Journal Editorial Board Editor-in-ChiefProf. Thomas Byeongnam YOON, PhD.
Founding Editor-in-Chief
ICACT Transactions on the Advanced Communications Technology (TACT) Editors
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Copyright ⓒ 2014 GiRI (Global IT Research Institute)Dr. Youssef SAID, Tunisie Telecom, Tunisia
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Copyright ⓒ 2014 GiRI (Global IT Research Institute)Dr. Il Kwon Cho, National Information Society Agency, Korea
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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
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Dr. Somkiat Kitjongthawonkul, Australian Catholic University, St Patrick's Campus, Australia
Dr. Dae Won Kim, ETRI, Korea
Dr. Ho-Jin CHOI, KAIST(Univ), Korea
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Dr. Myoung-Jin Kim, Soongsil University, Korea
Dr. Gyu Myoung Lee, Institut Mines-Telecom, Telecom SudParis, France
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Prof. Yoonhee Kim, Sookmyung Women s University, Korea
Prof. Li-Der Chou, National Central University, Taiwan
Prof. Young Woong Ko, Hallym University, Korea
Prof. Dimiter G. Velev, UNWE(University of National and World Economy), Bulgaria
Dr. Tadasuke Minagawa, Meiji University, Japan
Prof. Jun-Kyun Choi, KAIST (Univ.), Korea
Dr. Brownson ObaridoaObele, Hyundai Mobis Multimedia R&D Lab , Korea
Prof. Anisha Lal, VIT university, India
Dr. kyeong kang, University of technology sydney, faculty of engineering and IT , Australia
Prof. Chwen-Yea Lin, Tatung Institute of Commerce and Technology, Taiwan
Dr. Ting Peng, Chang'an University, China
Prof. ChaeSoo Kim, Donga University in Korea, Korea
Prof. kirankumar M. joshi, m.s.uni.of baroda, India
Dr. Chin-Feng Lin, National Taiwan Ocean University, Taiwan
Dr. Chang-shin Chung, TTA(Telecommunications Technology Association), Korea

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

Copyright ⓒ 2014 GiRI (Global IT Research Institute)

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Copyright ⓒ 2014 GiRI (Global IT Research Institute)Journal Procedure
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Copyright ⓒ 2014 GiRI (Global IT Research Institute)Volume. 3 Issue. 4

1

Optimizing a Cooperative Relay Network Using Advanced Power Allocation and Receiver Diversity Technique

6J

467

Muhammad H.D. Khan*, Muhammad D. Khan**, Mohammed S. Elmusrati** Communication and System Engineering Group, University of Vaasa, Finland. **National University of Science and Technology, Islamabad,
Pakistan

2

Enhancing the Implementation of Cloud-Based Open Learning with E-Learning Personalization

472

Nungki Selviandro1,2, Mira Suryani2, Zainal A. Hasibuan2
1

3Faculty of Informatics Telkom University, Indonesia, 2 Faculty of Computer Science University of Indonesia, Indonesia

An Efficient and Scalable Key Management Mechanism for Wireless Sensor Networks

480

Walid Abdallah*, Noureddine Boudriga*, Daehee Kim**, and Sunshin An**(*)Communication Networks and Security research Lab, University of Carthage, Tunisia; (**) Computer Network research Lab, Department ofElectronics Engineering, Korea University, Seoul, Korea

4

An Efficient LSDM Lighting Control Logic Design for a Lighting Control System494

Sung-IL Hong, Chi-Ho Lin
Schools of Computer, Semyung University, 65- Semyung-ro, Jecheon, Chungbuk, Korea

5

A Study on the Performance Evaluation of Container Tracking Device based on M2M
Eun Kyu Lee*, Hyung Rim Choi*, Jae Joong Kim*, Chae Soo Kim*
*Intelligent Container R&D Center of Dong-A university, 37 Nakdong-Daero 550 beon-gil Saha-gu, Busan, Korea

500

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 4, July 2014

467

Optimizing a Cooperative Relay Network Using
Advanced Power Allocation and ReceiverDiversity Technique
Muhammad H.D. Khan*, Muhammad D. Khan**, Mohammed S. Elmusrati** Communication and System Engineering Group, University of Vaasa, Finland.
**National University of Science and Technology, Islamabad, Pakistan
hazandanish@yahoo.com, danial356khan@gmail.com, moel@uwasa.fi

Abstract— Cooperative relaying is a relatively new technique inwireless communication systems that make use of all the nodes
present in a wireless sensor network by dynamically sharing theirradio resources in a distributed manner. All the nodes present in
the periphery of the two communicating nodes will act as relays
and forward the information until it reaches the recipient node. By
doing this, it achieves a significant diversity gain, which in turnincreases the robustness of the communication system. The
diversity gain offered by cooperative relay transmission can only
be exploited fully if a receiver diversity technique is being
employed at the recipient node. Moreover, theses cooperative relay
networks inherit the power limitation of a traditional wirelesssensor network. Therefore, in this paper an effort has been madeto optimize a cooperative relay system by addressing these twoissues. First, node powers expressions will be derived for a 3-nodeconfiguration using fixed gain amplify and forward protocol over aRayleigh Fading Channel. Second, performance of a 3-nodeconfiguration has been further analyzed by using different receiver
diversity techniques at the destination node. Simulation results arepresented to validate the performance gains when advanced power
allocation and receiver diversity is employed in a cooperative relay
network.
Keyword— Cooperative relay network, Maximal ratio
combining, Moment generating function, Diversity techniques,
Rayleigh fading channel, Fixed gain amplify and forward.

I. INTRODUCTIONO

ver the last few decades, many new techniques have beendeveloped to facilitate the process of wirelesscommunication. These techniques have enhanced theperformance of a wireless system in terms of throughput,———————————————————————
Manuscript received May 15, 2014. This work was supported in part by theCommunication and System Engineering Group at University of Vaasa, 65200
Vaasa, Finland.
Muhammad H. D. Khan is with the University of Vaasa, 65200 Vaasa,
Finland. (phone: +92-346-5280626; e-mail: hazandanish@yahoo.com).
Muhammad D. Khan is with the National University of Science andTechnology, 44000 Islamabad, Pakistan. (phone: +92-333-5486816; e-mail:danial356khan@gmail.com).
Mohammed S. Elmusrati is with the University of Vaasa, 65200 Vaasa,
Finland. (phone: +358-504-003763; e-mail: moel@uwasa.fi).

coverage, capacity, reliability, etc. One such technique is ofspatial diversity, which mitigates the effect of multi-path fadingand provides diversity gains.
The technique of relaying in wireless communicationemulates a multiple input multiple output (MIMO) system, as it
is not feasible to equip a small node with multiple antennas.
Cooperative relay transmission achieves spatial diversity,
higher rates and increases robustness by using the antennas of
the neighboring cooperating nodes [1] [2] and [3]. The source
and the cooperating nodes send the same information to thedestination node over independently fading paths, thus making
a virtual antenna array [4]. At the destination node a diversity
gain is achieved as it receives multiples copies of the same
information [5]. Among the trusted and proven cooperative
relaying techniques, amplify and forward protocol also known
as non-regenerative relaying protocol stands out because of its
simple execution and low computational cost. The simplicity
comes from the fact, as the nodes running this protocol simply
amplify the received signal and then forwards it to thedestination node without any complex processing [6]. On thecontrary, if each wireless node is running complex decoding
and encoding for each transmission hop the performance of awireless system is noticeably hampered. Therefore analysis of
the cooperative relay system has been performed employing
amplify and forward relaying protocol.
The process of optimizing the cooperative relay system has
been divided into two tasks. The first task is to develop an
advanced power allocation algorithm as the cooperative
communication system inherits the power limitation of a
traditional WSN. The power allocation algorithm is required toeffectuate the transmission powers of the corresponding nodes.
The prime objective is to allocate optimum transmission power
to the source and the cooperating relay nodes while maximizing
the quality of service at the receiver. The second task is toreplace the traditional receiver diversity technique likemaximum ratio combining with a more performance oriented
technique.
In [7], the power allocation algorithm has been investigatedfor a cooperative relay network but regenerative relaying
protocol has been used with a rician-fading channel.
Copyright ⓒ 2014 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 4, July 2014

In [8], an optimal power allocation OPA technique has been
proposed for a cooperative relay network with different network
configurations but maximum ratio combining has been
employed at the receiver node. In this paper, the powers
allocated to the involved nodes have been derived using the
moment generation function (MGF) approach and optimizationis achieved using the Lagrangian method [9]. Moreover, thesub-optimal receiver diversity technique (MRC) has been
replaced with, first equal ratio combining (ERC) and then withenhanced signal-to-noise ratio combining (SNRC).
The remainder of this paper is organized as follows. Section
II presents the system model and simulation parameters. In
Sections III, the power allocation expressions for the involved
nodes have been derived for the 3-node relay network
configuration over a Rayleigh channel. In Sections IV, thediversity combining expressions for the receiver nodes havebeen derived for the corresponding network configuration. The
efficiency achieved by the application of the above two
proposed techniques is presented in form of performancegraphs, in Section V. Section VI, concludes the paper.
II. SYSTEM MODEL
A. Cooperative Relay Network
The network configuration is shown below in Figure 1. The
network comprises of three nodes, namely a source node (S),destination node (D) and a relay node (R). Let GSD, GSR, and GRD
represent the channel gains for source to destination, source torelay and relay to destination links respectively. The
communication channel between the nodes is assumed to be aRayleigh fading channel and is normalized so that the fading
coefficient matrix is complex Gaussian. At all times, each andevery communicating node in the network will obey the rules ofhalf duplex transmission. Fixed gain AF protocol has been used
as a relaying technique. In the fixed gain AF relaying, theamplifier gain is based on the perfect CSI of the communication
link rather than instantaneous knowledge of CSI. In thesimulations the system performance has been measured for theend-to-end transmission. Therefore power constraints areapplied for the complete transmission cycle instead ofintermediate hops.
GSR

R	
  

GRD

468

respectively. The signal received at the relay and destination
will be expressed as YR and YD respectively. The expressions are
given below for the two received signals

YD [n] =  𝑃!        𝐺!" . X[n] + nSD[n](1)

YR [n] =  𝑃!        𝐺!" . X[n] + nSR[n](2)After that, the relay node amplifies the signal received YR andthen transmits it to the destination node with the amplifier gain
βR. Now, the transmission power of the relay node, PR. GRD isthe channel gain for relay to destination link and the noise at thedestination node, nRD. The modified signal received by thedestination Y’R, sent from the relay node is given below:

Y’R [n] = βR . YR[n] .   𝐺!" + nRD[n]                           (3)  Here, the fixed amplifier gain βR is used to accurately tweak thetransmission power at the relay to compensate the variations in
the relay to destination communication link. The gain isdependent on the channel between the source and relay node[10]. The amplifier gain of the relaying node can provide amaximum improvement, which is defined by the expression
below:βR =!!
  !! |!!"   |!   !  !!   

