point to estimate
the position as in Dhruv’s method [10], it is necessary to determine the observation position of the radio wave intensity
considering the base station. In contrast, our method creates
Wi-Fi and BLE fingerprints. Instead of a fingerprint for eachlocation, it creates a fingerprint for each room based on themeasured data. When you create a fingerprint for each room,
you cannot figure out where you are in the room. However,it becomes easy to grasp whether or not you are in the room.
When indoor position estimation using radio field strength ofa BLE device is carried out, radio waves of BLE are weaker
than Wi-Fi.
Kajioka’s method involving indoor positioning using BLE
radio intensity [11] targets small classrooms. Therefore, only
one beacon is placed for each room. Even though radio waves
may be weak, the beacon’s radio waves could be received everywhere in small rooms. It is considered that the possibility of a radio wave not reaching an estimation device is low.
However, we also assume a lecture room and a large lectureroom compared to Kajioka’s research. Therefore, there is apossibility that some places cannot be reached depending on
location. Therefore, we should install multiple beacons if aroom is so large.

3 BEACON DEFECT DETECTIONMETHOD USING WI-FI AND BLE
OBSERVATIONIn this research, we generate a Wi-Fi model and BLE modelfor each room, and detect defects such as battery outage or
breakdown or relocation of a beacon. An outline of this method
is shown in Fig. 1. In scene 1, when comparing the observed
data with the room model of each room, it can be estimatedthat the user is at room α from the Wi-Fi model. However, theBLE signal is not received from beacon A. Therefore, beacon
A is thought to have experienced a defect such as battery exhaustion or relocation. In scene 2, when comparing the modelof room α with the observed data, the existence of beacon B
can be confirmed. Since there is no beacon B in the room

Figure 1: Outline of proposed methodmodel of room α, there is a high possibility that beacon B
was moved from room β to room α.
By using this method, operation check of a beacon can bedone automatically. It is not necessary to periodically checkthe operation of each beacon. It is also considered that defects
such as in Fig. 1 can be detected during operation. By doing
this, it is possible to deal with the beacons which the defects
have occurred.

3.1

System Overview

An overview of the system is shown in Fig. 2. This method
is introduced together with room estimation-based applications such as attendance-management applications and coupondelivery applications. BLE/Wi-Fi observation data is acquired
for users who frequently use these applications. The timing
to acquire the data is when the BLE is received. Also, theuser here is not a system manager, but general users who use
the application on a daily basis. In the normal state, when
it receives BLE radio waves, it communicates with the room
estimation server and receives room information. There isa room recognition library as the foundation of location information service. Normally, in the library, room recognition is performed using only BLE in order to judge the occupancy situation necessary for location information service.
BLE models of all rooms are synchronized from maintenanceserver to room estimation server, and regular room estimation is done by using BLE model. Sometimes, when receiving BLE, it also receives Wi-Fi and sends two pieces of observed information to the maintenance server. Please notethat the BLE/Wi-Fi observation task is not explicit. Observed
BLE/Wi-Fi can be uploaded to the management database without the user s awareness. When observation information is
uploaded, the maintenance server estimates the beacon defectbased on the received radio wave information. The server
detects a problem with the BLE beacon using both the BLEmodel and the Wi-Fi model. When a problem is found in
the beacon, information on the room and the beacon in which
the problem occurred is presented to the manager. Then, theInternational Journal of Informatics Society, VOL.10, NO.3 (2019) 119-128

121

Figure 2: System overview
Figure 3: Collecting fingerprint
Table 1: Characteristics of beacon and Wi-Fi router
Installation cost
Installation place
Driving time
Detection range
Power consumption

