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Copyright ⓒ 2014 GiRI (Global IT Research Institute)Dr. Youssef SAID, Tunisie Telecom, Tunisia
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Copyright ⓒ 2014 GiRI (Global IT Research Institute)Dr. Il Kwon Cho, National Information Society Agency, Korea
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Dr. Feng CHENG, Hasso Plattner Institute at University of Potsdam, Germany
Prof. El-Sayed M. El-Alfy, King Fahd University of Petroleum and Minerals, Saudi Arabia
Prof. Lin You, Hangzhou Dianzi Univ, China
Mr. Nicolai Kuntze, Fraunhofer Institute for Secure Information Technology, Germany
Dr. Min-Hong Yun, ETRI, Korea
Dr. Seong Joon Lee, Korea Electrotechnology Research Institute, korea
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Dr. Dae Won Kim, ETRI, Korea
Dr. Ho-Jin CHOI, KAIST(Univ), Korea
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Dr. Gyu Myoung Lee, Institut Mines-Telecom, Telecom SudParis, France
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Prof. Yoonhee Kim, Sookmyung Women s University, Korea
Prof. Li-Der Chou, National Central University, Taiwan
Prof. Young Woong Ko, Hallym University, Korea
Prof. Dimiter G. Velev, UNWE(University of National and World Economy), Bulgaria
Dr. Tadasuke Minagawa, Meiji University, Japan
Prof. Jun-Kyun Choi, KAIST (Univ.), Korea
Dr. Brownson ObaridoaObele, Hyundai Mobis Multimedia R&D Lab , Korea
Prof. Anisha Lal, VIT university, India
Dr. kyeong kang, University of technology sydney, faculty of engineering and IT , Australia
Prof. Chwen-Yea Lin, Tatung Institute of Commerce and Technology, Taiwan
Dr. Ting Peng, Chang'an University, China
Prof. ChaeSoo Kim, Donga University in Korea, Korea
Prof. kirankumar M. joshi, m.s.uni.of baroda, India
Dr. Chin-Feng Lin, National Taiwan Ocean University, Taiwan
Dr. Chang-shin Chung, TTA(Telecommunications Technology Association), Korea

Copyright ⓒ 2014 GiRI (Global IT Research Institute)Dr. Che-Sheng Chiu, Chunghwa Telecom Laboratories, Taiwan
Dr. Chirawat Kotchasarn, RMUTT, Thailand
Dr. Fateme Khalili, K.N.Toosi. University of Technology, IranDr. Izzeldin Ibrahim Mohamed Abdelaziz, Universiti Teknologi Malaysia , Malaysia
Dr. Kamrul Hasan Talukder, Khulna University, Bangladesh
Prof. HwaSung Kim, Kwangwoon University, Korea
Prof. Jongsub Moon, CIST, Korea University, Korea
Prof. Juinn-Horng Deng, Yuan Ze University, Taiwan
Dr. Yen-Wen Lin, National Taichung University, Taiwan
Prof. Junhui Zhao, Beijing Jiaotong University, China
Dr. JaeGwan Kim, SamsungThales co, Korea
Prof. Davar PISHVA, Ph.D., Asia Pacific University, Japan
Ms. Hela Mliki, National School of Engineers of Sfax, Tunisia
Prof. Amirmansour Nabavinejad, Ph.D., Sepahan Institute of Higher Education, Iran

Copyright ⓒ 2014 GiRI (Global IT Research Institute)

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Copyright ⓒ 2014 GiRI (Global IT Research Institute)Volume. 3 Issue. 5

1Study on design and implementation of web-based audience measurement platform for digital signage service

6J

505

Wook Hyun*, MiYoung Huh*, SungHei Kim*, ShinGak Kang** Protocol Engineering Center, ETRI(Electronics and Telecommunications Research Institute ), 218 Gajeong-ro,
Yuseong-gu, Daejeon, Republic of Korea

2

Multipath Channel Characteristics for Propagation between Mobile Terminals in Urban Street Canyon
Environments

511

Myung-Don KIM, Juyul LEE, Jinyi LIANG, Jinup KIM
Electronics and Telecommunications Research Institute (ETRI) 218, Gajeongno, Yuseong-gu, Daejeon, 305-700, Korea

3

A TOR-Based Anonymous Communication Approach to Secure Smart Home Appliances

517

Nguyen Phong HOANG, Davar PISHVA
Institute of Information & Communications Technology, APU (Ritsumeikan Asia Pacific University), Japan

4

Smart Device Based Power Generation Facility Management System in Smart Grid
Young-Jae Lee*, Eung-Kon Kim*
*Department of Computer Science, Sunchon National University, 255 Jungang-ro, Suncheon, Jellanam-do, Republic Of Korea

526

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 5, September 2014

505

Study on design and implementation of
web-based audience measurement platform for
digital signage service
Wook Hyun*, MiYoung Huh*, SungHei Kim*, ShinGak Kang** Protocol Engineering Center, ETRI(Electronics and Telecommunications Research Institute ), 218 Gajeong-ro,
Yuseong-gu, Daejeon, Republic of Korea
whyun@etri.re.kr, myhuh@etri.re.kr, shkim@etri.re.kr, sgkang@etri.re.kr

Abstract—Digital signage service provides advertisement
and information to users using electronic displays with
network capabilities. Compared to traditional DID (Digital
Information Device) that just provides contents one-way,digital signage service can provide more advanced
functionalities such as user interaction and audiencemeasurements. By measuring audience behavior, it is possible
to provide appropriate contents to user and increases
advertisement effects. In this paper, we present
implementations of audience measurement using Kinect
camera since Kinect camera can track a human objects,
distance calculation and gesture recognitions, and web-based
analysis platform for audience measurement information.
Keyword— audience measurement, digital signage, Kinect,
AM, Web-based analysis platform, big data

I. INTRODUCTIONDIGITAL signage service

provides advertisements anduseful information using terminal equipped withelectronic displays, and it is also possible to aggregate
information using various kinds of sensors including
camera. Nowadays, digital signage services are evolving forproviding interaction and more intelligence services.
Especially, since digital signage terminals are installed inpublic space like bus stops, hallways, shopping mall, it isuseful for gathering audience and ambient information. This
information can be used for analysis of customer’s behavior
and venue characteristics.
When it comes to analyses the effectiveness ofadvertisement and provides interactive contents switching, it———————————————————————
Manuscript received September 15, 2014. This research was supportedby the ICT Standardization program of MSIP (The Ministry of Science,
ICT & Future Planning).
W. Hyun. Author is with the Protocol Engineering Center of ETRI, 218
Gajeong-ro, Yuseong-gu, Daejeon, Republic of Korea (corresponding
author : phone: +82428601565; fax: +82428615404; email:whyun@etri.re.kr)
M.Y.Huh. Author is with the Protocol Engineering Center of ETRI, 218
Gajeong-ro, Yuseong-gu, Daejeon, Republic of Korea (email:myhuh@etri.re.kr)
S.H.Kim. Author is with the Protocol Engineering Center of ETRI, 218
Gajeong-ro, Yuseong-gu, Daejeon, Republic of Korea (email:shkim@etri.re.kr)
S.G.Kang. Author is with the Protocol Engineering Center of ETRI, 218
Gajeong-ro, Yuseong-gu, Daejeon, Republic of Korea (email:sgkang@etri.re.kr)

needs to extract audience behavior information. ITU-T
SG16 is under standardization for audience measurement indigital signage. Different from audience measurements in
IPTV that service providers already knows subscriber’s
information, digital signage service are targeting anonymous
audience. Furthermore, it is forbidden to identify audiencefor privacy reason. In general, digital service providers use
cameras to extract audience information, and there areseveral products that extract basic audience informationsuch as ages and gender. In order to extract advanced
attribute of audience’s behavior, we uses Kinect[1] toextract height, distance, direction and staying duration, and
OpenCV[2] for gender detection and deciding whether
audience is watching or not. Each digital signage terminal
downloads policies for audience measurement frompreference server that resides in service provider’s domain.
Since audience information is reported periodically frommassive number of digital signage terminals continuously,
the information need to be stored into big data systems
rather than using file systems for further analysis. We have
surveyed several candidates for storing these log messages,
and we have used mongoDB for this.
In this paper, we present brief surveys of standardization on
audience measurement in digital signage services andrelated products regarding audience measurement in chapter
2, architectural service model for web-based audiencemeasurement platform of our digital signage service inchapter 3, audience metrics and implementation of audiencemeasurement functionalities using Kinect and OpenCV inchapter 4 and 5, and implementations of web-based
audience measure platform in chapter 6. We conclude inchapter 7.

II. RELATED WORKSIn ITU-T Q14/SG16, the standard for audiencemeasurement in digital signage is under development.
Figure 1 shows a generic digital signage architecture
including audience measurement. Terminal device can haveaudience measurement (AM) client, and the measured
information will be delivered to AM aggregation in service
provider’s domain.

Copyright ⓒ 2014 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 5, September 2014

Terminal Device
FunctionsApplication Functions

Playlog
Config data

DigitalSignage
ClientContent
ProviderFunctions

Metadata
Content

Digital Signage
Application

ReportAM
ClientSecurity
Client506

We designed digital signage server and terminal systems to
be implemented as a web application using Tomcat 6.0[7].

AM
Aggregation
Delivery Schedule
Playlists
Contents

Security
Functions

Service Control
FunctionsContent Delivery
FunctionsContent
Distribution
Content
DeliveryContent
Delivery
ClientContents
Playlist

Network Functions
RequiredFunctional Block

Optional
Functional Block

Figure 1. Generic digital signage architecture with audiencemeasurement[3]The audience measurement functionality of our
implementation will be embedded into AM client ofterminal device. It gathers audience and ambient
information using various sensors, and report to AM
aggregation functions. In this paper, we use Kinect 3D
camera to extract related metrics.
As well as standardization, there are several prototypes
that extract audience metrics such as gender and ages using
generic 2D camera.

Figure 3. Architectrual model for audience measurement in digital
signage system

As shown in Figure 3, the digital signage terminal is consistsof AM client application and terminal server. The terminal
server is a web application that is running on WAS platform,
and it is implemented Java. The AM client application is
implemented C++ since it must use Kinect SDK library and
OpenCV library. When digital signage application boots up,
it retrieves operation policy from remote controller signage
server. The policy includes play schedule, configuration ofterminal that contains AM configuration and log server
address. Whenever it generates AM report message, it sends
it by using WebSocket to AM server that will be located indigital signage service operator
A. Audience Measurement Client ModuleThis module receives video stream from Kinect camera,and extracts audience metrics using Kinect SDK and
OpenCV library. When it extracts audience information, it
sends the record to remote digital signage server by use of
WebSocket [8].
B. Local Terminal Server
Since digital signage terminal is designed to use generic
web browser, it needs to build local web application serverthat prepares contents prior to play. As well as contents
repository, it also acts as agency that receives configuration
regarding audience measurement policies such as resolution,
reporting frequency, reporting destination, etc.

Figure 2. Prototypes supporting audience measurement in Digital Signage
Expo 2013[4] and Digital Signage Japan 2013[5]Figure 2 shows several products regarding audiencemeasurement. As shown in Figure 2, most prototypes
provide detections for gender and ages since these are mostimportant factors for evaluation of advertisement. In thispaper, we have used Kinect 3D camera, since Kinect camera
trace human objects, distance calculation, moving directionsof audience and gesture recognitions.

III. ARCHITECTURAL MODEL FOR WEB-BASED AUDIENCEMEASUREMENT PLATFORM WITHIN DIGITAL SIGNAGE
SYSTEMS

C. Audience Measurement server
Unlike previous version of AM server [10], we havere-implemented it to use socket server, since the AM server
does not need to be implemented as a web application server.
When it receives AM report message, it just stores it tomongoDB for further analysis. When it needs to interact
with controller server, it uses RMI (Remote Method
Invocation), since those servers are tends to be locatedwithin service provider’s domain.
D. Service Controller server
The service controller server manages overall digital
signage services, and it provides web-based advertisement
effect analysis. In this paper, an advertiser or administrator
can take measures of effectiveness of advertisements or
terminals by use of web browser. This server is designed torun as Web application server on top of tomcat server.
Copyright ⓒ 2014 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 5, September 2014

In order to provide web-based analysis of the AM
information, we have designed user interface as shown infigure 4.

Presence
Audience
PersonGenderAgeDistance
HeightWatching

Count
Id

507

Presence Detection Value
Total number of audience
Identifier for differentiating eachhuman object
GenderRough Age
Distance between camera andaudience
Height of audience
Watching/Not watching

As well as venue characteristics, it is also possible toanalyse the relevance with content that had been played at
the time. This is useful for evaluating advertisement effect.
Since we have used 3D camera, it is possible to extract
moving direction, stayed duration, height and distance.
Other metrics are derived from OpenCV library.

