UBICOMP '14 ADJUNCT, SEPTEMBER 13 - 17, 2014, SEATTLE, WA, USA

Adapting Wi-Fi Samples toEnvironmental Changes
AutomaticallyTakashi Sakaguchi
Ritsumeikan University
1-1-1 Noji-Higashi
Kusatsu, Shiga 5258577
JAPAN
gucci@ubi.cs.ritsumei.ac.jp

Nobuhiko Nishio
Ritsumeikan University
1-1-1 Noji-Higashi
Kusatsu, Shiga 5258577
JAPANnishio@cs.ritsumei.ac.jp

Masahiro Mochizuki
Ritsumeikan University
1-1-1 Noji-Higashi
Kusatsu, Shiga 5258577
JAPAN
moma@ubi.cs.ritsumei.ac.jp
Kazuya Murao
Ritsumeikan University
1-1-1 Noji-Higashi
Kusatsu, Shiga 5258577
JAPANmurao@cs.ritsumei.ac.jp
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http://dx.doi.org/10.1145/2638728.2641302

843

Abstract
In recent years, a positioning method which utilizes
wireless LAN without using GPS has attracted attention.
Especially, in the case of a method which combinesabsolute position with a Wi-Fi radio environment inadvance, the cost of operation and management becomesenormous. Therefore, by sampling Wi-Fi radio information
observed at points where users stay frequently or in thelong-term, a method which automates to collect andupdate the Wi-Fi radio information has been proposed. In
the case of a long-term operating, the positioning
accuracy, however, decreases because this method does
not perform well in maintaining and managing samples. It
cannot adapt samples to environmental changes although
Wi-Fi radio signals change in case of long-term operating.
Accordingly, this paper proposes a new calculation formulafor improving a positioning accuracy. The formula iscalculated with the weight of each base station foravoidance of ill-behaving stations. In addition, this paper
also proposes the automated management system with
two steps. It adapts samples to changes of Wi-Fi radio
signals and a user’s behavior. As a result, a positioning
accuracy of the new system is higher than existing one.

Author Keywords
Lifelog , Activity recognition, Indoor positioning, Wi-Fi
based indoor positioning, Wi-Fi signal change

UBICOMP '14 ADJUNCT, SEPTEMBER 13 - 17, 2014, SEATTLE, WA, USA

ACM Classification Keywords
I.5.1 [PATTERN RECOGNITION]: Models-Statistical.;
K.6.4 [MANAGEMENT OF COMPUTING AND
INFORMATION SYSTEMS]: System ManagementQuality assurance.

Introduction
In recent years, a positioning method which utilizes
wireless LAN has attracted attention because it is diﬃcult
to use GPS in indoor environment. Regarding methods
utilizing wireless LAN, many researchers use the method
utilizing the Wi-Fi fingerprinting. As for the method, it isnecessary to collect Wi-Fi radio information at many
points in advance. After that, a Wi-Fi radio environment
map, which combines a Wi-Fi radio information collected
at a point and absolute location information of the point,
must be generated. A Wi-Fi radio information has BSSID
which distinguishes each base station, RSSI from them,and so forth. Positioning is to match a Wi-Fi radio
information observed in real time with this map. In
addition, this method needs to recollect Wi-Fi radio
information to update this map. Therefore, the cost ofoperation and management becomes enormous.
Accordingly, Nishio[1] proposed a method which
automates to collect and update the Wi-Fi radio
information to cut down the cost. This method assume
that our daily indoor activity could be considered astransportation between frequently visited places. Thismethod concentrate on frequently visited and long-term
stay places, while neglects transitional states between
them. The cost is cut down by positioning at theseplaces. These places can be represented as Wi-Fi radio
signal statistics when the accelerometer senses the user
stops. Many fragmented Wi-Fi radio signal statistics areanalyzed with similar radio signal statistics in order to844