BLE beacon
○
○
△
△
○

Wi-Fi router
△
△
○
○
△

manager can see the defect information and go to the place tocorrect the problem.
In fact, you can estimate a room without using beacons if
you use Wi-Fi. A comparison of the characteristics of WiFi routers and beacons is shown in Table 1. We decided toutilize beacons instead of Wi-Fi routers based on that. Beacons are inexpensive to install and many can be placed in thesame location. Also, since they are mostly battery-powered,installation is possible without being restricted by electricalaccess. Because beacons use BLE, they operate with powersaving, and the transmission range of radio waves is narrow.
However, the lifespan is as short as one to two years. Wi-Fi
routers are expensive to install and not suitable for installationin many rooms. Since they need a power supply to operate,installation locations are limited. However, since the possibility of interrupting operation is low, reliability is high andradio waves can be transmitted over a wide range.
In this study, we are targeting all the rooms of one building
or one campus such as a university, so it is considered that
beacons are suitable for installing in each room because they
are cheap and easy to install. Basically, Wi-Fi routers are used
to access the Internet, so it is not realistic to install them in aroom that is not used much. If we estimate the narrow range
of a room, it is considered that a narrower range is better thantransmitting a radio wave over a wide range. As such, beacons
are considered suitable.

3.2 Features of This Method
In this method, we generate a fingerprint for each room
rather than a fingerprint for each measured position [12]–[15],which is being done in many position estimation studies. For
each room, not for each location, no matter the detailed location of the observation point. Observe each room rather thanlocation. Then, it is estimated as that room which is regardless of where in the room. Therefore, room estimation is easy.
Also, compared to the method that requires observation of detailed positions, the number of observations of radio-wave fingerprints for information can be reduced. This leads to cost
reduction. Furthermore, there is no need to clearly decide
when to measure radio information. There is the advantagethat it is sufficient to observe data everywhere in the room.
The reason for combining Wi-Fi and BLE is to use Wi-Fi
to detect a beacon defect when it occurs. It also plays the roleof room estimation. When estimating a room by installing asingle beacon in a small room such as a small classroom ora laboratory, it is impossible to estimate a room if a beacon
fails or a beacon is taken out. Also, in the case of breakdown,
the beacon will be repaired if replaced. However, when it istaken out, it is necessary to search for a beacon terminal.
Data collection and modeling of this method are shown in
Fig. 3 and Fig. 4. In this method, the system knows all beacon ID’s and their room placements. In the case of a smallroom, beacon radio waves can be acquired anywhere in theroom. In the case of a large room, radio waves of the arranged beacon may not be acquired in some cases. If datacannot be acquired continuously, it could be erroneously detected that a defect has occurred in the beacon. Therefore, in
our assumption, several beacons are placed in a large room to
avoid this problem. The system collects Wi-Fi and BLE radio
wave information to estimate a room, and integrates the collected Wi-Fi and data for each BLE. We create a Wi-Fi modeland a BLE model for each room as in Fig. 4.
During operation, we gather BLE/Wi-Fi observation datafrom various people that use room estimation-based applications such as automatic attendance systems and stamp rally
games. Basically, BLE data is observed when entering a room
as in Fig. 3. The system also observes Wi-Fi data at regularintervals and compares the observed BLE list with the BLE

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Figure 4: Model creation methodmodel in every room and estimates the room. Room information is sent after room estimation is done. The system compares the Wi-Fi list observed every fixed period with the WiFi model of every room and estimates the room. The systemthen compares the observed Wi-Fi list with the Wi-Fi modelof the room.
Basically, room estimation for room estimation-based applications is performed using BLE data, but room estimationis performed simultaneously with BLE using Wi-Fi data at
regular intervals for detecting beacon defects. We believe that
estimation of beacon movement, malfunction, battery exhaustion, etc. will be possible. In addition, when room estimationis performed normally, each model is updated using the observed Wi-Fi and BLE data.
In this method, the system only knows the initial placement
room of the beacons. We do not consider it to be a defect situation if a beacon is slightly moved in the same room because
the beacon is still in the correct room. However, the BLE signal model for the room should be updated in such a situation.
We assume that participatory sensing is a suitable method to
update the BLE/Wi-Fi signal model for each room. In operation of room-estimation applications, a user’s smartphone uploads BLE/Wi-Fi observation information automatically. By
using the collected observation information, the BLE/Wi-Fi
model can be updated.