(4)

As, the relaying node running AF protocol may also amplify the
induced noise so in the next phase receiver diversity combining
technique will be used at the receiver node to mitigate thiseffect.
B. Reception Phase
In the reception phase, the overall performance of thecommunication system is increased by effectively combing themultiple copies of the transmitted message at the receiver node.
Receiver diversity combining particularly targets small scalefading. Therefore, the simulation has been performed using flat
Rayleigh fading channel as it is the easiest one to implement
and is manageable. Three different signal combining techniques
have been employed in the network. With each technique, theobjective is to calculate the weights, which compensate for thenegative effects of fading. These combining techniques aredifferent in the way they calculate their weight vectors.
1) Maximum Ratio Combining

S	
  

GSD

D	
  

Figure 1. A 3-Node Network Configuration

III. POWER ALLOCATION & DIVERSITY COMBINING
A. Transmission Phase
In the transmission phase, the source node S broadcasts data X
with transmission power PS to both relay and destination nodes,
as shown above in figure 1. The channel gains for the source torelay and from source to destination links are GSR and GSD
respectively. The additive white Gaussian noise (AWGN),
present at the relay node nSR and destination nodes nSD -In maximal ratio combining, the receiver node will perform
maximum ratio combining technique on the two received
signals. So, the output of the MRC system at the receiver node
will be:YMRC [n] = α0(   𝑃!     .𝐺!" . X[n]+ nSD[n]) +α1(βR . YR[n] .   𝐺!" + nRD[n])(5)

Substituting, equation (2) into (5):YMRC [n] = α0(   𝑃!     .𝐺!" . X[n]+ nSD[n]) +α1(βR. (βR . YR[n] .   𝐺!" + nRD[n]) .  𝐺!" + nRD[n])(6)Copyright ⓒ 2014 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 4, July 2014

In the above expressions, α0 and α1 are the weights of the MRC
for the communications links between source-relay and between
relay-destination. These weights compensate for the effects ofchannel impairments and are therefore calculated using theexpressions given below:!!   !!" ∗

α0 =
α 1=

(7)

!!

!!     .    !!   .  !!" ∗   .    !!" ∗   
!!

ΓT =

!!

!
!!"
.!!

+

!!

(

!

!!"
!

𝑌! n                                                     

!!"
!!"

!"

!!

!
!!"
.!!

!!

! ! .!

+ !" !
!!

!

𝑁!! = ! 𝑃!! 𝛾 𝑒 !" 𝑑𝛾

Substituting equation (10) & (11) into (12):

(14)

!

𝑁!!

!"#ө!

𝑁!!

!"#ө!

!!"#$

= 1+

!

𝛾!

!!"! ө

!!

(16)

!!

!!

= 1+

(15)

(17)

!"#! ө

Since (𝛾! >> 1) equation (17) becomes:𝑁!!

!!

≅    𝑁!!

!"#ө!

  

𝑃! =   

< 0.1  

(10)

(11)

!
!"#ө!

(18)

  !! .  ! ! !"    .  ! ! !"   !

    !! .  ! ! !"    .  ! ! !"   Minimize!! .!! .  ! ! !"    .  ! ! !"   

+   

!!!

!

  

  

!! .!! .  ! ! !"    .  ! ! !"   

(19)
  

(20)

!

!

!
!!! 𝑁!!

𝑃! =       !!
!

!
!"#! ө

𝑑𝜃

(21)

Subject to 𝑃! + 𝑃! ≤ 𝑃!

Since, the most common method for solving a constrained
optimization problem is Lagrange method. Therefore, the above
constrained optimization problem will be translated into a
Lagrange cost function. The Lagrange cost function comprisesof an objective function, constrained function and a Lagrange
multiplier as expressed below:

J  = 𝑃! +   𝜆  (𝑃! + 𝑃! − 𝑃! )(22)

Substituting equation (21) into (22):
!

!

(12)

!!!

!!

  

+   

Now, as the bit error probability has been formulated for thisconfiguration, the prime objective is to minimize this bit error
probability while maintaining the total power constraint [12].
The optimization problem can be expressed as:!

J  =    !!

The total SNR of the system ΓT will be:ΓT = Γ0 + Γ1𝑑𝜃!"#! ө

Substituting the values of γ0 and γ1 in equation (18):As, the transmission with relay is a multi-hop scenario, the SNR
can be calculated by considering it as cascaded single hoptransmissions. Therefore, the SNR Γ1 will be the summation of
all individual links.
Γ1 =

!!"#$

The moment generating function approach computes the bit
error probabilities by defining the error probability as an
exponential function of γ.

(9)

The SNR for the source-destination link and for thesource-relay-destination links are denoted by Γ0 and Γ1
respectively.
!
!!"
.!!

!
!!! 𝑁!!

𝑃! =    𝛾! . 𝛾! !!

C. Optimization Phase
In this phase, the network configuration will be translated into a
convex optimization problem, and the next step will be to findthe solution of this optimization problem for calculating the
optimal transmission powers for the source and all involved
cooperating relay nodes.

Γ0 =

)!

!

𝑃! =    𝑁!!

Where,

(13)

!!

!!!

𝑃! =       ! !

!

𝑌! n                                                      !" > 10    !"𝑌!"#$% = 𝑌! n + 𝑌! n               0.1 < ! > 10

! ! .!

   +    !" !

Here, the bit error probability has been calculated using
Moment Generating Function (MGF) approach for QPSK
modulation scheme [11]. As, it is known to be an effective tool
for performance analysis of any modulation scheme in a fading
scenario.

(8)2) Advanced SNR Combining
In a traditional signal to noise ratio (SNR) combining
technique, the weights are assigned depending on the
instantaneous SNR of the communication link, which was used
to transmit the signal. The signals coming from thecommunication link having high SNR will be assigned higher
weights and vice versa. In advanced SNR combining instead oftaking the instantaneous SNR an estimated factor of SNR is
calculated making it a less complex. In this technique, only thatsignal will be chosen whose SNR is greater than SNR of any
other signal by the estimated factor.

!
!!"
.!!

469

!
!!! 𝑁!!

J  ⁼    𝑁!!   

!
!"#! ө
!

𝑑𝜃 +   𝜆  (𝑃! + 𝑃! − 𝑃! )    !!   !   ! ! !"    ! ! !"   

+

!

  

!! !!   ! ! !"    ! ! !"                                                𝜆(𝑃! + 𝑃! − 𝑃! )(23)  +
(24)

Copyright ⓒ 2014 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 4, July 2014

The next step is to take the partial derivatives of the Lagrange
cost function J (P, 𝜆), with respect to Ps, PR and 𝜆. After thatthese equations will be equated to zero:
!!
!!!

= 𝑁!!

  

!!!! .  ! ! !"    .  ! ! !"     (!!   ! .  ! ! !"    .  ! ! !"    )

  

! −

!! .  ! ! !"    .  ! ! !"     (!! .!! .  ! ! !"    .  ! ! !"    )!

+                                                                                                    𝜆 = 0                                !!
!!!

⁼ 𝑁!! −

(25)

  !! .  ! ! !"    .  ! ! !"     (!! .!! .  ! ! !"    .  ! ! !"    )!

!!
!"

+𝜆=0

⁼𝑃! +    𝑃! − 𝑃! = 0

(26)
(27)

The above expression will be used to calculate the transmission
power values of source and all involved relay nodes.
𝑃!   =!!

!" !   !

𝑃!   =!

𝑃!                                                                         (28)!!    !"!

𝑃!                                                                       (29)

Where, in the above equation (28) and equation (29):A=(𝜎 ! !" + 8𝜎 ! !" )  
B=(3𝜎 !!" +    𝐴)  IV. SIMULATION RESULTS
Figure 2 compares the performance of OPA with that of EPA
in terms of ABER for a cooperative network having a single
relay node. The results indicate that for any given value of
Eb/No the performance of the cooperative system employing
OPA is better than the system having EPA as its power
allocation technique, also proposed by [13].

470

Second, when the relay node is closer to the destination node.
Finally, for the scenario when the relay node is closer to source
node. It can be seen clearly that the algorithm allocates different
power values based on the link quality between the two
communicating nodes at a given time.
TABLE I. OPTIMAL POWER VALUES (3-NODE CONFIGURATION)
Link Quality

OPA

EPA

  𝝈𝟐 𝑺𝑫 ; 𝝈𝟐 𝑺𝑹 ; 𝝈𝟐 𝑹𝑫

Source
Node

Relay
Node

Source
Node

Relay
Node

(1, 1, 1)

0.66670.33330.5

0.5(1, 1, 10)

0.81870.18130.5

0.5(1, 10, 1)

0.54720.45280.5

0.5

In figure 3, performance comparison of the above mentioned
receiver diversity combining techniques is presented. The
performance curves show that the traditional diversity
technique, i.e, MRC is lacking in performance for each valuesof SNR. The performance gap between the two receiver
diversity schemes increases with the increase in value of SNR,
as shown by the lower curve. Along with the visible
performance increase, ASNRC also does not require theknowledge of channel state information at the receiver node.
This in turn reduces the overall complexity of thecommunicating node.

Figure 3. Performance Comparison of MRC and ASNRC

Figure 2. Optimal Power Allocation versus Equal Power Allocation

Table I presents the transmission powers values of the nodes in
a 3-node network. These values are calculated using theexpressions derived earlier while satisfying the total power
constraint of “1”. The power values have been calculated forthree different scenarios. First, when the relay node is place inbetween source and destination node.

