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Copyright ⓒ 2018 GiRI (Global IT Research Institute)Dr. Youssef SAID, Tunisie Telecom, Tunisia
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Copyright ⓒ 2018 GiRI (Global IT Research Institute)Dr. Il Kwon Cho, National Information Society Agency, Korea
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Prof. Yoonhee Kim, Sookmyung Women s University, Korea
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Prof. Dimiter G. Velev, UNWE(University of National and World Economy), Bulgaria
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Prof. Jun-Kyun Choi, KAIST (Univ.), Korea
Dr. Brownson ObaridoaObele, Hyundai Mobis Multimedia R&D Lab , Korea
Prof. Anisha Lal, VIT university, India
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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
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Dr. Chang-shin Chung, TTA(Telecommunications Technology Association), Korea

Copyright ⓒ 2018 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

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Copyright ⓒ 2018 GiRI (Global IT Research Institute)Volume. 7 Issue. 4

6J

1 A solar power generation facility state monitoring system using drone aerial photographing

1128

Younlae Lee*, Young-Geol Lee**, Hyunah Kim***, Minjae Park***R&D Center, KGI, LTD., South Korea**Department of Computer Software, Daelim University, South Korea***Division of General Studies, Kyonggi University, South Kore

2 Security Enhancement for Access Control Mechanism in Real-time Wireless Sensor Network

1135

Mangal Sain*, Amlan Jyoti Chaudhray**, Satyabrata Aich***, and Hoon Jae Lee*
*Division of Computer Information Engineering, Dongseo University, Busan, South Korea**Department of ECE, Kaziranga University, Jorhat, 785-001, India***Department of Computer Engineering, Inje University, South Korea

Copyright ⓒ 2018 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 7, Issue 4, July 20181128

A solar power generation facility
state monitoring systemusing drone aerial photographing
Younlae Lee*, Young-Geol Lee**, Hyunah Kim***, Minjae Park***R&D Center, KGI, LTD., South Korea**Department of Computer Software, Daelim University, South Korea***Division of General Studies, Kyonggi University, South Korea
candy143@daum.net, yglee@daelim.ac.kr, hyuna486@kgu.ac.kr, mjpark@daelim.ac.kr

Abstract—Recently, there has been a lot of interest and issues
related to solar power generation, and accordingly, various
studies related to the solar power facility are being carried out.
We would like to describe a solar power generation facility statemonitoring system during research related to solar facility
research. Proposed system will be based on drone aerial
photographing technology, analyzing photographing data, andmanaging facilities based on the collected data. This paper
describes the process of data collection, processing, andmanagement, and proves its contents through the proposedsystem.
Keyword— solar power generation facility state monitoring
system, drone aerial photographing

I. INTRODUCTIONI

is becoming more and more likely due to the implementationof the Renewable Energy Supply (RPS) system in Korea. The
definition of RPS (New Renewable Energy Supply) means asystem requiring operators with power generation capacity of
500 MW or more to supply a certain amount of power withrenewable energy.It has implemented RPS and is doing well.
Until December 2016, four years after RPS was implemented,
its supply volume of FIT facilities (1 GW as of late 2011) was
about 7.6 times that as much as that of current FIT facilities(solar energy 3.3 GW, non-flowing energy). The replacement
rate of renewable energy in the year 2017 range is around 4 %.
The government aims for 10 % in 2023 and it is forecast to
rise to 28 % in 2030.
Therefore, a monitoring system for IT-related facilities will
be needed. So, we would like to propose a solar power
generation facility state system.

n recent years, the development of photovoltaic business
TABLE ⅠRPS facility confirmation by year, as of the end of 2016, Source: Korea Energy Corporation

Solar

NonSolar

Total

Number of power
plantsFacility Capacity(MW)
Number of power
plantsFacility Capacity(MW)
Number of power
plantsFacility Capacity
(MW)

Y2012

Y2013

Y2014

Y2015

Y2016

Total

1,670

1,898

5,501

6,944

4,056

20,069

245

385

869

986

804

3,289

74

43

67

51

34

269

1,731

509

873

441

711

4,266

1,744

1,941

5,568

6,995

4,090

20,338

1,975

895

1,742

1,472

1,515

7,555

———————————————————————
Manuscript received March 27, 2018. This work was supported by
Software Convergence Cluster, Incheon SW Convergence R&D Support
program of Incheon Business Information Techno park[Grant ID: R17-12802].
Yoonlae Lee is with R&D Center, KGI CO. LTD., 129, Gaetbeol-ro,
Yeonsu-gu, Incheon, 21999, South Korea(first- author, email:candy143@daum.net)
Youn-Geol Lee is with department of computer software, Daelim
University, 29, Imgok-Ro, Dongan-Gu, Anyang-Si, Gyeonggi-Do, 13916,

South Korea (co-author, phone: +82-31-442-4423, fax: +82-31-442-4428, email: yglee@daelim.ac.kr)
Hyunah Kim is with department of computer science, Kyonggi
University, 154-42, Gwanggyosan-Ro, Teongtong-Gu, Suwon-Si,
Gyeonggi-Do, 16227, South Korea (co-author, phone: +82-31-249-1467, fax:
+82-31-249-9173, e-mail: hyuna486@kgu.ac.kr)
Minjae Park is with department of computer software, Daelim University,
29, Imgok-Ro, Dongan-Gu, Anyang-Si, Gyeonggi-Do, 13916, South Korea(Corresponding author, phone: +82-31-442-4434, fax: +82-31-442-4428, email: mjpark@daelim.ac.kr)

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 7, Issue 4, July 20181129

Fig. 1 Various Applications of Drones

II. RELATED WORKS
We want to use drone technology to collect basic data for
facility management. Facilities mean solar power generation
facilities and collects thermographic information about thesolar power generation facilities. Therefore, not only solar
facilities technology, but also drone technology is also
relevant.
Currently, drone technology[1] is used in a wide variety offields[6,7,8,9] like topographic analysis, facility management,delivery service, disaster relief, and it is being used in fields
that were not predicted early on. As the navigation,communication, and sensor technologies related to drones
have developed dramatically, the convergence market fordrones such as facility inspection, disaster safety, disasterprevention, and logistics transportation is growing rapidly.
The global drone market is expected to grow from $ 6.6
billion in 2013 to $ 11.4 billion in 2022, and is growing very
rapidly[2]. The Korean Geographical Survey Institute isconducting ‘a study on UAV introduction[3]’ in the field of
public surveying in order to establish a standard for using thedrone in the field of public survey, and the research will becompleted in December 2017.
In the United States, a study was made on the use of dronesin the measurement of bridges, slopes and road pavement
aging for the purpose of eliminating traffic congestion,
improving safety, and reducing budgets.
In Europe, the efficiency of the survey was maximized byusing drones in the investigation of road pavement aging
mainly in Germany and Italy.
In China, research is being carried out on the use of drones
for life structure, current status, and transportation of
emergency goods in the event of slope failure using drone.
In the field of solar power, the drone technology can bevery useful. In order to measure the overheating of the solar
module, the drones can be equipped with an infrared camera
to collect millions of pieces of photo information. This would
save labor costs significantly compared to using individual
measuring devices to inspect solar cell module information.
First Solar, the leading solar power company in the UnitedStates, is using the SkyCatch Drones service[4] at the world's

