Journal Editorial Board Editor-in-ChiefProf. Thomas Byeongnam YOON, PhD.
Founding Editor-in-Chief
ICACT Transactions on the Advanced Communications Technology (TACT) Editors
Prof. Jun-Chul Chun, Kyonggi University, Korea
Dr. JongWon Kim, GIST (Gwangju Institute of Science & Technology), Korea
Dr. Xi Chen, State Grid Corparation of China, China
Prof. Arash Dana, Islamic Azad university , Central Tehran Branch, Iran
Dr. Pasquale Pace, University of Calabria - DEIS - Italy, Italy
Dr. Mitch Haspel, Stochastikos Solutions R&D, Israel
Prof. Shintaro Uno, Aichi University of Technology, Japan
Dr. Tony Tsang, Hong Kong Polytechnic UNiversity, Hong Kong
Prof. Kwang-Hoon Kim, Kyonggi University, Korea
Prof. Rosilah Hassan, Universiti Kebangsaan Malaysia(UKM), Malaysia
Dr. Sung Moon Shin, ETRI, Korea
Dr. Takahiro Matsumoto, Yamaguchi University, Japan
Dr. Christian Esteve Rothenberg, CPqD - R&D Center for. Telecommunications, Brazil
Prof. Lakshmi Prasad Saikia, Assam down town University, India
Prof. Moo Wan Kim, Tokyo University of Information Sciences, Japan
Prof. Yong-Hee Jeon, Catholic Univ. of Daegu, Korea
Dr. E.A.Mary Anita, Prathyusha Institute of Technology and Management, India
Dr. Chun-Hsin Wang, Chung Hua University, Taiwan
Prof. Wilaiporn Lee, King Mongkut’s University of Technology North, Thailand
Dr. Zhi-Qiang Yao, XiangTan University, China
Prof. Bin Shen, Chongqing Univ. of Posts and Telecommunications (CQUPT), China
Prof. Vishal Bharti, Dronacharya College of Engineering, India
Dr. Marsono, Muhammad Nadzir , Universiti Teknologi Malaysia, Malaysia
Mr. Muhammad Yasir Malik, Samsung Electronics, Korea
Prof. Yeonseung Ryu, Myongji University, Korea
Dr. Kyuchang Kang, ETRI, Korea
Prof. Plamena Zlateva, BAS(Bulgarian Academy of Sciences), Bulgaria
Dr. Pasi Ojala, University of Oulu, Finland
Prof. CheonShik Kim, Sejong University, Korea
Dr. Anna bruno, University of Salento, Italy
Prof. Jesuk Ko, Gwangju University, Korea
Dr. Saba Mahmood, Air University Islamabad Pakistan, Pakistan
Prof. Zhiming Cai, Macao University of Science and Technology, Macau
Prof. Man Soo Han, Mokpo National Univ., Korea
Mr. Jose Gutierrez, Aalborg University, Denmark

Copyright ⓒ 2014 GiRI (Global IT Research Institute)Dr. Youssef SAID, Tunisie Telecom, Tunisia
Dr. Noor Zaman, King Faisal University, Al Ahsa Hofuf, Saudi Arabia
Dr. Srinivas Mantha, SASTRA University, Thanjavur, India
Dr. Shahriar Mohammadi, KNTU University, IranProf. Beonsku An, Hongik University, korea
Dr. Guanbo Zheng, University of Houston, USA
Prof. Sangho Choe, The Catholic University of Korea, korea
Dr. Gyanendra Prasad Joshi, Yeungnam University, korea
Dr. Tae-Gyu Lee, Korea Institue of Industrial Technology(KITECH), korea
Prof. Ilkyeun Ra, University of Colorado Denver, USA
Dr. Yong Sun, Beijing University of Posts and Telecommunications, China
Dr. Yulei Wu, Chinese Academy of Sciences, China
Mr. Anup Thapa, Chosun University, korea
Dr. Vo Nguyen Quoc Bao, Posts and Telecommunications Institute of Technology, Vietnam
Dr. Harish Kumar, Bhagwant institute of technology, India
Dr. Jin REN, North china university of technology, China
Dr. Joseph Kandath, Electronics & Commn Engg, India
Dr. Mohamed M. A. Moustafa, Arab Information Union (AIU), Egypt
Dr. Mostafa Zaman Chowdhury, Kookmin University, Korea
Prof. Francis C.M. Lau, Hong Kong Polytechnic University, Hong Kong
Prof. Ju Bin Song, Kyung Hee University, korea
Prof. KyungHi Chang, Inha University, Korea
Prof. Sherif Welsen Shaker, Kuang-Chi Institute of Advanced Technology, China
Prof. Seung-Hoon Hwang, Dongguk University, Korea
Prof. Dal-Hwan Yoon, Semyung University, korea
Prof. Chongyang ZHANG, Shanghai Jiao Tong University, China
Dr. H K Lau, The Open University of Hong Kong, Honh Kong
Prof. Ying-Ren Chien, Department of Electrical Engineering, National Ilan University, Taiwan
Prof. Mai Yi-Ting, Hsiuping University of Science and Technology, Taiwan
Dr. Sang-Hwan Ryu, Korea Railroad Research Institute, Korea
Dr. Yung-Chien Shih, MediaTek Inc., Taiwan
Dr. Kuan Hoong Poo, Multimedia University, Malaysia
Dr. Michael Leung, CEng MIET SMIEEE, Hong Kong
Dr. Abu sahman Bin mohd Supa'at, Universiti Teknologi Malaysia, Malaysia
Prof. Amit Kumar Garg, Deenbandhu Chhotu Ram University of Science & Technology, India
Dr. Jens Myrup Pedersen, Aalborg University, Denmark
Dr. Augustine Ikechi Ukaegbu, KAIST, Korea
Dr. Jamshid Sangirov, KAIST, Korea
Prof. Ahmed Dooguy KORA, Ecole Sup. Multinationale des Telecommunications, Senegal
Dr. Se-Jin Oh, Korea Astronomy & Space Science Institute, Korea
Dr. Rajendra Prasad Mahajan, RGPV Bhopal, India
Dr. Woo-Jin Byun, ETRI, Korea
Dr. Mohammed M. Kadhum, School of Computing, Goodwin Hall, Queen's University , Canada
Prof. Seong Gon Choi, Chungbuk National University, Korea
Prof. Yao-Chung Chang, National Taitung University, Taiwan
Dr. Abdallah Handoura, Engineering school of Gabes - Tunisia, Tunisia
Dr. Gopal Chandra Manna, BSNL, India

Copyright ⓒ 2014 GiRI (Global IT Research Institute)Dr. Il Kwon Cho, National Information Society Agency, Korea
Prof. Jiann-Liang Chen, National Taiwan University of Science and Technology, Taiwan
Prof. Ruay-Shiung Chang, National Dong Hwa University, Taiwan
Dr. Vasaka Visoottiviseth, Mahidol University, Thailand
Prof. Dae-Ki Kang, Dongseo University, Korea
Dr. Yong-Sik Choi, Research Institute, IDLE co., ltd, Korea
Dr. Xuena Peng, Northeastern University, China
Dr. Ming-Shen Jian, National Formosa University, Taiwan
Dr. Soobin Lee, KAIST Institute for IT Convergence, Korea
Prof. Yongpan Liu, Tsinghua University, China
Prof. Chih-Lin HU, National Central University, Taiwan
Prof. Chen-Shie Ho, Oriental Institute of Technology, Taiwan
Dr. Hyoung-Jun Kim, ETRI, Korea
Prof. Bernard Cousin, IRISA/Universite de Rennes 1, France
Prof. Eun-young Lee, Dongduk Woman s University, Korea
Dr. Porkumaran K, NGP institute of technology India, India
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
Dr. Kwihoon Kim, ETRI, Korea
Dr. Jin Woo HONG, Electronics and Telecommunications Research Inst., Korea
Dr. Heeseok Choi, KISTI(Korea Institute of Science and Technology Information), korea
Dr. Somkiat Kitjongthawonkul, Australian Catholic University, St Patrick's Campus, Australia
Dr. Dae Won Kim, ETRI, Korea
Dr. Ho-Jin CHOI, KAIST(Univ), Korea
Dr. Su-Cheng HAW, Multimedia University, Faculty of Information Technology, Malaysia
Dr. Myoung-Jin Kim, Soongsil University, Korea
Dr. Gyu Myoung Lee, Institut Mines-Telecom, Telecom SudParis, France
Dr. Dongkyun Kim, KISTI(Korea Institute of Science and Technology Information), Korea
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)Evalution Procedure

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Copyright ⓒ 2014 GiRI (Global IT Research Institute)Anonymity:
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Copyright ⓒ 2014 GiRI (Global IT Research Institute)Journal Procedure
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Copyright ⓒ 2014 GiRI (Global IT Research Institute)Journal Submission Guide
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Copyright ⓒ 2014 GiRI (Global IT Research Institute)Volume. 4 Issue. 1

1 Design and Implementation of Vision-based Structural Safety Inspection System using

5J

562

Small Unmanned Aircraft
Sung-suk Choi *, Eung-kon Kim ** Department of Computer Science, Sunchon National University, Republic Of Korea