make clusters. By matching these samples and Wi-Fi
observation data, the user’s position is estimated. In
addition, samples are automatically managed with thesimilarity called virtual distance. The number of samples
are limited by setting a term of generating a sample. Also,
samples are adapted to environmental changes by setting
a term of updating a sample. In addition, a site conceptthat is a cluster of log periods each of which have samebase stations in common at certain ratio is introduced toshare ill-behaving base stations. In case of the operating
for a long time, this method, however, does not perform
properly to maintain and manage samples. It cannot
adapt samples to environmental changes. There are twokinds of environmental changes. One is slow
environmental change, such as adding, removing, ormoving Wi-Fi base stations, a change of RSSI by a changeof temperature and humidity, and a change of RSSI by
obstacles or rearranging a room.The other is rapid
environmental change such as a change of the point whereusers stay frequently or in the long-term by moving fromthe old desk to new one. Therefore, this paper proposes a
new calculation formula to improve and maintain thepositioning accuracy. Also, the automated management
system with two steps for adapting samples to that two
kind of environmental changes. One step is a preparatory
step to maintain and manage preparatory samples. The
other is a positioning step to select available samples for
positioning. Samples are automatically adapted to the twokind of environmental changes by using this system.
In the rest of this paper, problems and each of our two
approaches are explained. In addition, The automatedmanagement system with two steps is evaluated. In thelast section, a conclusion and directions for future
research are described.

WORKSHOP: HASCA

Problems

observation frequency of which are more than 40 percentof the most observation frequency of Wi-Fi base stations.
Therefore, a new Wi-Fi base station added to a sample
cannot be used for calculation. In addition, each Wi-Fi
base station’s RSSI used for calculation is the averagevalue. If the data of sample increases, the RSSI is hard tochange. It is diﬃcult to adapt samples to slow
environmental change if samples have long-term data.
Therefore, it is necessary to limit the amount of data used
for samples.

Nishio calculates the weight of each base station foravoidance of ill-behaving stations. A calculation formulaof virtual distance is used, as follow. It is calculated by
the Wi-Fi base stationfs MAC address and the strength of
Wi-Fi signals. Fw indicates a feature vector of basestations currently observed while Fc indicates the feature
vector already sampled. |vector| means the number ofbase stations. rssiw indicates a rssi of each base stationof Fw while rssic indicates a rssi of each base station ofFc .
√∑dist(Fw , Fc ) =(rssiw − rssic )2(|Fw | + |Fc | − |Fw ∩ Fc |)×|Fw ∩ Fc ||Fw ∩ Fc |The weight, however, cannot be reflected in this formula.
A new calculation formula for virtual distance needs toaccommodate the weight for improving the positioning
accuracy.
Adding, removing or moving Wi-Fi base stations, achange of RSSI by temperature and humidity, and achange of RSSI by obstacles or rearranging a room areadverse influences for positioning. It is necessary to adapt
samples to the slow environmental change for maintaining
the positioning high accuracy. Hence, samples are
updated with a Wi-Fi radio information currently observed
for maintaining when a user visits places where a sample
was generated. If the virtual distance satisfy the terms of
updating a sample, a new Wi-Fi radio information mustbe accumulated to the sample. Therefore, the observation
frequency of Wi-Fi base stations observed frequently in a
Wi-Fi radio information accumulated to a sample isnumerous. Wi-Fi base stations used for calculation of thevirtual distance are limited for removing ill-behaving basestations. The calculation uses Wi-Fi base stations, the

845

(1)

Positioning is worse aﬀected by rapid environmental
change such as a change of the point where users stayfrequently or in the long-term by moving from an old desk
to the new one. A new sample is generated if the virtual
distance between the sample and others satisfies the termsof generating a sample. If the distance cannot satisfy theterms of generating a sample, a new sample cannot be
generated. Therefore, in case a user visits a new placefrequently and in the long-term, a new sample cannot be
generated. For a higher precision positioning, samples
have to be generated in accordance with change of places
a user visits frequently or in the long-term.

Calculation Formula Accommodating the Weightfor Virtual Distance
The existing calculation formula cannot reflect the weight.
The weight can be reflected by using the observation
frequency. There are two kinds of the observation
frequency. One is the observed frequency of each Wi-Fi
base station in a sample. The other is the observed
frequency of each Wi-Fi base station of Wi-Fi radio signal
statistics of a currently stay. A virtual distance iscalculated via the following formula. Fw indicates afeature vector of base stations observed among eachuser’s stay. Fcom indicates a feature vector of base

UBICOMP '14 ADJUNCT, SEPTEMBER 13 - 17, 2014, SEATTLE, WA, USA

stations which are common to Fw and Fc . weightw
indicates a weight using the observation frequency
involved in a sample. weightc indicates a weight using
the observation frequency involved in Wi-Fi radio signal
statistics of a currently stay.