largest 290MW solar power plant.
Drones are also useful for protecting wildlife around large
solar power plants[5]. In the US, large-scale solar power
plants are obliged to maintain and manage wildlife habitats.
When drilling in the construction planning stage, wild
animals and plants in the construction area can be identified
and appropriate action taken. Based on this drone technology,
we are going to overlook thermal camera technology,
communication technology, and monitoring technology.
This
study
is
also
relatedtoremotemanagement[9,10,11,12,13] of facilities. Compared withvarious remote facility management methods, this study is
distinguished in terms of using solar facility management,
drone and thermal camera technique.
III. A SOLAR POWER GENERATION FACILITY STATEMONITORING SYSTEM USING DRONE AERIAL PHOTOGRAPHING
We propose a solar power generation facility statemonitoring system using drone aerial photographing. Figure
2 shows the overall of the proposed system.
The facilities can be monitored by the drones taking aerial
photographs, real-time or batch data transmission andprocessing. Of course, data collection and processing are
based on the IT infrastructure which consists of maps anddatabase servers, and application servers.
The system we propose consists of two main functions.
One is the thermal imaging camera image analysis function
and the other is the solar power facility state monitoring
function.
A. Thermal imaging camera image analysis function
We use a variety of information to recognize specificphenomena. In particular, we perceive phenomena based on
visual information. And we want to know about the state offacilities based on visual information. Drones generally
provide visual information for facility management. At thistime, the thermal imaging camera is mounted on the drone,
and thermal imaged photograph information is analyzed.
1) Location analysis of images
Drones thermal cameras are based on aviation cameras.
EXIF (EXchangeable Image File Format) information is
input into the image data taken for the air. In particular, theCopyright ⓒ 2018 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 7, Issue 4, July 2018

1130

Fig. 2 Overall of the proposed system which is solar power generation facility statemonitoring system using drone aerial photographingequipment equipped with GPS will store latitude, longitude
and altitude data when shooting. In order to find out thecharacteristics of a specific area, a color image analysis iscarried out after taking an image with an infrared camera, anda specific temperature point can be found based on the color
analysis. After finding a specific temperature point, map therelevant image data and corresponding point of the solar
facility site and display it on the screen. Map the position data
(latitude, longitude) to the scene image as a preliminary task.
Image formats that contain location (latitude, longitude)
data include GeoTiff information. For images without generallocation (latitude, longitude) data, you can enter the latitude
and longitude data using the map mapping system.
2) Metadata for data exchange: EXIFEXIF (Exchangeable Image File Format), which is
metadata recorded in an image file or the like, is recorded in
an image file of JPEG or TIFF format as an exchange image
file format.

The EXIF data includes the following information.
- Camera information- Camera settings- Copyright Information- Location information
TABLE Ⅱ
GPS information
Tag ID0
1
2
3
4

Variable name
GPSVersionID
GPSLatitudeRef
GPSLatitude
GPSLongitudeRef
GPSLongitude

5

GPSAltitudeRef

6

GPSAltitude

7

GPSTimeStamp8

GPSSatellites

9

GPSStatus

10

GPSMeasureMode

11

GPSDOP

12

GPSSpeedRef

13

GPSSpeed

14

GPSTrackRef

15

GPSTrack

16

GPSImgDirectionRef

17

GPSImgDirection

18

GPSMapDatum

Description
GPS version
N (north), S (south)
LatitudeE (east), W (west)
Hardness
Relationship between
altitude and sea level
Altitude
The time (hours,
minutes, seconds)
Satellite used formeasurement
The status of the GPS
receiver used in theshootingGPSmeasurement
mode
Accuracy of GPS data
Code for GPS receiver
speed
Speed of GPS receiver
Code for GPS receiver
direction
Direction of GPS
receiverSymbolforthedirection of the subject
The direction of thesubject
Informationongeopolitical location

GPSDestLatitudeRe
N (north), S (south)f
20
GPSDestLatitude
Latitude of target point
We want to use GPS information. There are various typesof GPS information as shown in the table 2. Of the EXIF GSP
19Fig. 3 EXIF information in JPG file

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 7, Issue 4, July 2018

information provided in Table 2, we use the latitude,
longitude, and altitude information ‘GPSLatitudeRef’,‘GPSLatitude’,‘GPSLongitudeRef’,‘GPSLongitude’,‘GPSAltitudeRef’, and ‘GPSAltitude’. We extract latitude,
longitude, and altitude information from images as shown
above.
3) Thermal imaging camera image analysis
And analyzes the captured images based on the positional
information and the thermal image information describedabove in a complex manner.
The data captured by the thermal imaging camera has its
own color depending on the temperature of each pixel of theentire image, so that the entire image has color. There arevarious modes as follows.
Figure 3 below shows the change information according to
the mode of the thermal imaging camera.
Among the various modes, White Hot is suitable for
specific temperature detection and Rainbow is suitable fortemperature distribution detection.
In case of ‘White Hot’ mode, it is suitable for specifictemperature detection. For ‘Rainbow’ mode, it is suitable fortemperature distribution. In other words, ‘Rainbow’ mode issuitable for ‘measuring temperature distribution’ and ‘WhiteHot’ mode is suitable for ‘measuring specific temperature
precision’.
The characteristics of the ‘measuring temperature
distribution’ are as follows:
The overall temperature distribution of the shooting area
can be distinguished by color- Quickly identify temperature changes around each shot
- Maximum temperature, minimum temperature andaverage temperature can be checked- It is possible to check temperature distribution oftemperature change by radiant heat over time
- It is possible to check temperature distribution according
to seasonal temperature change.
And, the characteristics of the ‘measuring specifictemperature precision’ are as follows:- prediction of specific temperature spots in large areas- Possible location determination at specific temperature- Data base can be analyzed by data base11314) Image analysis for specific temperature detection
The steps of image photographing and analysis are as
follows:①

Copying the original data shot by the thermal camera
to the system②

Copy the temperature distribution data and specifictemperature data separately③

Display temperature distribution image on screen④

Displays a specific temperature image on the screen,detects a specific temperature, and displays thecorresponding point

Figure 4 shows the steps of image photographing andanalysis.
5) Thermal imaging camera image analysis
The steps of thermal imaging camera image are as follows:①

The selected images stored on the SD card are copied
to a specific folder for analysis as shown Figure 5. If
we already have the same date data when we copy
images, we can create a new date folder and copy it
several times a day.

②

The original image can be called a temperature data
image and the spontaneous data image, respectively,
and the copied image is stored as an image by date as
shown Figure 6.

③

Select a temperature distribution image and check thetemperature distribution of the entire low-firing range
on the main screen. Map the latitude and longitude
coordinates stored in the respective images to thelatitude and longitude coordinates stored in the main
map image and outputs them to the corresponding
screen positions.

④

Select a specific temperature image as shown in
Figure 7 to start temperature analysis for each image,and the image with a specific temperature color isdisplayed in the same position as the temperature
distribution image.