3.3 Data Collection and Assumption
In the proposed method, the fingerprint for Wi-Fi and BLE
is collected in advance to generate the BLE/Wi-Fi model foreach room. As a premise, smartphones are used to collectdata. Observation data at the time of preliminary collection
are gathered with the correct room name known. In data collection, we walk around in each room, and record radio observation information of BLE and Wi-Fi at 10-second intervals.
In a general Wi-Fi fingerprint collecting method, radio waves
obtained while stationary for several seconds are regarded as
the fingerprint at that position. On the other hand, in the proposed method, we model the radio-wave environment of eachroom. Therefore, radio-wave information of the whole room

is necessary. BLE and Wi-Fi radio waves are affected by people and objects, and they decay as distance increases. Therefore, it is insufficient to observe only one point in the room,
especially in a large room. We walk around the room andcollect fingerprints in various places in the room.
At the time of data collection, we assume that beacons havealready been installed in each room. In addition, many kindsof BLE devices have been released in recent years. To avoid
confusion, only beacons with a specific UUID (ID that can be
set in the beacon) are targets for data collection. The transmission interval of the beacon device used was 300 ms, andthe transmission strength was set to -4 dBm.
Data collection is conducted by participatory sensing, andwhen a user with a smartphone enters a room, observation isassumed simultaneously with room estimation. Basically, thesmartphone collects BLE data, but also collects Wi-Fi data
at regular intervals. We integrate the collected Wi-Fi of theroom and data of each BLE, create a Wi-Fi model and BLEmodel for each room, and make a room model.

3.4

BLE/Wi-Fi Modeling For Each Room

We create Wi-Fi and BLE models for each room based on
the data observed as in Fig. 4 for room estimation. In the
proposed method, for the radio waves observed in the room,
the probability of observing the radio waves is calculated, and
the Wi-Fi model and BLE model are generated. However,for Wi-Fi, radio wave broadcast distance is greater than thatof BLE. Therefore, it is limited to those observed above acertain received signal strength. The main reason for using
such a simple model is to ease implementation and reduce
calculation cost.
Also, for both the Wi-Fi model and BLE model, radio waves
with observation probability of less than 50% are not includedin the model. Room estimation is performed based on theobservation probability of each radio wave. However, if extremely low radio-wave information is included at that time,
the probability of room estimation is considered to be low.
This is because radio-wave information, which is not frequently
observed, is used for room estimation.

3.5

Room Estimation

Room estimation during operation is explained here. We
compare the data observed at a certain timing, the list of WiFi and BLE in each room built in advance, the BLE modeland the Wi-Fi model, and estimate the room. We focus onroom estimation by the BLE model, but that using the Wi-Fi
model is done in the same way.
We use only the BLE model for room estimation for locationbased services such as attendance management systems. We
use the BLE model and the Wi-Fi model for maintenance as
to whether the BLE beacon is installed properly.
Let Ob be the set of BLE radio waves contained in observational data O. At this time, the probability p(r) existing in aroom r is calculated as follows. Here, we denote the observed
probability of radio wave a in room r as p(a|r) and the set ofradio waves contained in the BLE model of room r as Mbr .
First, we obtain the set of radio waves common to Ob andInternational Journal of Informatics Society, VOL.10, NO.3 (2019) 119-128

Mbr as Ob ∩ Mbr . Also, we obtain a set of radio waves that areincluded in Mbr and not included in Ob as Mbr − (Ob ∩ Mbr ).
Next, we obtain the probability that the radio waves of the set
element can be observed in room r as follows.
∏∏p(r) =
p(a|r) × (1 − p(b|r))123

Ebmh = Ob − (MbRb ∩ Ob )On the other hand, suppose that the room estimate Rw
based on the Wi-Fi model is correct. The beacon set thatis supposed to be impossible to observe can be obtained asfollows.

where
a ∈ Ob ∩ Mbr , b ∈ Mbr − (Ob ∩ Mbr ).
Next, we will explain room estimation with the Wi-Fi model.
It is the same as room estimation using the BLE model just bychanging the sign of the calculation.
Let Ow be the set of Wi-Fi radio waves contained in observational data O. At this time, the probability p(r) existingin a room r is calculated as follows. Here, we denote the observed probability of radio wave a in room r as p(a|r) andthe set of radio waves contained in the Wi-Fi model of roomr as Mwr . First, we obtain the set of radio waves common
to Ow and Mwr as Ow ∩ Mwr . Also, we obtain a set of radio waves that are included in Mwr and not included in Ow as
Mwr − (Ow ∩ Mwr ). Next, we obtain the probability that theradio waves of the set element can be observed in room r as
follows.
∏∏p(r) =
p(a|r) × (1 − p(b|r))wherea ∈ Ow ∩ Mwr , w ∈ Mwr − (Ow ∩ Mwr ).
Comparing the observation data with any room model as
described above, the probability of being a specific room isrequired. Let the room with the highest probability be theroom where the smartphone is currently.