2 Efficient 3D Design Drawing Visualization Based on Mobile Augmented Reality

568

Yeon-Jae Oh*, Kyoung-Wook Park**, Eung-Kon Kim*
*Department of Computer Science, Sunchon National University, Republic of Korea**Division of Culture Content, Chonnam National University, Republic of Korea

3 Study on Access Permission Control for the Web of Things
Se Won OH*/**, Hyeon Soo KIM*
*Department of Computer Science & Engineering, Chungnam National University, Korea**ETRI (Electronics and Telecommunications Research Institute), Korea

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 4, Issue 1, January 2015

562

Design and Implementation of Vision-based
Structural Safety Inspection System using Small
Unmanned Aircraft
Sung-suk Choi *, Eung-kon Kim ** Department of Computer Science, Sunchon National University,
255 Jungang-ro, Suncheon, Jellanam-do, Republic Of Korea
mstar2000@sunchon.ac.kr, kek@sunchon.ac.kr

Abstract—Safety inspection for high-rise structures should bedone regularly for follow-up test. However, as it is very
dangerous job and direct manual examination takes a lot of timeand money, its correct diagnosis is difficult. In this paper,computer vision-based structural safety inspection system is
proposed to be designed. The proposed system is an image
recognition monitoring system through wireless transmission of
sensor data by loading multiple sensors for facility monitoring in
UAV platform. The system loads various sensors such astemperature sensor, humidity sensor, smoke sensor, illuminancesensor, CO2 sensor, ultrasonic sensor, and infrared thermal
imaging sensor and can achieve stability inspection. So, It can
reduce inspection time and building’s maintenance cost
Keyword—small
unmannedaircraft,
Multi -Sensor,
Monitoring facilities, Safety inspection, Image Processing,Sensor Fusion, openAPI

I. INTRODUCTIONI

n domestic construction industry field, as number offacility that was constructed more than 20 years ago isincreased, accident by facility damage and collapse is takenplace frequently and its occurrence frequency is showing atendency of being increased due to facility deterioration andnegligence of safety check-up. Therefore, sustained safety
inspection and management are required but inspection ofhigh-rise buildings with its height over 10-100m by inspectors
with directly carrying inspection equipment is too dangerous
and requires lots of time and cost.
Therefore, in order to perform integrated inspection ofseveral structures of which access of inspector is limited and———————————————————————
Manuscript received October 30, 2014. This work was financially
supported by the Ministry of Education (MOE) and National Research
Foundation of Korea(NRF) through the Human Resource Training Projectfor Regional Innovation under Grant 2013H1B8A2032217, and theMSIP(Ministry of Science, ICT and Future Planning) and the
CITRC(Convergence Information Technology Research Center), Korea
supervising by the NIPA(National IT Industry Promotion Agency) underGrant NIPA-2014-H0401-14-1008 .
Sung-suk Choi, was with Sunchon National University, 255 Jungang-ro,
Suncheon, Jellanam-do, Republic of Korea (e-mail: mstar2000@daum.net)
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).

confirm damage status of structure to be inspected based ontime difference by each season and day, inspection method
using unmanned aerial vehicle (UAV) that is effective forshortening inspection time and reducing maintenance cost is
urgently required. In this study, computer vision-based
structural safety inspection system is proposed to be designed
Proposed system is an image recognition monitoring
system through wireless transmission of sensor data byloading multiple sensors for facility monitoring in UAV
platform.
This system has an advantage of being able to perform
more accurate, precise inspection and diagnosis throughimage processing on the ground by identifying problems after
approaching high-rise structures through remote control using
UAV equipped with video camera.

II. RELEVANT STUDY
A. Definition of UAV and its Utilization Field
UAV is a small aircraft on which pilot is not boarded and it
has a separate remote control operator on the ground and it isalso called as drone meaning that it looks like a buzzing bee.
In the past, it was mainly used for reconnaissance target or
military purpose but recently, its usage has been diversified
including private and research survey purpose. [1]At present, in order to apply UAV control technology todiversified fields including documentary shooting or pesticide
spraying, diversified researches are under progress [2] and incase of image analysis level, technology development for
substituting physical sensor including fire watch-out,behavioral watch-out has been advanced to a considerable
level. [3]
In overseas case, mainly Japan, Germany and U.K. show
unusual concern over maintenance and safety inspection of
structures and Fraunhofer ISE, Germany and Cyberhawk,
U.K. have developed and commercialized UAV for safetycontrol of wind power structures.
Recently, DHL, Germany started parcel delivery using
drone and Amazon and Google, USA are preparing
unmanned home delivery service.
B. Computer Vision Technology and Algorithm
In order to extract information contained in image by

Copyright ⓒ 2015 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 4, Issue 1, January 2015

563

analyzing it photographed from camera mounted in UAV,
computer vision technology is required. Computer vision
technology is widely applied to diversified applications
including text recognition, biometrics, positioning, 3D image
restoration, augumented reality [4]. Its utilization example is
as shown on below Fig. 1.

(a)Object recognition(b)SLAM(c)Augmented reality
Fig. 1. Example of computer vision utilization

In this study, a system of identifying damage part ofbuilding using edge detection algorithm by converting
photographed
image
tograyscale
using
light
intensity(illuminance) after tracking ground structure in the
air by mounting image camera in UAV is intended to be
developed. In addition, structures could be managed
efficiently by having inspector recognize facility condition
and information without difficulty through UAV equippedwith diversified sensor

Fig. 2 System Configuration

In order to perform safety inspection of structures,
Mikrokopter based open-sourced UAV, Germany is used.
Mikrokopter was developed into quardcopter (4), hexacopter(6) and octocopter (8) based on number of blade [5]. As asystem of controling flight depending on altitude-hold(acceleration sensor), horizontality-hold (gyro sensor),
posture stabilization function (pressure sensor), GPS is
mounted in FC(Flight Control) board that controls flight andso, it is suitable for basic aircraft of UAV for safety inspectionof structures [6]. By mounting image camera in UAV, image
frame of UAV is supplied to tablet PC on the ground througha synchronization proces and tablet PC on the groundprocesses image of each entered frame. In addition, by
mounting diversified sensors, more accurate safety control ofstructures could be ensured. General system outline is asshown on below Fig. 3.

III. MAIN SUBJECT
In this study, in order to supplement disadvantage ofexisting systems, development of a system that enables
internal, external defect detection of high-rise facility anddiversified inspections using UAV based diversified sensors
and HD camera is proposed.
A. Total System Configuration and Hardware Specification
Total system configuration to be proposed in this study is asshown on below Fig. 2.

Fig. 3. System Outline

Detailed hardware and functions of UAV to be equippedwith multiple sensors are as follows

Copyright ⓒ 2015 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 4, Issue 1, January 2015

564

TABLE IDETAILED HARDWARE SPECIFICATION OF UAV
ItemSpecification

Diameter

800mm

Frame

Aluminium

Weight (Battery
excluded)
Propeller

3Kg

Motor thrust

1.5Kg/motor

Operation time

Over 30 minutes

Gimbal14 inch300mm(W)×300mm(H), Internal dia.
200mm
Fig. 5. Operation mechnism of 3-axis gyro

MCU (Main Control Board) in which sensors will bemounted comprises multi Wii open-source base.
B. Sensor Convergence for Posture Control of UAV

In this study, Kalman filter was used in order to obtain
exact value by efficiently converging information acquired so
far from sensors in posture stabilization of UAV.

UAV to be used in this study is STM32 bit processor
mounted Black32 board based hexacoper and it comprises 6
motors, 6 ESC (Electronic Speed Controller) that controls
motor speed and 6 propellers. Fig. 4 shows Black32 board
mounted with sensors.

Fig. 6. Measurement of sensor data value through Kalman filter

Fig. 4 UAV internal sensor mounting

In order to ensure flight of hexacopter by taking stabilized
posture at the time of take-off and landing, balancing of
blades facing with each other is very important.[7]In order to measure posture of hexacopter, sensor isrequired and for exactly measuring roll, pitch, acceleration,
gyro sensor are required and for measuring yaw, geomagnetic
sensor is required. Acceleration sensor is a sensor measuring
acceleration and its size is expressed in figure by dividing
gravity acceleration into vector world of x, y, z axis. Gyro
sensor is a sensor detecting angular velocity and as exact
gradient measurement is difficult by accelerometer only incase that acceleration sensor measures other acceleration thangravity, it measures rotation angle. Therefore, gyro andacceleration sensor are used in convergence. Row, pitch value
could be obtained by acceleration sensor but yaw value could
be calculated by mounting geomagnetic sensor that measures
global magnetic field [8].
Operation mechanism of gyro sensor is as shown on below
Fig. 5.