Figure 4. Design of web-based analysis user interface

It is designed to provide three types of graph; line graph,ratio graph and bar graph. An user can select the type of
output for visual analysis of the result. In order to simplify
the procedure, the analysis can be performed on one
terminal group or one terminal. Since digital signage
terminals are turned on during the service, it will accumulate
huge volume of information; hence, it uses MongoDB [9]for storing the audience measurement information.
This platform can provide followings, but not limited;- The number of audience as per time
- The number of audience as per temperature- Stay duration for a specific content- The moving pattern of audience as per time
Since our AM client does not support temperature sensor,
it fetches the information from 3rd party open API services
with the location information of the terminal. It is also
possible to complex analysis by composing multiple
conditional attributes, since this platform supports multiple
selections of attributes for analysis.

IV. AUDIENCE METRICS DERIVED FROM AM CLIENTAPPLICATION
Table 1 shows audience metrics that will be embedded
into log message. When it comes to report log message, it
should contain terminal identifier since terminal is installedin various location. This information is used for analysis ofvenue characteristics.

Figure 5. Example log message for measured audience metrics

Especially, moving direction of audience can be used foranalyzing traffic pattern in accordance with time. This canbe used for authoring content for effective advertisements,
and advertisement strategy.
Figure 5 shows an example log message expressed using
XML.

V. IMPLEMENTATION OF AUDIENCE MEASUREMENT USINGKINECT CAMERATABLE 1. METRICS FOR AUDIENCE MEASUREMENT

Element
TerminalId
ContentId

Attribute

DateTime
Noise
AudioVolume
MoveInfo
Direction
InTimeOutTimeDescription
Terminal identifier
Content identifier that has been
playedDate and time for the log
Ambient noise (dB)Audio Volume (dB)Moving information of audience
Direction (L2R, R2L)
The time that audience come into
the sight of camera
The time that audience go out
from the sight of camera

In this chapter, we describe implementation details. We
have implemented AM client in Windows OS environment,
and used OpenCV for image processing in order to acquiregender and eye detection as shown in table 2.
TABLE 2. DEVELOPMENT ENVIRONMENTOS
Library

Windows 7/Visual Studio 10
Kinect SDK 1.6
OpenCV

A. Audience measurement procedure

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 5, September 2014

When the AM client boots up, it initializes two modules
as shown in Figure 5 after retrieving the configuration fromlocal WAS. In order to construct internal model(Initialize_openCV) for analysing face, eye and genderdetection, it loads pre-configured image for training. It also
initializes and registers call back function (KinectProc) for
controlling Kinect camera.
The call back function KinectProc takes event andstreams from camera and performs main analysis.
Initialization

Initialize_openCV

Initialize_Kinect

LoadFaceData

RegisterCBFunc

508

In case of fastest mode, it generates a log every 50ms.
Hence, it generates 200 messages in a second, but it has
some limits on producing accurate data due to lack of time
for analysis. When we used slow mode, it can extract mostof metrics what we want. By the way, since Kinect SDK can
support tracking of maximum four number of person
simultaneously, metrics derived from Kinect is constrained.
However, some metrics from openCV, such as number of
person, gender and eye detection, are not limited since it justextracts those metrics by using image processing engine of
openCV rather than using Kinect SDK. In this case, Kinect
is just used as a source of image.

LodaEyeData
KinectProc

LoadGenderData

ModelTrain

WaitForSingleObject

DetectGender
DetectEye(a) Normal mode(b) Fast mode

GetDistance
GetHeight
GetDirection

Figure 6. Example log message for measured audience metrics

When it comes to analyze gender and eye detection, it
passes the vector data and images into model analyzer
constructed within openCV library. The distance, height anddirection data are coming from Kinect SDK. When theanalysis is completed, it construct log message as shown in
Figure 4, and sends it to remote log server using WebSocket.

(c) Height and distance detection using Kinect in normal mode
Figure 7. Extraction of audience metrics using AM client

B. Acquisition mode of AM data
Since it takes time to analyse acquired image fromcamera depends on the size of crop area effects theperformance, we have defined four level of analysis asshown in table 3.
TABLE 3. PARAMETERS AND IT’S VALUES FOR FACE DETECTION

Mode
FastestFast

NormalSlow

Parameters
LogTick
FaceDetectSize
EyeDetectSize
LogTick
FaceDetectSize
EyeDetectSize
LogTick
FaceDetectSize
EyeDetectSize
LogTick
FaceDetectSize
EyeDetectSize

Value
50ms
180pixel
50pixel
200ms
120pixel
30pixel
500ms
80pixel
20pixel
1000ms30pixel5pixelFigure 6(a) shows a face detection using normal mode,and (b) shows in fast mode. The size of crop region in
Figure 6(b) is slightly larger than Figure 6(a) as configured
in table 3. The lower column of Figure(c) shows extracted
metrics including gender, height, distance and eye
detections.
C. Report of AM data
The AM information is delivered to AM server by using
WebSocket with XML format like Figure 4. This report willbe transformed into JSON format for storing into
MongoDB.

VI. IMPLEMETATION OF WEB-BASED AUDIENCEMEASUREMENT PLATFORM

In this chapter, we describe procedures of web-based
audience measurement platform briefly.
A. Audience measurement server operation
When AM server boots up, it establish RMI connection with
controller server for retrieving preference information such
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 5, September 2014509

as port number that AM server should listen throughstep1~3. When it is ready, AM client sends report message
to this server using WebSocket as shown in step 4. The AM
server converts XML data into JSON format to insert
mongoDB systems.

Figure 8. Procedures for audience measurement server

B. Analysis of advertisement effect procedures
When a customer or operator wants to take an analysis on
the effect of advertisement, it is needed to specify period,
output format, target terminals, attributes to be analyzed asshown in step 1 of Figure 9. When it submits the analysis
profiles to controller server, it gives an order to pull datafrom mongoDB for specified criteria as shown in step 2. The
result record will be stored into derby database for showing
the result as a format of graph. When the analysis iscompleted, the platform notifies it to user for checking out
the result.

Figure 9. Procedures for measuring advertisement effect

The Figure 10 shows an interface of composing analysis
request. As shown in the figure, it can express the resultwith three types of graph; line, bar and pie chart. It is
possible to analize for one terminal, and it is also possible to
do it for serveral terminals within a same group. These
analysis can be used for installation strategy and setting up
charing policy for an advertisement. It is also possible to
make a advertisement plan based on the types of users andtheir behaviors on a specific terminal or terminal group.
This implementation supports of analysis on of gender,height, stayed duration and moving direction, and those
attributes are gathered from Kinect devices.

Figure 10. Web-based analysis of audience measurement data

Some attributes such as ages, temperature are not fully
supported at this time, since those attributes are notgathered.
VII. CONCLUSIONSIn this paper, we have presented architectural model for
web-based audience measurement platform for digital
signage systems, and details regarding implementation ofaudience measurement functionalities collaborated with web
technologies. By use of 3D camera, we can get more
attributes of audience, such as distance, height, directions,etc. These metrics can be used for better marketing strategy
with additional analysis on aggregated audiencemeasurement data. For storing and analysing audiencemeasurement information, we have used MongoDB, since
there are lots of information would be stored. By use of thisplatform, it is possible to compose basic analysis on effectof advertisement by advertiser without any help of digital
signage service provider by their own.
ACKNOWLEDGMENT
This research was supported by the ICT Standardization
program of MSIP (The Ministry of Science, ICT & Future
Planning).

REFERENCES
[1]
[2]
[3]

Kinect , “http://www.microsoft.com/en-us/kinectforwindows/”
OpenCV – Open Source Computer Vision, “http://opencv.org/”
ITU-T Q14/SG16, H.DS-AM, “Audience Measurement for Digital
Signage”, May, 2013[4] H.780, “Digital signage: Service requirements and IPTV-basedarchitecture”, ITU-T, 2012.06[5] Digital Signage Expo 2013, “http://www.digitalsignageexpo.net/”[6] Digital Signage Japan 2013, “http://www.f2ff.jp/dsj/2013/en/”[7] Apache Tomcat, http://tomcat.apache.org/[8] The WebSocket API,
http://www.w3.org/TR/2011/WD-websockets-20110929/[9] MongoDB, http://www.mongodb.org/[10] W.Hyun, “Study on design and implementation of audiencemeasurement functionalities for digital signage service using Kinect
camera”, p597~600, ICACT 2014.

Copyright ⓒ 2014 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 5, September 2014

510

Wook Hyun is a research staff member with ETRI(Electronics and Telecommunications ResearchInstitutes) since 2000. He has received M.S. degree inInformation Communication Engineering from
Chungnam National University, Korea in 2000.
His research interests include VoIP, SIP, NGN, P2P,
overlay networking and digital signage.

MiYoung Huh is a research staff member with ETRI(Electronics and Telecommunications ResearchInstitutes) since 1990. She has received M.S. degree
in Information Communication Engineering from
Chung Nam National University, Korea in 2004.
Her research interests include VoIP, SIP, IPTV, andDigital Signage.
Sung Hei Kim is a research staff member with ETRI(Electronics and Telecommunications ResearchInstitutes) since 1991. She has received M.S. degreein Computer Science from Chung Nam National
University, Korea in 1995. Her research interests
include network
management,
NGN,
serviceengineering, multicasting, P2P systems, and overlay
networking.
ShinGak Kang received the BE and MSE inelectronics engineering from Chungnam University,
Korea, in 1984 and 1987, respectively and the Ph.D.
degree in engineering from Chungnam University,
Korea, in 1998. He is working for ETRI since 1984.
Since 2008, he is a professor of the school ofengineering, University of Science and Technology,
Korea. His research interests include VoIP, IPTV, andfuture network.

Copyright ⓒ 2014 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 5, September 2014

511

Multipath Channel Characteristics for
Propagation between Mobile Terminals in Urban
Street Canyon Environments
Myung-Don KIM, Juyul LEE, Jinyi LIANG, Jinup KIM
Electronics and Telecommunications Research Institute (ETRI)
218, Gajeongno, Yuseong-gu, Daejeon, 305-700, Korea
mdkim@etri.re.kr, juyul@etri.re.kr, liangjinyi@etri.re.kr, jukim@etri.re.kr
Abstract—In this paper, we focus on multipath channel
characteristics of low–height antenna links for mobile to mobile
communications in urban street canyon environments. We
present a wideband MIMO channel sounder and antennas used
to measure multipath channel characteristics in the 3.7GHz
frequency band and the result of calibration test to evaluate asystem performance before field measurement. We carried out
channel measurement campaigns in typical urban street canyon
environments in Seoul, Korea. The root mean square (r.m.s.)delay spread and angular spread characteristics are analyzed
with results of their distribution and cumulative density
function (CDF) in line of sight (LoS) and non-LoS (NLoS) caserespectively.
Keyword—Mobile-to-Mobile, multipath characteristics,
channel model, channel measurements, channel sounderI. INTRODUCTIONM

OBILE-TO-MOBILE

direct communications services,
commonly known as D2D (device-to-device), are now
being actively discussed in various standardization bodies,e.g., “ProSe” for the LTE-Advanced system [1]. Unlike
typical rooftop cellular networks, both transmitters andreceivers are generally found near street levels, since directcommunication links are established between mobile
terminals. Consequently, conventional propagation models
are limited in their ability to predict specific channel
environments and those characteristics for development of
the direct communication system using a system or link level———————————————————————
Manuscript received September 8, 2014. This work was supported in partby the MSIP (Ministry of Science, ICT & Future Planning), Korea in the ICT
R&D Program 2014, “Development of core technologies to improve spectral
efficiency for mobile big-bang (14-000-01-002)”. (Corresponding author:+82-42-860-6178; fax: +82-42-860-6732; e-mail: mdkim@etri.re.kr)
Myung-Don Kim is Principal Researcher with the Electronics andTelecommunications Research Institute, Daejeon, Korea (+82-42-860-6178;
fax: +82-42-860-6732; e-mail: mdkim@etri.re.kr).
Juyul Lee is Senior Researcher with the Electronics andTelecommunications Research Institute, Daejeon, Korea (+82-42-860-5503;
fax: +82-42-860-6789; e-mail: juyul@etri.re.kr).
Jinyi Liang is Researcher with the Electronics and Telecommunications
Research
Institute,Daejeon,Korea(+82-42-860-5289;
fax:+82-42-860-6732; e-mail: liangjinyi@etri.re.kr).
Jinup Kim is Principal Researcher with the Electronics andTelecommunications Research Institute, Daejeon, Korea (+82-42-860-5423;
fax: +82-42-860-6789; e-mail: jukim@etri.re.kr).