Fig. 4 Steps of image photographing and analysis

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 7, Issue 4, July 2018

1132

Fig. 5 Photographing images copy
B. Solar power facility state monitoring function
We can define and operate a monitoring process based onthermal imaging camera image analysis function.
Figure 8 is the operating process of the solar power facility
state monitoring system. It is a process that is configured toanalyze the thermal image information collected through thedrone, and to check and review the status of facilities.

Fig. 6 Save by image

Fig. 7 Temperature distribution andspecific temperature detection

Fig. 8 Operating process definitionof the solar power facility state monitoring
systemspecific temperature detection
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 7, Issue 4, July 2018

1133

Fig. 8 Operating process of the solar power facility state monitoring system specific temperature detection)

Figure 8 shows operation which is the solar power facility
state monitoring system specific temperature detection of theprocess defined in Figure 7. According to this operating
method, we can monitor the state of the solar power facility.
First step is to import the solar power facility image map.
The image map is composed of a slice map of (256*256) sizes.
Second step is to gather information from the drones. At thistime, the thermal image data and the latitude and longitude
information in the file are extracted together. Third step is toconvert latitude and longitude information as appropriate.
Fourth step, the location file name is extracted at the center.
In fifth step, the file name and the folder name are extracted.
The sixth step is the last step, extracting the temperature ofeach pixel, repeatedly storing the pixel information in a buffer
and overlapping the thermal image information.
It also provides information on the location of a specifictemperature with anomalous signs. In this way, themonitoring system can be operated and the management ofsolar power facility state can be systematically automated.

IV. CONCLUSIONSIn this paper, we propose a solar power generation facility
state monitoring system using drone aerial photographing.
We describe the thermal camera technology and the related
analysis technology that we are dealing with in the proposedsystem, and describe the operation method to monitor thefacility management using the technology. The technology
includes techniques for processing photographic data as wellas technologies for the thermal imaging camera itself,
including methods for managing images and for analyzing
and monitoring managed images.
We believe that this proposed system is a study on facility
management and that it will play an important role in the
future of new technology energy management.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 7, Issue 4, July 20181134

ACKNOWLEDGMENT
This work was supported by Software Convergence
Cluster, Incheon SW Convergence R&D Support program of
Incheon Business Information Techno park[Grant ID: R17128-02].

REFERENCES[1]DANIEL, Kai, et al. AirShield: A system-of-systems MUAV remotesensing architecture for disaster response. In: Systems conference,
2009 3rd Annual IEEE. IEEE, 2009. p. 196-200.
[2] US Teal Group, US Teal Group Report[3] Korean Geographical Survey Institute, a study on UAV introduction[4] Skycatch, Inc., Skycatch: Drone Image Processing Platform,
https://www.skycatch.com/[5] Quater, Paolo Bellezza, et al. "Light Unmanned Aerial Vehicles(UAVs) for cooperative inspection of PV plants." IEEE Journal of
Photovoltaics 4.4 (2014): 1107-1113.
[6] RESTAS, Agoston. Drone applications for supporting disastermanagement. World Journal of Engineering and Technology, 2015,
3.03: 316.
[7] KIMA, C.; MOON, H.; LEEA, W. Data Management Framework ofDrone-Based 3d Model Reconstruction of Disaster Site. ISPRSInternational Archives of the Photogrammetry, Remote Sensing andSpatial Information Sciences, 2016, 31-33.
[8] Ok Hyun and Kim Seong-Jin, “Application Method of Remote Site
Monitoring in Public Road Construction Projects,” Journal of the
Korea Academia-Industrial cooperation Society, Vol. 14, No. 12 pp.
6550-6557, 2013
[9] Kyoon-Tai Kim, “Development of a Mountainous Area Monitoring
System based on IoT Technology,” Journal of the Korea AcademiaIndustrial cooperation Society, Vol. 18, No. 3 pp. 437-446, 2017[10] Barker, William E., et al. "Method for computer internet remotemanagement of a telecommunication network element." U.S. Patent
No. 6,363,421. 26 Mar. 2002.
[11] Hunter, Robert R., David A. Vogt, and Leslie Cheong. "Multicapability facilities monitoring and control intranet for facilities
management system." U.S. Patent No. 6,363,422. 26 Mar. 2002.
[12] Wang, Shengwei, and Junlong Xie. "Integrating Building
Management System and facilities management on the Internet."
Automation in construction 11.6 (2002): 707-715.
[13] Yun, Chang Ho, et al. "Intelligent management of remote facilities
through a ubiquitous cloud middleware." 2009 IEEE International
Conference on Cloud Computing. IEEE, 2009.
[14] Suiter, F. J., and T. M. Cortes. "Considerations for a reliable
telecommunication power system at remote facilities utilizing valve
regulated lead-acid battery management system technologies."
Telecommunications Energy Conference, 1994. INTELEC'94., 16th
International. IEEE, 1994.

Younlae Lee is the director of KGI Corp., the South
Korea Software Development Co. He received B.S.
degrees in physics from Inha University and M.S.
degrees in civil engineering from Kangwon University.
He developed BlastAZ, the blasting simulation program.
And his Research interests include 3D modeling andImaging processing, Simulation systems, IoT(Internet ofThings) and location-based infrastructure maintenancesystems.
Young-Geol Lee is a full professor of computer
software at Daelim University, South Korea. He
received B.S., M.S., and Ph.D. degrees in computerscience from Inha University in 1993, 1995, and 1999,
respectively. His research interests include Database,Spatial Database, Geographic Information System,
Spatial Warehousing, Data-centric Constraint Language
and Process-aware facility management systems.

Hyunah Kim is an adjunctive professor and a faculty
member of the collaboration technology researchlaboratory in the department of computer science at
Kyonggi University, South Korea. She received her
B.S. degree in computer science from Korea Nazarene
University in 2001. Also, she received her M.S. and
Ph.D. degrees in computer science from Kyonggi
University in 2003 and 2009, respectively. Her
research interests include workflow systems, SCORMbased e-Learning process models, BPM, BPI, ACM,
workflow-supported social networks discovery and analysis, and processaware Internet of Things.
Minjae Park is an assistant professor of computer
software at Daelim University, South Korea. He
received B.S., M.S., and Ph.D. degrees in computerscience from Kyonggi University in 2004, 2006, and2009, respectively. His research interests include
groupware, workflow systems, BPM, CSCW,
collaboration theory, process warehousing and mining,
workflow-supported social networks discovery andanalysis, process-aware information systems, data intensive workflows, andprocess-driven Internet of Things and process-aware factory automation
systems.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 7, Issue 4, July 20181135

Security Enhancement for Access Control
Mechanism in Real-time Wireless Sensor Network
Mangal Sain*, Amlan Jyoti Chaudhray**, Satyabrata Aich***, and Hoon Jae Lee*
*Division of Computer Information Engineering, Dongseo University, Busan, South Korea**Department of ECE, Kaziranga University, Jorhat, 785-001, India***Department of Computer Engineering, Inje University, South Korea
mangalsain1@gmail.com, choudhuryamlanjyoti@gmail.com, satyabrataaich@gmail.com, hjlee@dongseo.ac.kr
Corresponding author email id: mangalsain1@gmail.com