Figure 4, compares the performance of the 3-nodecooperative relay system having two different power allocation
algorithms and receiver diversity techniques. The first
configuration is employing EPA, as its power allocation
technique while MRC technique is being used at the receivernode. In the second configuration both of the proposed optimal
power allocation and receiver diversity technique are being
used. The performance curve of the second configuration
outperforms that of the first one for every value of SNR. The
performance gaps increases with the increase in the values of
SNR.
Copyright ⓒ 2014 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 4, July 2014

471

[7]

Figure 4. Performance Comparison of Traditional Vs. Proposed Techniques

V. CONCLUSION
In this paper, we have investigated a cooperative relay network
employing advanced power allocation and receiver diversity
techniques. First, the expressions for different phases in acooperative communications were derived. After that theexpression for the optimal power allocation and the proposed
receiver diversity techniques were derived. The results aboveshow clearly that the system performance is enhanced
significantly with the use of these techniques. OPA is an ideal
candidate for a cooperative network having un-balanced
communication links with nodes placed asymmetrically. Whereas, EPA performs only well if the nodes are placed
symmetrically. It was also shown that if only a rough estimate
about the channel quality is available then more advanced
techniques like ASNRC could be employed to increase theperformance of the system.
ACKNOWLEDGMENT
The authors would like to thank Vaasa University Foundation
for the financial support provided by them and National
University of Science and Technology.
REFERENCES
[1]
[2]
[3]
[4]
[5]

[6]

K. J. Ray Liu and A. K. Sadek, “Cooperative Communications andNetworking,” Cambridge University Press, 2009.
I.
E.
Telatar,“Capacityof
multiple-antenna
Gaussianchannels,”Europ.Trans. Telecommun., vol. 10, p. 585, Nov. 1999.
A. Sendonaris, E. Erkip, and B. Aazhang, “User cooperation diversityPart
I: System description,” IEEE Trans. Commun., vol. 51, no. 11, pp.1927–
1938, Nov. 2003
J. N. Laneman, G. W. Wornell. “Distributed space-time coded protocols
for exploiting cooperative diversity in wireless networks [J],”. IEEE
Trans. on Information Theory, 2003, 49(10).
Youngpil Song; Hyundong Shin; Hong, Een-Kee, "MIMO cooperative
diversitywithscalar-gain
amplify-and-forward
relaying,"
Communications, IEEE Transactions on , vol.57, no.7, pp.1932,1938, July2009
J. N. Laneman, D. N. C. Tse, and G. W. Wornell, “Cooperative diversity
in wireless networks: Efﬁcient protocols and outage behavior,” IEEE
Trans. Inf. Theory, vol. 50, no. 12, pp. 3062–3080, Dec. 2004.

Mulugeta K. Fikadu, Mohammed Elmusrati, and Reino Virrankoski,“Power Allocation in Multi-node Cooperative Network in Rician Fading
Channels,” IEEE 8th International Conference on Wireless and Mobile
Computing, Networking and Communications (WiMob) 2012: 496-501.
[8] Khan, M.H.D.; Elmusrati, M.S.; Virrankoski, R., "Optimal power
allocation in multi-hop cooperative network using non-regenerative
relaying protocol," Advanced Communication Technology (ICACT), 2014
16th International Conference on , vol., no., pp.1188,1193, 16-19 Feb.
2014[9] S. Boyd and L. Vandenberghe, Convex Optimization. Cambridge
University Press, 2009.
[10] Diomidis S. M, Zoran H., George K. K. and Robert S, “PAPR of
Variable-Gain and Fixed-Gain Amplify and Forward Relaying,”. 9thInternational Conference on Systems, Communications and Coding 2013:1-5.
[11] Ramesh, A.; Chockalingam, A.; Milstein, L.B., "Performance analysis of
TCM with generalized selection combining on Rayleigh fading channels,"
Global Telecommunications Conference, 2002. GLOBECOM '02. IEEE ,vol.2, no., pp.1403,1407 vol.2, 17-21 Nov. 2002[12] Rasouli, H.; Anpalagan, A., "SNR-based vs. BER-based power allocation
for an amplify-and-forward single-relay wireless system with MRC at
destination," Communications (QBSC), 2010 25th Biennial Symposium on, vol., no., pp.429,432, 12-14 May 2010[13] Bin Shen; Rumin Yang; Kyungsup Kwak, "Optimal power allocation
schemes for amplify-and-forward relay networks with different levels ofchannel knowledge," Communications and Information Technologies(ISCIT), 2010 International Symposium on , vol., no., pp.510,513, 26-29
Oct. 2010

VI. AUTHOR (S)Muhammad H. D. Khan (S’13) was born in Rawalpindi,
Pakistan, in 1986. He received the B.E. degree in electricalengineering from the COMSATS Institute of Information
Technology, Islamabad, Pakistan, in 2010, and the M.Sc.
degree in technology from University of Vaasa, Vaasa,
Finland, in 2013.In 2010, he joined the Department ofAvionics Engineering, Air University, Pakistan, as a
Research Assistant, and in 2011 became a Research
Associate. His current research interests include wirelesscommunication systems, sensor networks and resource allocation management.
Muhammad D. Khan (S’14) was born in Islamabad,
Pakistan, in 1992. He received the B.E. degree from the
National University of Science and Technology (NUST),
Islamabad, Pakistan, in 2013. In 2013, he joined theDepartment of Electrical Engineering, NUST, Pakistan, asa Researcher. His current research interests include
Communication systems and Signal Processing.
Mohammed Elmusrati (S’00-M’04-SM’12) received the
B.Sc. (with honors) and M.Sc. (with high honors) degrees
in telecommunication engineering from the Electrical andElectronic Engineering Department, Benghazi University,
Libya, in 1991 and 1995, respectively, and the Licentiateof Science in technology (with distinction) and the Doctorof Science in Technology degrees in control engineeringfrom Aalto University - Finland, in 2002 and 2004,
respectively. Currently, Elmusrati is full professor andhead of communications and systems engineering group at University of Vaasa- Finland. His main research interests include Radio resource management inwireless communication, wireless networked control, game theory, and smart
grids.

Copyright ⓒ 2014 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 4, July 2014

472

Enhancing the Implementation of Cloud-Based
Open Learning with E-Learning Personalization
Nungki Selviandro1,2, Mira Suryani2, Zainal A. Hasibuan2
12Faculty of Informatics Telkom University, Indonesia
Faculty of Computer Science University of Indonesia, Indonesia

nselviandro@telkomuniversity.ac.id, mira.suryani@ui.ac.id, zhasibua@cs.ui.ac.id

Abstract — Indonesia is a developing country that began toutilize information technology in education. A form of its
implementation is the use of e-learning. However, in practicethere are still some obstacles, such as learning resources are
not evenly distributed, limited access to services provided,
qualified educators resources are concentrated in specificareas. This led to the emergence of disparities educational
process, and technology gap due to differences in ICT
infrastructure owned by any educational institution.
Therefore this study proposes architecture of cloud-based
open learning to solve these problems. The term open learning
is used in order to encouraging the development of the concept
of Indonesia Open Educational Resources (IOER) and as wellas the adoption of concept of cloud computing. There areseveral phase that we conducted in this research such as
analysis, design, implementation, testing, and evaluation phase.
The design of the proposed architecture consists of six layers:(1) Infrastructure, (2) Platform, (3) Application, (4) Service, (5)Access, (6) User. As a result of the implementation from thisarchitecture is a prototype of Indonesia - Virtual Open
Learning System (iVOLS).
In experiment, personalization e-learning runs as a service
that need large storage and other shared facilities to conduct
the program so the system can delivered different learningmaterials to different learners. The e-learning personalizationin cloud environment classified successful if the learners got
the best performance on learning and it shown by theirevaluation score. Based on the test results and evaluation
showed that the availability on Cloud-Based Open Learning
further meet user needs. This is indicated by the presence of asimple infrastructure services, application services with just
one stage and the availability of a wider range of data and theresource sharing. In accessibility, Cloud-Based Open Learning
provides easy access to the user. By economically, the result of
evaluation showed that Cloud-Based Open Learning has aninvestment of 35.61% efficiency, increase Return On
Investment (ROI) of 60.95% and Net Present Value (NPV) of
81.97% from the user's perspective. While from the provider’s
perspective, Cloud-Based Open Learning has an investment of
200% efficiency, increase Return On Investment (RoI) of
220.4% and Net Present Value (NPV) of 109.55%.
———————————————————————
Manuscript received May 19th , 2014. This work was supported byFaculty of Computer Science University of Indonesia.
N. Selviandro is with the Faculty of Computer Science University ofIndonesia. (corresponding author to provide phone: +62856 9720 4002;
e-mail: selviandro@yahoo.co.uk).
M. Suryani is with the Faculty of Computer Science University ofIndonesia. (e-mail: mira.suryani@ui.ac.id).
Z. A. Hasibuan is with the Faculty of Computer Science University ofIndonesia. (e-mail: zhasibua@cs.ui.ac.id).

Keyword — Cloud Computing, E-Learning, Indonesia Open
Educational Resources, Personalization.

I. INTRODUCTIONE-LEARNING provides many benefits such as
flexibility, diversity, measurement, and others [1], eventhough its implementation still exist many difficulties. The
main problem experienced when to start applying e-learningis the high initial cost or in other words is the economic
factor [2]. It is becoming a major focus for the institutions
that will implement e-learning. The initial cost consists ofthree main problems: (1) Infrastructure; (2) Human
Resources; (3) Maintenance. Another problem mightoccured when implementing e-learning is access to thelearning material. This problem experienced in Indonesia asa country with thousands of islands.
Along with the development of the IT world, cloudcomputing is gradually become the new paradigm ofinnovation in the IT world, cloud computing is a computing
services that can be used through the Internet in accordancewith the needs of users with little interaction between
service providers and users. Cloud computing technology as
well described as a computing resource that provides ahighly scalable as external services through the Internet.
Therefore, cloud computing can be considered as analternative to minimize the cost of infrastructure and humanresources for development and maintenance process of
e-learning systems [3].
In this paper the author will discussed previous cloud
learning architecture and the basic concept of open
educational resources. The proposed open learning
architecture also will be described in Chapter 4. Further
more in this paper also will discuss the approach of theimplementation, experiment in personalization learning, andthe evaluation. For final chapter author will described theconclusions and discussed the future works of this study.

II. CLOUD COMPUTING
Cloud Computing is a new paradigm to organize andmanage ICT resources. There are various definitions ofcloud computing, one of which is the definition according to
The National Institute of Standards and Technology (NIST)
which defines cloud computing as “model for enabling
convenient, on-demand network access to a shared pool ofconfigurable computing resources that can be rapidly

Copyright ⓒ 2014 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 4, July 2014provisioned and released with minimal management effort
or service provider interaction” [22]. Generally speaking,the cloud computing service model consists of three layers[5], among others: (1) Software as a Service (SaaS); (2)Platform as a service (PaaS); (3) Infrastructure as a service(IaaS) [6].
In practice, cloud computing has four implementation
models where each model has certain characteristics [7],among others: (1) Private, the model is aimed at anorganization where cloud operations are managed by a third
party or the organization itself; (2) Public, service on thismodel is intended for the general public or the industry inwhich the various services provided by the cloud computing
service provider organization (3) Community, this model ismanaged by several organizations that form a community ofpractice in which the operations are managed by thecommunity with the division of tasks particular; (4) Hybrid,
this model is a combination of various models existing
cloud distribution. Typically, this is done with a
combination of specific purposes where there is anattachment for example: technological standards and data
ownership.