mh
Ew= Ob − (MbRw ∩ Ob )Here, if Rb and Rw are different, elements of Ebmh andmhEw are also different. In that case, their union is regarded asa candidate for a problem.
mh
E mh = Ebmh ∪ Ew

Next, we show an algorithm to examine the beacon set
E mt (mt means “move to somewhere”), which is supposed tobe observed but is not observed. What we can discover withthis pattern is a malfunction, a battery exhaustion, or that the
beacon has been moved out of the observed room.
Suppose the room estimate Rb based on the BLE model iscorrect. A beacon set that is supposed to be observed but is
not observed is obtained as follows.
Ebmt = Mb − (MbRb ∩ Ob )On the other hand, suppose that the room estimate Rw
based on the Wi-Fi model is correct. A beacon set that is
supposed to be observed but is not observed is obtained asfollows.
mt
Ew= Mb − (MbRw ∩ Ob )mt
Here, if Rb and Rw are different, elements of Ebmt and Ew
are also different. In that case, their union is regarded as a
candidate for a problem.
mt
E mt = Ebmt ∪ Ew

3.6 Beacon Defect Detection
Based on the results of BLE room estimation and Wi-Fi
room estimation, we compare BLE radio wave list Ob with
the BLE model of the estimated room and detect the defect ofa BLE beacon. The malfunction of a BLE beacon is such thatradio waves are not transmitted due to battery exhaustion or
breakdown, or it has been taken out of the room. For these defects, we will discover two types of inconsistencies: “Should
not be observable and “Observable but are not . Then, we
perform defect analysis.
As a precondition, Rb is the room estimated by the BLEmodel. Rw is the room estimated by the Wi-Fi model. Ob is
the set of BLE beacons received at a given observation. MbR
is the set of BLE beacons included in the BLE model in room
R.
First, we will show the algorithm for finding the beacon
set Emh (mh means “move here from somewhere), which
is supposed to be unobservable. What can be found with thispattern is that the beacon was moved to a room observed fromsome room.
Suppose the room estimate Rb based on the BLE model iscorrect. The beacon set that is supposed to be impossible toobserve can be obtained as follows.

3.7

Comparison With Other Methods

As related research, compare Asahi’s method, described in
Section 2, with our method. Table 2 compares attributes of
the two methods. In Asahi’s method, a beacon is arranged as acheckpoint of a stamp rally. Therefore, it is necessary to transmit data at every checkpoint. Since it is sufficient to transmit
only the BLE data, it is considered that the power consumption does not increase very much. However, with this method,
it is necessary to transmit Wi-Fi and BLE data when entering
the room. Since entrance/exit is repeatedly performed, it isthought that power consumption will increase.
With regard to estimation of defects, Asahi’s method does
not transmit data unless a person passes near a beacon. Furthermore, we cannot observe data. Also, defect cannot be
detected if data cannot be observed. However, in our method,data observation is done at entry. Except for rooms that are
not used much, we believe that we can respond quickly to a
problem with beacons.
Regarding the type of defect, Asahi’s method can only detect defects such as a beacon’s battery outage. Also, if a beacon is taken out, you do not know where it has gone. In our124

S. Ikeda et al. / BLE Beacon’s Defect Detection Method Based on Room Model of BLE and Wi-Fi

Table 2: Comparison of methods
Power consumption
Immediate nature ofdefect detection
Types of defects

Asahi’s method [8]○This method
△

△
△

○
○

method, a room model is created, and observed data are compared. As a result, in addition to a defect of the beacon, movement such as replacement can be detected.