Abstract— A wireless sensor network (WSN) based real-time
application, both physical nodes (i.e., unguarded nodes) as well as
open communication channels are accessible to the adversaries.
Such channel openness and unguardedness of the WSN nodes
may lead to various attacks to the application. Therefore anaccess control mechanism is essential for such WSNs that are
deployed in the hostile environments. In this regards, recently,
two practical access control protocols (PACPs) are being
proposed for WSNs. The authors claimed that their proposed
protocols are suitable for practical implementation and are
secure against most of the known attacks. Unfortunately, PACPs
have inherent security weaknesses and difficulty in real-time
implementation. In this paper, we identify few security pitfalls.
In addition, a new node addition phase is impractical in the realworld deployment. In order to overcome the PACPs issues, we
also proposed an enhanced practical access control protocol thatprovides more security features at low computation andcommunication costs.
Keywords— Access control protocol, authentication, key
establishment, wireless sensor networks

I. INTRODUCTION
Wireless sensor networks (WSNs) are known as novel andintelligent systems, and are continuously deploying in wide
range of real-world applications (military, healthcare, smart
building, security systems, etc) [1].
———————————————————————
Manuscript received January 2, 2018. This work was supported by the
Basic Science Research Program through the National Research Foundationof Korea (NRF) funded by the Ministry of Education, Science andTechnology. (Grant number: NRF-2011-0023076). This paper is a follow-up
the invited journal to the outstanding paper of the 20th International
Conference on Advanced Communication Technology (ICACT 2018).
Mangal Sain is with the Department of Computer Engineering, Dongseo
University, South Korea. He is the corresponding author of this paper.
Amlan Jyoti Chaudhray is with Department of ECE, Kaziranga University,
Jorhat, India (e-mail: choudhuryamlanjyoti@gmail.com)
Satyabrata Aich is with the Department of Computer Engineering, Inje
University, South Korea (e-mail: satyabrataaich@gmail.com)
Hoon Jae Lee is with the Department of Computer Engineering, Dongseo
University, Busan, South Korea, South Korea (e-mail: hjlee@dongseo.ac.kr)
Mangal Sain is with the Department of Computer Engineering, Dongseo
University, South Korea. He is the corresponding author of this paper.
(Corresponding
authorphone:+8251-320-2009;e-mail:mangalsain1@gmail.com).

WSN have emerged as a field of research. WSN have longterm economic potential and capability to transform daily
lives. In addition, Wireless Sensor Networks increase many ofthe latest problems such as abstractions and optimization
problems, tracking, localization etc.
The incorporation of several types of sensors, such as
acoustic, seismic and optical, in a network platform and thestudy of the general scope of the system presents several
interesting challenges. Due to recent development in WSN
technology Wireless sensors, they are a great tool for military
applications related to admission, monitoring of outline andinformation gathering and elegant logistic support in an areathat is implemented. Some additional applications: site
detection, personal health monitoring based on sensors withsensor and motion sensor networks [2]Low-cost deployment is one of the acclaimed benefits ofsensor networks. Limited power and memory are two biggest
constraints in WSN. But with the development of in
fabrication technique these two problems can be resolved infuture. Also, due to the unattended nature of sensor nodes anddangerous sensing environments, replacing battery is not a
viable solution. Alternatively, the monitoring characteristics
of many sensor network applications require a long service
life. Therefore, providing a form of energy efficiency
monitoring service for geographical areas is a very importantresearch topic.
These sensor nodes are deployed in a wide area for
performing their intended task efficiently. Due to the novelties
of WSNs such as, large scale deployment, resource scarcity
and wireless communication nature makes them vulnerable tovarious attacks. It is possible that an adversary can introduce
the malicious nodes into the network and may disturb the
network functionality. However, to protect WSNs fromadversaries and maintain the network working continuously(life-time), security mechanisms (e.g., access control [3] [4])are highly desirable for the applications.
Zhou et al. proposed an access control protocol based on
ECC [5], which is more efficient than RSA-based public-key
cryptography schemes. The authors state that the new node(with the timestamp) could join the network at any time andsupport key exchange. However, to authenticate a sensor node,
the Zhou et al. scheme incurred extremely high computing and

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 7, Issue 4, July 20181136

communication costs. In real WSN, high consumption rates identifiers). Finally, BS installs a secret key and AS into thecan be the real problem. Thereby, based on ECC and hash nodes, which are selected of the network deployment. Morechain, Huang proposed a novel access control protocol (NACP) general example, BS randomly chooses KX and {KRi}i∈{1,2,…,x}[6] which is quite good for low power sensor nodes. He also from LKS for the node X. Then, BS computes ASX= {(HIDi,
showed that NACP can be easily implemented as a dynamic h(IDX||KRi))} i∈{1,2,…,z} for node X. Here, HIDi means the owneraccess control system because all the secrets and information of secret key KRi. Thereafter, BS installs KX and ASX into thetransmission information in existing nodes should not be node X. Now sensors are ready for the deployment.
updated once a new node has been added to the network.
In 2009, Kim and Lee proposed an enhanced novel access
2) Authentication and key establishment phase: Assume
control protocol (ENACP) which exploits the hash-chainthat two nodes (e.g., node A and node B) are neighbors andapproach and performs the node authentication and key
each node recognizes the identities of its neighboring nodes
establishment [7]. Unfortunately, Zeng et al., [8] and Shen etal., [9] demonstrated that ENACP has natural design flaws and using some beaconing technique which includes the node
vulnerable to many attacks. In 2012, Lee et al pointed out that identity in the beacons. If node A shares h(IDA||KB) with node
ENACP is susceptible to message forgery and new node B, then two nodes (A and B) start key establishment as
masquerade attacks, and proposed practical access control follows.
protocols (also known as PACPs) for WSNs [10]. PACPs i. Node A generates a random integer tA, and computes thepoint NA= tAP = (NxA, NyA) over the elliptic curve E andconsist of two sub-schemes, namely, secure PACP (secPACP)
SA= h(IDA||NxA||h(IDA||KB)). Now, it (Node A) broadcasts
and memory-efficient PACP (ePACP). Moreover, authors
IDA, NA, and SA.
claimed that PACPs are secure against many attacks and very
ii. After receiving the broadcasted message from the node A,
practical for the real WSNs.
However, in this paper we demonstrate that PACPs are notnode B checks whether h(IDB||KA) is in ASB = {(HIDi,
secure against message replay attack, Sybil attack andh(IDB||KRi))} i∈{1,2,…,z} or not. If it is not true then aborts theimpersonation attack. More importantly, we will show that thesystem. Otherwise, node B verifies h(IDA||NxA||h(IDA||KB))
new node addition is very limited (i.e., only for certain nodes)= SA with its own key KB . If SA is verified then node B
and hence, PACPs are not highly scalable. Next section willassured that NA is generated by a legal node who knows
briefly review the PACPs. In order to mitigate the issue ofthe h(IDA||KB). After that, node B generates a random
pacps we, also we also proposed an enhanced access control
integer tB and computes NB= tBP= (NxB, NyB) and SB=
protocol for real time WSN. The proposed scheme is strongh(IDB||NxB || h(IDB||KA)). And it broadcasts IDB, NB, andagainst message replay attack and Sybil attack. We also
S B.
discuss the enhanced security features of our proposediii. Upon receiving the broadcasted message from the node B,
protocol and prove that the scheme is secure against message
node A checks h(IDB||NxB||h(IDB||KA)) = SB with its own
replay attack, strong against Sybil attack and possess
key KA . If SB is verified then node A assured that NB is
important security features such as user anonymity. Similar to
generated by a legal node who knows the h(IDB||KA).
PACP our proposed algorithm exploits hybrid cryptosystem
Thereafter, node A computes SKAB= tANB = (SKxAB, SKyAB)
i.e. elliptic curve and symmetric cryptography.
and ZA= h(IDA||SKxAB||h(IDA|| KB)), and broadcasts ZA.
iv.
Node
B computes SKAB= tBNA = (SKxAB, SKyAB) and checks
The Remainder of this article is organizes as follows.
h(IDA||SKxAB||h(IDB|| KA))= ZA. If it is true, then node B
Section II consist a review of PACPS. Section III presents theapproves SKAB. Now node B computes ZB= h(IDB|| SKxAB
analysis of security pitfalls in PACPs. Section IV presents an|| h(IDA|| KB)) and broadcasts it to the node A.
Enhanced Access Control Protocol. Section V presents
security analysis. Finally, Section VI concludes our results v. Finally, node A checks h(IDB||SKxAB||h(IDA|| KB))= ZB. If
and future research.
it holds, then node A also approves SKAB.
II.
REVIEW OF PACPS[7]PACPs have two variant, namely, secPACP and ePACP.