Figure 1 indicates our system concept. Preparatory
samples are used for maintaining and management. Also,these can be used for detecting samples a user frequently
visits. Available samples is important samples for the user.
This system estimates user’s position with available
samples.

√∑

weightc (rssiFw − rssiFc )2∑
weightc (Fcom )∑∑(weightw (Fw−com ) +weightc (Fc )∑×weightc (Fcom )∑∑)| weightc (Fcom ) −weightw (Fcom )|∑+weightc (Fcom )f requency
weight =maxF requency

Firstly, preparatory step is described. There were two
thresholds with the virtual distance. These were used as
terms for generating and updating a sample. Additionally,a new threshold is added for generating a preparatory
sample. Preparatory samples are generated for adapting asample to the environmental changes. These are
generated at as many places which a user visits frequently
or in the long-term as possible. Also, these are updatedwith Wi-Fi radio signal statistics of a currently stay.

dist(Fw , Fc ) =(2)

The Automated Management System with
two steps

Update a sample

Samples are automatically managed with two steps foradapting samples to the two kind of environmental
changes. One step is a preparatory step to maintain andmanage preparatory samples. The other is a positioning
18'"
step2to selectavailable samples for positioning.
2

18'"5#8

Generate a new
preparatory
sample

UpdatethresholdGenerate
threshold
VirtualDistance

Generate a new sample

7.

Generate threshold(Preparatory)

Stay

Stay

Day

0

Figure 2: Relationships among Three Thresholds
10

Generate/Update
Samples

Preparatory Samples• SamplesManagement

Positioning
choosesamples

Available Samples• Positioning
with thesesamplesFigure 1: The Automated Management System with Two
Steps

846


4

Figure 2 describes the relationship between thesethresholds. ”Generate threshold” is set as a term forgenerating samples. Samples are generated if a virtual
distance between Wi-Fi radio signal statistics and asample is more than ”Generate threshold”. The distance
between each sample is enough to use it for positioning.
In addition, ”Update threshold” is set as a term for
updating samples. Samples are updated if a virtual
distance between Wi-Fi radio signal statistics and a

WORKSHOP: HASCA

sample is less than ”Update threshold”. Furthermore,”Generate threshold(preparatory)” is set as a new term forgenerating preparatory samples. This threshold is lessthan ”Generate threshold” and more than ”Update
threshold”. Preparatory samples are generated at as many
places a user visits frequently or in the long-term as
possible if a virtual distance between Wi-Fi radio signal
statistics and a sample is more than ”Generate
threshold(preparatory)”. Preparatory samples are not
suﬃcient to use for positioning. For the preparatory step,”Generate threshold(preparatory)” and ”Update
threshold” are used to maintain and manage preparatory
samples. The amount of data used for samples , however,is needed to limit because it is diﬃcult to adapt samples
to slow environmental change in case of long-term data.
Therefore, samples consist of the data within the limits offixed intervals called window size. Figure 3 shows thewindow. This window slides if the amount of data is morethan the window size. Specifically, in case of that, new/radio,'$"(# to?
#after)
Wi-Fi
signal statistics are added
a samplethe oldest Wi-Fi radio signal statistics of the data
accumulated to the sample were removed.

used to select available samples from preparatory samples
for positioning. Available samples are selected frompreparatory samples to be adapted to rapid environmental
change. It is not suﬃcient for positioning to use
preparatory samples because some distance between eachof preparatory samples are less than ”Generate threshold”.
Therefore, some samples which are suﬃcient for
positioning have to be selected from preparatory samples.
Each sum of time of staying on each preparatory sample
can be used for selecting available samples. It is possible
to select available samples which generated at places auser frequently visits by selecting available samples fromthe longest sum of time of preparatory samples. It issuﬃcient for positioning to use available samples because
all virtual distance between each of available samples are
more than ”Generate threshold”. Available samples,
however, do not change frequently according to rapid
environmental change. Accordingly, available samples are
re-selected every fixed interval called refresh interval.
Also, Figure 4 shows the refresh interval. This interval isused for calculation of each sum of time of staying oneach preparatory sample. It is possible to re-select* '"(6)
available samples quickly to use this interval if a places auser visits frequently or in the long-term change.