channel simulation.
Recently, new prediction methods and channel
characteristics for propagation between mobile terminals
based on field measurement in various outdoor environments
are reported widely. Lu et al [2] developed simplified
site-specific path loss formulas for street grids. They
assumed that surrounding buildings are infinitely high so that“vertical plane” effects can be ignored. Consideringreflection and diffraction along with the horizontal plane,
they developed path loss formula based on two-ray models,
which can account for up to two-turn NLoS links. M. Sasaki[3] proposed a new path loss model for propagation between
terminals located below roof-top in residential environments.
J. Lee et al [4] investigated the effects of surrounding
building heights on path loss characteristics, especially inurban street grid environments in Korea. From thecomparison results of the ITU-R propagation models such as
ITU-R Recommendation P.1411 [5] and Report M.2135 [6],it is noted that conventional channel models areoverestimated and the path loss characteristics can beaffected by surrounding building heights. Furthermore, fromthe aspect of radio propagation between low-antenna
terminals in high-rise environment, reflected waves atcorners in horizontal plane seem to play a dominant role.
Most of these researches are relevant to the path loss
characteristics for mobile to mobile direct communications.
However, when we consider direct communications between
terminals using MIMO antennas, the study of multipathcharacteristics (i.e. delay spread, angular spread, etc.) is also
very important.
This paper investigates multipath channel characteristics
for propagation between mobile terminals based on channel
measurements in Seoul, Korea. Section II presents a
wideband MIMO channel sounder used to measure multipath
channel characteristics, and the result of calibration
measurement to evaluate a system performance before fieldmeasurement. In Section III and Section IV, the fieldmeasurement campaign and analysis results of multipathcharacteristics such as delay spread and angular spread aredescribed. Finally, to wrap up the work, conclusions are
given in Section V.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 5, September 2014

II. MEASUREMENT SYSTEM AND CALIBRATION OF SYSTEMPERFORMANCE BEFORE MEASUREMENT-135o).
180o
90o

-90o

A. Wideband MIMO Channel Sounder
Measurement campaigns were conducted with the
wideband MIMO channel sounder system developed atElectronics and Telecommunications Research Institute(ETRI), which can measure multipath characteristics in the
3.7 GHz frequency with 100 MHz bandwidth [7][8]. Table I
represents a detailed specification of the channel sounderwhich can measure the channels received from multiple
antenna elements and estimate multipath components with atime delay resolution of 10 ns.
TABLE ISPECIFICATION OF CHANNEL SOUNDER AND MEASUREMENT

512

RX
90o
(b)

0o

(a)
-90o

0o

0o
θ
θ
TX 0o

180o

180o
9m

90o

-90o

90o

-90o

180o

0o

0o

(d)
180o

180o
90o

-90o
180o
(c)

direction of 0o for RX
direction of 0o for TX

-90o

90o

Fig. 2. Configuration of calibration measurement for channel sounders(TX-RX) and antennas

CONFIGURATIONS

Items
Frequency
Channel bandwidth
PN code length
Maximum Tx power
Multipath Resolution
Frequency stability
Number of Antenna elements

Specifications
3.7 GHz
100 MHz
2047 chips
36 dBm10 ns (3m)10-11
TX: 8 UCA, RX:8 UCA

For the measurement campaign, we installed a transmitter(TX) and a receiver (RX) channel sounder in separate
vehicles respectively as shown in Fig. 1. A uniform circulararray (UCA) antenna which has eight vertically polarized
elements aligned circularly with a spacing of 0.5 λ is mounted
on the rooftop of each vehicle with the height of 1.9 meters.

8-UCA(a) θ = 0o(b) θ = 45o(c) θ = 180o(d) θ = -135o

8-UCA

Fig. 3. Estimated result of AoA and AoD at the position of θTransmitter (TX)

Receiver (RX)Channel SounderFig. 1. Installation of channel sounder system and antennas (mounted on therooftop of vehicle) for channel measurement campaign

B. System Calibration Measurement and Results
Before measurement campaign, we have conducted asystem calibration measurement at ETRI playground (openspace) with channel sounders (a transmitter and a receiver)and antenna arrays. For this measurement, full radiation
patterns of all the elements of each array antenna were
measured in the anechoic chamber in advance. The azimuth
plane radiation pattern with two polarizations (vertically andhorizontally from -180o to +180o) and the elevation plane(from -45o to +45o) have been obtained enabling theestimation of the spatial channel characteristics. Fig. 2
depicts a configuration of this test. The position of atransmitter (TX) was fixed, and a receiver (RX) was locatedat 9 m away. The UCA8 array antennas for both TX and RX
were installed on the ground with a height of 1.5 m. We
measured a channel from TX to RX, and changed the positionof the RX and collected measured samples of total 8 points
with different angles (θ = 0o, 45o, 90o, 135o, 180o, -45o, -90o,

In this paper, we observed the estimated results of an angleof departure (AoD) and an angle of arrival (AoA) by using a
Barlett beam-forming method [9]. Fig. 3 shows estimatedresults of AoA and AoD at each point of θ. We can see that adominant path component having the strongest channel
power (red part) starts from each angle of 0o, 45o, 180o and-135o and correctly arrives at each angle of 0o, 45o, 180o and-135o in Fig. 3(a)-(d). From this measurement results, theperformance of our channel sounders was evaluated, and we
confirmed the ability to estimate time and spatial
characteristics of multipath channels through fieldmeasurement campaign.

III. MEASUREMENT CAMPAIGN
To obtain multipath characteristics in urban street canyon
environments, we conducted a measurement campaign at 3
sites which are carefully selected places among typical urban
low-rise, high-rise, and very high-rise environments in Korea.
All sites are composed of rectilinear flat street grids, but theiraverage building heights are different, as specified in Table
II.
Fig. 4 shows the measurement routes and surrounding
environment of each area. The measurement campaigns were
performed along the planned routes in three different sites
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 5, September 2014

respectively. Each site has two TX points i.e. TX1 and TX2,
and all RX positions for LoS and NLoS scenario assigned
along the road as shown in the map.
TABLE IIMEASUREMENT SITES AND ENVIRONMENTS
Class
Location
FeatureLow-rise buildings (3-5 story, 11-14m
Urban
Ilsan,
height) are located at both side of 2
low-riseSeoullanes road (avg. 18 m wide)
A commercial area where high-rise
Urban
Yeouido,buildings (10-15 story, 35-45m
high-riseSeoulheight) are located at both side of 2
lanes road (avg. 24 m wide)
A downtown area with skyscrapers
Urban
where very high-rise buildings
Gangnam,very(50-195m height) are located at bothSeoul
high-riseside of 2-4 lanes road (avg. 26 m
wide)

Sites
Site 1Site 2Site 3

TX1-NLoS

TX2-NLoS

TX1-NLoS

TX2-LoS

TX1-LoS

TX2

513

The measurement campaigns were taken during daytime
and outside of normal rush hours, when few people are on
sidewalks and vehicle traffic running at about 30km/h.
During measurement, we held the TX vehicle at a stationary
position and moved the RX vehicle at a speed under 10 km/h
along measurement routes. The RX channel soundercollected measured data including channel impulse responses
and GPS information.

IV. MEASUREMENT RESULTS AND ANALYSIS
We analyzed multipath channel characteristics such as
r.m.s delay spread and angular spread of arrival frommeasured results.
A. r.m.s delay spread
The r.m.s delay spread (DS) is the standard deviation valueof the delay of multiple paths, and it is weighted proportional
to the energy in the reflected waves. To obtain delay spreadfrom measured data, we estimate the multipath components
from power delay profile (PDP) from measured channel
impulse responses (CIRs). CIRs can be obtained from theauto/cross-correlation function between an original PN
sequence and a received signal as follows:

TX2-NLoS
TX1

hmn   Transmitter position
Measurement Route (LoS)
Measurement Route (NLoS)

TX2-NLoS

Rxy  
Rxx 0F 1 ( X ( f )*  Y ( f ))

 x 

2

(1)

i

i

(a) Site 1, where Y ( f ) is a Fourier transform of the received signal
TX1-LoS

TX2-NLoS

TX1-NLoSy ( ) , x( ) is a sequence for transmission, Rxx means an

TX2-LoS

auto-correlation to remove a system impairment [9], andRxy   means a cross-correlation of x( ) and y ( ) .
The power delay profile (PDP) can be calculated by:
TX2-NLoS

TX1-NLoSPDP ( ) NM h  
mn

2

(2)

n 1 m 1

TX1

TX2

Transmitter position
Measurement Route (LoS)
Measurement Route (NLoS)(b) Site 2

TX1

TX2-LoS

TX2
TX1-LoS

TX2-NLoS

TX1-NLoS

TX1-NLoS

TX2-NLoS

Transmitter position
Measurement Route (LoS)
Measurement Route (NLoS)(c) Site 3
Fig. 4. Measurement routes and environments of each site ((a) Site 1: Ilsanarea, (b) Site 2: Yeouido area, (c) Site 3: Gangnam area), where hmn   has an N × M matrix (n and m are theindex of Rx and Tx antennas respectively). To extract
multipath components, we set a threshold (20 dB below thepeak level in this paper) considering the dynamic range ofchannel impulse responses [11], and then pick the multipath
components h l  with a related delay  l , l  1 : L . L is a
total number of multipath components founded within
threshold. Finally, delay spread can be calculated by the same
method in [11].
Fig. 5 shows the distribution of r.m.s. DS values
corresponding to travel distance between the TX station andRX station for each scenario (LoS and NLoS) andenvironments (urban low-rise, urban high-rise and urbanvery high-rise). From the measurement results, we can seethat distributed DS values of the NLoS are much larger thanthe LoS for all sites. Furthermore, in case of NLoS, we canobserve that the DS values are gradually increased proportion
to the distance. In addition, the cumulative distributionfunctions (CDF) of the delay spread for LoS and NLoS cases
are plotted in Fig. 6.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 5, September 2014

r.m.s delay spread (Site1)

514

CDF of delay spread (Site 1-3, LoS)
1

120

0.9

LOS
NLOS

100

0.8
0.7
0.6

CDF

Delay Spread (ns)

80

60

0.5
0.4

40

0.3
0.2

Site 1Site 2Site 3

20
0.1

0

0

0

50

100
150
Travel distance (m)

200

250

0

20

40

60

160

180

200

CDF of delay spread (Site 1-3, NLoS)

r.m.s delay spread (Site2)

1

400

0.9

LOS
NLOS

0.8

300

0.7
0.6

250
CDF

Delay Spread (ns)140(a) LoS cases(a) Site 1

350

80
100
120
Delay spread (ns)

200

0.5
0.4

150
0.3

100

0.2

50

0.1
0

0

0

50

100
150
Travel distance (m)

200

250

Site 1Site 2Site 3

0

100

200

300

400
500
600
Delay spread (ns)

700

800

900

1000

(b) NLoS cases(b) Site 2

Fig. 6. CDF of delay spread for LoS and NLoS scenarios (site 1 to 3)
r.m.s delay spread (Site3)
1000
900

LOS
NLOS

800

Sites
Delay Spread (ns)

700
600

Site 1500

Site 2400
300

Site 3200

TABLE IIIMEASUREMENT RESULTS OF R.M.S DELAY SPREAD
r.m.s. delay spread (ns)Class
Scenario
10%
50%
95%
LoS
0.9
5
31.9
Urban
low-rise
NLoS
16.7
33.1
50
LoS
2.2
7.1
69
Urban
high-rise
NLoS
40.5
81.3
149.4
Urban
LoS
2
8.1
31
very
NLoS
58
142
610.5
high-rise

100
0

0

50

100
150
Travel distance (m)

200

250

(c) Site 3
Fig. 5. Measurement results of r.m.s. delay spread corresponding to Tx-Rx
distance ((a) Site 1: urban low-rise environment, (b) Site 2: urban high-rise
environment, (c) Site 3: urban very high-rise environment)

We summarize the measured r.m.s. delay spread for thedifferent cases with cumulative probability of 10%, 50% and
95% as shown in Table III. When we compared medianvalues (50% of CDF) of r.m.s. delay spread in Table III, Site
3 (urban very high-rise environment) has the largest value,
Site 2 (urban high-rise environment) has a medium, and Site
1 (urban low-rise environment) has the smallest value. From
this result, we can understand that the higher buildings
around the TX or the RX station, the more multipath
components can be generated.