The authentication and key establishment phase of
secPACP is shown in Fig. 1.

A. secPACP (secure PACP): It is composed of three phases:
initialization, authentication and key establishment, andnew node addition.
1) Initialization phase: This phase is performed off-line by
the base station (BS); it generates a large key space (LKS), key
identifiers, and identities (IDs) for all sensor nodes (i.e., N
sensor nodes). BS randomly chooses Q nodes for the initial
deployment (or network). Thereafter, BS randomly picks one
secret key and m keys from LKS for each node and computes
an authentication set (AS) (i.e., set of hash values, and their

3) Node addition phase: This phase is invoked when a newnode is entering into the existing network. First, BS assigned
an identity to the new node (IDQ+1) and also preloads secret
key KQ+1 and ASQ+1= {(HIDi, h(IDQ+1||KRi))} i∈{1,2,…,z}.
Thereafter, new node will perform the authentication and key
establishment phase as shown in Fig. 1, and becomes the legal
member of the network.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 7, Issue 4, July 2018
Node A
NA= tAP = (NxA, NyA)
SA= h(IDA||NxA||h(IDA||KB))
msg1: IDA, NA, SA

Node B

Verifyh(IDA||NxA||h(IDA||KB)) = SA
NB= tBP= (NxB, NyB)
SB= h(IDB||NxB||h(IDB||KA))
msg2: IDB, NB, SB
Verifyh(IDB||NxB||h(IDB||KA)) = SB
SKAB= tANB = (SKxAB, SKyAB)
ZA= h(IDA||SKxAB||h(IDA|| KB))
msg3: ZA
SKAB= tBNA = (SKxAB,SKyAB)
Verifyh(IDA||SKxAB||h(IDB||KA))= ZA
ZB=h(IDB||SKxAB||h(IDA|| KB))
msg4: ZB
Verifyh(IDB||SKxAB||h(IDA|| KB))= ZB

1137

Node A
NA= tAP = (NxA, NyA)
SA= h(IDA||NxA||h(IDA||KB))
msg1: IDA, NA, SA

Node B

Verifyh(IDA||NxA||h(IDA||KB)) = SA
NB= tBP= (NxB, NyB)
SB= h(IDB||NxB||h(IDB||KA))
msg2: IDB, NB, SB
Verifyh(IDB||NxB||h(IDB||KA)) = SB
SKAB= tANB = (SKxAB, SKyAB)
ZA= h(IDA||SKxAB||h(IDA|| KB))
msg3: ZA
SKAB= tBNA = (SKxAB,SKyAB)
Verifyh(IDA||SKxAB||h(IDB||KA))= ZA
ZB=h(IDB||SKxAB||h(IDA|| KB))
msg4: ZB
Verifyh(IDB||SKxAB||h(IDA|| KB))= ZB

Fig. 1. secPACP: Authentication and key establishment phaseFig. 2. ePACP: Authentication and key establishment phase

B. ePACP (memory-efficient PACP): It is composed of twophases, namely, initialization, and authentication and key
establishment. This subsection reviews ePACP, which is avariant of secPACP except the initialization phase.
1) Initialization phase: This phase performed offline by thebase station (BS); it generates a large key space (LKS), key
identifiers, and identities for all N sensor nodes. BS randomly
chooses Q nodes for the initial network deployment. Now it isassumed that the identities of all nodes are in a circular order(i.e., the last identity is equal to the first identity). Therefore,
each sensor node has its inner nodes and outer nodes incircular order. The number of all candidate node is Qʹ (Q ≤ Qʹ≤ LKS), we describe the inner nodes of node X as {IDYi}(X ˂Yi≤

Next section will demonstrate the security pitfalls in
PACPs.

X+⌈Qʹ/2⌉) and the other nodes are represented as the outer nodes

of node X.
Thereafter, BS randomly chooses one secret key from thelarge key space (LKS) and installs it into the each node. Then,it (BS) chooses m keys from LKS for each sensor’s inner
nodes; derives an authentication set (AS); and finally, installs
AS into its corresponding sensor node. For example, BS
randomly chooses KX and {KRi}i∈{1,2,…,x} from LKS for node X.
Here X is a node, and KRi are randomly selected secret keys fornode X’s inner nodes. For node X, BS computes ASX= {(HIDi,h(IDX||KRi))} i∈{1,2,…,z}, here HIDi means the owner of secret
key KRi. Thereafter, BS installs KX and ASX into node X.
2) Authentication and key establishment phase: Assume
that two nodes (e.g., node A and B) are neighbors and eachnode recognizes the identities of its neighboring nodes using
some beaconing technique which includes the node identity inbeacons. If sensor node B is an inner node of node A, then A
starts the pairwise key establishment with node B, otherwise,
node B starts. The authentication and key establishment phaseis same as in secPACP (refer to the secPACP authenticationand key establishment phase). However, the flow of ePACP
is depicted in Fig. 2.