Kalman filter developed by Rudolf Kalman in early 1960s
is an algorithm being widely used in computer vision, robot,
rocket, satellite, missile and control field [9]. This algorithm
is estimating a new result after removing noise included indata by using past and new measurement data [10] and in thisstudy, more exact image was intended to be processed
through UAV positioning and posture control by using thisKalman filter.
C. Design and Development of multiple sensor mounted
moduleActually, inspector may require a lot of time and cost andexperience a lot of difficulties due to exposure to risk if hewishes to check visually in order to confirm damage status ofinspection site based on time difference by each season andday but by performing inspection through UAV mounted withsensors, such risk factors could be reduced and structures
could be managed efficiently.
Therefore, in this study, by using diversified sensors
including sensor of temperature, humidity, CO, CO2, Ozone,
dust, VOC (detection of organic compound), safety ofstructures is intended to be inspected. In order to mountsensors in UAV, as large or heavy sensor module isdetrimental to flight, flight stability is ensured by using small,light sensor module.
D. Development of multiple sensor data transmission
technology

Copyright ⓒ 2015 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 4, Issue 1, January 2015

Multiple sensor data collected from UAV is required to betransmitted to monitoring personnel on the ground and inorder to ensure transmission distance over min. 100m, IEEE
802.11n based wireless transmission module and 7dbi
antenna are required to be composed. By visualizing sensor
data being obtained from diversified sensors including
sensors of temperature, humidity, CO, CO2, Ozone, dust andVOC (for detection of organic compound), data shall beprocessed.
By developing meta data management module that controls
data type, unit, validity range of sensor data, sensor data ofdiversified forms are processed. Information could bemanaged more efficiently by implementing visualized module
by each sensor data and displaying it in tablet PC monitor of
the worker.
E. Design of video camera mounted module
Visual inspection is the most basic inspection method
performing structural stability inspection but inspection ofhigh-rise building facility by inspector with directly carrying
inspection equipment is too dangerous and it requires a lot oftime and cost. In this study, through more clear and precise
photographing of video camera mounted in UAV, precisionand efficiency of structural safety inspection are intended to
be increased. Camera image is obtained by projecting dots of
3D space in 2D image plane. Therefore, conversion,
movement and rotation of world coordinate to camera
coordinate are taken place and in order to explain thiscoordinate conversion nature, a process of finding out
parameter value is required and this is called calibration.

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Fig. 8. Camera calibration

As a result of performing experiment with two image files,
focal length was [762.873 766.547] +/- [26.743 26.669] andmain pointer [298.977 231.074] +/- [3.814 6.803]. distortion[0.024516 -0.243971 -0.005740 -0.000509] +/- [0.030206
0.361247 0.000665 0.000902] and pixel error [0.26 0.22].
In order to transmit acquired image to the ground fromvideo camera, IEEE 802.1x based wireless transmission
technology was used. Motion JPEG video compression that
may transmit clear image at every scene was used and safetyof structure was identified promptly by transmitting image
with transmission speed over 20-30fps and resolution of
4CIF/2CIF
F. Development of image acquisition and processing
technology
In order to process images acquired from UAV, image
processing technology is required. Image transmitted byvideo camera may be sometimes damaged and not clear and ifinterested area is desired to be observed, data could bemanaged more effectively by using image processing
technology. In addition, as acquired image shows difference
depending on climate or surrounding environment, image isrequired to be improved to be fit for intended purpose. As atechnology of improving image, there are smoothing,sharpening and noise removal.
A work of extracting a specific area is called image analysis
and it is mainly used for finding out size, form and contour
line. In addition, in order to recognize image, following
processing stage is required.

Fig. 7. Camera and image coordinate

As video camera to be mounted in UAV has to identify
position of ground facilities in the air and perform inspectionof external damage status and cracking, more precise anddelicate image acquisition is required. Calibration is
performed in order to correct camera distortion. Result of
performing camera calibration is as shown on below Fig. 8.

Fig. 9. Image recognition processing stage

In detecting internal, external damage status of structuresby receiving input of color image being photographed by
UAV, it is hard to process color image immediately and so,
pre-treatment process of converting to grayscale is required.
In order to change RGB color image to grayscale, it isrequired to change color information to light intensity
information.
Various methods are available for changing RGB colour
value to light intensity value and in this study, NTSC
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566

(National Television System Committee) method that is transmission. As the quantity of image being transmitted bymainly used in analogue broadcasting system in our country video camera using UAV is relatively larger than that ofand the USA is used. Required formula could be obtained as general data, this feature is required to be considered.
In this study, image compression technology is used forfollows.
Grayscale = 0.2999 ∗ Red + 0.587 ∗ Green
removing
overlapped or unnecessary data in order to store or(1)transmit data efficiently. Image compression technology is+ 0.1114 ∗ Blue
usually classified into loss technique and lossless technique.
As Loss technique reduces resolution at the time ofcompression and data is wasted, its quality is decreased. Inorder to restore image again, linear interpolation is used but it
is hard to restore to original image. On the contrary, losslesstechnique makes no difference even though image is restored
again after compression. In order to ensure fast image(a)RGB Color(b)Grayscale Conversion
transmission
in this study, Motion JPEG (M-JPEG) video
Fig. 10 PreProcessing
compression is performed.
Image acquired from UAV requires more precise image
processing through pre-treatment process and at this timeedge detection method is used for removing noise of image.
Edge is taken place at boundary between image object andobject or object and background and it means boundary ofobject in image [11].
Detection technique using edge does not require many
operations and such noise is usually identified by brightnesschange at edge that is boundary with background aroundimage being entered. Edge information is used for detectionof first differential function such as operators of Prewitt,
Roberts, Sobel, second differential function such as Laplacian
operator or many other methods. [12]In most cases, as edge extraction mask is sensitive to noise,even small noise is frequently extracted by regarding it asnoise. As a method of supplementing this disadvantage, thereis edge extraction method using Canny mask and its objective
is to extract strong edge by actually making such mask not tobe sensitive to noise.
This algorithm presents a method of finding out edge thatsatisfies good detection, good localization and clear response.
In this study, Canny edge detection technique that is
resistant to noise is used.

IV. EXPERIMENT AND IMPLEMENTATIONIn this study, in order to perform inspection for cracking
detection status of internal structures by importing video
image photographed by UAV as a part of safety inspection of
structures using UAV, window application program was
implemented. Implemented image acquisition system is a
program of identifying cracking status of internal, external
structures by detecting contour line in order to obtain required
information in screen after importing colour image file andconverting it to Grayscale.
As a result of extracting cracking image of internal,
external building, cracking image was extracted relatively
well but very fine cracking image was not extracted asintended.

(a)Grayscale(a) Original image
Fig. 11 Segmentation(b)Threshold= 0(b)Canny edge

Image processed like this uses extraction algorithm throughimage segmentation and its processing speed is fast andstorage capacity could be saved as well. It has an advantagethat after extracting characteristics using segmented image
and comparing it with existing photographed image, its
normality status could be determined without difficulty.
Image processing technology like this is effective for
identifying and managing structure condition.
Pixel is basic unit of image and image is composed ofnumerously gathered pixels. Therefore, as the quantity ofimage data is relatively larger than that of general text data,considerable loss of time and data may be taken place during(c)Threshold= 76(d)Threshold= 136
Fig. 12 Result of cracking extracting

V. CONCLUSION
In this study, system design for structure safety inspection
using UAV was suggested and a system processing
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 4, Issue 1, January 2015

photographed image was implemented. It could be realizedthat in order to process image acquired by UAV, noise wasremoved by using image processing algorithm and throughmore clear image, appearance of structure could bediscriminated.
By providing more convenient working environment after
visualizing data by mounting video camera and diversified
sensors in UAV, more safe and objective inspection process
and result could be obtained compared with existing method.
Based on future design, it is intended to be applied to safety
inspection of T/L tower, plant smoking stack, bridges andpiers, wind power generators of which access to workers isdifficult and dangerous. In case of T/L tower, inspection of its
corrosion and cracking by using UAV rather than manual
inspection would be more safe and fast.
As inspection of plant smoking stack should be performed
manually after stopping plant operation, a lot of cost isrequired but in case of safety inspection method using UAV,
its inspection could be performed while operating plantcontinuously and analysis of gas being emitted during
operation is enabled.
Besides, UAV could be utilized for safety inspection
including exfoliation, cracking of concrete structures such as
high-rise buildings and apartments and by mounting
thermo-graphic camera in UAV, UAV could be used for
detection using thermal sensing in wide area including forest
fire watch-out. Therefore, it is expected that a systemsuggested in this study could be effectively utilized in safety
inspection of diversified industrial fields in the future.

567

Determining Orientation by Inertial and Magnetic Sensing”, IEEE
TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 53, NO.
7, JULY 2006
[9] Kalman, R. E. 1960. “A New Approach to Linear Filtering andPrediction Problems,” Transaction of the ASME—Journal of BasicEngineering, pp. 35-45 (March 1960)[10] Gan. Q, Harris. C. J, “Comparison of two measurement fusion
methods for Kalman-filter-based multisensory data fusion”, Aerospaceand Electronic Systems, Vol37, Issue 1, pp.273-279, Jan, 2001[11] Hea-jung Lee, Sung-tae Jung, Suck-tae Joung, “Noise Elimination andEdge Detection based on Fuzzy Logic”, The journal of the Korea
Institute of Maritime Information & Communication Sciences, Vol7,
No3, pp.506-512, 2003[12] S. E. El-Khamy, “A Modified Fuzzy Sobel Edge Detector.”, Radio
Science Conference, pp. 1-9, 2000,Feb, 22-24.
Sung-Suk Choi
Sung-Suk Choi received the B.S. degree from Korea
National Open University, Korea, in 2010. She iscurrently a PhD student in computer science at theSunchon National University, Korea, Her current
research interests include augmented reality, image
processing,computergraphics,
android
programming.
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, Ko-rea, in 1987, his
Ph.D. degree from Chosun University, Gwangju,
Korea, in 1992. His current research interests arecomputer vision, virtual/augmented reality, image
processing, and computer graphics. Currently he is a
professor in department of computer engineering,
Sunchon National University, Korea.