B. r.m.s angular spread
The r.m.s. angular spread is the power-weighted standard
deviation of the direction of arrival and departure, and it isgiven by the second moment of the power angular profile[11]. We calculate the power azimuth spectrum (PAS)corresponding to the only founded multipath components.
The PAS calculation function is based on Bartlett
beam-forming theory [9], which is written as follows:
PASi (AOA )  RX AOA   hmn  i 2(3), where  RX is a radiation pattern of RX antennas (360 x
N matrix), and  AOA is an angle of arrival.
Then, to overcome the limitation of angular resolution for
estimation of direction of arrival using a small number ofantenna elements [12], we find the peak angle l from the
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 5, September 2014

result of PAS as below:

l  arg max PASl (AOA )

(4)

After all, we obtained the r.m.s. angular spread of arrivalfrom extracted coefficients such as arrival angle  l andrelated power for each multipath components.
Fig. 7 depicts the CDF curves of the angular spread ofarrival (ASA) for LoS and NLoS case, respectively. When
we observed the distribution of ASA values, they are notincreased or decreased depending on the distance, but thecase of NLoS is highly distributed than the LoS for allenvironments.
CDF of ASA (Site 1-3, LoS)
1
0.9
0.8
0.7

CDF

0.6
0.5
0.4
0.3
0.2

Site 1Site 2Site 3

0.1
0

0

10

20

30
40
Angular spread (degree)

50

60

515

that the median values (50% of CDF) of r.m.s. angular spreadin NLoS cases are higher than the LoS cases for allenvironments. Furthermore, the height of surrounding
building seems to affect the angular spread characteristics at
50%, but not much at other probabilities.

V. CONCLUSION
In this paper, we investigated the multipath channel
characteristics between mobile terminals with low-height
antennas considering for mobile-to-mobile communication
scenarios. We carried out channel measurement campaigns in
the 3.7GHz frequency band in three different urban street
canyon environments. In our measured results, r.m.s delay
spread values of the NLoS case are much larger than the LoS
for all sites. Furthermore, especially in case of NLoS, we canobserve that the DS values are gradually increased proportion
to the distance. On the other hand, r.m.s. angular spreadvalues are not looked increase or decrease depending on the
distance in those circumstances. The ASA values in case of
NLoS are highly distributed than LoS case for allenvironments. The height of surrounding building, inparticular at 50% of CDF, seems to slightly affect the angularspread characteristics. This issue should be further studied on
the basis of more measurement data. The statistical angularcharacteristics of departure (ASD) are expected to similar
with those of arrival based on reciprocity if two mobile
terminals are located at the same environment and scenario.

(a) LoS cases
CDF of ASA (Site1-3, NLoS)REFERENCES

1

[1]

0.9
0.8
0.7

CDF

0.6
0.5
0.4
0.3
0.2

Site 1Site 2Site 3

0.1
0

10

20

30
40
Angular spread (degree)5060(b) NLoS cases
Fig. 7. CDF of angular spread of arrival for LoS and NLoS scenarios

Sites
Site 1Site 2Site 3

TABLE IV
MEASUREMENT RESULTS OF R.M.S ANGULAR SPREAD
r.m.s. angular spread (degree)
Class
Scenario
10%
50%
95%
LoS
0.7
13.6
42
Urban
low-rise
NLoS
6.1
27.3
56.6
LoS
2.3
15.4
36.5
Urban
high-rise
NLoS
13.9
25.9
54.9
Urban
LoS
1.5
16.7
45.6
very
NLoS
8.7
30.7
47.5
high-rise

Table IV summarizes the measured results of r.m.s.
angular spread with the probability of 10%, 50% and 95% for
the different environments and scenarios. We can observe

3GPP TR 22.803, Feasibility study for Proximity Services (ProSe),
Mar. 2013.
[2] J. S. Lu, H. L. Bertoni, C. Chrysanthou, and J. Boksiner, “Simplified
path gain model for mobile-to-mobile communications in an urban
high-rise environment,” in Proc. IEEE Sarnoff Symp., Princeton, NJ,
Apr. 2010.
[3] Motoharu Sasaki, Wataru Yamada, Naoki Kita, Takatoshi Sugiyama,“Path Loss Model with Low Antenna Height for Microwave Bands in
Residential Areas”, IEICE Transactions 96-B(7), pp1930-1944, 2013[4] J. Lee, H. K. Chung and M. D. Kim, "Building Height Effects on Path
Loss for Low Antenna Links in Urban Street Grid Environments",
IEEE Asia Pacific Wireless Communication Symposium, Aug. 2013.
[5] Rec. ITU-R P.1411, Propagation data and prediction methods for theplanning of short-range outdoor radiocommunication systems andradio local area networks in the frequency range 300 MHz to 100 GHz,
Feb. 2012.
[6] Rep. ITU-R M.2135-1, Guidelines for evaluation of radio interface
technologies for IMT-Advanced, Dec. 2009.
[7] J.-J. Park, M.-D. Kim, H.-K. Kwon, H.-K. Chung, X. Yin, and Y. Fu,“Measurement-Based Stochastic Cross-Correlation Models of aMultilink Channel in Cooperative Communication Environments,”
ETRI Journal, vol. 34, pp. 858–868, Dec. 2012.
[8] M. D. Kim, J. J. Park, H. K. Kwon, and H. K. Chung, “Performance
Evaluation of Wideband MIMO Relay Channel Sounder for 3.7 GHz,”IEEE Asia Pacific Wireless Communication Symposium, Aug. 2011.
[9] M. Barlett, “Smoothing periodograms from time series with continuous
spectra,” Nature (London), vol. 161, no. 8, pp. 686–687, 1948.
[10] H. Chung, et al., “MIMO channel sounder implementation and effects
of sounder impairment on statistics of multipath delay spread,” Proc.
IEEE VTC, vol. 1, pp. 349–353, Sep. 2005.
[11] Rec. ITU-R P.1407, "Multipath propagation and parameterization of its
characteristics", Oct. 2009.
[12] Gerd Sommerkorn, et.al. "Performance Evaluation of Real Antenna
Arrays for High-Resolution DoA Estimation in Channel Sounding",
COST 273 TD(03)196, Sept. 2003.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 5, September 2014

516

Myung-Don Kim (BS’93–MS’95) is a Principal
Researcher in the Advanced Communications
Research
Laboratory
atElectronics
andTelecommunications Research Institute (ETRI). He
joined ETRI, Daejeon, Rep. of Korea, in 1995, and heworked on the development of mobile test-beds forCDMA, IMT-2000 and WCDMA systems. Since
2006, he has been involved in the development of
wideband MIMO channel measuring system,measurement and channel estimation of MIMO channels. His researchinterests include MIMO, channel measurement and channel modelingfor next generation mobile communications
Juyul Lee (BS’96-MS’98-PhD’10) is a SeniorResearcher in the Advanced Communications
Research
Laboratory
atElectronics
andTelecommunications Research Institute (ETRI)
since 2000. Prior joining with ETRI, he was a
Research Engineer with the Agency for Defense
Development (ADD) from 1998 to 2000. His
research spans the fields of information theory andwireless communications, with special interests in
multiple-antenna/multiple-user/multi-cellresource
allocations,device-to-device communications, and wireless propagation channel
measurements and modeling.
Jinyi Liang (BS’04–MS’13) is a Researcher in the
Advanced Communications Research Laboratory atElectronics and Telecommunications ResearchInstitute (ETRI). He is Chinese and joined ETRI,
Daejeon, Rep. of Korea, in July 2013, and he’s
working on the project ‘Wireless Channel and
Frequency Characterization based on Field
Measurements for Broadband Mobile Hot-Spot
Applications’. His research interests include MIMO,
channel measurement and channel modeling for next
generation mobile communications.
Jinup Kim (BS’85-MS’87-PhD’96) has been with
Electronics and Telecommunication ResearchInstitute since 1987. And also he has been a professorof University of Science and Technology in the fieldof Wireless communications since 2005. He has
researched in the field of the wireless communication
system. He is recently interested in the Digital RF,
channel modeling, Software Defined Radio andCognitive Radio technologies, etc.

Copyright ⓒ 2014 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 5, September 2014

517

A TOR-Based Anonymous Communication
Approach to Secure Smart Home Appliances
Nguyen Phong HOANG, Davar PISHVA
Institute of Information & Communications Technology, APU(Ritsumeikan Asia Pacific University), Japanhoang.phong.37e@st.kyoto-u.ac.jp, dpishva@apu.ac.jp, Fax: +81 977 78 1001, Tel: +81 977 78 1000

Abstract— Digital information has become a social
infrastructure and with the expansion of the Internet, network
infrastructure has become an indispensable part of social life
and industrial activity for mankind. The idea of using existing
electronics in smart home appliances and connecting them tothe Internet is a new dimension along which technologies
continue to grow, and in recent years mankind has witnessed anupsurge of usage of devices such as smart phone, smart
television, home health-care device, smart LED light bulbs
system, etc. Their build-in internet-controlled function has
made them quite attractive to many segments of consumers andsmart phone has become a common gadget for social
networking. There are, however, serious challenges which need
to be addressed as these tiny devices are designed for specificfunctions and lack processing capacity required for mostsecurity software. This research explores how theseinternet-enabled smart devices can be turned into very
dangerous spots for distributed attacks purposes bycybercriminals for various ill intensions in a pinpointed manner.
It then introduces a new approach to deal with such problems
by taking advantage of the anonymous communication of theOnion Router (hereafter: TOR). It compares pros and cons ofusing anonymous communication scheme and justifies it be an
efficient countermeasure to most attack scenarios.
Keyword—Smart Appliances, the Internet of Things, Security,
Anonymous Communication, the Onion Router (TOR)

I. INTRODUCTIONNOWADAYS, thanks to the impressive achievement ofmaterial science especially in semiconductor materials
and nanomaterials, all the electronic devices are getting
smaller in size, and microprocessors can be found in mostappliances. Such developments have been leading mankind
to a new era of technology, the era of the “Internet of Things”(hereafter: IoT), where all the appliances are getting tiny andcontrollable via the Internet, thus enabling people to enjoy
network based services, such as Video on Demand (VOD),Music on Demand (MOD), remote update, e-commerce,
remote control, and other similar services. Furthermore,———————————————————————
Manuscript received on September 9, 2014. This work is a follow up of apresentation done at the 16th International Conference on Advanced
Communication Technology which received Outstanding Paper Award.
Nguyen Phong HOANG is a graduate student at the Graduate School ofInformatics at Kyoto University Japan (hoang.phong.37e@ st.kyoto-u.ac.jp).
Davar Pishva is a professor in ICT at Ritsumeikan Asia Pacific University(APU) Japan (corresponding author: +81-977-78-1000, fax: +81-977-781001, e-mail:dpishva@apu.ac.jp).

researchers around the world have come up with anabundance of resourceful ideas on how to effectively use
microprocessors and Internet in other everyday household
appliances. ‘Smart’ has become the new buzzword that wehave kept on hearing in recent years, for example, in ‘smart’homes, ‘smart’ kitchens, ‘smart’ ovens, ‘smart’ refrigerators,
etc. [1]. Table 1 indicates functional classification of smart
home appliances. There is also a tremendous businesspotential for them because of foreseen future demand byelderly people, where the number of people over the age of 65
is expected to double to 70 million by 2030 [2]. According toa study conducted by International Data Corporation, 212
billion “things” will be installed based on IoT with anestimated market value of $8.9 trillion in 2020 [3]. Those“things” will be nothing special but daily used appliances
ranging from watch, light bulb to smart television,refrigerator and so on.
TABLE IFUNCTIONAL CLASSIFICATION OF SMART HOME APPLIANCESNo
Function
Example of Product or Usage
Broadband TV, Microwave Oven, HDD
1 Content Retrieval
Recorder (for TV program, etc)
Content
HDD Recorder (for TV program, etc),
2
Storage/Usage
MP3 Player
Communication/
VoIP , IP-TV Phone, All kinds of Emails
3
Messaging
System, Healthcare System
Security Camera, Gas/Fire Sensors,
4 Remote Surveillance
Refrigerator, Lighting Fixture, Door Lock
Air Conditioner, Lighting Fixture, TV,
5 Remote Control
TV Program Recording
6 Remote Maintenance
Firmware Update, Trouble Report
7 Instrument Linkage
Networked AV Equipments
8 Networked Game
Family Type Game Machine

Connecting smart home appliances to the Internet,
however, makes us vulnerable to malicious attacks. An
intruder can steal private information such as contact info,
shopping or eating preferences, lifestyle and relaxation habits,
or credit card information used to pay for such services. They
can also use smart appliances as launching pads to carry out
malicious attacks into other systems. Table 2 shows a list ofcommon attacks that can be carried out through smart home
appliance and the next section discusses some specific attack
cases.