III. ANALYSIS OF SECURITY PITFALLS IN PACPS
Indeed, PACPs are strong against eavesdropping, message
forgery attack, and new node masquerade attack. However, asingle loophole can become a big danger to the network, if all
possible security threats are not considered (with theirdestructive impact) while designing the protocol. In thissection we present the inherent PACPs security pitfalls, suchas, message replay attack, Sybil attack and impersonation
attack, and other practical issues.
For the comprehensive
analysis of PACPs, we have assumed that an attacker has fullcontrol over wireless channels (e.g., it can insert, drop, modify
or replay the wireless messages). Based on above
assumptions, we generalize the message replay attack in
PACPs, as follows.
1) Message replay attack: In this attack, an adversary
actively captures on-air wireless messages between two
communicating entities (e.g., node A and node B) and replays
the captured messages, later, as it is. Although, it is a very
common attack on wireless communication protocols but it(replay attack) could cause of one of the network destructive
denial-of-services attack if it would not be protected
efficiently and resultant, node’s (AA) battery power depletion.
Attack description: In PACPs, it is worth noting that, as shownin Fig.1 (secPACP) and Fig.2 (ePACP), an active adversary
easily captures the wireless messages (msg1) between thenode A and the node B (refer-Section II, authentication and
key establishment phase). In secPACP, assumed that aftersome later time adversary transmits, msg1 (IDA, NA, SA) to thenode B. Upon receiving msg1 from adversary, node B starts
computations as follows: verifies h(IDA||NxA||h (IDA ||KB )) =SA. It will be verified easily because every time node B
considers msg1 as a fresh message (because random number/
nonce is not properly verified) and node B computes: NB= tBP

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 7, Issue 4, July 2018

= (NxB, NyB) and SB= h(IDB||NxB||h(IDB||KA)) and sends msg2
(IDB, NB, SB ) to attacker. Note that, here the node B is notaware about that it has sent msg2 to an attacker or to a legal
node. Now upon receiving the msg2 from the node B, anattacker generates a fake msg3 (ZAʹ= h(IDA|| SKxABʹ || h(IDA||KB))) and sends it to the node B. Here, SKxABʹ is attacker’s
fake key. Now, the node B computes the key (SKAB = tBNA =(SKxAB,SKyAB)) and verifies the message (ZAʹ). Obviously,attacker’s fabricated fake message (i.e, ZAʹ) will not be
verified by the node B because SKxAB ≠ SKxABʹ and hence ZAʹ
will not be verified. Thus, due to the very late detection of an
attacker, secPACP is vulnerable to the message replay attack.
By imposing the message replay attack again and again, an
attacker can make sensor node battery depletion which is not
acceptable in the mission-critical WSN applications.
Likewise, ePACP is also vulnerable to the replay attack.
Authors of [11] argued that preloading the number of keys
(i.e., either pairwise or not) onto exposed devices (i.e., nottamper-proofed) strengthens the incentive for attackers tocompromise a node. In PACPs, authors exploit the pairwise
key pre-distribution scheme and suggested that each PACPs
node contains number of keys (e.g., 5,740 keys in secPACP
and 1650 keys in ePACP). Though, Kim et al claimed that
secPACP and ePACP are resilience against node captureattack and node fabrication attacks means if a node is captured
then the pairwise keys of non-captured nodes are node
reveled. However,
the high number of keys in a node
motivates to the attackers for corrupting more nodes.
Moreover, Tyler Moore demonstrated that a small colluding
node (less than 5% of the entire network) can control half’s of
its neighbors’ communication channels. Thus in PACPs, anadversary can collect the energy-exhausted sensor nodes fromthe terrain and can dig outs the all secrets from a node. Basedon above assumptions, we generalize the Sybil attack andimpersonation attack on secPACP and ePACP.
2) Sybil attack: In this attack, a malicious sensor node canpresent itself with multiple fake identities (IDs) andimpersonates other legitimate nodes as a legal node [12].
Moreover, it can manifest in a severe form leading to thefailure of basic protocols functioning, such as network
routing, network resource allocation and network functioning.
Attack description: In mission-critical applications (e.g.,
military, homeland security, etc) where sensor networks areoften deployed in hostile environments. Consider secPACP
case, where 5,750 keys suggested for an exposed sensor node.
Assumed that a motivated adversary collects some energyexhausted sensor nodes and reprogram them or makereplication of the nodes (known as clone). Thereafter
adversary deploys these malicious/clone nodes into the terrain,authenticates itself with non-compromised nodes and may
control the network, accordingly. Now onwards, we call amalicious node as a Sybil node. It is assumed that a Sybil node
can recognize the identities of its neighboring nodes using
some beaconing technique which includes the node identity inbeacons. A Sybil node illegitimately takes on multiple

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identities [12]. Moreover these identities may belongs to its
authentication set (i.e., ASX= {(HIDi, h(IDX || KRi )) i∈{1,2,…,z}) or
belong to the existing nodes identities, here, HIDi means theowner of secret key KRi. Fig.3 depicts the Sybil attack running
example.

Fig. 3. Sybil attack in secPACP scheme

For the simple generalization of the Sybil attack, we assume
the size of a large key space (LKS) is 50. As shown in Fig. 3, aSybil node presents its multiple identities to its neighboring
nodes and tries to authenticate and establish a pairwise key, asa legal node. For instance, it (Sybil node) shows own multiple
identities as follows: ID42 to the node 25, ID8 to the node 39,
ID16 to the node 45 and ID46 to the node 20. The solid (red)line represents that the node 25 has HID42, the node 39 has
HID8, and the node 45 has HID16 are corresponding to the
Sybil node. Hence, the node 25, node 39 and node 45
authenticate to the Sybil node as a legitimate node andestablish pairwise keys with the Sybil node.
The flow of Sybil attack between the Sybil node (i.e., ID42)
and the node 25 (says node B) is as follows.
A. Sybil node generates a random integer StA and computes
the point SNA= StAP = (SNxA, SNyA) over the elliptic
curve E, and computes SSA= h(SIDA||SNxA||h(SIDA||KB)).
Now Sybil node sends SIDA, SNA, and SSA to the node 25(i.e., B).
B. After receiving the message from the Sybil node, node B
checks whether h(SIDB||KA) is in ASB = {(HIDi,
h(SIDB||KRi))} i∈{1,2,…,z}. Since, the node B holds, and it
verifies h(SIDA||SNxA||h(SIDA||KB)) = SSA with its own
key KB . Here, SSA will be verified and node B assured
that SNA is generated by a legal node. Note that, here thenode B does not know whether this message (SIDA, SNA,
and SSA) is received from legitimate node or an attacker(Sybil node). After that, node B generates a random
integer tB and computes NB= tBP= (NxB, NyB) and SB=h(IDB||NxB||h(IDB||KA)). And it sends IDB, NB, and SB tothe Sybil node.
C. Upon receiving the messages from the node B, Sybil