473

Fig. 1. Conventional E-Learning

III. CONVENTIONAL E-LEARNING TOWARDS CLOUD-BASEDE-LEARNING
Based on Carroll et al [9] the main advantage of the
adoption of cloud computing is the efficiency to manage thecost that user will spend for the services. This is aninteresting point of view that with this advantage we could
adopt cloud concept in terms of implementation ine-learning. Conventional e-learning commonly used by theuniversity developed by the university itself tend to cause
lots of problems such as time to designing e-learning
systems will be developed, costs for infrastructure, selecting
commercial or open source e-learning platform, the cost tohire professional staff to maintain and upgrade the system of
e-learning, and so on. This process is more likely need moretime [7].
The implementation of e-learning based on cloud possibly
could help educational institutions to use a single e-learning
service that running on cloud environment. This model can
reduce the initial costs incurred by the institution for the
implementation of e-learning by using cloud computing
services, because institutions do not need to pay for thepurchase of infrastructure, both in terms of procurement ofservers and storage. By the adoption of cloud computing,the educational institution can rent the infrastructure of thecloud computing providers [10]. Likewise with the humanresources for the development stage, the cloud environmentof e-learning has been provided by the cloud service
provider, as well as maintenance of the e-learning [11].
Figure 1 illustrated the conventional e-learning
implementation and Figure 2 illustrated the cloud-basede-learning implementation. From both of these pictures
explain the paradigm shift in the implementation ofe-learning, shifting from conventional e-learning
implementation to cloud-based e-learning implementation.
By using this approach might help educational institution in
implementing e-learning with less cost. Generally, theimplementation of conventional e-learning consists of somebasic element such as e-learning system development,

Fig. 2. Cloud-Based E-Learning

system upgrade, and system maintenance [12]. It had a lot of
problems, both in terms of flexibility, scalability, andaccessibility [13]. According to [14] one of the main
important features that can be presented in the use of
e-learning in the cloud is scalability, which allows
virtualization provide infrastructure layer provided by thecloud service provider. Virtualization helps solve theproblem of the physical barriers that are generally inherent
in the lack of resources and infrastructure to automate themanagement of these resources as if they were a single
entity through hypervisor technologies such as virtual
machine (VM).

IV. OPEN EDUCATIONAL RESOURCES
Open Educational Resources (OER) initiative is an
initiative that enables to share all educational resources topublic domain with open access, open license, open format,
and open system. This OER implementation can be seen in
many countries in the world like MIT Open Courseware,
China Open Resource for Education, or Paris OCW Project.
Manyeducational
resource
sharingsystemimplementations have been developed all over the world
with many different techniques. Web service architecture

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 4, July 2014

more often used in the recent past year. This implementation
uses web service as an integration, retrieval and dataexchange application [6]. The newer trend that is often used
today is the semantic web technology where the resourceswere formed in the semantic description [7]. Other
researchers also are using P2P technology combined withsemantic web technologies and formed a super-peer P2P
semantic grid where the semantic metadata retrieved frommany educational sources [8].

V. CLOUD-BASED OPEN LEARNING ARCHITECTURE
There are several architectural cloud-based e-learning
have been proposed by previous researcher. In this paper
will discuss three architectural cloud-based e-learning, suchas architecture proposed by [4], [1], and [5].
In [4] they proposed e-learning architecture based on
cloud computing that consists of three layers that are
infrastructure, platform, and application layer. They
explained that infrastructure layer is a hardware layer that
supplies the computing and storage capacity for the higher
level and this layer, which is used as e-learning and software
virtualization technologies, ensures the stability andreliability of the infrastructure. The second layer is Platform
layer, which is a middle layer consisting middleware that is
Web service they use here. It purpose is for providing thelearning resources as a service. This layer consists of two
modules, the first module is Item Classification Module(ICM) and the second module is Course Selection Module(CSM). Main jobs both of these modules are focusing onaccessing the items from the item bank and selecting
suitable learning content from the content database. The lastis the third layer they called it as a Application layer whichis responsible for interface provision for the students.
The next architecture proposed by [1]. Their proposedarchitecture consists of five layers. The First layer isinfrastructure layer. It is composed of information
infrastructure and teaching resources. Information
infrastructure contains internet/intranet, system software,
information management system and some common
hardware. Teaching re-sources stored up mainly in
traditional teaching model and distributed in different
departments and domain. The second layer is software
resource layer. This layer is composed by operating system
and middleware. A variety of software resources areintegrated through middleware technology to provide a
unified interface for software developers to develop
applications and embed them in the cloud. The third layer isresource management layer. In order to effectuate on
demand free flow and distribution of software over various
hardware resources, this layer utilizes integration ofvirtualization and cloud computing scheduling strategy. The
fourth layer is service layer. This layer has three levels ofservices namely, SaaS, PaaS, and IaaS. In SaaS, cloudcomputing service is provided to customers, contrasting totraditional software, cloud customers use software via theinternet without any need to purchase, maintain, andupgrade, so they only pay a monthly fee for rent the cloudservices that used by the customer. The last layer isapplication layer. This layer is a specific layer consisting of
applications of integrated teaching re-sources, including

474

interactive courses and the teaching resources sharing. The
teaching resources include teaching material, teaching
information, as well as the full sharing human resources.
The last architectures that we referred in this study isfrom [5]. They proposed architecture of e-learning-based on
cloud computing consists of three layers, namely: (1)infrastructure layer, (2) middleware layer, and, (3)application layer. The first layer is infrastructure layer. It is
employed as the e-learning resource pool that consists ofhardware and software virtualization technologies to ensure
the stability and reliability of the infrastructure. This layer
also supplies the computing and storage capacity for thehigher level. The second layer is middleware layer. It
focuses in providing a sharable platform. The final layer isapplication layer. At this layer, cloud computing provides
convenient access to the e-learning resources.
In this study we propose the architecture that we havedesigned by modifying previous architectures that we used
as references. Our proposed architecture depicted in Figure
3 consists of six layers, namely: (1) infrastructure layer; (2)platform layer; (3) application layer; (4) service layer (5)access layer; and (6) user layer.
We have modified the user layer. Our user layer consistsof all stakeholders that might involve to the system. We also
add two more layers which is Access layer which is consistof multi-channel access from multi devices for addressing
the access issue for Indonesian local context and service
layer that describes the services that provided by the system,
which is: e-learning as a service, data as a service, andinfrastructure as a service.

Fig. 3. Cloud-Based Open Learning Architecture

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 4, July 2014

VI. IMPLEMENTATION
Cloud environment developed using Proxmox platform.
Table 1 Describes the Hardware and software specification
for the environment.
TABLE IHARDWARE SPECIFICATIONS
HardwareCPU
MemoryHard Drive
Network

ProxmoxMinimum
64 bit1 GB RAM
Hard Drive
1 NIC

Current
Hardware
64 bit2 GB RAM
Hard Drive
1 NIC

RemarkFulfilled
Fulfilled
Fulfilled
Fulfilled

Main activity in this process is developing a working
prototype portal. Authors used Java Script and PHP
programming language to develop the portal. This portal
will be the gate for the users to use their e-learning systemand the portal illustrated in Figure 4.
This portal main service called as a E-Learning as aService. The objective is to provide a e-learning system forthe users. This service will provide three possible cases,
which is: (1) Enable users to request a e-learning system forusers who do not have an e-learning system and create a
new one for educational purpose only; (2) Enable users toenroll to existing e-learning system for users who do nothave an e-learning system or institution; (3) Enable users tomigrate their e-learning system to join the e-learning basedon cloud environment for users who already have an
e-learning system and willing to entrust the maintenanceduty to cloud provider.
Two another services that provided by this portal are dataand infrastructure services. By joining open learning portal
users automatically rewards by free data storage and cloud
based infrastructure. Data services consist of multimedia
data that uploaded by another users and every user could
store and share their data with another users.
Infrastructure service will be provided to the users byusing virtual machine. Virtualization helps solve theFig. 4. Prototype of Open Learning Portal

475

problem of the physical barriers that are generally inherent
in the lack of resources and infrastructure to automate themanagement of these resources as if they were a single
entity through hypervisor technologies such as virtual
machine (VM).
One of the services that run in Open Learning Portal is“Student-CenteredE-learning
Environment
Personalization Dynamic E-learning” or usually called as
SCELE-PDE. This service is an e-learning that built frommodified Moodle LMS so the system can provide
personalization based on triple-factor model concept. The
learners that registered use the e-learning to improve theirperformance in learning. They learn the materials that begiven by teachers in the way they like. The e-learning
recorded learner’s activity such as access to learningmaterial and involved in forums. The learner’s activity
determined learning behavior patterns of the learner.
Learning behavior patterns filled the triple-factor parameterthat consists of learning style category, level of motivation,and knowledge ability.
Learning style of the learner determined level of learningmaterial that suitable with the learner preferences. Based on[8], learning style of learner calculated based on mean infrequent table of the group as a threshold. Learning style
divide into 3 categories, they are seldom access category for
number of access learning materials below the threshold,discipline category for number of access equal with therange of threshold, and diligent access category for numberof access greater or equal than the threshold.
Level of motivation determined which forum activity thatshould be improved by the learner. Level of motivation
calculated from mean of activities in frequent table of groupas threshold and divide into 3 categories, they are low,
medium, and high motivation. Low motivation category
gives to learner with number of access to forum discussions
is below than the threshold. Medium motivation category
gives to learner with number of access to forum discussion
equal with the range of threshold. High motivation category
gives to learner with number of access to forum discussion
is greater than the threshold.
Knowledge ability determined the performance of thelearner after they use the e-learning. Knowledge ability
calculated based on evaluation score of users. Knowledge
ability divide into 4 categories, they are fail for intervalscore 0-60, fair category for interval score 61-80, good
category for interval score 81-90, and very good category
for interval score 91-100. The outcomes of thepersonalization are suitable learning contents for every
learner that registered in the system. The architecture of thepersonalization e-learning illustrated in Figure 5.
In order to improve their performance, level of learningmaterial has been proposed. Level of learning materials thatdelivered to learner consists of three level, they are LV1 isshort material (M), LV2 are short and explanation material(M+P) and, LV3 is short, explanation, and additional
material (M+P+T). Short materials delivered in slide form.
Explanation materials delivered in audio, video, andmultimedia form such as slide-audio mixing and video-slide
mixing. Additional materials delivered in link, example, andother references form. Many form of learning materials needlarge repository and cloud environment used as a solution to
Copyright ⓒ 2014 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 4, July 2014

store and delivered it. In the experiment, there are 118
learners that registered in Science Writing subject during asemester. In the subject there are 40 learning materials withdifferent format and size that have been delivered. The
composition of learning materials describe in Table 2.