No
1
2

TABLE IIA LIST OF COMMON ATTACKS
Common Threat
Example of an Attack
User Impersonation
Impersonation using password
DeviceImpersonation of a device using its faulty

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 5, September 2014

3
4
5
6
7
8
9

Impersonation
Service Interruption
Data Alteration
Worm/Virus
Infection
Phishing/Pharming
Data Wiretapping
Firmware
Alteration
OS/Software
Vulnerability

certificate
Distributed Denial of Service (DDOS)
Data alteration of transmitted or stored data
Infiltration and/or damaging of a computer
systemImpersonation of users’ destination
Information leakage through wiretapping
Replacing of firmware at will
Launching of worms and attacks using such
vulnerabilities

II. TYPICAL ATTACK CASES ON SMART APPLIANCES
In a previous research, the authors showed how the natureof Internet Protocol could accidentally put its user’s identity
into high risk of being revealed due to the existence of private
information behind the IP address in the packet header, which
can be easily extracted and observed by various IP tracer anddeep packet inspection tools [4]. In addition, the use ofsniffing tools such as Wireshark or other network monitoring
applications, though not new, turn out to be very efficient for
attacking IoT networks too. Table 3 shows threat likelihood
level of a given smart home appliance type for a particular
attack and the rest of the section briefly discusses some of theclassical techniques that have recently been employed tocarry some of these attacks on the smart home appliancesystem not only to steal personal information but also abuse
the devices and make them serve cyber criminals’ numerous
illegal purposes.

518

B. Denial-of-Service (DOS) Attack
DOS attack is not a new technique and the main attacking
mechanism is that a huge amount of packets are generated
and sent simultaneously to a targeted appliance. As aconsequence, the appliance is either brought down causing
permanent crash, or reset to factory setting automatically andmaking it lose its configuration, stored data and applications.
This kind of attack has recently been reported (August2014) [6]. A hacker named, Hemanth Joseph, shared on his
blog a very simple way to carry a DOS attack on a Pebble
Smart Watch. The attacker just needs to know the victim’s
phone number, Facebook ID, or any other way to interact
with the Watch’s IP address. Considering that the watch has afunction of showing messages received from Facebook,tablet or phone on its screen without character limitation, theattacker can keep on sending many lengthy messages so as tocause a DOS attack on the watch. As a consequence, theSmart Watch could be brought down, reset to factory setting,and lose all of its data as shown in Fig. 1:

TABLE IIITHREAT LIKELIHOOD LEVEL OF A GIVEN SMART HOME APPLIANCE

Fig. 1. After DOS attack, the Watch’s screen is full of white straight lines,
all data and applications are erased because of reset to factory setting [6].

A. Man-In-The-Middle Attack
In March 2014, a Vulnerability Research Firm named
ReVuln, published a video which describes how to employ
man-in-the-middle attack to penetrate into the Philips Smart
Television through the wireless network that the device
connects to. Consequently, the cyber criminal could steal thecookies from the built-in web browser of the television andgenerate a session hijacking attack to gain access to victim’s
personal pages [5].
After observing the attack video, one can easily say thatTV’s configuration for connecting to wireless network
through a default hard-coded password is not appropriate.
Though it may be convenient for the users, it is quite
dangerous if the cyber criminal is also within the range of
wireless router. It could even cause more serious aftermath
since remote control TV application can easily be
downloaded from the Internet. Through such application the
hacker could obtain the TV’s configuration files and control
the TV if he knew the IP address of the television.

In addition to the IoT network of home appliances, cybercriminals can also easily penetrate into internet-based-control
public appliances. A study published in August 2014 bysecurity researchers from the University of Michigan
demonstrates how a series of vital security vulnerabilities intraffic light systems in the US could allow adversaries toquite easily take control of the whole network of at least 100
traffic signals from a single point of access [7].
By carefully examining the above cases, the authors foundthat the main reason behind such vulnerability was because
those appliances make use of unencrypted wireless radio
signals thus simply monitored and compromised bycybercriminals.
C. Thingbot
Thingbot is derived from the word botnet which itself is acombination of the words “robot” and “network”. In a similarmanner, thingbot is comprised of the words “thing” and“robot”.
In order to create a huge botnet network, many computers
are compromised and abused by malware to launch cyberattacks without awareness of internet users. In a very similarmanner, botnet composed of smart home appliances and

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 5, September 2014

other devices in IoT network, can be infected and easily
turned into slaves by the attackers because of lack of proper
security. After knowing the real IP addresses of suchcompromised devices, it becomes easy for the hacker togenerate cyber attacks such as spamming, or executing
Distributed Denial of Service (DDOS) by manipulating them
via standards-based network protocols such as Internet Relay
Chat (IRC) and Hypertext Transfer Protocol (HTTP) [8].
Although no serious DDOS attack originating from IoT
network has been reported as of this moment, it is predictablethat DDOS attack scheme from IoT will be on its upward
trend in a near future as mentioned in a warning press from
Kaspersky blog [9]. Just to do a small calculation as areference, let us assume that only 0.01% of the IoT network iscompromised by 2020. This will make around 20 million
appliances vulnerable to cyber attacks. Even granting thatmost of the IoT will only transmit relatively small amounts ofdata, considering their enormous size, the DDOS attack will
be severe enough and should have no difficulty in bringing
down a server, or any single host. Moreover, unlike DOS
attack which is generated in a pinpointed manner from asingle computer or server to flood a target, DDOS attack is an
integrated effect of a huge number of compromised devices.
Once it occurs, blocking becomes extremely difficult sinceeach compromised element has its own unique IP address.
D. Some Specific Attack Cases in JapanA DVD/HDD video recorder in Japan, which implemented
a proxy server and was accessible without authentication
under its default configuration, was used as an open proxy
server base for spamming [10], as shown in Fig. 2. In anotherincident, a music player, which was infected with a virus in
the factory, corrupted its user’s computer upon connection[11]. In an example of privacy violation, a poorly
implemented ‘referer’ feature in a cellular phone constantly
transmitted previously accessed page information even when
the page was reached via direct addressing (i.e.,
non-hyperlink access). The browser flaw caused private
information, which may had been required to access aprevious page (e.g., user name, password), to be revealed to
the next link. It also revealed the user’s favorite sites bytransmitting information on a previously accessed page [12].

519

Fig. 3. A heterogeneous home network and its service Providers

services complex to both users and service providers, as canbe seen from Fig. 3.
Implementing security on these devices also presents morechallenges than traditional computer security due to thelimited resources (e.g., toy CPUs that cannot handle
computationally expensive cryptographic computations andbattery power that prohibits long-lasting or high-peakcomputations). Furthermore, because security of a network
depends on its weakest link, security of networked smart
home appliances would rely on the security of its mostprimitive home appliance e.g., a coffee maker or a toaster.
The problem is further aggravated by the fact that home
appliance users cannot be considered as “skilled”
administrators, but are instead technology-unaware people inmany cases.
In order to cope up with such security challenges, thecorresponding author in a previous work proposed the idea ofhandling them through network operator. The
recommendation was to engage a network operator to build
dedicated but nonproprietary home gateways and become thepreferred trusted third party and motivate internet-enabled
smart appliance manufacturers to develop device drivers andapplication software that can run on such universal home
gateways to control and operate the appliances. This idea isschematically shown in Fig. 4, where a universal home
gateway, managed by a network operator, functions as anentry point to the networked appliances. In this architecture,
all transactions with the smart appliances, whether local or
remote, are done via universal home gateways. [13][14].

Fig. 4. Smart home appliance security via network operator

Fig. 2. A spamming incident

III. SECURITY IMPLEMENTATION CHALLENGES
Continuous growth of diverse smart home appliances anddevelopment of numerous networking technologies makemanagement of home network security and their associatedUnfortunately, however, as of today no such universal
home gateway has yet been manufactured, without which it
becomes impossible for a network operator to take the role oftrusted third party.

IV. MOTIVATION BEHIND THE STUDY
In a recent previous work on anonymous communicationand its application to social networking, the authors
conducted some experiments to test the confidentiality level
and anonymity level of several anonymous tools such as
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 5, September 2014

Incognito Mode in Chrome browser, proxy server, virtualprivate network (VPN), and TOR. In conclusion, the study
was able to point out some significant pros and cons in eachparticular anonymity tool. Additionally, the authors
recognized that TOR is indeed the strongest anonymous toolcompared with other tools [4]. This is mainly because “TorBrowser Bundle” operates as an internet browser whichanonymizes all internet surfing activities that pass throughand sends them to the TOR network. After concluding that
TOR is a powerful tool for protecting internet user’s privacy
from being compromised by most contemporary attack
techniques and learning about a new-style of attack; reportedby Proofpoint, an innovative security-as-a-service vendor, inJanuary 2014, the authors realized that smart appliances’
security have not yet been realized via network operator and
TOR may play an effective role in this endeavor.
The Proofpoint’s report was about cyber attack involving
commonly used smart home appliances and in its press
release, the company stated: “The attack that Proofpoint
observed and profiled occurred between December 23, 2013
and January 6, 2014, and featured waves of malicious email,typically sent in bursts of 100,000, three times per day,targeting Enterprises and individuals worldwide. More than
25 percent of the volume was sent by things that were notconventional laptops, desktop computers or mobile devices;
instead, the emails were sent by daily consumer gadgets suchas compromised home-networking routers, connected
multi-media centers, televisions and at least one refrigerator.
No more than 10 emails were initiated from any single IP
address, making the attack difficult to block based on
location” [15]. Thus, this study is motivated by the
aforementioned scenario and our relevant previous work. In
this paper, the authors will demonstrate how TOR can help
remedy the recent security problems in smart home appliancesystem.
V. TOR – A NEW APPROACH TO SECURE SMART HOMEAPPLIANCES
Taking into consideration all of the aforementioned cyberattack cases aimed at smart appliances, it is apparent that theattack techniques are not new. However, what have beenchanged are the attack targets, which are the smart home
appliances and devices in IoT network. In most cases, theattackers make complete use of the nature of Internet
Protocol to have the access to those appliances; andoftentimes, the devices do not have full-functional display or
screen so it is really hard for the victims to even detect thatthey are being attacked and abused internally. Furthermore,different from human-controlled computers, most of smart
appliances (such as LED light bulbs smart system, smart
refrigerators and smart meters) can easily be accessed due totheir 24 hours around-the-clock availability on the Internet.
Last but not least, because each appliance is designed to serve
only a specific purpose, marketability factors such as low
cost, portability, tinier size, etc. make built-in full
cryptography capability infeasible in most of suchappliances.
While the previously proposed concept of universal home
gateways and involvement of network operators in security
challenges of such appliances is not achieved, it is going to bedifficult for producers to produce economically feasible, safe

520

and tiny smart appliances. This is because for instance, an
LED in smart light bulb systems would become unaffordable
to buy if cryptography process and sufficient memory forencrypting information were built into each single bulb.
Hence, it is certainly not the right approach to equip all smart
appliances with built-in security function against cyberattacks in the same manner that has been done on personal
computers and web servers.
An intermediate solution, which is proposed in this paper,is to make use of TOR and this section will demonstrate how
to utilize TOR to make the IoT network more robust andsecure against most of the contemporary cyber attacks.
A. Some Important Properties of TOR
Before moving into detailed technical demonstrations, it isnecessary to briefly introduce some important properties of
TOR. In the previous research we already described its
internal working mechanism and showed that it is indeed an
ideal anonymous communication tool which employs
asymmetric cryptography, takes advantage of public-key
encryption and transmits data from a source to a destination
through a randomly selected route of multiple nodes [4]. Raw
data having its destination IP address encrypted andre-encrypted with public key of the selected nodes,
something which resembles an onion ring where in each layer
is a re-encrypted version of an encrypted data by the public
key of the node. In the transmission process, each node
decrypts a layer of the encryption to extract the next layer’s
IP address, an operation resembling an onion-peeling-off
process. The final node decrypts the last layer of theencryption and sends the original data to its real destination
without revealing or even knowing its original sender [16].
In this paper, we would like to expand the research into a
deeper level of TOR and employ its subproject, The Amnesic
Incognito Live System (Hereafter: Tails); a live operating
system, Debian-based Linux distribution which can run on
almost any CPU based gadget like DVD, USB stick, or SD
card [17].
Usually ordinary internet users often use TOR as aninternet browser and the Tor Browser Bundle is one of its
most well-known subprojects. While the Tor Browser Bundle
operates as an anonymous browser where only those surfing
activities done within the browser are anonymized, Tails acts
as a complete stand-alone operating system causing not only
net surfing activities in the browser but also all of the
networking activities running within other applications andoperating system be anonymized by forcing them to passthrough TOR network before going out to the public internet
environment. Through this mechanism, the data transmission
process gets firmly secured because of the multilayer
asymmetric cryptography feature of TOR. As the process
also encrypts the destination IP address, real IP address of thedevice also gets hidden from the adversaries. Thus we havetaken advantage of these two features of Tails operating
system and investigated its potential for addressing thesecurity problems of the IoT network.
B. Test Platform Configuration and Monitoring Process
In this section we describe how to configure TOR to securesmart home appliance system. The main idea is to set up Tails
to be the central control gateway thus forcing all thetransmitted data packets of smart home appliances to pass

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521

through before going out to the public internet environment.
Since no universal home gateway has yet been produced, we
made use of existing devices and software to create a new
system and used it to conduct the experiment as illustrated inFig. 5:

Fig. 5. Setting up a TOR network for smart home appliancesFig. 7. A list of TOR circuits obtained from TOR network map tool

Our experimental system consisted of a laptop with WiFi
internet connection interface, local area network (LAN) cableconnection interface and a Tails-contained storage device(such as DVD, USB stick, or SD card). Initially we loaded
Tails into the laptop, configured the LAN cable to be its maininterface to the Internet and reserved WiFi interface as an
Access Point for smart home appliances. The LAN and WiFi
interfaces were then bridged to each other so that the Internet
connection from LAN cable is shared with WiFi interface toform the Access Point. All the smart home appliances werethen configured to connect through this Access Point insteadof directly connecting to the Internet.
This way, because of the built-in anonymity feature of
Tails, all the networking activities passing via laptop’s WiFi
Access Point are anonymized and multilayer-encrypted bypublic-key cryptography before being sent through the TOR
nodes network to their real destinations. Fig. 6 shows theoperating mechanism of this process.