ACKNOWLEDGMENT
This work was financially supported by the Ministry of
Education (MOE) and National Research Foundation of
Korea(NRF) through the Human Resource Training Projectfor Regional Innovation under Grant 2013H1B8A2032217,
and the MSIP(Ministry of Science, ICT and Future Planning)
and the CITRC(Convergence Information Technology
Research Center), Korea supervising by the NIPA(National
IT
Industry
Promotion
Agency)under
Grant
NIPA-2014-H0401-14-1008 .
REFERENCES
[1]

[2]

[3]

[4]
[5]
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Hundley, Richard, and Gritton, EugeneC, “Future Technology-Driven
Revolutions in Military Operations”, RAND Corporation , Document
No. DB-110-ARPA, 1994.
Jae-Ha Ryi, Wook Rhee and Jong-Soo Choi. “Effective Perceived
Noise Level Prediction for a Propeller driven UAV by using Wind
Tunnel Test Data”, Journal of Korean Society for Aeronautical &Space Science, No. 41(1), pp. 10-16 , Jan. 2013.
Min Goo Yoo and Sung Kyung Hong, “Target Tracking Control of
Quadroto UAV using Vision Sensor”, Journal of Korean Society for
Aeronautical & Space Science, No. 40(2), pp. 118-128, 2012.
Computer
VisionIntroduction,
http://www.comp.nus.edu.sg/~cs4243/lecture/motion.pdf
http://mikrokopter.de/en/home
AscTec Hummingbird with AutoPilot User’s Manual, Ascending
Technologies GmbHC. J. Kaufman, Rocky Mountain Research Lab.,
Boulder, CO, private communication, May 1995.
Gabriel M. Hoffmann, Haomiao Huang, Steven L. Waslander, Claire J.
Tomlin., “Quadrotor Helicopter Flight Dynamics and Control: Theory
and Experiment”, AIAA Guidance, Navigation and Control Conference
and Exhibit 20 - 23 August 2007, Hilton Head, South Carolina.Angelo M. Sabatini, “Quaternion -Based Extended Kalman Filter for

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 4, Issue 1, January 2015

568

Efficient 3D Design Drawing Visualization
Based on Mobile Augmented Reality
Yeon-Jae Oh*, Kyoung-Wook Park**, Eung-Kon Kim*
*Department of Computer Science, Sunchon National University,
255 Jungang-ro, Suncheon, Jellanam-do, Republic of Korea**Division of Culture Content, Chonnam National University,
50 Daehak-ro, Yeosu, Jellanam-do, Republic of Korea
oksug10@sunchon.ac.kr, zergadiss73@chonnam.ac.kr, kek@sunchon.ac.kr
Corresponding Author: Eung-Kon Kim

Abstract—Recently,inmanufacturing,machinery,
construction and shipbuilding industry, diversified researchesof visualizing design drawings without difficulty utilizing
augmented reality technology have been performed. In thispaper, we propose efficient 3D design drawing visualization
technique based on mobile augmented reality. Proposed
technique first recognizes design drawing region, and only
performs image tracking in its region. Through performance
analysis, it could be seen that in technique being suggested inthis paper, drawing recognition time was reduced by 4-33%,
drawing matching recognition rate was increased by 5-15% andnumber of output frame per second was increased by 7-8
frames per second.
Keyword—Augmented Reality, 3D Structures, Drawing
recognized

I. INTRODUCTIONA

s augmented reality provides users with enhanced
immersion and a sense of reality, it has been applied indiversified fields including broadcasting, shopping, medical
service, education, military, game, architecture, shipbuilding
and manufacturing business[1-6].
As design drawing is 2D plane and it expresses relevant
data numerically, it is hard for construction manager,engineer and users to understand solid form of structures. In
order to solve this problem, diversified researches visualizing
design drawing without difficulty by utilizing augmented
reality technology have been performed.
In this paper, 3D model visualizing technique ofaugmenting virtual 3D structures on 2D drawing utilizing
augmented reality technology is suggested.
Suggested visualized technique recognizes design drawing
through smart phone camera without marker and displays it
out on camera image by augmenting 3D model.
First, in order to recognize design drawing, edge point is———————————————————————
Manuscript received November 9, 2014. 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).
Yeon-Jae Oh, was with Sunchon National University, 255 Jungang-ro,
Suncheon, Jellanam-do, Republic of Korea (e-mail: oksug10@naver.com)
Kyoung-Wook Park, was with Chonnam National University,
Jellanam-do, Republic of Korea(e-mail : zergadiss73@chonnam.ac.kr)
Prof. Eung-Kon Kim, was with Sunchon National University, 255
Jungang-ro, Suncheon, Jellanam-do, Republic of Korea(corresponding
author:+82-61-750-3627;
fax:+82-61-750-7999;
e-mail:kek@sunchon.ac.kr).

extracted from camera image and it is set as design drawing
region by calculating rectangular region. And as feature point
for total image is extracted by performing matching fordesign drawing region only, its calculation volume is lessthan existing matching method.
Through performance analysis, it could be seen that intechnique being suggested in this study, drawing recognition
time was reduced by 4-33%, drawing matching recognition
rate was increased by 5-15% and number of output frame persecond was increased by 7-8 frames per second.
II. AUGMENTED REALITY 3D MODEL VISUALIZATIONTECHNOLOGY IN INDUSTRIAL FIELD
A research on introduction of augmented reality for site
support of 3D design data utilized augmented reality
technology for a system in which 3D design data could be
supported to site workers in terms of operation andmaintenance after ship hand-over in order to ensuresustainable performance of ship[7].
Augmented Reality Technology based plant piping
construction management plan aimed at efficiency of piping
construction management through real time confirmation of
piping information in plant piping construction site andconfirmation of 3D piping model based piping installation
error being overlapped in actual environment by utilizing
augmented reality[8].
In a research on application of augmented reality for
inspecting rebar arrangement of civil works, its application
could increase understanding of rebar workers by visualizing
3D modeled rebar in actual site in an overlapped form and by
utilizing it even to construction inspection stage, suppress
cost increase by defect and rework that may be taken place inrebar fabrication work effectively[9].
In a research on application of augmented reality
technology for operation and maintenance education for shipbuilding/marine equipment, in order to ensure sustainabilityof ship building/marine equipment, material and support
product sustainability by introducing augmented reality
technology at the site, a system that enables education andmaintenance support for the system that may support workers
more effectively at the time of operation and education formarine equipment/material was provided[10].

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 4, Issue 1, January 2015

III. EFFICIENT 3D DESIGN DRAWING VISUALIZATION

569

recognized is shown in “fig. 2”.

Augmented reality has been used in diversified fields
including construction, machinery, ship building industry as
communication tool between managers and engineers
because structure of 3D model is visualized on 2D design
drawing so that design process of structures to be
manufactured could be explained to the users without
difficulty and exact user’s requirements could be identified.
“Fig. 1” show structural diagram of visualizing 3D model ofdesign drawing.

Fig. 2. Design drawing recognition process of design drawing recognition
Fig. 1. structural diagram of visualizing 3D model of design drawing

A. Design drawing management server system
Designdrawingmanagement
serverincludesconfiguration of design drawing management server, design
drawing information management module and feature point
matching and search module.
Configuration of design drawing management server:
Design drawing management server system play a role ofsaving image, feature points and 3D model of design drawing,
searching relevant design drawing by receiving feature point
data transmitted from smart phone and transmitting design
drawing information and 3D model.
Design drawing information management module:
Design drawing management module is to manage design
drawing information in design drawing image registered inserver. System manager is able to manage overall sideinformation and authorized user is able to manage permitted
information only after approaching module through user
authentication. In total natural image being used inaugmented reality system, each main information andadditional information are managed.
Feature point matching and search module: Featurepoint matching and search module receive information
request if image entered from smart phone is available andsearch information in DB management system. Searched
result is printed out in a form of XML.

Firstly, design drawings are entered to the server by using
scanner. Feature points are extracted from the drawings using
feature points extraction algorithm. The extracted featurepoints are stored in database together with 3D model.
Subsequently, drawing image is acquired by taking photousing a camera embedded in a smartphone. From theobtained image, boundary and borderline are extracted. Thenregions of interest (ROI) for a rectangle area of the drawing
are calculated. After extracting feature points within the ROI,
a matching operation is carried out by comparing between
extracted feature points and the feature points of drawings
stored in a cache. If there exists the drawing with matchedfeature points, fetch the information about the drawing fromcache memory. Otherwise, send the feature points to theserver in order to search data stored in server. Once the server
receives the feature point data, it retrieves the design
drawings stored in its data-base using the method of featurepoint matching. In this case, the drawings with highest
similarity are searched using SURF(Speeded Up Robust
Features) algorithm. When the search is completed, the foundfeature points and 3D model would be transmitted back tocache memory in a smartphone. The smartphone shows themessage to the user, so that the user recognizes that thedrawing data with matched feature point now exists in cachememory. However, if the server couldn’t find the matcheddrawing in its database, it returns failure message back to thesmartphone. Then smartphone displays failure message to theuser. “Fig. 3” shows matching process after setting design
drawing region based on suggested region setting.