TABLE IILEARNING CONTENT THAT DELIVER IN EACH WEEK IN INDENTIFICATION
STEP

feedback

When learner login into personalization e-learning, thesystem will record the activities. In experiment, learning
process with e-learning divided into 2 step. Step 1 is
identification step and Step 2 is personalization step. The
step 1 held from week 1 unttill 7. The learner usede-learning without personalization and learning content
delivered in many form but in limited amount. Based onstep 1, learning activities that have been recorded describe
in Table 3 and the scenario of personalization learning
illustrated in Figure 6.

assignment

10outline789reference

6

video

5

trigger4

animation

3

What is scientificwriting
Fundamental conceptof reserach
Scientifi Inquiry and
Logical Thinking
Writing anddeveloping paragraph
Problem Identification& Hypothesis
How to Review
Literature
Quantitative Analysis
Quanlitative Analysis
Writing Research
Proposal
Plagiarsm &Bibliography

forum

2

Topic

audio
slide

Week
1

Fig. 5. Architecture of Personalization Learning Content in E-learning

476

x

x

x

-

-

-

x

-

-

-

x

-

x

x

-

-

-

x

-

x

x

x

x

-

x

x

-

-

x

-

x

x

x

-

x

x

x

-

x

-

x

x

x

-

x

-

x

-

-

-

x

x

x

x

x

-

x

-

-

-

x
x
x

x
x

x
x

-

x
-

-

x
x
-

-

x

x
-

x

-

-

-

-

-

x

-

-

-

There are so many activities in a week. Both of activities
need a large storage. Based on experiment, number ofactivities will increase equally with number of users andlearning materials. Cloud environment as a service gives thefacilities to enjoy the learner when they use personalizatione-learning.
In order to improve performance of learners when use
personalization e-learning in cloud environment, relation
between level of learning materials and knowledge ability oflearners has been observed. The relation in step 1 will becompared with the relation in step 2. Tabel IV shows the
distribution of learners about relation between level oflearning materials and knowledge ability in identification
step.

Copyright ⓒ 2014 GiRI (Global IT Research Institute)Fig 6. The Experiment Set Up of Personalization Learning

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 4, July 2014

TABLE IIILEARNING ACTIVITES IN IDENTIFICATION STEP

6

55

53

4

118

TABLE VI
DISTRIBUTION OF LEVEL OF LEARNING MATERIALS AND
KNOWLEDGE ABILITY IN PERSONALIZATION STEPTABLE IV
DISTRIBUTION OF LEVEL OF LEARNING MATERIALS AND
KNOWLEDGE ABILITY IN IDENTIFICATION STEP

Fail (0-60)
Fair (61-80)
Good (81-90)
Very Good (91-100)
Sum

LV1
(M)

LV2
(M+P)

LV3
(M+P+T)

Sum

4
43
46
2
95

1
4
0
1

1
9
6
1
17

6
56
52
4
118

6

Based on the table IV, the distribution of learners focus
on LV 1. There are 6 learners which belong to fail category,
4 of them are in LV1 who only access short learningmaterial but fail to gain more information because lack ofknowledge ability. In fair category there are 43 learners that
only access short material and gain enough information. The
others in this category distributed to different level oflearning materials but in small number. In good category, 46
learners have preference to access short learning materials
too. They have higher knowledge ability than the category
before, so their evaluation score belong to interval 81-90. In
very good category there are only 4 learners, 2 learners
belong to LV1 and the others belong to LV2 dan LV3.
The experiment continues to step 2 or personalization
step. Based on preferences and calculation of means fromfrequent table of access learning materials, thepersonalization of learning materials delivered to learners.
The step 2 held from week 8th until week 12th. The learnerused personalization e-learning and got more different formand size of learning materials. 40 learning materials
delivered in the system got the feedback such as number ofaccess and other learning activities. Number of learning
activities in learning step 2 describe in tabel V.
TABLE VLEARNING ACTIVITES IN PERSONALIZATION STEP

564
344
609
1113
1301
3931

4

45

61

Very
Good

221
554
252
728
380
2135

Good

8
9
10
11
12
Sum

Evaluation Score
Fair

Amount ofaccess to
forum
discussions

Fail

Week

Amount ofaccess tolearning
materials

Table V shown that learning activities in personalization
step increased more than learning activities in identification
step. Number of access to learning materials increased from1793 to 2135 activities and number of access to forum
discussions increased from 2804 to 3931 activities. It shownthat personalization e-learning is able to improve learner’s
participation when they learnt because system deliver typeof learning materials that suitable with the learner’s need. So
the learners will be focused on their exploration to get the
information when they learn.
In personalization step, distribution of learners in relation
between level of learning materials and knowledge ability
was observed and describe in table VI below.

8

Fail (0-60)
Fair (61-80)
Good (81-90)
Very Good (91-100)
Sum

LV1
(M)

LV2
(M+P)

LV3
(M+P+T)

Sum

1
26
27
5
59

0
5
5
1
11

3
14
29
2
48

4
45
61
8
118

Tabel VI shown that learners in fail category decreased
into 4 learners. 45 learners in fair category distributed to
LV1, LV2, and LV3 in 26, 5, and 14 respectively. In good
category, there are 61 learners. This number increase from52 in identification step. The last category is very good that
increased from 4 to 8.
In addition, this study also observed the activities oflearners in discussion forums The main purpose of theactivity observed in the discussion forums are to be used asa benchmark for determining the level of motivation of thelearner. The discussion forum is divided into three
categories of discussion include: lounge F1), self add post
(F2), and trigger forum (F3). The observation is divided into
two Steps: Step 1 is identification and Step 2 ispersonalization (see Figure 5). In Step 1, the lounge (F1) is
a forum with the highest activity, there is about 2,567(91.55%) of the 2,804 discussion activities for 7 weeks.
Activity on trigger forum there are only 153 (5.45%) of theoverall activity in the existing forums. Then for the self add
post activity is the lowest activity with only 84 events(2.99%) of the total 2.804 activities. The Observation resultof discussion forum activity in step 1 can be seen in Figure 7
below.

Amount	  of	  Activity	
  

1162
118
197
477
409
441
2804

Very
Good

451
236
372
219
247
268
1793

Good

1
2
3
4
5
6
7
Sum

Evaluation Score
Fair

Amount ofaccess to
forum
discussions

Fail

Week

Amount ofaccess tolearning
materials

477

2567	
  
3000	
  
2500	
  
2000	
  
1500	
  
1000	
  
500	
  
0	
  

84	
  

153	
  

F1	
  
F2	
  (self	  F3	
  (lounge)	
   add	  post)	
   (trigger	
  forum)	
  
Type	
  of	
  Dicussion	
  Fourm	
  	
  

118

Fig 7. Learner's Discussion Activities in Step 1 : Identification

Copyright ⓒ 2014 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 4, July 2014

Amount	  of	
  Ac*vity	  Then, the special treatment given to all learners who still
have low activity discussion forums through the suggestions
given in the e-learning during step 2 of personalization. At
the end of step 2, the observation of the activity indiscussion forums carried back. The comparisons between
the activity of step 1 and step 2 in learning activity
conducted to determine the increased activity of learnerdiscussion. The observation is illustrated in the Figure 8 as
follows.
In step 2, there are 3,237 activities in a public forum(F1). This represents an increase of step 1 which only 2,567
learning activities. In self add post (F2), a very high amountof increased activity at the step 2 where there are 1,401
compared with step 1 with only 84. In the triggers forum,
the activity also increased from the previous 153 to 193
activities. This increased activity is assumed to be caused by
given recommendations.

478

the result will conclude the cost efficiency. The formula for
calculating cost analysis (for NC stands for Non
Cloud-Based system and C stands for Cloud-Based system
could be describes as follows :(1)
The simulation process with this approach conclude thatby using cloud-based system could decrease the investation
cost up to 35.61%.
Net Present Value (NPV) is measured by calculating thethe difference between the present value of cash inflows andthe present value of cash outflows. In this case NPV noncloud based formula could be describes as follows [6]:

(2)
4000	
  
3000	
  

3237	
  
2567	
  
1401	
  

2000	
  
84	
  

1000	
  

and NPV for cloud based formula described as follows:153	
   193	
  

(3)

0	
  
F1	
  (lounge)	
  F2	
  (self	
  add	  post)	  F3	  (trigger	
  forum)	
  

Type	
  of	
  Discussion	
  Forum	
  
Step	
  1.	
  
IdenAﬁcaAon	  The simulation process needs several assumptions such as
salary of programmer, analyst, and server procurement cost.
Furthermore, the result by calculating the NPV approachshows that the value of NPV is positive (greater than 0) byusing NPV percentage formula :Fig 8. The Comparison about Discussion Forum Activity in Step 1 and Step
2 of Learning Activities

In general, personalization e-learning success toimprove performance of the learners. Many learners moved
from low category to higher category in knowledge ability,
dicussion activity, and get the higher score. It can behappened because in environment level, personalizatione-learning runs in cloud computing environment which canprovide wide access to broad storage of learning materials,
facilities, and others services that support personalization.
VII. EVALUATIONThe evaluation process for technical system will be usedfunctional testing method. The system will be tested by
input scenario and the output will be recorded and matchedby the expected output. This scenario aimed to tested thatthe system will be running properly.
After making sure that the system has running properly
by evaluated the functional system, the next evaluation
process is concerning to economical aspect. Authors
approach for this evaluation is by comparing two cases:
non-cloud e-learning and cloud e-learning. This two cases
will be evaluated by two approach: (1) Cost (Capex & Opex)
analysis; (2) Net Present Value (NPV).
Cost anaylsis is measured by calculating sum of Capex
and Opex between non-cloud and cloud open learning then(4)with the results shows positive value (43,9%) of NPV thatmeans by using cloud based system could give more
benefits than using non cloud based system.

VIII. CONCLUSIONS AND FUTURE WORKS
This paper discussed the problems while developing andimplementing the e-learning system stressing the initial costissue. Authors proposed a solution for these problems byadopting cloud technology and the concept of open
educational resources to implement the e-learning andexpected become a cloud based open learning system.
Authors steps for solved the initial cost problem aredesigning a architecture of cloud based open learning andimplementing this architechture to a working prototype
system. Final step is evaluating the prototype system bystressing the initial cost using the Net Present Value method.
The results of the evaluation shows that by implementing
the cloud based open learning portal could decrease theinfestations cost up to 59% in compares to non cloude-learning systems and with NPV approach shows that theresults is 43,9% of NPV percentage that means by using

Copyright ⓒ 2014 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 4, July 2014

cloud based system could give more benefits than using non
cloud based system.
In our future work, we will design and develop a semantic
based search engine for enhanced the system and integration
it with personalization e-learning.

ACKNOWLEDGMENT
Authors wish to thank to Dr. Ismail Khalil from Johannes
Keppler University, Austria, for his positive comments andsuggestions that improve this article.