4 ROOM ESTIMATION AND BEACON
DEFECT DETECTION EXPERIMENT
By examining the room-estimation method conducted in
Section 3, we can verify the accuracy of room estimation.
We also conducted experiments as to whether beacon defects
could be detected. If the accuracy of room estimation obtained by experiment is low, there is a possibility that estimation of beacon defects may be affected.
As the first experiment, after creating the BLE/Wi-Fi model,
we observe the Wi-Fi and BLE data in each room and obtain
the accuracy of room estimation. Since beacon radio waves
are weaker than Wi-Fi, it can be considered observable in theroom and still not be observed. Therefore, we observe data atvarious places in the room. It is considered that room estimation is possible with a high probability if the room is isolated.
However, it is considered difficult if it is possible to observe
radio waves of the same Wi-Fi or beacon, such as in a roomnext door. In addition, it is thought that the probability ofroom estimation will be low if all the beacons with low radio
field intensity are used. Then, estimation of a beacon defectis affected. Therefore, the boundary value of the radio field
intensity is also found.
As a second experiment, we will detect malfunctions assuming beacon movement, defect, etc. We anticipate the following kinds of defect: one cannot observe the beacon in theroom; the beacon in the room has malfunctioned; a beaconthat should not be observable originally is detected; and a beacon installed in one room has been moved to another room.
As a result, we believe that by comparison with BLE/Wi-Fi
observation and the BLE/Wi-Fi model of each room, beacon
movement and malfunction can be detected.
The factors that affect detection accuracy are considered tobe the size of the room, location of the room, and number of
beacons.
These factors, along with multiple potential defects, may
combine variously to affect detection accuracy.

4.1 Experiment Setting
As the experimental setting, data collection is done in eachroom using a smartphone as in Section 3. The room used in
the experiment is shown in Fig. 5. One Wi-Fi access point
is installed in each room. Data collection takes place everywhere in the room.
The beacon arrangements for each room are shown in Fig.
6. Everywhere in the room at least one beacon’s signal can be

Figure 5: Floor map

Figure 6: Beacon arrangement diagram

received. All UUIDs are unified. In room A, three beacons
are installed in different corners of the room, three in total,
and it is apart from the other rooms. In room B, two beacons
are installed on opposite walls; and room B is in a different
building from the other rooms and thus furthest away. In roomC, two beacons are installed, and it is next to room D. In room
D, one beacon is installed in the center. Room E is a large lecture room and has beacons installed in three places: entrance
1, entrance 2, and next to the central pillar. Observe BLE and
Wi-Fi while walking through the room. Also BLE and Wi-Fi
data are acquired 10 times in each room, 50 times in total. The
quality of room model will be better when observation time
becomes long. Also, the accuracy of room estimation will beapproved. However, we decided the number of observation
per room is 10 times, because the purpose of the experimentis to confirm the ability of defect detection in the experiment.
Actually, the setting of the number of observation per room isrigid condition.

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125

Table 3: Room estimation result-50dBmRoomestimation
RoomA
RoomB
RoomC
RoomD
RoomE
Overallprobability-75dBm-90dBm

BLE
100%
100%
100%
30%
100%

Wi-Fi
100%
100%
70%
100%
100%

BLE
100%
100%
100%
40%
100%

Wi-Fi
100%
100%
80%
100%
100%

BLE
100%
100%
100%
30%
100%

Wi-Fi
100%
100%
100%
80%
100%

86%

94%

88%

96%

86%

96%

4.2 Room Estimation Experiment
In the room-estimation experiment, we used the Wi-Fi and
BLE observation data and experimented on how accurately
room estimation could be estimated. We compared the observed data with the model created in Section 3. If the accuracy of room estimation is low, it is thought that estimation of
beacon defects will be affected. Also, if all the base-station
information is used, there is a possibility that the probabilityof room estimation will be low. Since there is a possibility
that the experiments to be performed next may be affected,
the boundary value is also examined. Boundary values of -50
dBm, -75 dBm, and -90 dBm were used for room estimation.
Experimental results are shown in Table 3.
For room A, the room estimate for BLE was 100% at any
boundary value of the radio field strength. Room B was 100%
different at any boundary value because it is in a different
building from the other rooms. For room C, the estimate for
BLE was 100%, but for Wi-Fi the estimate at -50 dBm was
70% and the estimate at -75 dBm was 80%. For room D, BLE
estimates were less than 40% at any boundary value; estimate
for Wi-Fi at -90 dBm was 80% and others were 100%. Room
E was 100% in BLE and Wi-Fi estimates.
From the experimental results, we could estimate a roomwith high probability except for ones with a room next door.
However, in rooms next to each other, we could observe theBLE and Wi-Fi radio waves from either room. Therefore, it isconsidered difficult to estimate only by whether the base station can be observed. Moreover, accuracy is high when room
estimation is performed at -75 dBm from the experimental result. Therefore, the boundary value considered to be suitablefor defect detection is -75 dBm.
As a result, the influence of the number of beacons and thesize of the room is small. However, when the position of theroom is close, the beacon radio waves can be acquired, so thedetection accuracy is low.