B. Recognizing Design Drawings
The mobile augmented reality system for design drawing
proposed by this paper is implemented using non-marker
based AR technology which enables to recognize existing
design drawings without inserting specific marker into thedrawing. The processes in which design drawings are

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570

drawing as marker, after detecting rectangular shaped
drawing region in previous stage, features points are
extracted for only relevant regions.
Feature point matching of design drawing: Data thatextracted typical feature points by its compression andfeature points of DB are matched. In system resources such as
smart phone or tablet PC and mobile devices having frequent
processing speed limit, feature point is matched by applying
SURF algorithm.
C. Design Drawing 3D Model Visualization(a) DB of design drawing feature point(b) Detection of design drawing region(c) Extraction of design drawing region feature point
Fig. 4. Process of 3D Model Visualization(d) Result of design drawing matching
Fig. 3.Matching process after setting design drawing region

DB of design drawing feature point: First, design
drawing management server saves design drawing image,feature point data, 3D model as DB and extracts featurepoints by using SURF algorithm after converting to image
size similar to resolution of smart phone.
Detection of design drawing region: Generally, design
drawing is rectangular shape and by utilizing this feature,design drawing region is detected. First, unnecessary noise isremoved by performing Up-Scale immediately followed by
Down-Scale in camera image of smart phone. And then,contour line is detected utilizing inter-regional contour
algorithm by changing threshold. After approximating thesepointers, whether it is rectangular shape is determined.
Coordinate of rectangular vertex is calculated by determining
whether line is 4 and it is rectangular shape utilizing edge
angle, region.
Extraction of feature point of design drawing: After
extracting rectangular drawing region, feature points inregion only are extracted. In order to recognize design

Once a drawing is completely recognized, the process toaugment 3D model on top of the 2D drawing is being
conducted as illustrated in “fig. 4”. 3D model and the featurepoints for the design drawing stored in cache memory areloaded into a smartphone visualization module. Then camera
takes a photo of a target drawing. Based on the photo image,
ROI coordinates are detected for the rectangle area of thedrawing. The edge coordinates of the rectangle are tracked
and then a distance and an angle between a camera and adrawing are estimated. Corresponding to an estimateddistance and angle, 3D model would be adjusted like moving,
expanding, shrinking or rotating. Finally shading effect is
added to 3D model. The resulting 3D model is visualized on ascreen of the smartphone. In the meanwhile, if there is no user
input, all of the above processes are executed under an
iterative loop starting from getting input of a camera image.
When a user input occurs, correspondent events are handledsuch as expanding, shrinking, rotating and terminating.
Geometric conversion of 3D Model: If four vortex
coordinates of design drawing is known, rotated position andposture of design drawing could be calculated based on
camera coordinates system. In this study, camera coordinates
system is obtained from vortex of design drawing ROI using
a method suggested in geometric conversions. “Fig. 5” shows
movement, rotation, enlargement of 3D model

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 4, Issue 1, January 2015(a) Movement(b) Rotation(c) Enlargement
Fig. 5. Movement, Rotation, Enlargement of 3D Model

Conformity of design drawing 3D model: Image
conformity is a process of making two images to be exactly
coincided when actual drawing and virtual 3D model are
shown in overlapped form. By receiving video image data in
Frame buffer of graphic system, 3D model is expressed inoverlapped form in setting position. After matching 3Dmodel including its depth value on 3D design drawing, it isdisplayed.

571

camera of the smartphone which was used in this study.
When taking pictures, camera angle and distance to thedrawings were varied. Those pictures were entered to thesystem as inputs. “Fig. 8” shows a scene how a design
drawing is taken a picture using the smartphone which is acomponent of mobile AR 3D model display system.

Fig. 6. AR execution demonstration

IV. IMPLEMENTATION AND PERFORMANCEANALYSIS

A. System Implementation
Developing environment: To analyze feasibility of the
proposed mobile AR system for design drawing visualization,
android-platform smartphone application and a drawing
management server were developed. For a design drawing
management server, hardware specification is as follows:
INTEL i7 3.40GHz, 16GB Memory and SSD 256GB. As for
smartphone, hardware specification is Qualcomm
Snapdragon S3 Dual Core 1.5GHz and 1GB memory.
Android 4.1.2 Jelly-bean is used as platform. Screen
resolution is 1280×800.
Design drawing and 3D model database: For
implementing the proposed system, management server for
dealing with design drawings were developed. In conjunction
with the server, database was also developed. The database
was de-signed to be used for storing 20 types of design
drawing images, feature points and correspondent 3D model.
The 20 types of models were generated using Catia modelingtool.
Implementation result: The implementation result is
explained here. Total 20 models were entered to the system as
an input. The system supports to expand, shrink and rotate theimage as user wants. Each part comprising the whole image
can be displayed. “Fig. 6” shows a demonstration of runningmobile AR-based 3D model display system. “Fig. 7” shows
screens where an augmented 3D model is presented as rotatedimage and as partial display.
B. Performance Analysis
Performance regarding recognition of design drawing: To
estimate system performance, a comparison between the
proposed technique and an existing technique was conductedby measuring a recognition rate and recognition time
duration for the design drawings. In the proposed technique,
ROI is firstly detected within the drawing image and thenmatching is done by using only feature points extracted from
ROI. In contrast, the existing technique extracts featurepoints from an entire region of the drawing image. For
carrying out the sys-tem performance analysis, 20 different
types of design drawings were taken pictures using a rear(a) 30 degree(b) 70 degree(c) Car loading(d) Truck loading
Fig. 7. Rotation and Partial display screen of an augmented 3D modelFig. 8. Drawing test process

Total 20 tests were carried out. In each test, the design
drawing was taken from a different direction, for example,
front, side and diagonal direction. For each angle anddirection, tests were conducted for the pro-posed method andan existing method. Therefore, total number of tests

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 4, Issue 1, January 2015

conducted is 180. In this test, measurement items are two:recognition rate and recognition time duration. To get rid of
any effect of a lagging time which might occur incommunicating between the server and the smartphone,feature points and 3D models were stored in a cache memoryof the smartphone prior to conducting tests. The process todetect feature points by extracting ROI from the design
drawing is shown in “fig. 9”. The technique pro-posed in thisstudy detects and extracts ROI first as illustrated in (a).
Secondly, remaining area except for the ROI is marked to be
black as presented in (b). Finally, feature points are extractedusing SURF algorithm.
“Fig. 10” (a) and (b) shows a matching result for the
proposed method and an existing method respectively. In
case of Fig.10(a), matching process is carried out only for
ROI region. It implies that time to be taken for matching can
be lower and matching accuracy can be better than that of an
existing technique. In case of Fig.10(b), however, matchingneeds to be conducted for an entire region of a drawing. It
indicates that time to be taken for matching would be
increasing. Furthermore, other objects surrounding the core
part affects and distracts the matching task.

572

relatively low recognition rate in side and diagonal and it isunderstood that in side and diagonal direction, as design do
cements backdrop or surrounding objects occupy moreregions in camera image, it lowers exact recognition. In 70oangle, suggested technique showed recognition time shorter
than existing technique by app. 4-7% and in 50o and 30o,recognition time was represented to be shorter by app.
24-33%. The reason of this result is that in existing technique,as more backdrop or surrounding objects occupy most part ofimage in low angle, number of extracted feature point isplenty. Table 3 is 3D Model visualization test Results
TABLE Ⅰ.
DESIGN DRAWING RECOGNITION RATE TEST RESULTS
Proposed technique
Traditional technique
Test Item
Angle
Diago
Front
Side
Front Side Diagonalnal
Recognition
Rate(%)

30

95

90

85

80

75

70

50

100

95

90

90

90

75

70

100

100

100

100

95

95

TABLE Ⅱ.
Test result of recognition time of design drawing
Proposed technique
Traditional technique
Test Item Angle
Front Side Diagonal Front Side Diagonal
Recognition
Time(%)

10

410.2

424.6

426.7

548.4 562.3

569.8

45

427.3

432.5

420.9

530.0 541.1

547.3

80

410.5

423.1

426.3

478.7 492.6

491.2

TABLE Ⅲ.
3D Model Visualization Test Results
The number of frames per
second(a) only within ROI region(b) an entire region of image(Proposed method)(traditional method)
Fig. 9. Feature Points Extraction from Design Drawing(a) Drawing Region ROI detection(b) Feature points extraction
Fig. 10. Feature points matching for design drawings

Recognition results for the drawings are listed as table 1.
According to the figures in table 1, the proposed techniqueshows higher recognition rate than a traditional method by
5~15%. In terms of recognition time duration, the proposed
method shows 23~32% lower than a traditional method.
These results come from the fact that the proposed method
excludes remaining region by detecting ROI of rectangle area
for the drawing prior to carrying out feature point matching.
The total number of feature points to match becomes lessthan that of a traditional method. Therefore, calculation
amount would be decreasing.
Table 2 shows a test result of design drawing recognition
rate. In case of suggested technique, its recognition rate is app.
95-85% in side and diagonal and it shows difference
depending on each angle but in most cases, it recognizedalmost accurately. However, existing technique showed