0

Day

Preparatory Samples Preparatory Samples
Preparatory Samples





0

: SamplePreparatory Samples Preparatory Samples
Preparatory Samples: Window size

Day

Available Samples

Available Samples
Available Samples
calculate stay term ofeach preparatory sample: Sample
2

Figure 3: Preparatory Samples

Secondly, positioning step is described. For the positioning
step, ”Generate threshold” and ”Update threshold” are847: Refresh interval

Figure 4: Available Samples

UBICOMP '14 ADJUNCT, SEPTEMBER 13 - 17, 2014, SEATTLE, WA, USA

A user, however, has the possibility to visits palaces
except where available samples are generated. Therefore,
available samples are merged for positioning such theseplaces. The label which is a name to define each sample isused for merging. There are some preparatory samples not
to be selected as available samples. Each virtual distancefrom these samples to available samples is calculated todetect the nearest sample. This virtual distance iscalculated with Equation 1 because each weight of Wi-Fi
base stations is diﬀerent in each sample. Figure 5 shows
the merging process. The two samples are the nearest are
merged. The merged label is generated from these twolabels.
These samples are renamed with the merged label.
S[JB.
Preparatory Samples3

ill-behavior base stations but also manage the samples
limited number. The samples limited number are easily
managed. Equation 1 is used for generating a site.

Evaluation
The automated management system with two steps isevaluated by a simulation of human behavior. An
experiment is carried out in our laboratory. A fixed
observation was conducted at three places for two weeks.
The three places are described in Figure 6.

Available Samples

2

3
1

1

Available Samples3

1 or 2
1 or 2ITUX[IVZB.

4

Figure 5: Merge of Samples

C

5~6m

15m

B

3~4m

A

Figure 6: Fixed Observation
PointsFinally, the merged available samples are used for
positioning. The distance between a Wi-Fi radio signal
statistics which a user observes currently and thesesamples are calculated. The virtual distance is calculatedwith Equation 2 in real time. Position is detected by theavailable sample when the distance value is the smallest.
Furthermore, preparatory and available samples arepossessed by each site. Sites can not only share
5848

We have already verified that positioning is suﬃcient if
the distance between samples is more than 6 meters. The
distance between ”A” and ”C” is more than 6 meters.
Therefore, the samples generated at ”A” and ”C” aresuﬃcient for positioning. Although, The distance between”A” and ”B” is less than 6 meters. The samples
generated at ”A” and ”B” are not suﬃcient. The samples
generated at ”B” and ”C” are also the same. In thesimulation, a user visits each place at 5-minute interval. In
addition, a Wi-Fi radio signal statistics are collected for 5
minutes. After 5 minutes, a user visits other or same placerandomly. A Wi-Fi radio signal statistics are also collected
for 5 minutes. The simulation is carried out by repeatingthese steps. This simulation represents the humanbehavior like a user repeats stay for 5 minute and transfer.
A sample is generated and updated with Wi-Fi radio
signal statistics collected at each place for 5 minutes.
Also, positioning is conducted every 5 minutes. Somepatterns exists by changing a transition probability that auser visits after. These patterns are described in Table 1.

WORKSHOP: HASCA

Table 1: Simulation Patterns
Pattern Name
PatternAC

PatternB

PatternACtoB

Description
A transition probability to”A” and ”C” is 40 % each,a transition probability to ”B” is 20 %A transition probability to ”B” is 60 %,a transition probability to”A” and ”C” is 20 % eachpatternAC is carried out at first,
patternB is after a fixed intarval

Adapting sample to slow environmental change isevaluated with PatternAC and PatternB. A user often
visits ”A” and ”C” in PatternAC. Two samples are
selected as available samples because the samples
generated at ”A” and ”C” are suﬃcient for positioning.
Slow environmental change is evaluated with PatternAC incase the number of available samples is two . A user often
visits ”B” in PatternB. A sample is selected as available
samples because the samples generated at ”A” and ”B”
are not suﬃcient. The samples generated at ”B” and ”C”
are also the same. Slow environmental change is evaluatedwith PatternB in case one sample is generated. Adapting
sample to rapid environmental change is evaluated withPatternACtoB. In PatternACtoB, a user changes the point
where he stays frequently or in the long-term. A user
stays frequently or in the long-term at ”A” and ”C” atfirst. A user changes the places where he stays frequently
or in the long-term to ”B” after fixed interval.
”Window Size”, ”Refresh Interval”, ”Generate threshold”,”Generate threshold(preparatory)”, and ”Update
threshold” must be set before carrying out theexperiment. ”Window Size” is set for one week. The
shorter it is set, more easily the system adapts the849