4.3 Beacon Defect Detection Experiment
In the defect-detection experiment, a beacon defect was detected, assuming a malfunction such as battery exhaustion,
defect or movement of a beacon. The verified defect situationis shown in Fig. 7. In defect 1, one of three beacons in room A
is unusable due to battery outage or malfunction. In defect 2,
the beacons of room B and room C were arranged mistakenly,
or moved. In defect 3, it is assumed that the beacon of room E
has been moved to room D. In fact, in simple situations with

Figure 7: Assumed defect situation

Table 4: Defects that expected to be detected
Room name
RoomA
RoomB
RoomC
RoomD
RoomE

Should not be
observable
None
C1, C2
B1, B2
E1
None

Observable but
are not
A1
B1, B2
C1, C2None
E1

only one beacon defect, our method can detect the defect easily. Therefore, we set these more complex situations for theexperiment.
Figure 4 represents the details of defect situations. As Defect 1, Beacon A1 is broken, so that A1 cannot be observed in
Room A. Therefore, the defect situation named “Observable
but are not” should be detected in Room A. Also, there are no
beacons to be moved to Room A, so that the defect situation
named “Should not be observed” should not be detected in
Room A. We set Defect 2 in Room B and C. The defect suppose misplacement by the system operator. Beacon B1 andB2, they should be placed in Room B, are misplaced in Room
C. Also, Beacon C1 and C2, they should be placed in RoomC, are misplaced in Room B. Therefore, both of the defectsituations named “Observable but are not” and “Should not
be observed” should be detected in Room B and C. Defect 3
is mischief situation. One of the beacon named E1 in Room
E is moved to Room D by someone. Therefore, “Observable
but are not” should be detected in Room E1, also “Should not
be observed” should be detected in Room D. The result ofdetection is presented in Fig.6
Table 5 shows the detailed result of room estimation anddefect detection. At first, the method estimates existing room.
If observed room and estimated room are same, room estimation result is correct. Then, each defect is checked on thecondition of the user observed in estimated room.
In defect 1, existing room is estimated as room A, and theestimation is correct. On the condition that the user observed

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S. Ikeda et al. / BLE Beacon’s Defect Detection Method Based on Room Model of BLE and Wi-Fi

Table 5: Detailed result of defect detection
Observed room

Room estimation
resultEstimated room

Defect detection
resultDefect1

RoomA

Correct

RoomA

Defect2

RoomB

Correct

RoomB

RoomC

Wrong

RoomB

Correct

RoomC

Wrong

RoomD

Wrong

RoomB

Correct

RoomD

Correct

RoomE

Correct
Wrong
Correct
Wrong
Correct
Wrong
Correct
Wrong
Correct
Wrong
Correct
Wrong
Correct
Wrong
Correct
Wrong

Defect3

RoomD

RoomE

Table 6: Accuracy of defect detection

Total number ofdefect detections
Number of correct
answers fordefect detection
Incorrect number ofdefects detected
Correct answer
rate