Traditional Method(Tracking an entire
region And then performing image
registration)

14~20 Frames

Proposed Method (Tracking ROI Rectangle.
And then performing image registration)

21~28 Frames

C. Performance Analysis for Design Drawing Visualization
To estimate the performance especially for image
registration technique used in the proposed visualization
method, time to be taken for augmenting process wasmeasured by the number of frames per second. In case of a
traditional method, feature points are extracted first.
Secondly, a distance and an angle between camera and thedesign drawing are calculated by matching the feature points.
And then, 3D model is geometrically transformed for
performing image registration. In the process of feature point
matching, a significant amount of calculation is required, sothat the image registration cannot be performed in smoothway. In contrast, the proposed method firstly detects ROI of
rectangle from a camera image. A distance and an angle
between the smartphone and design drawing are to be
calculated only within the ROI. Then image registration is
performed after geometrically transforming 3D model. An
amount of calculation necessary for each frame would belower. Hence image registration can be performed smoothly.
Model visualization test results are presented in table

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 4, Issue 1, January 2015

V. CONCLUSION
In case of visualizing 2D design drawing as 3D modelusing mobile augmented reality technology, understandingfor each design drawing could be enhanced and exact andefficient design and manufacturing process control areenabled.
A system suggested in this study recognizes design
drawing based on non-marker by limiting entered image. In
particular, as matching is made by recognizing design
drawing region and extracting feature point in this region
only, its recognition rate is high and calculation volume ismarginal. And by tracking once recognized rectangular shape
only, 3D model could be visualized on drawing based onlimited calculation volume only.
Through performance analysis, it could be seen that intechnique being suggested in this study, drawing recognition
time was reduced by 4-33%, drawing matching recognition
rate was increased by 5-15% and number of output frame persecond was increased by 7-8 frames per second.
For future work, it is necessary to generate 3D model for
an internal structure of specific part of a design drawing as
panoramic images and to display them efficiently.

573

a Ph.D. student in computer science at the Sunchon National University,
Korea, Her current research interests include augmented reality, image
processing, computer graphics.
Kyoung-Wook ParkHe received the B.S. degree from Sunchon National
University, Suncheon, Korea, in 1997, his M.S degreefrom department of computer sci-ence and statistics,
Chonnam National University, Gwangju, Korea, in
1999, his Ph.D. degree from department of computerscience, Chonnam National University, Gwangju,
Korea, in 2004. His current research interests areparallel and distribution processing, graph theory,
theory of computation, bioinformatics and augmented reality.
First A. Author (M’76–SM’81–F’87) and the otherauthors may include biographies at the end of regularpapers. Biographies are often not included inconference-related papers. This author became aMember (M) of IEEE in 1976, a Senior Member (SM)in 1981, and a Fellow (F) in 1987. The first paragraphmay contain a place and/or date of birth (list place,then date). Next, the author’s educational backgroundis listed. The degrees should be listed with type ofdegree in what field, which institution, city, state, and country, and year
degree was earned. The author’s major field of study should be lower-cased.

REFERENCES[1]

Fonseca, David, et al., "Relationship between student profile, tool use,
participation, and academic performance with the use of Augmented
Reality technology for visualized architecture models." Computers in
Human Behavior, vol. 31, 2014, pp. 434-445.
[2] Yeh, Kai-Chen, Meng-Han Tsai, and Shih-Chung Kang, "On-sitebuilding information retrieval by using projection-based augmented
reality." Journal of Computing in Civil Engineering, vol.26, no.3, 2012,
pp.342-355.
[3] Irizarry, Javier, et al. "InfoSPOT: A mobile Augmented Reality
method for accessing building information through a situation
awareness approach." Automation in Construction, vol. 33, 2013,
pp.11-23.
[4] Pachoulakis, Ioannis, and Kostas Kapetanakis. "Augmented reality
platforms for virtual fitting rooms." International Journal ofMultimedia & Its Applications, vol. 4, no.4, 2012, pp. 35-46.
[5] G.D. Michelis, M. Loregian, P. Martini, “Directional Interaction with
Large Displays Using Mobile Phones”, 4th Annual IEEE International
Conference on Pervasive Computing and Communications Workshops,
2006. pp.196-200,[6] Nee, A. Y. C., and S. K. Ong, “Virtual and Augmented Reality
Appli-cations in Manufacturing”, Manufacturing Modelling,
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[7] Lee, Jung-Min, et al. "Active insp ection supporting system based on
mixed reality after design and manufacture in an offshore structure."
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[8] Kim, Chang Hwan, "A Development of Automated Pipe Spool
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2010,[9] Kim, Young-Tae, et al. "Construction and inspection management
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[10] Park, Chan-Sik, and Hyeon-Jin Kim. "A framework for construction
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Yeon-Jae Oh
She received the B.S. degree from Korea National
Open University, Korea, in 2007, her M.S. degreefrom department of computer science, Sunchon
National University, Korea, in 2009. She is currently

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 4, Issue 1, January 2015

574

Study on Access Permission Control for the Webof Things
Se Won OH*/**, Hyeon Soo KIM*
*Department of Computer Science & Engineering, CNU (Chungnam National University), Yuseong, Daejeon,305-764, Korea**ETRI (Electronics and Telecommunications Research Institute), Yuseong, Daejeon, 305-700, Korea
sewonoh@etri.re.kr, hskim401@cnu.ac.kr
Abstract—The Web of Things (WoT) research is exploring
ways on the interoperation among the smart things, since the
Web has proven its potentials as open communication
environment for accommodating a variety of Web resources.
The Web technologies has enabled the Web-enabled devices topublish and exchange their resource information over the Web,
whereas the Web-enabled devices should cope with the security
threat regarding the information exposures over the Web,particularly, access permissions for the resources about thethings. Thus, in this paper we analyse access permission control
mechanism considering both the WoT characteristics and the
REST-compliant resource-oriented Web architecture. In
contrast to existing access control logics, the proposedmechanism utilizes not only the requester information such as
the typical identity and the internet addresses, but also thecontext of the thing itself. Based on this mechanism, we present
web-resource structure for access permission control, anddescribe an exemplary procedure in detail. This researchcontributes to the flexible and decentralized access permission
control for WoT.
Keyword—Access Control, REST, Security, Web of Things,
Web Resource

T

I. INTRODUCTION

HE number of smart things connected to the Internet evenexceeds the population of human beings nowadays. As
more smart things are capable of data communicating over theInternet, the concepts of the “Internet of Things (IoT)” or“Machine-to-machine (M2M)” are having been realized inmany fields, as in [1]–[8], including smart home, smart meters,
remote healthcare, and logistics process automation. Different
sources predict that by 2020, as in [9] and [10], the number ofconnected things would be more than 25 billion, even up to 50
billion with an optimistic view, and the IoT market size will———————————————————————
Manuscript received November 28, 2014. This work was supported byElectronics and Telecommunications Research Institute (ETRI) grant fundedby the Korea government [13ZC1100 (2013~2015) – Unit Research Project3, Development of USN/WoT Convergence Platform for Internet of Reality
Service Provision, 15ZC1300].
Se Won OH is with the department of Computer Science & Engineering,
Chungnam National University, as well as ETRI, Daejeon, 305-700, Korea(e-mail: sewonoh@etri.re.kr).
Hyeon Soo KIM is with the department of Computer Science &Engineering, Chungnam National University, Daejeon, 305-764, Korea(corresponding author; phone: +82-42-821-6657; fax: +82-42-822-9959;
e-mail: hskim401@cnu.ac.kr).

grow up to $7 trillion according to [11] and [12].
Whereas many IoT researches mainly have focused on how
to establish connectivity among the networked devices, theWeb of Things (WoT) or the Web-based IoT have been
extensively studied for integrating these smart things with thewell-known Web technologies, as in [13]–[15]. The
Web-enabled things can interoperate freely over the Web
utilizing the open Web standards, since the World Wide Web(WWW, Web) can provide flexible, scalable communication
channels for any web clients to share web resources. Also, thescope of the traditional web services can be broadened into
the physical-world, not only cyber-world. Moreover, theWeb-enabled things can reuse and adopt the proven Web
mechanisms such as discovery, searching, browsing, linking,and caching, as in [16]. Further, REST (Representational statetransfer) architectural style with URIs (Uniform Resource
Identifier), HTTP, and standardized media types, is very
promising to make these things to share their data andresources over the Web.
A pressing open problem in this WoT environment is how
to allow smart things to grant clients access to their own
resources, since the Web-based open environment often
leaves information vulnerable to disclosure resulting insecurity threats such as:--Malicious clients and unwanted data sharing--Attacks in any time and from anywhere--Unpredictable work load and availability risk
Unfortunately, there are few studies on these concerns andseveral system prototypes just have shown inadequate
functionality for security. In particular, from a security point,
the existing access control mechanisms for the web resources,
as in [17]–[18], are used to require either rigid access policy
enforcement or dependency on the pre-configured accessmanagement procedures like a central authentication service(CAS) system. And these ways have limitations for dealing
with the dynamic interactions and the scalability in the WoT.
Thus we analyzed the access permission control for the
resources of web-enabled things, as in our previous research[19]. And a decentralized access permission control
mechanism for Web resources for WoT is proposed. The
proposed mechanism adopts REST-style resource-oriented
architecture for things, in order to enable a thing itself (or its
owner) to manage access permissions to its own information
resources by means of simple CRUD (Create, Read, Update,and Delete) actions. To explain the mechanism in detail, we
described the prototype module of resource access control as
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 4, Issue 1, January 2015

well as resource structure XML.
The remainder of this paper is organized as follows:
Section 2 presents the related researches and the basic
concept of access permission management. Section 3
proposes access permission control mechanism consideringboth the WoT characteristics and the resource-oriented Web
architecture with the exemplary cases in detail. Then,concluding remarks are provided in Section 4.
II. RELATED STUDYThis section introduces the basic model for providing
access control and discusses the existing researches on accesspermission management, with describing research challenges
coming from the WoT characteristics.
A. Access Control Model
The rudimentary form of access control model in acomputer security system suggests that an active subject
requests a specific access operation to a passive object, then areference monitor between them decides whether to grant theaccess request or not, as shown in Fig. 1.