Copyright ⓒ 2018 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 7, Issue 4, July 2018

node easily checks h(IDB||NxB||h(IDB||KA)) = SB with its
own key KA . Thereafter, Sybil node computes SSKAB =StANB = (SSKxAB, SSKyAB) and SZA= h(SIDA||SSKxAB||h(SIDA|| KB)), and sends SZA to the node B.
D. Node B computes SKAB= tBSNA = (SSKxAB,SSKyAB) andchecks h(SIDA||SSKxAB||h(IDB|| KA))= SZA. Since it will
be verified and node B computes ZB= h( IDB || SSKxAB ||h(SIDA|| KB)) and sends it to the Sybil node.
E. Now Sybil node computes h(IDB||SSKxAB||h(SIDA|| KB))
and establishes a pairwise key with the legitimate node(i.e., node B).
Similarly, Sybil node can establish a pairwise key with thenode 39, 45, and many more. The Sybil node authenticationand key establishment phase is shown in Fig. 4.
Moreover, in Fig. 3, the (red) dotted line represents that thenode 20 do not contain any HID46, and hence, cannot
authenticate to the Sybil node. The double (green) solid line
represents that a Sybil node can impersonates its neighboring
nodes. For example, it (Sybil node) sends own neighbor’s
identity (i.e., ID20) to the node 46 and impersonates as a legal
node. Since, the node 46 has HID20; it authenticates andestablishes a pairwise key (as shown in Fig. 4) with the node46.
Likewise, ePACP is also susceptible to the Sybil attack andimpersonation attack, where 1,650 keys are recommended foran exposed sensor node.
Resultant, PACPs are not secure against the Sybil attackand impersonation attack where a sole Sybil node can control
PACPs’s neighbouring nodes communication channels
without misbehaviour detections.
3) Limited scalability in secPACP (new node addition):
Recall a new node addition phase in secPACP (refer sectionII), where a new node is entering into the existing networks.
The base station (BS) assigned a new identity to the newsensor node (IDQ+1) and also preloads secret key KQ+1 andASQ+1= {(HIDi, h(IDQ+1||KRi))} i∈{1,2,…,z}. However, secPACP
allows only limited scalability (i.e., new node addition) to the
network. In secPACP network, where
N numbers ofidentities were generated offline for the N nodes and Q nodes
were selected for the initial network deployment (recall
initialization phase in secPACP, Section-II). Now, only Q+1(i.e., new node)) can easily enter into the existing network
because it may have shared secrets (i.e., KQ+1 and ASQ+1={(HIDi, h(IDQ+1||KRi))} i∈{1,2,…,z}) with the existing nodes. Note
that, here an N+1 node can never be entered into the network
since it does not contain any secret shared (i.e., KN+1 and
ASN+1= {(HIDN+i, h(IDN+1||KRi))} i∈{1,2,…,z}) with the existing N
nodes. For more simple generalization consider a simple
running example. Assumed that a BS generates offline 50
nodes (N) identities and the size of key space is 50. Then BS
randomly chooses 45 nodes (Q) for the initial deployment (ornetwork). Then only, 5 nodes (N-Q) can be easily added into
the network, because these (N-Q) nodes may have secretshared with the existing (Q) nodes. Therefore, N+1 (e.g., node51) node cannot join the network. Consequently, secPACP has

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limited scalability, which is not practical for the MAMMOTH
size distributed WSNs, where scalability is highly required.
Sybil Node

Node B

SNA= StAP = (SNxA, SNyA)SSA= h(SIDA||SNxA||h(SIDA||KB))
msg1: SIDA, SNA, SSA
Verifyh(SIDA||SNxA||h(SIDA||KB)) = SSA
NB= tBP= (NxB, NyB)
SB= h(IDB||NxB||h(IDB||KA))
msg2: IDB, NB, SB
Verifyh(IDB||NxB||h(IDB||KA)) = SB
SSKAB= StANB = (SSKxAB, SSKyAB)
SZA= h(SIDA||SSKxAB||h(SIDA|| KB))
msg3: SZASKAB= tBSNA = (SSKxAB,SSKyAB)
Verifyh(SIDA||SSKxAB||h(IDB||KA))= SZA
ZB=h(IDB||SSKxAB||h(SIDA|| KB))
msg4: ZB
Verifyh(IDB||SSKxAB||h(SIDA|| KB))= ZB
Fig. 4. Authentication and key establishment phase for Sybil attack in
secPACP

4) Node anonymity: In secPACP and ePACP schemes,
nodes IDs of all nodes are openly transmitted. This will help
adversaries to perform Sybil attack and make life much easierfor them. In any access control or user authentication scheme,
user anonymity is an security feature and the protocol designer
has to make sure that the user IDs of nodes are kept secret[10].
Other practical issues: PACPs also have other practicalissues, which are highly desirable for the real WSNs, as
follows.
 In PACPs, if node A shares h(IDA||KB) with the node B,
only then both the nodes (A and B) can start key
establishment. Otherwise, it is possible that a big part of
network may isolates from the entire network, if sharedsecrets are not found. Hence, in PACPs shared secret is
not guaranteed (i.e., 100%).

Fig. 5. The average time to establish a pairwise key [7]Copyright ⓒ 2018 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 7, Issue 4, July 2018

More importantly, in PACPs, the computation time (orcomputation cost) is very high (as depicted in Fig.5), where
the average time for establishing a pairwise key is about 9
seconds, which is expensive for the real WSNs.
IV. ENHANCED ACCESS CONTROL PROTOCOLIn this section, we propose an enhanced access control
protocol which is strong against message replay attack andSybil attack.
In the initialization phase of the proposed scheme, basestation randomly choses kX and {kYi }i∈{1,2,...,m} from LKS for
node X, and a common random number, q for all nodes.
Subsequently, BS then computes ASX = {(HIDi, h(IDX∥kYi )}i∈{1,2,...,m} where HIDi is the identity of hash value h(IDX∥kYi ).
Afterward, the base station puts kX and ASX, and q into node X.
In the Authentication and key establishment phase, twonodes (e.g., node A and node B) are neighbors and each node
recognizes the identities of its neighboring nodes using somebeaconing technique which includes the node identity in the
beacons. If node A shares h(IDA||KB) with node B, then two
nodes (A and B) start key establishment as follows.
i. Node A generates a random integer tA, and computes thepoint NA= tAP = (NxA, NyA), d=IDA⊕ q and SA=
h(IDA||KB) which is already stored in the node and sends
over the elliptic curve E.
ii. After receiving the broadcasted message from the node A,
node B computes IDA=d⊕q and checks if h(IDB||KA) is in
ASB = {(HIDi, h(IDB||KRi))} i∈{1,2,…,z} or not. If it is not true
then aborts the system. Otherwise, node B verifies
h(IDA||KB)= SA with its own key KB . If SA is verified thennode B assured that NA is generated by a legal node who
knows the h(IDA||KB). After that, node B computes e=q⊕IDB, generates a random integer tB and computes NB=
tBP= (NxB, NyB) and SB= h(IDB||KA). And it broadcasts e,
NB, and SB.
iii. Upon receiving the broadcasted message from the node B,
node A computes IDB=e⊕q and checks if checks ifh(IDA||KB) is in ASB = {(HIDi, h(IDA||KRi))} i∈{1,2,…,z} or not.
If it is not true then aborts the system. Otherwise, A
verifies if h(IDB||KA) = SB with its own key KA holds trueor not. If SB is verified then node A assured that NB isgenerated by a legal node who knows the h(IDB||KA).
Thereafter, node A computes SKAB= tANB = (SKxAB, SKyAB)
and generate current timestamp t1 and compute C1= SKAB
mod t, and ZA= h(IDA||SKxAB), and broadcasts t1, C1, ZA.
iv. Node B computes SKAB= tBNA = (SKxAB, SKyAB) and checksh(IDA||SKxAB)= ZA. If it is true, then node B approves
SKAB. Node B checks if t1’-t1 does not exceed maximum
threshold time Δt (to check message freshness).
Subsequently, only if message freshness is justified, thennode B computes SKAB= tBNA = (SKxAB, SKyAB) andgenerate current timestamp t2 and compute C2= SKAB modt, Now node B computes ZB= h(IDB|| SKxAB) andbroadcasts t2, C2, and ZB to the node A.