479

interests include digital library, e-learning,
information system, information retrieval, andsoftware engineering.
Prof. Zainal A. Hasibuan, Ph.D. was born in Pekan
Baru, Indonesia in 1959. He received BSc. degree in
Statistic from Bogor Institute of Agriculture,
Indonesia, 1986, MSc. and PhD in Information
Science, Indiana University, in 1989 and 1995
respectively. Currently, he is a lecturer and PhD
supervisor at Faculty of Computer Science,
University of Indonesia. He is also the Head of
Digital Library and Distance Learning. His researchinterests include e-Learning, Digital Library,
Information Retrieval, Information System, andSoftware Engineering.

REFERENCES[1]

Masud, Md.A.H., & Xiaodi Huang. “An E-learning System
Architecture based on Cloud Computing”. IEEE. 2012.
[2] Chuang, S., Chang, K., & Sung, T. “The Cost Effective Structure For
Designing Hybrid Cloud Based Enterprise E-Learning Platform”.
IEEE CCIS. 2011.
[3] Chandran, D., & Kempegowda, S. “Hybrid E-Learning Platform
Based On Cloud Architecture Model: A Proposal”. IEEE. 2010.
[4] Phankokkruad, Manop. “Implement of Cloud Computing For
E-Learning System”. IEEE ICCIS. 2012.
[5] Wang, Chun-Chia, Wen-Chang Pai and Neil Y. Yen. "A Sharable
e-learning Platform Based on Cloud Computing." 3rd International
Conference on Computer Research and Development (ICCRD).
Shanghai, 2011. 1 - 5 .
[6] Yang, Z. “Study on an Interoperable Cloud framework for
e-Education”. International Conference on E -Business and E-Government (ICEE) (pp. 1 - 4). China: IEEE. 2011.
[7] Selviandro, Nungki, Zainal A. Hasibuan, "Cloud-Based E-Learning:
A Proposed Model and Benefits by Using E-Learning Based on Cloud
Computing for Educational Institution", ICT-EurAsia, Springer,
pp192-201 2013.
[8] Sfenrianto, N. Selviandro, Z. A. Hasibuan, H. Suhartanto, “AnApproach for Learning Type Triple Factor In E-learning Process.”,
Journal of Next Generation Information Technology. 2013.
[9] Carroll, M., Merwe, A., & Kotzé, P. Secure Cloud Computing
Benefits, Risks and Controls. IEEE. 2011.
[10] Ghazizadeh, Aida. Cloud Computing Benefits And Architecture In
E-Learning. IEEE. 2012.
[11] Pocatilu, Paul. Cloud Computing Benefits for E-learning Solutions.
Academy of Economic Studies, Bucharest, Romania. 2010.
[12] Méndez, J. A., & González, E. J. Implementing Motivational Featuresin Reactive Blended Learning: Application to an Introductory Control
Engineering Course. IEEE. 2011.
[13] Masud, Md.A.H., & Xiaodi Huang. An E-learning System
Architecture based on Cloud Computing. IEEE. 2012.
[14] Jones, M. Tim. Cloud computing and storage with OpenStack:
Discover the benefits of using the open source OpenStack IaaS cloud
platform. Developer Works. 2012.

Nungki Selviandro was born in Curup, Indonesia in
1988. He received bachelor’s degree in Computer
Science from University of Indonesia, Indonesia,
2011, and master's degree also in computer sciencefrom University of Indonesia, Indonesia, 2013.
Currently, he is a lecturer at Faculty of Informatics
Telkom University. His research interests include
cloud computing, e-Learning, information system,and software engineering.

Mira Suryani was born in Bandung, Indonesia in
1989. She received bachelor degree in Computer
Science Education from Education University ofIndonesia, Indonesia, 2011, and master's degree in
Computer Science from University of Indonesia,
Indonesia, 2014. Currently, she is a research assistantin research laboratory at Faculty of Computer
Science, University of Indonesia. Her research

Copyright ⓒ 2014 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 4, July 2014480

An Efficient and Scalable Key Management
Mechanism for Wireless Sensor Networks
Walid Abdallah*, Noureddine Boudriga*, Daehee Kim**, and Sunshin An**(*)Communication Networks and Security research Lab, University of Carthage, Tunisia;(**) Computer Network research Lab, Department of Electronics Engineering, Korea University, Seoul, Korea
ab.walid@gmail.com, noure.boudriga2@gmail.com, dhkim@dsys.korea.ac.kr, sunshin@dsys.korea.ac.kr

Abstract—A major issue in many applications of WirelessSensor Networks (WSNs) is ensuring security. Particularly, in
military applications, sensors are usually deployed in hostile
areas where they can be easily captured and operated by anadversary. Most of security attacks in WSNs are due to thelack of security guaranties in terms of authentication, integrity,and confidentiality. These services are often provided using
cryptographic primitives where sensor nodes need to agree on aset of secret keys. Current key distribution schemes are not fully
adapted to the tiny, low-cost, and fragile nature of sensors that are
equipped with limited computation capability, reduced memory
size, and battery-based power supply. This paper investigates
the design of an efficient key distribution and management
scheme for wireless sensor networks. The proposed scheme canensure the generation and distribution of different encryption
keys intended to secure individual and group communications.
This is performed based on elliptic curve public key encryption
using Diffie-Hellman like key exchange that is applied at different
levels of the network topology. In addition, a re-keying procedure
is performed using secret sharing techniques. This scheme is more
efficient and less complex than existing approaches, due to the
reduced number of messages and the less processing overhead
required to accomplish key exchange. Furthermore, few numberof encryption keys with reduced sizes are managed in sensor
nodes, which optimizes memory usage and enhances scalability
to large size networks.
Index Terms—Wireless sensor networks, Security, Key distribution and management, Elliptic curve cryptography, thresholdsecret sharing

I. I NTRODUCTION
Since their advent, Wireless Sensor Networks (WSNs),
have been the subject of an increasing interest from theacademic and industrial communities, due to their wide andvaried number of applications in military and civilian domains.
Manuscript received July 1, 2014. This work was supported in part byby the National Research Foundation of Korea (NRF) grant funded by the
Korean government and the Tunisian government (No. NRF2012K1A3A1A09026959).
Walid Abdallah is an Assistant Professor and member of the Communication Networks and Security research Lab, Tunisia (corresponding author; Phone: +21698229840; e-mail: ab.walid@gmail.com). Pr. Noureddine
Boudirga is a Professor of Telecommunications in the School of Communication Engineering, SupCom, Carthage University and the Director of theCommunication Networks and Security Research Laboratory (CNAS), Tunisia(email:noure.boudriga2@gmail.com). Deahe Kim is a Phd student in theComputer Network research Lab, Department of Electronics Engineering,
Korea University, Seoul, Korea (email: dhkim@dsys.korea.ac.kr). Pr. Sunshin
An is a Professor of Electronic and Computer Engineering in Korea University,
Seoul, Korea (email: sunshin@dsys.korea.ac.kr )These networks demonstrated high effectiveness in the development of many innovative applications such as battlefieldsurveillance, border control, structural health monitoring ofaircraft, environment parameters measurement, and patient
health care[1], [2], [3].
Conceptually, a WSN is composed of a number of sensor
nodes, deployed in a specific zone to detect particular eventsand transmit messages to a base station (sink node) in a
multi-hop communication fashion using the wireless medium.
Sensor nodes are characterized by their reduced size, limited processing capability, and battery-based power supply.
These characteristics must be taken into consideration indeveloping appropriate communication protocols. Particularly,
ensuring communication security is one of the major issues in
WSNs, especially when they are distributed in hostile regions
where sensors can be captured and easily manipulated by anadversary. Furthermore, with advances achieved in wirelesstechnology, WSNs are being used in critical domains, suchas controlling aircraft and avionic systems, surveying healthstates, and monitoring toxic gas emission where security attacks can have very dangerous consequences on human safety.
Therefore, providing security services for data communicationin WSNs becomes a main requirement to avoid malicious
activities and even terrorist attacks.
Typically, to ensure communication security, at least four
services must be provided, namely, confidentiality, authentication, integrity, and availability. Most of these services are basedon the implementation of cryptographic techniques which
require the establishment of a set of shared encryption keys. A
wide range of cryptographic algorithms and schemes had beendeveloped to enable dynamic key distribution and management
in classical networking infrastructure, such as asymmetricencryption techniques, digital signature schemes, and Public
Key Infrastructure (PKI). However, these techniques cannot bedirectly used in WSNs. Indeed, sensor nodes cannot sustain thehigh processing overhead and complexity of these techniques
due to their limited computational capacity and reduced memory size. In addition, most of the existing key management protocols must perform extensive message exchanges to establish
keys, which increases power consumption, depletes the sensor
node limited energy, and shortens the network operational lifetime. Besides, in a WSN, nodes can leave the network because
they run out of energy or are captured and eliminated by anadversarial party. Therefore, new sensor nodes must be added
after the initial deployment phase to replace the removed nodes
Copyright ⓒ 2014 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 4, July 2014or to enhance the connectivity of the network. Consequently,
the key management scheme should deal with this issue to
avoid the failure of the network and optimize the re-keying
procedure when nodes are deleted or added.
Several research works[4], [5], [6], [7], [8], [9], [10], [11],[12], [13], [14] had been devoted to design appropriate key distribution schemes for WSN. The proposed schemes were basedon pre-distribution of symmetric or asymmetric keys before
the network deployment or on secret sharing using threshold
cryptographic techniques. Although some of these schemes
can offer extensive security to data transfer on wireless sensor
network, they are complex to apply in real environment anddo not scale to large networks. In addition, key establishment
protocols require a high number of message exchanges which
exhausts the limited available energy of sensor nodes andshorten the network operational live time. Moreover, all existing key distribution and management schemes assume static
topology and do not consider the case where mobile sensor
nodes can be employed.
This paper proposes the design of a key management
scheme for wireless sensor networks adapted to hierarchicaltopologies. It is an enhancement of the work presented in[15]. Our key distribution scheme can perform efficient andscalable generation and sharing of cryptographic keys toprovide authentication, integrity, and confidentiality services
to all types of data traffic exchanged at the different layersof the network topology. Our proposal uses elliptic curve
based Diffie-Hellman like exchange procedure to establish
individual secret keys between different elements of the WSN,
such as sensor node and base station, the sensor and its
cluster head, and each cluster head and the base station. Theseexchanges are exploited to generate secure group keys toensure intra-cluster and inter-cluster communications privacy.
Authentication of the exchanged values is implemented toovercome vulnerability to the man-in-the-middle attack. Our
secret key establishment approach is less complex and requires
reduced message exchanges than existing schemes whilst it
improves offered security level. Moreover, the use of elliptic
curve techniques allows shorter key sizes and decreases theprocessing overhead of the cryptographic operations while
ensuring the same security level as current public key schemes.
The main contributions of this work with regard to existing
literature are as follows:• The development of an Elliptic Curve Public Key Cryptography (ECPKC) based key management mechanism
for WSNs, allowing dynamic establishment of manykinds of secret keys intended for different usages invarious levels of the network topology and taking into
consideration node mobility.
• The design of an efficient group key establishment procedure to enable in-network processing and secure intracluster and inter-cluster broadcast traffics. This procedure
achieves group key sharing in only two rounds, which
reduces the processing and communication overheads andsaves sensor’s energy.
• The proposal of a re-keying procedure based on secretsharing techniques to ensure backward and forward secrecy and improve resilience to node capture attack.