575--Multilevel security using information flow [23],--Role-base access control (RBAC) [24]–[25], and--Attribute-based access control (ABAC) [26]–[27].
Here, RBAC represents the concept of role, in order tologically relate users and permissions. That is, the RBAC
model assigns permissions to roles, and roles to the users.
However, this model mainly focuses a kind of ‘static’authorization assignment a priori, whereas many practicalinformation systems require more flexible ways to update thesubject’s permissions. In the meantime ABAC can supportchanging needs much easier, since only subjects with valid setof attributes are permitted to access the data and this approach
do not rely on the pre-defined roles for the specific sets of
attributes. Also ABAC supports the notion of distributed
access control enforcement, called policy enforcement point.
In addition, in consideration of environmental and context
information, a concept of context-aware authorization model
has been suggested as in [28]–[29]. And it is recognized thatthese semantic information can help to specify access control
measures and to provide machine-interpretable description of
security requirements [30], as the WoT environment enables
things to communicate over the Web with disparate parties
and users.
III. ACCESS PERMISSION CONTROL FOR WOT

Fig. 1. Basic access control model including subject, object, and referencemonitor

In order to protect the information system against malicious
actions which could damage data and resources, it is essential
to control access requests from subjects [17]. So, thereference monitor as a guard should perform two types oftasks; 1) to identify the subject who made the request, and 2)
to decide who is allowed to do what to the object.
Traditional access control systems have usually relied on a
local database which can maintain either user passwords,
group membership, and/or access control matrices. Then,several modifications have been adapted for the Web security
environment, including 1) SSL usage for securing the user
channels, 2) checking host information for authentication, and3) centrally managed database for a domain access control.
However, the centralized Web security architecture often
imposes limitations like a single point of failure as well as adependency on the dedicated security configuration.
To assure access control in a distributed system
environment considering an ad-hoc security domain for thething-to-thing communication, as in [20]–[21], it is necessary
to handle all the access requests even without asking to thecentral security manager. Likewise, the number and/or
diversity of subjects/objects in the web of things make theaccess control issues more challenging [13].
B. Existing Access Control Logics in Computer Network
With the advancement of information security, in order tofacilitate the management of access permission, many security
models for computer network systems have been coming up
as the followings, to name just a few:--Subject/object access control matrix [22],A. WoT Access Control Requirements
In the WoT environment a thing is expected to have adynamic connection with other things over the web. That is,each thing may be either subject or object interchangeably inorder to perform its own task. Further, according to the
REST-style web architecture, each thing may represent itself
as web resources which can be identified with unique URIs.
According to the aforementioned properties, the accesspermission control for WoT should meet the following
requirements:
1) Each thing may publish its information as one or more
web resource(s) over the web.
2) Resources for a thing can be accessed by the basic
HTTP/REST request from a subject (e.g. a HTTP client),
as outlined in Fig. 2. Since each thing may communicate
with other things in the WoT environment, Thing A canrequest access for the specific resources about the hosting
entity, Thing B. Then, Thing B needs to assure the accesspermission about the requested resources against theThing A’s access request.
3) Permission assignment can be described as a web
resource representation, then the decision to grant accessfor a given request to the specific resource should bemade with referring to this type of resource.

Fig. 2. Control flow about access request and response between two
web-enabled things in WoT

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 4, Issue 1, January 2015

B. Resource-oriented Architecture for WoT
Since the WoT makes use of the current resource-oriented
Web architecture, a web-enabled thing can exchange a
specific resource with the HTTP/REST methods about the
CRUD (Create, Read, Update, and Delete) operations. Also,
the given resource and its attribute(s) information, if any, canbe identified with unique URI, of which situation is describedin Fig. 3. The resource representation contains three resource
objects (‘a’, ‘b’, and ‘c’) and the relative contents can beaccessed with the usual HTTP/ REST CRUD methods. For
example, in order to retrieve the content information of object‘a’, which is hosted at 192.168.0.1, a HTTP request for GET
operation should specify the URI of the given object asfollowing: ‘http://192.168.0.1:8080/things/a/contents’. Like
any web page or image file which might be linked using URI,
WoT resource information could be accessible via Web.

576

might be several resource objects (Object 1 to Object N) andeach object could be dealt with different access permissions.
Particularly the request handling function is required to checkthe suitability for access permission to the corresponding
resource objects in the Step 5, just after the existence of therequested resource has been checked from the Step 4. Then,
access operation to the requested resource should be executedin the Step 6, and the relevant HTTP/REST response would
be sent back to the requester.

Fig. 4. Process flow from the arrival of a HTTP/REST request to sending therelevant HTTP/REST response back

Step
1
2
Fig. 3. Exemplary resource representations about Web-enabled things andthe associated HTTP/REST request and response

In this situation, we considered that the hosting entity might
accommodate a number of resource objects, even resourcesfrom the other thing (e.g. /things/b/contents and/things/c/contents) which do not have a HTTP server
capability. It is because many smart devices practicallyprovide constrained features as well as limited powers, sothese constrained things may exchange its resource
information through the support of much powerful entity,
called WoT Gateway or WoT Broker [16]. Thus, in this case,the way to make each resource configured with different
access permission should be provided.
C. Proposed Mechanism for WoT Access Control
Fig. 4 outlined the detailed steps (i.e., from the Step 1
through the Step 6) to process a request for accessing
resources in the WoT. Each steps aims to filter out any
incomplete or ambiguous access request, as summarized in
TABLE I. Even though it seems the advanced secure channels
as well as cryptographic algorithms improve the entire
security level for the HTTP protocol communication, we
presumed the participant things in the WoT at least supports
the standard HTTP protocol basically. For example, if therequest could not be interpretable in Step 2 due to any
malformed syntax, then the original requestor should receive
HTTP response with a ‘Bad Request (400)’ status code
according to the HTTP protocol [31]. Likewise, if therequested resource is not found at the hosting entity, ‘Not
Found (404)’ HTTP response should be generated.
We recognized that the matter of granting an access request
to the specific WoT resource is very closely associated with
the Step 5. Here, as shown in Fig 5, we considered that there

3
4
5

TABLE ISTEPS TO ASSURE WOT ACCESS CONTROL
PurposeTo filter out
To filter TCP/IP packets
Unallowable transaction
To parse HTTP/REST
requestTo check HTTP header forbasic authentication
To check whether therequested resource exists ornot
To check the assigned accesspermission for the request
operation

Invalid request, usuallywith abnormal parameters
Unverified client
Requests for the
expired/outdated orirrelevant resource
Unassigned accesspermission for therequested operation

Fig. 5. WoT request handling function handling secure access control using
the steps described in Fig 4 and TABLE I

Fig. 6 describes an exemplary case for assessing a
HTTP/REST requests using the typical access control matrix,
where a thing may have N resources (i.e. Object 1 to ObjectN) which can be requested from the other M things (i.e.
Subject 1 to Subject M). The access permissions about Object
1 should be simply assigned for all the subjects, but this model
do not provide scalability due to the overhead for maintaining
the M by N matrix. That is, if a web-enabled thing has 10
resources and other 10 things are expected as subjects, the 10
by 10 matrix should be maintained and searched through. So
the access permission about a resource object ‘Object 1’ canbe specified for every subjects (Subject 1 to Subject M). Then,the hosting entity checks the matrix if the subject of the access
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 4, Issue 1, January 2015

request has the enough right to access the given resource
object. For example, Subject 1 have all the C/R/U/D accessrights to Object 1, whereas Subject 2 has only R access rights.
Here, C means Create, and likewise R for Retrieve or Read, U
for Update, and D for Delete. Though additional classificationof subjects into several associated groups based on
Role-based Access Control (i.e. the role of ‘SuperUser’ may
be assigned to several subjects like {Subject 1, Subject 2,…}) might alleviate this burden, but each thing still needs tomaintain the lists of roles and compare the assigned accesspermissions against each HTTP request about CRUD
operation to the specific resource object.

577

assumed all the Retrieve access operation to this permission
rule resource should be allowed for interoperability.