Should not be
observable

Observable but
are not

107

51

38

48

69

3

35%

94%

BLE/Wi-Fi signals in room A, BLE A1 was estimated 10
times as “Observable but are not”. The defect detection iscorrect, so that “A1:10” is appeared in a cell of “Defect detection result - Correct” and “Observable but are not”. Also,
the defect “Should not be observable” should not be observedin room A, so that “None” is set in a cell of “Defect detection
result - Wrong” and “Should not be observed”. There are no
estimation error in terms of defect 1. Therefore, “None” is set
both of the cells of “Defect detection result - Wrong. ”
In defect 2, room estimation was succeeded when the user
exists in room B. On the condition that the user observed
BLE/Wi-Fi signals in room B, BLE C1, C2 were estimated 10
times as “Should not be observed”. Also, BLE B1, B2 were
estimated 10 times as “Observable but are not”. The defectdetections are all correct, so that “C1:10, C2:10” appears in“Defect detection result - Correct” and “Should not be observable” and “B1:10, B2:10” are appears in “Defect detection result - Correct” and “Should not be observable”. The number
after colon means the number of defect detection times. On
the other hand, in room C, room estimations contain errors
as room B and room D, so that they are shown as “Wrong”.
If room estimation failed, the method tends to derive wrong
defects. For example, when the room is wrongly estimated as

Estimated number of defects
Should not be
Observable but
observable
are not
None
A1:10
None
None
C1:10, C2:10
B1:10, B2:10
None
None
None
None
D1:7. E1:5
None
B1:4, B2:4
C1:4, C2:4
E1:1
None
None
None
B1:6, B2:6, E1:4
D1:3
None
None
D1:10, E1:10
None
E1:10
None
B1:10, B2:10
None
None
E1:10
None
None

room B, wrong defects of “D1:7. E1:5” appears in “Defectdetection result - Wrong” and “Should not be observable”.
When the room is wrongly estimated as room D, wrong defects of “B1:6, B2:6, E1:4” appears in “Defect detection result- Wrong” and “Should not be observable”. Also, wrong defects of “D1:3” appears in “Defect detection result - Wrong”
and “Observable but are not”. Even if the room estimation iscorrect, defect detection may contain errors. When the room
is correctly estimated as room C, wrong defects of “E1:1” appears in “Defect detection result - Wrong” and “Should not
be observable”.
In defect 3, as same as defect 2, there are several defectdetection failures even the room estimation is correct. The
corresponding cell is, “Defect 3 - Room D - Correct - Room
D - Wrong” and “Should not be observable”, and the concrete
defect detections are “B1:10, B2:10”.
From Table 6, the number of defect detections is larger
than the number of observations. In this research, we estimate rooms using BLE and Wi-Fi and detect defects of BLE.
Therefore, the result will come up two estimating of the rooms
for BLE and Wi-Fi. If the room estimation results using BLE
and Wi-Fi are different, defects are detected for each estimated room. When the estimation of BLE is room A and theroom estimation in Wi-Fi is room B, the result of defect detection in room A and defect detection in room B are obtained.
Also, even if it is in the same room, there are cases wheremultiple defects such as “BLE A1 can not be observed” and“BLE A2 can not be observed”. In the situation, the numberof defect is counted for each defect. Therefore, the total number of defect detection will be larger than 50 observations. For
example, BLE C1 and BLE C2, which do not exist in room
B, were observed. The total number of defect detections isrepresented by the sum of correct and incorrect detections of“Should not be observable” and “Observable but are not” inTable 5, respectively. The number of correct answers for de-International Journal of Informatics Society, VOL.10, NO.3 (2019) 119-128127fect detection is expressed by the sum of “Correct” each defect detection. The incorrect number of defects detected isexpressed by the sum of “Wrong” each defect detection. The
correct answer rate is represented by “total number of defects
estimated / correct defect detections”.
The first “Observable but are not” targets are rooms A, B,
C and E from Table 6. The number of observations and thenumber of defects detected are not much different. In addition, the number of correct answers for defect detection islarge, and the correct answer rate is 94%. For defect detection, rooms A, B, D and E are accurately detected. However,room B has detected a different BLE than expected. The next
deficiency is “Should not be observed”. Detectable estimation
results are for rooms B, C, and D. The number of estimateddefects is almost twice the number of observations. About
one third of the correct answers are correct, and the correct
answer rate is 35%.
As a consideration of defect detection, as shown in Table 5,
in rooms A and E, the BLE of the other room cannot be seen.
Therefore, “Should not be observable” and “Observable but
are not” defects are detected correctly. It is thought that thisis because the rooms are far away from each other, so their
BLEs do not interfere with each other. Room B is separate
from other rooms. Therefore, it is considered that it is not
influenced by other beacons, and defects can be accurately
detected. Rooms C and D are next to each other. Therefore,
they are considered to be more affected by BLE comparedto other rooms. As a result, as shown in Table 5, unexpected
BLE is detected, and erroneous detection becomes a problem.
From these results, it can be considered that it is possible todetect malfunction accurately when the room is separated andnot affected by other beacons. However, if the room to be observed is close to another, the estimates of the room by BLE
and Wi-Fi may be different. Therefore, it is considered thatthe number of detected defects increases and the number ofcorrect answers for defects decreases. In this hypothesized
defect experiment, it was executed assuming that all BLEs
located in rooms B and C were replaced. Therefore, it is considered that a significant estimation error occurred between
BLE and Wi-Fi.