481

The remaining parts of the paper are as follows: Section
II exposes related works to the key distribution problem inwireless sensor networks. Section III describes the proposed
key management scheme. Section IV, analyzes the security
level provided by the proposed scheme. Section V performs
a performance analysis of the proposed scheme, Section VI,
presents simulation work conducted to evaluate the effectiveness of the key management approach and demonstrate its
scalability. Section VII, concludes the achieved work in thispaper and gives some perspectives.
II. R ELATED W ORK
Key distribution problem in wireless sensor networks, hadbeen the subject of many research works during the last
decade[16]. Key distribution and management procedure playa crucial role in guaranteeing the security of any data exchangeusing cryptographic primitives. Key management encompasses
the processes of generating, distributing, storing, and updating
encryption keys. The main target is to prevent attacker from
exploiting weaknesses in the key management procedure toderive encryption keys and break the security of the wirelesssensor network. Due to the limited resources of sensor nodes,
the large number of deployed nodes, and missing of infrastructure, key distribution and management is a major issue inwireless sensor networks.
Secret key cryptographic techniques are the most suitable
to secure communication in wireless sensor networks. These
techniques can be executed in reduced computational capability processors with an acceptable delays. In addition, they
manipulate short key sizes requiring few memory occupancy.
This has the advantage to reduce the energy consumption,
increase efficiency, and ensure reliability of the network.
TinySec [17] is an effective implementation adapted to wireless sensor nodes to ensure link layer security in terms ofconfidentiality, integrity and authentication. Authors studied,specific operational modes for a set of secret key encryption algorithms and message authentication codes, that cansatisfy resource constraints of sensor nodes. For instance,in TinySec, RC5 and Skipjack are considered as the mostsuitable encryption algorithms to offer data confidentiality.
One of the main problems in secret key cryptosystems is
key distribution consisting in the procedure to securely sharesecret keys between sensor nodes. Many research works[5],[6], [8], [18], [7], [9], [10], [19], [20], [4], [21], [14] havebeen devoted for studying key distribution and management
issue in wireless sensor networks proposing multiple schemeswith various approaches.
Most of the proposed key distribution schemes are basedon the pre-distribution of a set of secret keys in the sensor
nodes before network deployment [5], [6], [8], [7], [12]. The
work presented by Eschenauer et al [5] is the first in thiscontext. The authors proposed a key distribution scheme basedon pre-loading a set of secret keys in each sensor node that are
randomly selected from a common pool of keys. At the initial
phase of network deployment, each sensor node exchanges
information about the pre-configured keys with its neighbors
to find those that share with it the same keys. When two
Copyright ⓒ 2014 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 4, July 2014neighbors find that they have a common key, they establish asecure communication link between themselves. Furthermore,
the established secure links can be used to negotiate thesharing of pairwise keys between nodes that their key sets did
not overlap. It was proved, using random graph theory that, if
the probability that two selected sets of keys share at least one
key is greater than a given value, then secure connectivity of
the network can be achieved with high probability. Although,this scheme may be very efficient in establishing shared keys
in wireless sensor networks, its main drawback is that when
the number of jeopardized nodes increases, the security levelsignificantly degrades. When sensor nodes are captured, allshared keys can be discovered and encrypted data will be
disclosed to an adversary. Moreover, given that the same key
can be used to secure many links, the attacker may evenbe able to decrypt data being currently transmitted between
non compromised nodes. Besides, to offer full communication
security, each sensor node needs to store and manage an
important number of keys, which requires a high memory
capacity and limits the scalability of this solution to large size
networks.
Chan et al [6] introduced the concept of q-composite. In
this approach, a secure link between two neighbor nodes isestablished if they have at least q common keys, where q ≥ 2.
Authors show that increasing the number of shared keys, q,boosts the resilience to node capture, in the sens that theattacker will need to compromise a higher number of nodes
than in the original scheme described in [5] to decrypt thesame amount of data. Despite resilience enhancement against
node capture, this scheme has not resolved the main limits,which are, the complex procedure and the communication
overhead needed to establish a full one-hop secure connectivity
between neighbor nodes, and the required memory to store andmanage shared keys. To enhance random key pre-distribution
approach, Du et al [7] presented a technique to establish
pairwise keys between sensor nodes based on the random
selection of rows and columns in a key matrix. In this scheme,
multiple key generation spaces are used to enhance resilience
to node capture. Nevertheless, this approach cannot guaranteethat two nodes can share a direct secure link, and the key
path establishment procedure of the original scheme [5] is stillneeded. All these schemes are developed for wireless sensor
networks configured in a flat topology where all nodes havethe same capabilities and thus a key pairwise must be setup
between each pair of sensors. This can reduce scalability tolarger size networks due to higher power consumption, extensive processing requirement, and communication overhead.
Using a hierarchical topology can simplify and improve thescalability and efficiency of the key distribution procedure. In
this case, the sensor node doesn’t need to establish a pairwise
keys with all nodes in the network, but only with those thatare in its communication range. Particularly, a sensor will
share keys with its cluster head (CH) and cluster members;
this contributes in reducing the communication overhead andsaving energy. Several works have studied the design of key
distribution mechanism for hierarchical sensor networks [18],[22], [12], [14], [10].
Localized Encryption and authentication protocol (LEAP)482[18], [22] is an energy efficient key distribution mechanism
developed for large scale hierarchical sensor networks, that isable to generate specific keys for securing various types of
uni-cast and broadcast traffics. Four kinds of encryption keys
are defined: individual key shared between the base station andeach sensor node, pairwise key that is a unique key establishedbetween the node and its cluster head, cluster key is a common
key used to secure data intended for all members of the cluster,and group key used to secure data broadcast to all nodes of the
network. All these keys are derived from a unique master key
that is pre-loaded in each node before deployment. This masterkey is erased from the memory at the end of the initial key
distribution process, to avoid that an adversary party capturing
a single node, can compromise all data transmitted in the
network.
A similar approach was described in [12], where a security
architecture was proposed for wireless sensor networks basedon a master secret key that is embedded in the source code of
the operational system in every sensor node. The authors claimthat this can prevent the disclose of the encryption keys derived
from the master key and stored in non-volatile memory even if
the node is captured. Although, we can agree that configuring
the master key in the source code of the application program
can harden the task of an attacker to retrieve it, but this is not
impossible. Also, it will be very hard to upgrade the security
parameters of the encryption scheme such as the key size,
encryption algorithms, and the master key itself, because thiswill require the upload of another operation system in all
deployed nodes.
Secret sharing techniques have been investigated in [14] todesign a key management mechanism in hierarchical wirelesssensor networks. This scheme allows the distribution of keys
in different levels of the topology. Indeed, individual secrets
are distributed to all nodes of the network. Group keys can beconstructed by resembling a minimum number of individual
secrets and applying a polynomial interpolation. This has theadvantage of ensuring the survivability of the network if aminimum number of nodes are still active and a maximum
number of nodes had not been compromised. Nevertheless, toensure security of the transmitted data, the shared secrets must
be modified for every session to prevent key compromising
due to the capture of a single node participating in the reconstruction process. This generates an extensive communicationand processing overheads and increases power consumption.
In addition, sensor nodes are required to store an importantnumber of session secrets overloading the limited memory
capacity of sensor nodes.
The previously described key distribution schemes for
WSNs are static in the sense that they are based on the preloading of secret keys that remain valid during all the network
life-time. More specifically, these schemes do not define rekeying procedures to update encryption keys. This fact, canconstitute a serious security limit in these mechanisms, as
using an encryption key for a long period of time can increase
the probability of being compromised. Some dynamic key
management schemes that enable key updating had been
proposed for wireless sensor networks [21], [23], [24]. These
schemes are mainly based on secret key encryption techniques
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 4, July 2014

which makes them vulnerable to node capture attacks. In
these dynamic key management schemes, capturing a specificnumber of sensor nodes can lead to revealing keys used by noncompromised nodes. Public key encryption techniques can beinvestigated to resolve these problems.
Although, public key cryptosystems have not been considered at the beginning in WSNs to secure key distribution owing
to their key sizes and high computation capacity requirement,
they are being investigated in some research works[25], [19],[4]. This is motivated by the advances achieved in physicalnode architecture technology and the enhancement of their
computation capacity. In addition, a promising solution is theuse of elliptic curve cryptography which significantly reduces
key size, achieves key generation in a limited delay, andconsumes a few amount of power [13].
In this paper, we investigate the design of a key distribution
and management scheme for wireless sensor networks withhierarchical topology. Our approach consists in combining
different techniques, each one will be used in a specific context
in order to ensure the highest security level while guaranteeing
efficiency and scalability of the key distribution process. Our
proposal is based on using elliptic curve public key cryptography, in particular on the Diffie-Hellman like key exchange
procedure, to establish secret keys in the different levels of
the hierarchy. A unique private key is generated by eachsensor node at the initial phase of the network deployment.
The generation process is based on the identity of the node
and a key that is pre-loaded in the sensor node and deleted
after the generation of the private key. The validation of thecorresponding calculated public key is achieved by the basestation. The public and private keys are then used to establish
individual and group secret keys to secure different kinds of
uni-cast and broadcast traffics. In addition, our scheme enables
secure re-keying procedure by using secret sharing techniques
to regenerate the initial key and reconfigure the overall security
parameters.
III. K EY DISTRIBUTION AND MANAGEMENT SCHEMEDESCRIPTIONIn this section, we describe the proposed scalable key
distribution and management scheme to secure wireless sensor networks. Firstly, we introduce the considered networkarchitecture; then we detail the initial key generation anddistribution procedure; finally we investigate issues related tonode addition, deletion, and mobility.
A. Network topology and assumptions
Wireless sensor networks can be configured into two main
topologies: flat homogenous and heterogeneous hierarchical.
In flat topology, all sensors have the same capabilities interms of sensing, computing and communication. Whereas,in hierarchical topology, the network is composed of manykinds of nodes with divers capabilities and perform different
functions. Flat wireless sensor networks are more simple todeploy, however hierarchical architectures are more efficientand scalable.