Node A
NA= tAP = (NxA, NyA)
d=q⊕IDASA= h(IDA||KB)

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Node Bd, NA, SA
Compute IDA=d⊕q
Verifyh(IDA||KB) = SA
NB= tBP= (NxB, NyB)
SB=h(IDB||KA)e= q⊕IDBe, NB, SB

Compute IDB= q⊕eVerifyh(IDB||KA) = SB
SKAB= tANB = (SKxAB, SKyAB)
A generates timestamp t andCompute C1=SKAB mod t
ZA= h(IDA||SKxAB)
t1,C, ZA
SKAB= tBNA = (SKxAB,SKyAB)
Compute C’= SKAB mod t andverify if C=C’
Check if t1’-t1< Δtverifyh(IDA||SKxAB)= ZA
B generates timestamp t2 andCompute C2=SKAB mod t
ZB=h(IDB||SKxAB)
t2, C2, ZB
Check if t2’-t2< ΔtVerifyh(IDB||SKxAB)= ZB
Fig. 6. Authentication and key establishment phase of enhanced access
control protocolv.

Node B computes SKAB= tBNA = (SKxAB, SKyAB) andchecks h(IDA||SKxAB)= ZA. If it is true, then node B
approves SKAB. Node B checks if t1’-t1 does notexceed maximum threshold time Δt (to checkmessage freshness). Subsequently, only if message
freshness is justified, then node B computes SKAB=
tBNA = (SKxAB, SKyAB) and generate current
timestamp t2 and compute C2= SKAB mod t, Now
node B computes ZB= h(IDB|| SKxAB) and broadcasts
t2, C2, and ZB to the node A.

vi.

Finally, node A checks h(IDB||SKxAB))= ZB. If it
holds, Node A checks if t2’-t2 does not exceed
maximum threshold time Δt (to check message
freshness). Subsequently, only if message freshness
is justified, then node A also approves SKAB.

V. SECURITY ANALYSIS
In this section we will compare between different proposed
schemes with our access control protocol. We will also
discuss the enhanced security features of our proposed
protocol and prove that the scheme is secure against message
replay attack, strong against Sybil attack and possess
important security features such as user anonymity.

Copyright ⓒ 2018 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 7, Issue 4, July 2018

TABLE I

Tpm
Thc
Th
Tc

COMPUTATION COST COMPARISONENACP[7][16]Sec
ePACP[10]PACP[10]
2Tpm
5Tpm
2Tpm
2Tpm
2Thc
4Th
2Th
5Th
4Th
-----

EPACP
2Tpm
4Th
4Tc

Table I illustrates the computational overhead comparison
between ENACP [7], Huangs [16] and PACPs [10]. We cansee ENACP need two point multiplications (2Tpm), two hashchain operations (2Thc) and four hash computations (4Th); on
other hand Huangs scheme requires five point multiplications(5Tpm) and two hash computations (2Th), and secPACP andePACP (in PACPs) requires (2Tpm + 5Thc) and (2Tpm +4Th), respectively. Proposed scheme computes a two point
multiplication operation (2Tpm), and four-way hashoperations (4Th). However the proposed is more secured thensecPACP and ePACP.
Strong against message replay attack: In this attack, an
attacker wants to perform a message replay attack using
previously broadcasted messages. In the proposed enhanced
access control p rotocol individual nodes verify message
freshness mutually (refer section IV, authentication and key
establishment phase points iii, iv, and v) and make sure that noadversaries can replay the existing messages after certain
duration of time, giving them less time to perform different
types of attacks.
Strong against Sybil attack: In this attack, a malicious
sensor poses multiple fake identities to other noncompromised nodes. Practically it is very difficult to prevent
Sybil attacks as it is a type of physical attack trying to temper
existing legitimate nodes by some means. However, ourscheme do not transmit node IDs openly in the public channel.
Hence, the individual user IDs of the nodes are not available
to the adversaries. In addition, the adversaries cannot use
session messages as these expires once loses freshness as
discussed earlier this section. Hence, even if the adversaries
capture some energy exhausted nodes, they cannot determine
node IDs and making them impossible to impersonate theother nodes.
In addition, intrusion detection techniques based on mutual
protection have been proposed by Buse et al. [14] [15] meansthat if the attacker manages to send a false identity to a legal
node, then it is practical to detect the Sybil attack using amutual protection mechanism. For this mechanism, when two
or more nodes are in the direct transmission range in which
the transmitted data sent by both nodes can be received bythem, they are said to be mutually protected.

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VI. CONCLUSIONSIn this paper, we have pointed out that PACPs are neither
secure nor practical for the real mission-critical WSN
applications. PACPs have still inherent security pitfalls; andcan give enough incentives to the attackers. We have shownthat how a sole energy-exhausted node (i.e., a Sybil node) can
easily control the big part of a mission-critical application. We
have also designed an enhanced practical access control
protocol which overcomes the previous drawbacks andprovide practical implementation platform in WSN
environment.
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[11] M. Tyler, “A Collusion Attack on Pairwise Key Predistribution Schemesfor Distributed Sensor Networks,” In the proceeding 4th IEEE
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against Sybil attack in Sensor Networks,” In the proceedings of the 8thInternational Conference on Distributed Computing and Networking,
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User anonymity: Our scheme do not transmit node IDs
openly as already mentioned in this section. Hence, node IDs
are kept secret, providing anonymity to the nodes.

Copyright ⓒ 2018 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 7, Issue 4, July 2018

1142

Mangal Sain received the M.Sc. degree incomputer application from India in 2003 andthe Ph.D. degree in computer science in 2011.
Since 2012, he has been an Assistant Professor
with the Department of Computer Engineering,Dongseo University, South Korea. His
research interest includes wireless sensor
network, cloud computing, Internet of Things,
embedded systems, and middleware. He has
authored over 50 international publications
including journals and international conferences. He is a member of
TIIS and a TPC member of more than ten international conferences.

Amlan Jyoti Chaudhary received his MS fromDongseo University in 2012 in computerscience. Since then he has been an assistantprofessor at Department of ECE, Kaziranga
University, India. His research interest include
cryptography, Network Security, Security in
Cloud computing and WSN. His publication
include paper on network security, Secure
Authentication and designing new algorithm forsecure network architecture.
Satyabrata Aich is working as a researcher inthe field of computer engineering He has overfour years of teaching, research and industry
experience in India and abroad. He has
published many research papers in journals andconferences in the realms of Supply Chain
Management and data analytics. His researchinterests are natural language processing,
Machine learning, supply chain management,
data mining.
Hoon-Jae Lee received his BS, MS, and PhD.
degrees in Electrical Engineering from
Kyungpook National University, Daegu,
South Korea, in 1985, 1987, and 1998,
respectively. He is currently a professor in
the Department of Information andCommunication Engineering at Dongseo
University. From 1987 to 1998, he was a
research associate at the Agency for Defense
Development (ADD). His current researchinterests include developing secure communication system, sidechannel attack, and ubiquitous sensor network/radio frequency
identification security.

Copyright ⓒ 2018 GiRI (Global IT Research Institute)