samples to environmental changes. The longer it is set,more stable the samples are against environmental
change. ”Refresh Interval” is set for one day to re-selectavailable samples quickly. ”Generate threshold” is set to
16. The threshold is the virtual distance between ”A” and”C”. Also, the virtual distance is suﬃcient for positioning.
”Generate threshold(preparatory)” is set to 11. The
threshold is less than the virtual distance between ”A”
and ”C”. Also, it is less than the virtual distance between”B” and ”C”. Therefore, three samples are generated
with the threshold. ”Update threshold” is set to 6. We
have verified updating process with various thresholds. As
a result, we set to 6 for the threshold.
The Precision and the Recall are calculated by thefollowing formulas for the evaluation.
Precision =Recall =Number of correct system output(%)
Number of all positioning(3)

Number of correct positioning(%)
Number of all positioning(4)

Precision indicates what is correct for the system.
”Update threshold” is used for positioning. The distance
between Wi-Fi radio signal statistics which a user observesin currently and samples are calculated. A sample isselected if the distance is the smallest. The sample isselected as an output if the distance is smaller than
”Update threshold”. Correct behaviors for the system arethat outputting an available sample if a user visits a place
the sample is generated, and not selecting an available
sample if a user does not visit places samples are
generated. The Recall indicates the correct for
positioning. Correct behaviors for positioning are that
outputting an available sample if a user visits a place the

UBICOMP '14 ADJUNCT, SEPTEMBER 13 - 17, 2014, SEATTLE, WA, USA

sample is generated. It is wrong for positioning not to
output a sample.
First, the evaluation of PatternAC is described. Adapting
sample to slow environmental change is evaluated in caseof two samples are selected as available samples. The
automated management system with two steps iscompared with the system with one step. There are two
versions of the automated management system with two
steps. One is the system which is conducted with amerging process. The other is the system which is not.
Figure 7 indicates the Precision and the Recall every one
day. Samples are generated at ”A” and ”C” in case of thesystem with one step. Preparatory samples are generated
at ”A”, ”B”, and ”C”. Two samples generated at ”A”
and ”C” are selected as available samples. 80% of
positioning is conducted at places where samples that are
selected as available samples are generated. On the other
hand, 20% of positioning is conducted at places where
samples that are not selected as available samples are
generated. The Precision of both the system with one
step and with two steps is about 80% on the 9th day
because these system output sample is generated at ”A”
when a user visits ”B”. The Precision of two steps system
is improved slightly on the 13th and 14th day. The reason
why the Precision is improved is samples of the two steps
system can be maintained adequately. The Recall of both
systems is about 80% because the Recall decreases whena user visits ”B”. In addition, the Precision and the Recallof the two steps system which conducts that the systemmerges available samples is about 100% in the whole
period because the system could merge samples generated
at ”A” and ”B”.

850

(%)

100
90
80
70
60
50
40
30
20
10
0

(one step)Precision(one step)Recall(two steps)Precision(two steps)Recall(two steps)Precision(merge)(two steps)Recall(merge)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 (Day)

Figure 7: Precision and Recall of PatternAC

7

Second, the evaluation with PatternB is described.
Adapting sample to slow environmental change isevaluated in case a sample is selected as an available
sample. The systems to be compared are the same used
before. Figure 8 indicates the Precision and the Recall
every one day. Samples are generated at ”B” in case of
the system with one step. Preparatory samples are
generated at ”A”, ”B”, and ”C”. A sample generated at”B” is selected as an available sample. 60% of positioning
is conducted at places where samples that are selected asavailable samples are generated On the other hand, 40%of positioning is conducted at places where samples thatare not selected as available samples are generated. The
Precision of both the system with one step and with two
steps is about 80% on the 5th day because these system
output sample which is generated at ”B” when user visits”A”. The Precision of both systems are almost the samein whole period. The Recall of both systems about 60%
because the Recall decreases when a user visits ”A” and”C”. In addition, the Precision and the Recall of the two
steps system which conducts with a merging process isabout 100% in whole period because the system could