The data transmission process is done anonymously
through distributed TOR nodes system operated by a huge
number of volunteers around the world. As indicated in Fig. 7,
each circuit has 3 nodes called entry, intermediate and exit
which are highlighted in red, blue and green respectively. The
entry nodes (in this case: servbr2a, EdwardSnowden, TSR1)
are rarely changed. Tails sometimes reuses the same set ofentry nodes in a working session, while the intermediate andexit nodes keep changing from circuit to circuit. This isbecause the entry node is the first node where the connection
gets into the TOR network. As it is vital to make theconnection steady and robust as much as possible, TOR
directory nodes often have longer uptime than othervoluntary nodes (in this case: the directory node servbr2a has
118-day uptime). In order to provide more thorough details,
we collected information about all the nodes shown in Fig. 7,
the results of which are indicated in Fig. 8.

Fig. 6. The overview of TOR WiFi network for smart home appliances

Once the Internet connection is available, Tails
immediately send a request to TOR directory server node toobtain the list of other nodes in TOR network and establish asufficient amount of circuits for routing data through the
TOR network anonymously. It is easy to monitor how Tails
forms the available TOR nodes to create circuits by means ofa built-in tool called TOR network map. Fig. 7 shows a seriesof random routing paths that Tails forms to transmit data
through the TOR network.

Fig. 8. List of TOR nodes in a random working session of Tails with theirdetailed information (country, uptime, IP address, sever type and Operating
System information)

TOR nodes of Fig. 8 are obtained from the TOR-network
monitoring
websitenamed
http://www.torstatus.
blutmagie.de, which contains detailed information of theavailable TOR nodes. In a similar manner, Tails user cancheck through which node the data packets will betransmitted and decide to either let the data pass through a
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 5, September 2014

particular route or not. For instance, by examining the uptime
and properties of the entry node EdwardSnowden, an
experienced Tails user may not want his/her data passthrough that node. This is because its uptime is not long; it is
not a Guard and Stable node, hence, vulnerable tocompromise because the data could be dropped duringtransmission process and result in leaking the identity
information of Tails users. To prevent data transmission
through such an unwanted circuit, the user just needs to rightclick on it from connection window, choose Delete circuit,and then Tails will remove it from the list.
Another noteworthy feature of this Tails is that all data aresent and received in a distributed manner, a particular case ofwhich is shown in Fig. 9.

522from internal attack and prevent local network admin or otherusers in the same LAN to monitor Tails-installed device fromwithin.
To verify the MAC address spoofing feature, we used a
network tool called Fing [19] and monitored the LAN towhere Tails-based laptop was connected. As shown in Fig. 10,
devices without Tails-installed operating system could be
easily traced thus revealing their names, real interfaces and
MAC addresses; while Tails-installed laptop changed its
MAC address every time the machine got connected again to
the local network and did not leak its name, thus mademonitoring hard as indicated in the highlighted red section ofFig 10.

Fig. 9. Data Transmission is done in distributed manner by means ofdispersed voluntary TOR nodes around the world

For example, in the same working session of Tails, we
executed sudo apt-get update in root terminal to run an
update task for the Debian operating system. Although it is asingle task in which the system communicates with Debiandatabase servers to update newest functions for Tails, the task
gets divided into many subtasks and communicates with
Debian servers through separate streams as described
technically in Tor proposal 171 [18]. The update task shown
on Fig. 9 is done in distributed manner through the following
3 separate circuits:
1.
2.
3.

Fig. 10. MAC address spoofing feature of Tails

C. Testing with Multimedia Transmission
In this section we demonstrates feasibility of transmitting
multimedia data over the TOR-based network using Sony
BRAVIA smart television (hereafter: the smart TV) as a
typical testing object, shown in Fig. 11:

TSR1, geekbox, linuxthefish
EdwardSnowden, LT, IPredator
servbr2a, Atlantic CoastNE, spfTOR1e1

As a result, there is no way for any single node in thecircuits, including the LAN admin or Debian servers’ admin
nodes to have all the information about transmission route,original IP address of TOR user, and the raw data. This meansthat each component in the system can only know a part of
the whole transmission process. Thanks to this unique
working mechanism of Tails, altering transmission routefrequently in about every 10 minutes and providing maximal
anonymity for Tails user. Therefore, if functionality of thesystem shown in Fig. 5 is constructed as a Tails-embeddedrouter, it is certainly going to be a promising solution for
securing smart home appliance system.
Last but not least, MAC address spoofing feature is also an
indispensable feature of Tails. Although MAC address is notsent over the Internet and only used within the local network,
there is a risk of an internal attack from an adversary who isalso in the same network. Tails also fakes MAC address ofthe system, laptop in our example, so as to protect the system

Fig. 11. Testing object - Smart Sony BRAVIA television

The smart TV was intentionally selected for theexperiment since it is integrated with many applications andcan be considered as a typical representative of other smart
appliances as well. Apart from the function of a traditional
television, the smart TV has also many other multimedia
applications and internet-based services such as highCopyright ⓒ 2014 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 5, September 2014

definition (hereafter: HD) movies from the Internet, online
music, net surfing, social networking and so on as shown in
Fig. 11.
Considering that TOR only supports the Transmission
Control Protocol (hereafter: TCP), as stated in TOR project
documentation [16], we had to first examine which protocol
is mainly used in the smart TV to communicate with theInternet and ensure proper rerouting of its data stream
through the TOR network. To monitor the data transmission
process of the smart TV, we used a network monitoring tool,
called Microsoft Network Monitor, to tap to the connection
channel between the smart TV and the Internet. We then run
all the essential applications, ranging from Youtube, Sony
online HD movies service to gaming applications, at the TVside and Fig. 12 shows captured result at the network
monitoring tool side.

523

time-to-live and need to hit their final destination before thetime-to-live is over. Hence, applications like Skype, LINE
and Viber conventionally operate based on the UDP.
Our initial attempt to launch Skype via TOR network in thesame manner as other TCP-based applications over theInternet having a regular speed failed mainly because of theshort time-to-live duration of UDP packets. In order to provethat TOR-based network could also work with voice over IP
application, we conducted the next phase of our experimentat Japan Advanced Institute of Science and Technology(hereafter: JAIST) where the Internet connection speed is 10
Gbit/s, one of the highest in Japan [20]. Fig. 13 shows the
setup of our experiment where at one side, we connected thesmart TV to the Internet via the Tails-installed laptop and at
the other side; a regular Windows equipped with Skype wasconnected to JAIST network. At both side, Microsoft
Network Monitor was used to tap to the transmission
channels.

Fig. 12. Data stream between Sony BRAVIA smart television andmultimedia resources

As can be seen in the captured result, most of applications
communicate with the Internet via TCP. In this particularcase, Youtube communicates with r5.sn-xgmnpoxuopp-ogue.
googlevideo.com to load videos via TCP; or otherSony-based services like online HD movies, gaming
applications communicate with Sony’s service-providing
servers such as bivl.np.ac.playstation.net, ssm1.internet.sony.
tv, applicast.jp.sonystyle.jp, and tv.app.bs.sony.jp using TCP.
It is evident that such TCP-based data streams can be routed
through the TOR network in order to make the connection
more secure. Though the Internet connection speed may
theoretically get slower and adversely affect quality of theservices when routed through the TOR network since it has topass through 3 TOR nodes before reaching the finaldestination, the experiment showed that all the above
mentioned applications and Sony-based services still works
smoothly over the TOR network. Despite the fact thatbuffering speed of HD movies and videos from Youtube was
not as fast as the normal and direct connection environment,
the videos from Youtube server still loaded with anacceptable speed and caused no interruption in between.
D. Testing with Voice-over-Internet Protocol Applications
This section examines potential of using the TOR-based
network for voice over IP application like Skype. Unlike
Youtube videos and HD movies which are TCP-based
applications, Skype which is a voice over IP service andinstant messaging client software developed by Microsoft,
operates based on The User Datagram Protocol (hereafter:
UDP). It is one of the vital applications in the application
suite of the smart TV, but has to operate in a real-time manner.
In other words, the transmitted data packets have really shortFig. 13. Testing VoIP over TOR network

We then made a call from the Skype account in the smart
TV to the Skype account in the JAIST computer while
monitoring the transmission process at both sides by two
separate network monitoring tools. This time, the Tailsinstalled laptop instead of dropping out the UDP connection,
encrypted and encapsulated all the UPD packets of Skype
into TCP packets and sent them through the TOR network as
shown in the captured screen of its monitoring tool in Fig. 14,
where 192.168.123.27 is the local IP address of theTails-installed laptop, and 62.210.137.27, 188.138.1.229 and94.23.105.27 are the IP addresses of the entry TOR nodes atthat time.

Fig. 14. Tails-installed PC encapsulates UPD packet inside TCP packet

Copyright ⓒ 2014 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 5, September 2014

At the exit node of the TOR, the TCP packets got
decrypted thus disclosing the original UDP packets andsending them to the final destination. Consequently, at JAIST
computer side the incoming Skype UDP packets created aring sound of the incoming call and established a channel forvoice conversation as shown in the captured screen of its
monitoring tool in Fig. 15.

Fig. 15. Incoming UDP Skype packets after being decrypted and dispatchedfrom TOR exit node (in this case, TOR exit node IP address is 46.9.176.21).

However, by the time the Skype account at the JAIST
computer side clicked answer, the channel only survived fora few seconds and without being able to transmit any voice it
got dropped. By analyzing the monitoring results of bothsides, it is not difficult to show the cause of the drop out
shortly after the ring. As shown in Fig. 14, the TCP packets
are sent out at the time the call was made (from 1:26:04).
However, at the JAIST computer, the UDP packets werereceived interruptedly at 1:26:5 and at 1:26:13. It is therefore
clear that connection delay and short time-to-live duration of
UDP packets is the main cause, something which could beeventually offset by the expected increase in the Internet
connection speed in the next coming years through theevolution of optical fiber technology.
To further investigate the potential of TOR-based
network for with voice over IP applications, we also tested itwith other voice over IP applications like LINE and Viberand observed the same phenomenon and have no doubt that
UDP-based smart home appliances could also operatesmoothly in TOR environment in the near future.

524

carried out by the authors is to utilize the TinyOS
programming language and simplify The Amnesic Incognito
Live System so as to create a Tails-embedded router that can
act as the central control gateway to support and secure thesmart home appliance system. The idea is to come up with apractical version of the universal home gateway while
waiting for the development of its ideal version andinvolvement of network operators in the security challengesof smart home appliances. Practical realization of “VoIP over
TOR” is another area which needs further investigation while
waiting for further increase in the Internet connection speed
through the evolution of optical fiber technology.