Fig. 7. Modified access matrix focusing on the each CRUD operation
instead of the original subject-object relationship shown in Fig. 6

Fig. 6. Naive form of access control matrix specifying the Subject-Object
relationshipAs all the access operation can be categorized into the fourtypes among C, R, U, and D, then we concentrated on the each
CRUD operation with a modified access control matrix asshown in Fig. 7. When compared to the naïve form of access
control matrix, now the 4 by N columns should be searchedthrough. That is, the access permission for each specific
CRUD operation can be assigned to the given subject(s) in theform of ‘permission flag’. The permission flag value 1 of thespecific operation means the corresponding subject is allowed
to access the given resource object. For example, the Read
operation to Object 1 is allowed to all the subjects (so, thepermission flag R includes Subject 1 to Subject M), whereas
only Subject 1 is assigned to have rights to the Update
operation. We also noticed that a specific permission setting
can be applied to more than one REST requests, for instance,
the two operations, Create and Update, for the resource‘Object 1’ in Fig. 7 can be specified identically. Likewise,
more than one resource object, Object 1 and Object 2, may
have the identical access permissions for Read operation.
Moreover, regarding the third WoT access control
requirements, the set of permission flags can be represented asa web resource, a specific access permission rule could beapplied to the multiple resources synchronously, as far as
there exists a reliable web connection. Fig. 8 conceptualizes
that resource objects of Thing A are referencing the same setof permission flags (i.e., ‘Local Permissions A1’ and ‘Local
Permissions A2’). And Object 1 and Object 2 have configured
with Access Right A1. Since web resource can link other web
resource readily using URI. Meantime, Object N of Thing A
is referencing the external access permission rule, ‘Global
Permissions B1’ which is managed by other host. Here, weFig. 8. Linking the set of permission flags as a type of web resource object

D. Access Permission Abstraction for WoT
Now we generalized the access permission by replacing the
subjects with the generic conditions, which aggregate not only
the subject information like typical identifiers for therequester (i.e. IP address, hostname, and domain information),but also context and environmental information related withthe web-enabled thing itself (i.e. point of time, time schedule,
location, device hardware state, and configuration limit value).
Therefore, we can express a permission flag for each CRUD
access operation as the following:
Permission Flag C [/R/U/D] = {Condition about subjects} +{Condition about the context of the requested
resource object}
For example, as presented in Fig. 9, a web-enabled thing as
the hosting entity for the resource objects may allow the‘Create’ operation by judging from the combinative results
throughout the several conditions (e.g. C1 to C3) as
followings:
C1) if the requester is from the specific domain like‘*.cnu.ac.kr’,
C2) if the current time is between 9 AM and 3 PM, and
C3) if the CPU usage of the hosting entity is less than 90%.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 4, Issue 1, January 2015

In addition, an internal policy should be settled in order todeal with more than one condition. For instance, a policy haveto be determined considering the following two situations:--Situation 1: Permission should be granted only when
all the conditions meet.
--Situation 2: Permission should be granted if any of theconditions meets.
Here, we considered the case of Situation 1 and all theconditions have the equal level of priority or weight for
simplicity. So, in Fig. 9, only if the specified conditions are
satisfied, access permission for each CRUD operation can bepassed. For example, the permission flag C of the given
resource object includes three conditions C1 to C3, whereas
the flag R includes only C2.

Fig. 9. Exemplary representation of permission flags specifying various
combination of conditions

E. Proof of Concept for WoT Access Control
We implemented a request handling function as a proof ofconcept with the example of Fig. 9, with two Java framework
libraries, Jersey RESTful Services Framework (Available:
https://jersey.java.net/) and Grizzly Web Framework(Available: https://grizzly.java.net/). The implementation
accepts the HTTP request for accessing the example resource,‘/root/object/’, and checks the set of permissions related withthe given resource, as shown in Fig. 10.

578

We used Postman REST client utility to send HTTP
request in order to retrieve a web resource located at‘192.168.219.106:8080/root/object/’, as shown in Fig. 10 (a),and receive the responses, as shown in Fig. 10 (b) or (c)accordingly. Since the retrieve access operation from (a) do
not violate the condition about ‘TimeBetween’, the requested
resource was successfully retrieved. The response payloadpresents the set of permission flags described in the example
of Fig. 9. (c) If the request do not satisfy all the relevant
conditions regarding the given access operation (in this case,the request time was beyond the time range), the requestor
might get the HTTP response with a status code 401(‘Unauthorized’). The implementation showed that the
web-enable thing as the hosting entity for the requested
resource can control access permission as far as the relevant
conditions are being checked well, apart from centralized
authorization procedure.
F. Elaboration on Specifying Access Permission
For the more elaborated access control, we noted that abinary true-function can be adopted for processing themultiple conditions, then each condition can be classified into
two types, i.e., inclusive and exclusive. For example, we set arule about ‘access permission would be granted only if any ofinclusive conditions meets and if any of exclusive condition
does not meet at the same time’. Then, the regarding
permission flag might be expressed as the following:
Permission Flag C [/R/U/D] = {inclusive-type conditions}- {exclusive-type conditions}Fig. 11 outlined an access permission rule (<permissions>), in
XML document format, including the permission flag about‘Retrieve’ operation (<permission type=“R”>) which consistsof 3 inclusive and 3 exclusive conditions. That is, the resource
object referencing this permission rule can be accessed, only
if any of 3 inclusive conditions (i.e. conditions about Subject
id, Subject IP address, and Subject domain) can be satisfied,and at the same time if any of 3 exclusive conditions (i.e.
conditions about Thing operation duration, Thing CPU usage,and Subject domain) is not applicable at all. Likewise, otherthree operations (i.e. C/U/D) can be listed as the otherelements of this access permission rule.

Fig. 11. Exemplary structure for expressing the access permission

Fig. 10. (a) HTTP request to retrieve a web resource located at‘192.168.219.106:8080/root/object/’; (b) HTTP response with successful
retrieval of the requested resource; (c) HTTP response with a status code 401(‘Unauthorized’)

Fig. 12 describes the resource structure of a web-enabledthing using the web-based access permission mechanism,
illustrated in Fig. 8. In order to configure resource objects
with the specific access permissions, each resource object
Copyright ⓒ 2015 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 4, Issue 1, January 2015

within the web-enabled thing needs just a reference link to theappropriate permission rule resource, for example,‘/root/permission_A1’ or ‘/root/permission_A2’. Here, these
permission rules include one or more conditions following theway specified with the example of Fig. 11. Thus, theweb-enabled thing can authorize any access operation toresource object (i.e. ‘/root/object_1’) by referencing apermission rule resource (i.e. ‘/root/permission_A1’) andchecking the access conditions.
Also, the permission rule resource ‘/root/permission_A1’
might be referred by two resource objects, ‘/root/object_1’
and ‘/root/object_2’, so these objects could be accessible bychecking the identical sets of access conditions. The object‘/root/object_N’ refers to the external permission rule‘/permissions_B1’ maintained by other hosting entity.
Eventually, through the inter-linking among the web
resources, a web-enabled thing manages access permission
without any external authorization server’s support. Besides,
as far as the external web connection keeps alive, a number ofthings can be managed under the identical access permission
policy when configured to adopt a global access permission
rule resource.

Fig. 12. Exemplary resource structure maintained by a web-enabled thingaccording to the case of Fig. 8.

IV. CONCLUSIONSIn this paper, we described the requirements for the accesspermission control to the resources of web-enabled devices in
the web of things environment. Then, we presented an accesspermission control mechanism considering for the WoTcharacteristics.
The
proposedmechanism adopts
resource-oriented architecture for Web-enabled things,
utilizes the access conditions accommodating both therequester information and the context of the thing itself.
The generalized access permissions can be represented also
as a web resource, easily reachable via Web architecture. Thismechanism enables each Web-enabled thing to authorize
access requests locally, even without dependency on a
specific security domain. Moreover, it helps to apply an
identical permission rule to the multiple resources just byreferencing same access permission web resource. Thus, theproposed mechanism contributes to the flexible anddecentralized access permission control for the Web ofThings.

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RFC Editor, 1999.
Se Won OH is a senior member of engineering andresearch staff working for ETRI, and also under a PhD
course at Chungnam National University (CNU),
Korea. He received He received the BS (1999) and the
MS degree (2001) from Pohang University of Scienceand Technology (POSTECH), Korea. Since 2001, hehas been involved in several large research projects on
software platform (such as RFID Event Management
System, USN Middleware Platform, Software System Infrastructure) which
integrates legacy applications with various data resources including RFID
tags and Sensor Node. His recent research interests are Internet of Things(IoT) and Web of Things (WoT), particularly about interfacing Web-enabled
devices. He has also participated standardization activities on automatic
identification and data capture(AIDC) techniques as a member of JTC 1/SC
31, and as a secretary for JTC 1/SC 31/WG 6 (Mobile Item Identification andManagement, 2008-2014).
Hyeon Soo Kim is a professor at Chungnam National
University (CNU), Korea. He works for Department ofComputer Science and Engineering at CNU. His
current research areas include Software Engineering(Software Testing, Software Architecture, Software
Maintenance, and Software Reengineering) andApplications (Nuclear Engineering) as well as
Distributed Computing (J2EE/EJB, .NET, Web
service, SOA, Internet of Things, and Large Scale Data
Processing). He received the BS degree (1988) from Seoul National
University (SNU), Korea, and the MS (1991) and the PhD degree (1995)
from Korea Advanced Institute of Science and Technology (KAIST), Korea.
He is a member of KIISE and KIPS.

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