WORKSHOP: HASCA

merge samples generated at ”A”, ”B”, and ”C”.
(%)

100
90
80
70
60
50
40
30
20
10
0

(one step)Precision(one step)Recall(two steps)Precision(two steps)Recall(two steps)Precision(merge)(two steps)Recall(merge)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 (Day)

Figure 8: Precision and Recall of PatternB

8

Finally, the evaluation with PatternACtoB is described.
Adapting sample to rapid environmental change isevaluated. the systems to be compared are the same used
before. A user frequently visits ”A” and ”C” at first.
Although, he frequently visits ”B” after a fixed interval.
The pattern change is conducted on the 7th day. Figure 9
indicates the Precision and the Recall every one day.
Samples are generated at ”A” and ”C” in case of thesystem with one step. Preparatory samples are generated
at ”A”, ”B”, and ”C”. Preparatory samples generated at”A” and ”C” is selected as available samples in PatternAC
and PatternB. A preparatory sample generated at ”B”,
however, is selected as an available sample from 9th day.
The Precision of the system with one step decreases on
the 10th, 13th, and 14th day because the system outputs
a sample which is generated at ”A” when a user visits”B”. The Recall of both of the system with one step andwith the two steps decreases to about 40 % from the 8thday. After changing pattern, 40% of positioning is
conducted at places where samples selected as available

851

samples are generated. On the other hand, 60% of
positioning is conducted at places where a sample that are
not selected as available samples are generated. Hence,
the Recall decreased. The Recall of the system with two
steps, however, is improved to 60% from 9th day. The
system with two steps can adapt samples to rapid
environmental change because samples which areimportant for a user are selected according to a patternchanges. In addition, the Precision and the Recall of thetwo steps system which conducts with a merging process
is about 100% in whole period. It is exceedingly highpositioning accuracy in all patterns. The range of
positioning, however, is rougher than positioning which
does not conduct with a merging process. Therefore, thepositioning accuracy and the range of positioning are a
trade-oﬀ relationship.
(%)

100
90
80
70
60
50
40
30
20
10
0

(one step)Precision(one step)Recall(two steps)Precision(two steps)Recall(two steps)Precision(merge)(two steps)Recall(merge)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 (Day)

Figure 9: Precision and Recall of PatternACtoB

9

RelatedWork
There are many researches to address the problem is that
positioning accuracy decreases because of environmental
change. Two kinds of these research is introduces.

UBICOMP '14 ADJUNCT, SEPTEMBER 13 - 17, 2014, SEATTLE, WA, USA

Indoor positioning methods using absolute position
The method must combines a Wi-Fi radio information andabsolute location information at first. Pan[2] proposed amethod to update a database of Wi-Fi finger printing with
Manifold co-Regularization. Yin[3] proposed a method therelationship between a Wi-Fi radio signal from a user’s
device and one from devices were set in the environmentis predicted. These method, however, need to combine a
Wi-Fi radio information collected at a point and absolute
location information of the point. Therefore, the cost of
the maintenance and management is numerous.
Indoor positioning methods not using absolute position
The method don’t have to combine a Wi-Fi radio
information and absolute location information. Jiang[4]proposed a method automates to correct and update a
Wi-Fi radio information with samples fragmented byaccelerometer. Positioning is conducted with Bayesianestimation. The range of positing, however, is limited to aroom. In addition, a user has to visit five times at a place
and a sample has to be consisted of a Wi-Fi radio signal
information for over twenty minutes. Therefore,
positioning can not be conducted in smaller range and atplaces a user does not visit frequently.

Conclusion
This paper proposes a new calculation formula and theautomated management system with two steps for
improving positioning accuracy. The present result
suggested that the system can adapt samples toenvironmental changes. Furthermore, it is exceedingly
high positioning accuracy if the two steps system which
conducts with a merging process although the positioning
accuracy and the range of positioning is trade-oﬀ
relationship. We examined the system in our laboratory.
Positioning accuracy, however, decreases if the number of852

Wi-Fi base stations is a few. We have to examine thesystem in outdoor, house, and so forth in the future.

Acknowledgements
Part of this research is supported by SCOPE(#132307011) (Strategic Information andCommunications R&D Promotion Program) from the
Ministry of Internal Aﬀairs and Communications(MIC),
Japan.

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