ACKNOWLEDGMENT
Firstly, Nguyen Phong HOANG would like to sincerely
thank his supervisor, Professor Davar PISHVA (Dean ofCollege of Asia Pacific Studies at APU), for his dedicatedhelp and support during the last 2 years. Without such help
and academic advice this research could not have beenaccomplished. Next, we would like express our deepest
gratitude to Professor Atsuko Miyaji from the School ofInformation Science, Japan Advanced Institute of Scienceand Technology (JAIST). Through an Academic Exchange
Program which exists between APU and JAIST, Mr. Nguyen
Phong HOANG was able to enjoy her state-of-the-art
scientific laboratory and precious advice while conductingsome experiments for this work.

REFERENCES[1]

Herper, Matthew. Emerging Technologies: 'Smart' Kitchens A LongWay Off, Forbes, Feb. 2003.

[2]

Staff. Wired News: Caregiver Tech Slowly Evolves, AssociatedPress, September 2003.

[3]

VI. CONCLUSION
Since the Internet of Things is still at the early stage of its
development, smart home appliance producers need moretime and effort in order to produce cost-effective and safe
products. Until then, however, being aware of cyber attacks
aimed at smart home appliances and its numerous risks,having an alternate solution to remedy the potential problems
is quite important.
In this paper we first showed numerous vulnerabilities thatsmart home appliance users are facing and how networking
monitoring tool and regular DDOS attack technique caneasily be used to attack the IoT network and simultaneously
abuse them for illicit purposes. We then proposed theimplementation of TOR-based anonymous communication
into the IoT network as an effective alternative way to help
smart home appliance users protect their privacy and makethe smart home appliance system more secure from
aforementioned cyber attacks. Our results show that Tails
having many security features such as multilayer encryption,
data transmission in distributed manner over a huge amountof voluntary nodes around the world, and MAC address
spoofing, is indeed a suitable approach to solve recent
security problems in TCP-based smart home appliances.
Future work along this research which is already being

Carrie MacGillivray, Vernon Turner, Denise Lund, Worldwide Internetof Things (IoT) 2013–2020 Forecast: Billions of Things, Trillions of
Dollars. International Data Corporation, 2014. Available:
http://www.idc.com/getdoc.jsp?containerId=243661[4] Hoang Nguyen Phong, and Davar Pishva. "Anonymous
communication and its importance in social networking.” the 16th
International Conference on Advanced Communication Technology(ICACT 2014), pp. 34-39 (February 2014)[5] Having fun via WIFI with Philips Smart TV. Revuln, 2014. Available:
http://vimeo.com/90138302[6] Hemanth Joseph, Dosing Pebble Smart Watch And Thus Deleting All
Data Remotely, August 2014 [Online]. Available:
http://www.whitehatpages.com/2014/08/dosing-pebble-smartwatch-and-thus.html[7] Ghena, B., Beyer, W., Hillaker, A., Pevarnek, J., & Halderman, J. A.
“Green lights forever: analyzing the security of traffic infrastructure.”
In Proceedings of the 8th USENIX conference on Offensive
Technologies (pp. 7-7). USENIX Association. (2014, August).
[8] Ramneek Puri, SANS Institute InfoSec Reading Room. SANS Institute,
August 2003. Available:
http://www.sans.org/reading-room/whitepapers/malicious/bots-botnet-overview-1299[9] Brian Donohue, Beware The Thingbot! Kaspersky Lab, January 2014.
Available: https://blog.kaspersky.com/beware-the-thingbot/[10] Katagi, Kizu. Vulnerability of Toshiba's RD Series HDD-DVD
Recorder ‘Stepping-stone’ for Danger, Internet Watch, October 2004
(In Japanese)
http://internet.watch.impress.co.jp/cda/news/2004/10/06/4882.html.
[11] Press Release, A Report to Customers on the Issue of ‘Creative Zen
Neeon’ Digital Audio Player and its Response, Creative, September
2004 (In Japanese) Available:
http://jp.creative.com/corporate/pressroom/releases/welcome.asp?pid=12181.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 5, September 2014525

[12] AU Announcement, EZweb Browser’s Home Page URL Transmittalon AU and TU-KA Mobile Phones, KDDI News, December 2005 (InJapanese) Available:
http://www.au.kddi.com/news/topics/au_topics_index20051209.html.
[13] D. Pishva, K. Takeda, A Product Based Security Model for Smart
Home Appliances, 40th Annual IEEE International Carnahan
Conferences on Security Technology, pp. 234-242 (2006).
[14] D. Pishva, K. Takeda, A Product Based Security Model for Smart
Home Appliances, IEEE Aerospace and Electronics System Magazine,
Vol.23, No.10, pp. 32-41 (October, 2008).
[15] Proofpoint Uncovers Internet of Things (IoT) Cyberattack. Proofpoint
Inc., Sunnyvale California, 2014.
[16] "The solution: a distributed, anonymous network", Tor: Overview.
TOR project. Available:
https://www.torproject.org/about/overview.html.en#thesolution[17] Koen Vervloesem, the Amnesic Incognito Live System: A live CD foranonymity. Linux Info from the Source, April 2011. Available:
https://lwn.net/Articles/440279/[18] Robert Hogan, Jacob Appelbaum, Damon McCoy, Nick Mathewson,
Separate streams across circuits by connection metadata. The Tor
Project, October 2008. Available:
https://gitweb.torproject.org/torspec.git/blob/HEAD:/proposals/171-separate-streams.txt[19] Fing, the ultimate network toolkit. Overlook Soft. Available:
http://www.overlooksoft.com/features[20] Information Environment, Information Environment (Information
Society Research Center) - Campus Network. Japan Advanced Instituteof Science and Technology. Available:
http://www.jaist.ac.jp/is/keyword/research/info.html

Nguyen Phong HOANG was born in Tien Giang
Province, Vietnam in 1992. He received his
undergraduate degree in Business Administration
majoring in Information & Communications
technology (ICT) from Ritsumeikan Asia Pacific
University (APU), Japan. He is presently pursuing his
graduate studies at the Graduate School of Informatics
at Kyoto University in Japan. He has received
numerous scholarship and awards; APU Tuition
Reduction Scholarship from 2010-2014, JASSO(Japan Student Services Organization) Scholarship from 2011-2012, and
TOYOTA Tsusho Scholarship from 2013-2014. His research interests
include information security, privacy and anonymous communication. He
hopes to advance his research on TOR (The Onion Router), one of the mostrobust anonymous tools, during his graduate studies. He participated in the
16th International Conference on Advanced Communication Technology andreceived Outstanding Paper Award from the Conference. He has been an
IEEE member since 2013.

Davar Pishva is a professor in ICT at the College of
Asia Pacific Studies, Ritsumeikan Asia Pacific
University (APU) Japan and presently serves as the
Dean of both College and Graduate School of Asia
Pacific Studies. In teaching, he has been focusing on
information security, technology management, VBA
for modelers, structured decision making and carries
out his lectures in an applied manner. In research, hiscurrent interests include biometrics; e-learning,
environmentally sound and ICT enhanced technologies. Dr. Pishva received
his PhD degree in System Engineering from Mie University, Japan. He is
Secretary General of IAAPS (International Association for Asia PacificStudies), Senior Member of IEEE, and a member of IEICE (Institute ofElectronics Information & Communication Engineers), IAAPS andUniversity & College Management Association.

Copyright ⓒ 2014 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 5, September 2014

526

Smart Device Based Power Generation Facility
Management System in Smart Grid
Young-Jae Lee*, Eung-Kon Kim*
*Department of Computer Science, Sunchon National University,
255 Jungang-ro, Suncheon, Jellanam-do, Republic Of Korea
leeyoungjae@sunchon.ac.kr, kek@sunchon.ac.kr
Corresponding Author: Eung-Kon Kim

Abstract— As energy consumption is gradually increased due
to rapid development of industrialization, the whole world
including our country is faced with an issue of lack of back-up
power, exhaustion of fossil energy and global warming. Under
this background, as a method of maximizing energy efficiency
by preventing global warming and reduction of greenhouse gas
emission, smart grid that converged existing power networkwith IT technology receives concentrative attention as a growthengine of next generation [1-3]. Currently, maintenance ofdomestic solar power plant management is provided at thecenter through remote monitoring by using measuring sensor
being installed at power plant and as regular check-up or repair
being performed at site is progressed by site management
personnel by directly moving power generation facility, there isa difference in time and accuracy depending on ability of sitemanagement personnel. In this paper, a system of managing
smart grid power generation facility by internet of things (IOT)
technology is suggested. Maintenance of suggested system for its
regular check-up and failure is allowed by site manager
conveniently and by using facility recognition based technology
instead of existing QR code, its direct application is allowed
without additional equipment to smart grid power generation
facility being operated at present and by developing power
generation facility recognition service using markerless based
facility recognition technology, it may be expanded to atechnology of recognizing other smart grid power generation
facility in the future.
Keyword—Smart Grid, Power plant facility management,
Smart Phone, Location Based Service

I. INTRODUCTIONAs energy consumption is gradually increased due to rapid
development of industrialization, the whole world
including our country is faced with an issue of lack of back-up
power, exhaustion of fossil energy and global warming.
Under this background, as a method of maximizing energy
efficiency by prevention of global warming and reduction of———————————————————————
Manuscript received June 23, 2014. This work was supported in part byThis research was financially supported by the Ministry of Education (MOE)and National Research Foundation of Korea(NRF) through the Human
Resource
Training
ProjectforRegionalInnovation(No.
2013H1B8A2032217).
Young-Jae Lee, was with Sunchon National University, 255 Jungang-ro,
Suncheon, Jellanam-do, Republic of Korea (e-mail: skyit89@nate.com)
Prof. Eung-Kon Kim, was with Sunchon National University, 255
Jungang-ro, Suncheon, Jellanam-do, Republic of Korea(corresponding
author to provide phone: +82-61-750-3627; fax: +82-61-750-7999; e-mail:kek@sunchon.ac.kr).

greenhouse gas emission, smart grid that converged existing
power network with IT technology receives concentrative
attention as a growth engine of next generation [1-3].
Smart grid power generation facilities are under operation
by it being spread in various places from small-scaled power
generation being installed in general housing, building,parking lot and public facilities to large-scale power
generation system being composed in a grid form and thesefacilities require sustained management and check-up and incase of occurrence of problem such as facility malfunction,
power failure or damage, rapid and exact check-up and repair
are required to be performed at the site.
However, at present, management of solar energy power
generation facilities is performed at the center through remotemonitoring by using measuring sensor being installed at thepower generation facility and as regular check-up or repair atsite is progressed by site management personnel by directly
moving power generation facility, there is a difference of timeand accuracy depending on ability of site management
personnel.
In addition, in case of solar energy power generation
facility or wind power generation facility, as it has diversified
products, standards and forms, there are a lot of difficulties
for exact check-up and in case of small-scaled power
generation facility, in view of its features, as maintenance costis high and a lot of cost is required once a failure is taken place,
prevention of failure in advance is required through regularcheck-up and operational check-up.
Recently, due to popularization of smart phone, a study on
industrial application by using smart phone is under way andas smart phone is mounted with wireless internet
communication module (3G, 4G, Wifi, etc.), GPS sensor,
electromagnetic sensor, image sensor, diversified information
provision by using facility recognition technology is enabled[4].
In this paper, a system of managing smart grid power
generation facilities by using power generation facility
recognition based technology is suggested. Suggested system
recognizes smart grid power generation facility image in realtime by using image sensor and GPS sensor and it provides
site manager with detailed information, hardware drawing,
sensor data, facility history of power generation facility.
In addition, it provides location information of facilities sothat site manager could identify other surrounding facilities
conveniently. When utilizing suggested system, site manage

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 5, September 2014

could perform regular check-up and maintenance of repair
conveniently and by using facility recognition based
technology instead of existing QR code, its direct applicationis allowed without addition equipment to smart grid power
generation facility being operated at present and by
development of power generation facility recognition based
technology using markerless based facility recognition
technology, it may be expanded to a technology ofrecognizing other smart grid power generation facility in the
future.
II. RELATED WORKS
A. Cimphony (Open Grid System)
Cimphony is data management and analysis application ofmultiple platform smart grid power generation system having
OSGI modular system and Eclipse based user interface.
Cimphony module provides an independent service such as
geographic information visualization by using data editing,
OCL based verification, model based conversion, distributed
data and KML.
This system is able to support new standard and data model
without change of framework by using model-driven
architecture, open standard such as CIM (Common
Information Model) and smart grid facility monitoring andmanagement function through data visualization from IPadand IPhone [5].
B. Field Force Data Visualization App with Augmented
Reality
This is augmented reality based smart grid site monitoring
application that was promoted in renewable energy and smart
grid research institute EPRI being composed of consortium
including a lot of universities and enterprises.
This system is a conception model of light-weighted,
mobile access platform that integrated diversified back officesystems and this is a project in which user visualized smart
grid facility information in actual world by augmenting it
using GIS sensor and electromagnetic sensor built in tablet
PC.
As a mobile application program for data control andvisualization aiming at supporting site workers, it provides
information of site details, location and manual, sensor
provided to site staffs and cheaper platform built in powersource by utilizing mobile system.
In addition, application user may reserve request for workin real time.
This project is a kind of demonstration project that proves
strong power of CIM(Common Information Model) in aconceptual way under actual application environment and it
has characteristics of having efficiency of visualizing CIM
data, stability of site workers, low cost mobile data platform
and CIM base utilizing actual data and system, light-weightand mobile data platform [6].
C. Integrating geographical information and augmented
reality techniques for mobile escape guidelines on nuclear
accident sites
As a study on integrating augmented reality that provides
counterfactual information service for escape and location