room with a neighboring room or a room with a small space.
The reason for this is that beacons are confused because they
are more vulnerable to nearby BLE radio waves. Isolatedrooms could be detected with 100% accuracy. However, adjacent rooms could only be detected with 35% accuracy.
In this experiment, we observed data only in rooms. We did
not observe BLE/Wi-Fi information in hallways. Therefore,
there is a possibility that room estimation could be done evenfrom a hallway. If you are in a room, you may mistake someroom estimation if tracking for several tens of seconds. However, if the number of times estimated in the same room islarge, you can presume that you are in the room.Also, in thecase of a hallway, room estimation is considered to change
one after another. Therefore, it can be presumed that you arewalking in the hallway.
As future work, we should modify the BLE/Wi-Fi roommodel and improve room estimation accuracy. One idea is tointroduce a Gaussian distribution model to express possible
signal strength. By modeling with Gaussian distribution, we
can understand the range of the radio-wave intensity seen ina specific room and consider that the room can be accurately
estimated even with an adjacent room. If the room can be
estimated accurately, it can be expected that the accuracy of
estimation of beacon defects should be improved.
At the time of operation, it is possible to automatically update the room model by using the uploaded BLE/Wi-Fi observation data. If we keep the model we created first, we may not
be able to estimate rooms, such as when a beacon is changedout or there are many people or things in the rooms. Therefore, we should update the model in operation. We believethat data of various situations can be observed and changed
according to the situation and changes in the environment.

5 CONCLUSION
In this paper, we proposed a BLE beacon defect detection
method. The method is based on WiFi and BLE fingerprints
for each room. We modeled the observed Wi-Fi and BLE
data for each room. Basically, we compared the BLE modelwith the observed BLE list and estimated the room. We also
compared Wi-Fi models and Wi-Fi lists at regular intervals.
A room-estimation experiment was conducted. We calculated the probability that room estimation would succeed.
Also, we obtained the boundary value of the signal strengththat does not affect beacon-defect estimation. We experimented with three boundary values: -50 dBm, -75 dBm, and-90 dBm. The best result was -75 dBm. Therefore, Wi-Fi
signal that is under -75 dBm is removed for our method.
We conducted a beacon defect detection experiment, and it
was possible to detect a beacon in which a malfunction had
occurred. However, it was difficult to estimate a defect in a

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(Received November 18th, 2017)(Revised April 18th, 2018)

Shota Ikeda is a graduate student at Aichi Institute of Technology. His current research focuses
on estimating rooms using BLE beacons and detecting their defects. He is also interested in theInternet of Things and in creating a more convenient and less cumbersome society using BLE
beacons.

Fumitaka Naruse is an undergraduate student at
Aichi Institute of Technology. His current research
focuses on estimation of people in rooms using
BLE beacons. His team developed an application linked to the web for acquiring real-time occupancy information and past occupancy history.

Katsuhiko Kaji received his Ph.D. in in- formation science from Nagoya University in 2007. He
became a RA at NTT Communication Science Laboratories in 2007 and an assistant professor in Nagoya
University in 2010. Currently, he is associate professor of Faculty of Information Science, Aichi
Institute of Technology from 2015. His researchinterests include indoor positioning and remote interaction. He is a member of IPSJ and JSSST.