527

guidance at the time of nuclear accident, it not only provides
guidance of escape route by using augmented reality but alsovisualizes current nuclear power plant condition in
geographical map.
This system supports site workers escape from nuclear
accident site in real time by identifying current location after
capturing surrounding image by using camera built in user’s
own working system and visualizing escape route in a map[7].
However, it has a disadvantage that high quality map isrequired, time of loading escape route is long and people atnuclear accident site may consume a lot of time while waiting
for this map.
III. FACILITY RECOGNITION BASED MOBILE POWER PLANT
MANAGEMENT SYSTEM
A. System Configuration
In this paper, power generation facility management systemthat enables convenient and exact management is suggested ina way that site workers recognize smart grid power generation
facilities by using power generation facility recognition
technology, transmit it to server through wirelesscommunication such as 3G, WiFi and then provide
information of current condition, past history, design drawing
by matching it with data stored in server.
Power generation management system may be mainly
divided into two parts including smart grid power generation
facility management server system for providing site workers
with formation relevant to current condition, check-up andmanagement history and design of power generation facility
and smart application that recognizes and manages power
generation facility by using power generation recognition asshown on Fig. 1.

Fig. 1. System layout

B. Power generation facility recognition and registration
using power generation facility recognition
Location and image information of power generation
facility in power generation facility management server are
registered by recognizing power generation facility afterusing image being shot in screen view of site worker’s smart
phone and information for power generation facility
requested by site worker is provided.
Recognition and registration process of smart grid power

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 5, September 2014

generation facility is as shown on Fig.2.

528

shown on Fig. 4, information of relevant power generation
facility is transmitted to worker but if its matching failed,unrecognizable message is transmitted.

(a) Feature point matching1

Fig. 2. Recognition and registration process of power generation facility

Registration and recognition process of smart grid power
generation facility is that site worker approaches power
generation facility within distance of 10cm and makes camera
direct at power generation facility and at this time GPS sensor
and electromagnetic data are read and at angle of app. 5o to
the right/left, power generation facility is shot as panoramic
image.
By transmitting GPS sensor, electromagnetic sensor andfeature point data to server after extracting feature point ofphotographed panoramic image as shown on Fig. 3, power
generation facility is registered in server data base.

(a) Panoramic image(b) Feature point matching2
Fig. 4. Feature point matching test and facility recognition

In this paper, feature point was extracted by using SURF
(Speeded Up Robust Features) algorithm so that fast
performance ability and feature points that are not changed torotational change and scale conversion could be found infeature points of panoramic image being transmitted fromserver and images being obtained from smart phone camera
and camera and object would be recognized as identicalobject even though its angle and distance are different at thetime of shooting smart grid power generation facility[8].
C. Power generation facility management server
Smart grid power generation facility management systemprovides information of current condition, check-up,
management history and design drawing of power generation
facility based on mobile web application server (WAS: Web
application server) in order to ensure convenient interface
with smart phone.
Smart grid power generation facility management server
system is as shown on Fig. 5.

(b) Feature point extraction by using SURF algorithm
Fig. 3. Recognition and registration process of power generation facility

In a process of recognizing power generation facility bysite manager, power generation recognition of worker isrequested through the same method as power generation
facility registration process.
And then server searches data most similar to facility
requested by worker among GPS stored in real time andelectromagnetic sensor data and when a result is obtained bymatching feature point of panoramic image of searchedfacility with feature point data transmitted by worker as

Fig. 5. Power generation facility management server system

Diversified smart phone and tablet PC are supported by
Copyright ⓒ 2014 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 5, September 2014providing an independent interface to platform by utilizing
Rest API and a complete interface of smart application ispossible by providing power generation facility management
service through Rest API.
Management server system of power generation facility iscomposed of power generation history, sensor data, matching
power generation facility data base of its management modulethat provides situation information service with feature points,
location management of power generation facility and featurepoint data base of power generation facility recognition
module that provides its feature point management service.
As shown on Table 1, smart grid power generation
management server mainly provides functions of log-in,
history, recognition management of power generation facility,
environment setting and log-in.

529

generation facility location stored in server based ontransmitted coordinates and guides location by transmitting
location of power generation facilities to site manager througha stage of transmitting ID, GPS coordinates of power
generation facility within radius of 5km.

TABLE I
SMART GRID POWER GENERATION MANAGEMENT SERVER
MAINLY PROVIDES FUNCTIONS

No
1
2
3
4

Div

User log-in
Log-inPowergeneration
facility
history
management

5
6

Power
generation
facilityrecognition
management
module789Function

Environment
setting

Password change
Inquiry of power
generation facility
information
Addition of power
generation facility
history
Power generation
facility recognition
information
storagePower generation
facility recognition
information inquiry
Deletion of power
generation
information
Server setting
User setting
Power generation
facility recognition
modulemanagement log

10
Log function

11

Power generation
facility history
management log

Description
Function of user authentication
Function of changing user ID andpassword
Detailed information inquiry of
power generation facility
Addition of power generation
facility history
Feature point data storage of GPS,
electromagnetic sensor data andpanoramic image of power
generation facility
Feature point data inquiry of GPS,
electromagnetic sensor data andpanoramic image of power
generation facility
Function of deleting registered
power generation facility
information
Setting of server network andaccessSetting of power generation facility
manager account and authority
Storage/inquiry of log record for afunction ofregistration/inquiry/deletion being
performed in power generation
facility recognition management
moduleStorage/inquiry of log record forinquiry/addition of facility history
being performed in power
generation facility history
management module

D. Guidance service of location-based smart grid power
generation facility
Guidance service of location-based smart grid power
generation facility is a service of guiding location of power
generation facility so that worker may perform inspection forsurrounding power generation facility based on location
information obtained through mobile communication network
or GPS as shown on Fig. 6 and by guiding location of smart
grid power generation facility that is distributed widely, site
worker could perform next inspection rapidly.
Location guidance service of power generation facility
first reads latitude/altitude coordinates from GPS sensor ofsmart phone, transmits it to power generation facility
management server and power generation management server
obtains lineal distance (Euclid distance) with power

Fig. 6. Guidance service of location-based smart grid power generation
facility

E. Smart phone application for smart grid power
generation facility management
Major function of smart phone application using power
generation facility recognition technology is to recognize
power generation facility through smart phone image at siteusing power generation facility recognition technique andidentify location information including its latitude/altitude
being interfaced with Google map.
In addition, it may monitor current measurement
information of facility such as voltage, current, active,
reactive power, provide its detailed information for model
name, manufacturer, hard ware layout and manage history of
power generation facility.
Intuitive and convenient use is allowed by providing
history management, current condition and HW related
detailed information in a way of recognizing power
generation facility through camera view of smart phone.
Smart phone application supports both android smart
phone being utilized most widely and IPhone and its
application was implemented in a hybrid mode using
PhoneGap in order to ensure application to smart phone ofdiversified platforms based on development of just one time.
In addition, user interface of mobile application wascomposed by using HTML5 and JQuery Mobile and control
module of GPS sensor and camera were developed byutilizing PhoneGap library and JavaScript.
Screen of the result of implementing smart phone for smart
grid power generation facility management being suggested inthis paper is as follows.
Fig. 7 is a screen of log-in implementation that certifies
user by transmitting manager ID and password to server after
receiving it at site and main menu implementation.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 5, September 2014(a) Log-in
Fig. 7. User log-in and main menu(b) Main menu

Main menu is composed of three menus including facility
recognition that recognizes power generation facility throughcamera, power generation list that may inquire overall power
generation facility and its location guidance and whentouching relevant menu, each function is performed.
Power plant recognition transmits data to server after
extracting feature points through SURF algorithm followedby photographing panoramic image by using GPS and image
sensor of smart phone as shown on Fig. 8.

(e) Facility image 3
Fig. 8. Facility recognition(f) Feature point extraction 3

In server, by matching transmitted data with stored data,coincided power plant is found out and as shown on Fig. 9,
detailed information, design drawing and management history
information of power plant are provided to workers throughwireless communication.

(a)Detailed information(a) Facility image 1

530(b) Facility design drawing(b) Feature point extraction1(c) Facility management history
Fig. 9. Facility information(c) Facility image 2(d) Feature point extraction2

Site manager could receive information of current locationand overall location of power generation facility as shown on
Fig. 10.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 5, September 2014

531

TABLE II
FACILITY RECOGNITION TEST

Evaluation item
Recognition rate (%)
Recognition time (ms)

Front
100
452

30ºSide92456

45ºSide78
470

The power generation facility recognition rate of the
proposed system was 100%, 92% and 78%, respectively infront, side angles 30° and 45° like Table 2. The recognition
time was 452ms, 456ms and 470ms, respectively in front, side
angles 30° and 45°.
It turned out that in spite of a little difference by angle, it
recognized the power generation facility accurately, overall.
(a)Current location guidance of(b)Total location informationsite manager
Fig. 10. Location guidance service of facility

When implementing location guidance service of power
generation facility, it is guided through visualized Google
map and when selecting marker of visualized power
generation facility, type and information of relevant power
generation facility are outputted.
marker being visualized in Google map indicates current
location of site manager,marker indicates location of wind

power generation facility andmarker indicates location of
power generation facility, respectively.
IV. EXPERIMENTS
Experiments were composed to test whether the proposedsystem recognizes power generation facilities and provides its
status and detailed information.
The test-bed consists of a small solar panel, an inverter, abattery, a gateway, a power generation facility management
server like Figure 11.

V. CONCLUSION
In this paper, facility recognition based mobile smart gridfacility system by which site manager recognizes smart
facilities by using GPS sensor and image sensor of smart
phone or tablet PC and diversified information including
detailed information, HW drawing, sensor data, management
history of facility and location guidance are provided to sitemanager was suggested.
By having developed smart application based on hybridarchitecture, it is expected that simultaneous support toalmost all the smart phone platforms would be allowed basedon its development of just one time and intuitive andconvenience use would be possible by recognizing power
generation facility through camera view of smart phone andproviding its information to site manager.
In addition, as site manager is able to receive detailed
information of facility at site, efficient maintenance forregular check-up and failure could be performed conveniently,
overall operation cost could be reduced and furthermore, byusing facility recognition technology instead of existing QRcode, its direct application is possible without any need ofadding equipment to smart grid facility currently underoperation.
Owing to development of facility recognition service
through markerless based facility recognition technology, it isexpected that it may be expanded to technology recognizing
T/R facility of smart grid and other facilities in the future andits cheaper construction would be possible as well.
ACKNOWLEDGMENT“This research was financially supported by the Ministry of
Education (MOE) and National Research Foundation of
Korea(NRF) through the Human Resource Training Projectfor Regional Innovation (No. 2013H1B8A2032217).”.

Fig. 11. Diagram of Test-bed

A test was conducted whether it would be possible to checkthe current status, measurement information, details andtemporal data of the power generation facility module using asmart phone.
In addition, the results of a test on recognition rate andrecognition time of the power generation facility by angle arelike the following Table 2.

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Young-Jae Lee
Young-Jae Lee received the B.S., M.S degree from
Korea Sunchon National University, Korea, in 2012,2014 She is currently a Ph.D. student in computerscience at the Sunchon National University, Korea,
Her current research interests include augmented
reality, image processing, computer graphics.

Eung-kon Kim(Corresponding Author)
Eung-kon Kim received the B.S. degree from Chosun
University, Gwangju,, Korea, in 1980, his M.S
degree from department of electronics, Hanyang
University, Seoul, Korea, in 1987, his Ph.D. degreefrom Chosun University, Gwangju, Korea, in 1992.
His current research interests are computer vision,
virtual/augmented reality, image processing, andcomputer graphics. Currently he is a professor in
department of computer engineering, Sunchon National University, Korea

Copyright ⓒ 2014 GiRI (Global IT Research Institute)