483

Fig. 1. Network architecture

In this work, we consider a hierarchical wireless sensor
network composed of a large number of sensor nodes thatare organized into a number of clusters. Each cluster iscontrolled and managed by a cluster head which is a device
with higher processing and communication capabilities thansensor nodes. After deployment, the cluster heads need to
execute an appropriate clustering algorithm [26] to divide the
network into an optimized number of clusters.
The considered network topology is depicted by Figure 1.
As we can see, the network architecture encompasses three
types of network devices, sensor nodes, cluster heads, and thebase station (or Sink node). In the sequel, we describe thefunctionality of each one of these devices.
1) Sensor nodes : Sensor nodes are in the lowest level ofthe hierarchy. They are low-cost devices with a very limited
computing, storage, and communication capabilities. Also,
they are power supplied using a finite life battery. The main
mission of a sensor node is to detect particular events and to
exchange messages with its cluster head and the base station.
Also, a sensor node can relay messages transmitted by sensor
nodes which their communication ranges do not reach thecluster head. In some situations, the base station can exchange
messages with the sensor nodes. This can happen for example
when the configuration of sensor nodes needs to be updated
or when a particular event happens. In addition, we supposethat at any time a sensor can be attached to only one cluster.
However, sensor nodes can be mobile and move from one
cluster to another with a very low speed.
2) Cluster heads : The cluster head is responsible ofcollecting data from the members of its cluster and aggregatingthem in order to optimize transmission channel usage. Also,
it manages and controls all procedures of member join anddeparture. A cluster head needs to be equipped with anextensively higher amount of resources than the sensor node.
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 4, July 2014

In our architecture we suppose that cluster heads encompasses
a higher processing devices with large storage capacity andmore powered and long live batteries. Moreover, we consider
that they are able to achieve more complex operations andhave a wider communication range than sensors. Cluster heads
can communicate with each other directly and relay data to
the base station. Due to their limited number, it can be costeffective to assume that cluster heads are endowed with atamper-proof hardware that ensures resistance to node captureattack. Moreover, some advanced security functionality suchas auto-destruction and memory eraser in case of unauthorizedaccess attempts can be implemented in these devices.
3) Base station : The base station is the network elementthat implements the most higher capabilities. We assume thatit has unlimited resources such as, computing power, storage
capacity and energy. Moreover, the base station has a verylarge communication range that can reach all nodes in the
network. Depending on the application, the base station canbe localized either in the center or the corner of the network.
In any case, it is supposed that the base station is installed ina well known and secure location. Also, it is considered as
the most secure element of the topology and is trusted by all
entities of the wireless sensor network.
B. Key generation and distribution procedure
The main objective of our work is to design a key management procedure that ensures robust authentication, integrity
and confidentiality services in the sensor network and takes
into consideration the limited resources and reduced processing capability of the sensor nodes. The key management
mechanism should allow secure generation and distributionof keys in every level of the hierarchy. In addition, it must
enable the establishment of different group communication
keys that can be used to perform in-network processing.
The in-network processing capability consists in a the abilityof sensor nodes to decrypt packets transmitted by neighbornodes in order to avoid event detection redundancy and allow
data aggregation. These operations are very useful in many
applications and permit energy saving and channel usage
optimization. Consequently, we can distinguish the following
kinds of keys:• Individual keys: used to secure communication between
a sensor node and the base station.
• Intra-cluster pairwise keys: shared between a sensor nodeand its cluster head and neighbor sensor nodes belonging
to the same cluster• Cluster key established between all sensor nodes of thesame cluster to secure group communications.
• Inter-clusters key: used to secure communication between
all clusters heads and the base station• Network key: shared between all nodes of the network
and used to secure message broadcast.
In this work, we investigate the use of elliptic curve publickey cryptography to enable efficient and secure key exchange
in wireless sensor networks. In the upcoming subsections, we
present our Elliptic Curve Public Key Cryptography (ECPKC)
based key management mechanism proposed to carry out

484

dynamic establishment of the aforementioned kinds of keys.
First, an overview of elliptic curve cryptography is given inthis paper. Then, the generation and distribution processes aredescribed.
1) Elliptic curve fields selection: Elliptic curve techniques[27] offer a valuable opportunity to efficiently apply publickey cryptography approach to secure wireless sensor networks.
These techniques are able to provide equivalent security level
as classical public key cryptosystems, namely the DiffieHellman key exchange procedure, with significantly reduced
key size. For example, in Diffie-Hellman a minimum key sizeof 1024 bits is required to ensure the security of the key
exchange procedure. Indeed, the discrete logarithm problem,on which is based the security of this key establishment
protocol, becomes intractable for a key size higher than thisvalue. However, with elliptic curve equivalent approach a key
size of 160 bits is sufficient to ensure security. In the following
we explain how this was achieved.
Given a Galois field Fp , where p is an integer number, anelliptic curve, E(Fp ) is defined by the set of points that satisfy
the Weierstrass form defined by the following equality:
y 2 + a1 xy + a3 y = x3 + a2 x2 + a4 x + a6

(1)

where, ai ∈ Fp .
In cryptography, two forms of the Galois finite fields are ofinterest. The first form considers a field Fp , with p a prime
number, and an elliptic curve satisfies the equation:
E(Fp ) = {(x, y) ∈ Fp2 , y 2 = x3 + ax + b} ∪ {O}(2)where a, b are satisfying 4a3 + 27b2 6= 0 and Ois the neutral
element of the curve. This form is very useful for a software
implementation of the elliptic curve encryption paradigm.
The second form considers a field Fp , with p = 2k , and k isprime number. The elliptic curve in this case is characterized
by the equality:
E(Fp ) = {(x, y) ∈ Fp2 , y 2 + xy = x3 + ax + b} ∪ {O} (3)This form is more adapted for hardware implementation of
elliptic curve based encryption algorithms.
These two forms are not vulnerable to the sub-exponential
attack and can guarantee the security of the key exchange
procedure. For both forms a specific addition operation isdefined. The more interesting is the equivalent form of thediscrete logarithm problem in the elliptic curve field. Recall
that, the discrete logarithm problem consists in, given a prime
number p, and a generator g and a value h belonging to Z∗p ,it is difficult to find, x where h = g x mod(p). In elliptic curve
cryptography, it is believed that, given a field Fp satisfying oneof the aforementioned forms, and two points, P and Q belonging to E(Fp ), the problem of finding, an integer n, such thatQ = nP = P + P + ... + P is more difficult than the discrete
logarithm problem. Therefore, mapping between the classical
Diffie-Hellman key exchange scheme and its equivalent using
elliptic curve paradigm can be simply performed by replacing
the exponentiation operation by an integer multiplication (ormore precisely n-time addition) in E(Fp ).
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 4, July 2014

2) Individual keys establishment : Individual keys are established between each sensor node and the base station during
the initial phase of network deployment. We assume that thehierarchical network topology has been created and that sensor
nodes can communicate with the base station to establish
secret keys. This is performed in our scheme using elliptic
curve based Diffie-Hellman key exchange procedure according
to the following steps:
Pre-deployment : Before deployment, the base station randomly selects an integer number p, the elliptic curve E(Fp )according to the second form as discussed above, and agenerator point G ∈ Fp . Then, it generates its private key,xB ∈ Zp , where 2 ≤ xB ≤ p − 1 and calculates thecorresponding elliptic curve public key, YB = xB G. The
parameters p, E(Fp ), G , YB , and an initial key K0 will be
pre-loaded in each deployed sensor node. The initial key, K0
will be used to verify the genuineness of the deployed sensor
nodes. It is valid only during the short period of the initial
deployment phase and will be deleted immediately after theaccomplishment of the key establishment procedure. In thefollowing , we denote by N the total number of deployed
nodes, where each node is uniquely identified by an id value.
Private/public key pair generation and individual key
calculation: Immediately after network deployment and theestablishment of clustered communication architecture, every
node i, 1 ≤ i ≤ N , will generate its private key, xi ∈ Zp .
This is performed by applying a hash function as follows:

485

Pairwise keys are established in a similar way as individual
keys described above. The only difference is that public values
must be retrieved from the base station and each party verifies
its validity before key establishment. To this end, the basestation calculates a MAC of the public key using the individual
key shared with the sensor node that had generated the key
request message. After that, neighbor nodes i and j canestablish a pairwise key, Kij = xj Yi = xi Yj = xi xj G.
Similarly, the pairwise key Kic = xi Yc = xc Yi = xi xc G
can be established between the node i and its CH c.
To set the cluster key a group communication secret key
sharing procedure was proposed. This scheme is more efficient than the existing techniques [28] because it allows key
establishment in only two rounds. To this purpose, for eachnode j of the cluster, the CH calculates and sends a publicvalue
Ycj = xc

mc
X

Yn

(5)

n=1, n6=jwhere mc denotes the number of sensor nodes in the clusterc.
The cluster key, K c can be determined in each node bysimply adding this value to the already established intra-cluster
pairwise key asKc= Kjc + Ycj = xc

mc
X

Yn

(6)

n=1

xi = Hash(idi ||K0 ||Ni )mod(p)(4)

where Ni is a randomly generated nonce. This generation
procedure ensures that all private keys are different from eachother, which can enable data origin authentication.
Then, the sensor node calculates its elliptic curve public key,
Yi = xi G. At this point the sensor node is able to calculate
its individual pairwise secret key, Ki = xi YB = xi xB G.
The sensor node sends its public key Yi to the base station
to be validated and stored in the public keys repository. The
message is authenticated by a Hash Message Authentication
Code (HMAC) using K0 , to ensures that it is sent by a genuine
deployed sensor node.
Public key validation and individual key establishment in
the base station: After verifying the identity of the sensor
node and the MAC of the received request, the base station
validates the public key of the sensor node, saves it in its publickeys repository, and establish the shared individual pairwise
key, Ki = xB Yi = xi xB G. Finally, the base station sends tothe sensor node an acknowledgment that is authenticated by a
MAC calculated using the individual key, Ki . Then, the sensor
deletes immediately the initial key, K0 from its memory.
3) Intra-cluster pairwise keys and cluster key establishment: Intra-cluster pairwise keys must be established to securecommunication between each sensor node and its cluster head.
In addition, the sensor node can establish a pairwise key witheach one of its node neighbors in order to communicate withthe cluster head. In addition, a cluster key shared between
all nodes of the cluster is established to enable in-network
processing and optimize resources usage.

4) Inter-cluster key and network key establishment: Using
the same procedure as for the cluster key, CHs and the baseMPstation can share an inter-cluster key K B = xB Yc to securec=1

message broadcast in the second level of the hierarchy. M isreferred to as the number of clusters.
In addition, a network key KN can be securely distributed
to all sensor nodes using two encryption stages. In the firststage, the base station randomly generates KN , encrypts itwith the inter-cluster key, KB and transmits it to all CHs. In
the second stage, each CH, c decrypts the network key andencrypts it with the cluster key, K c before broadcasting it to
all cluster members.
5) Session keys derivation: All the keys that they had been
described in last subsections are note used directly to ensure
data confidentiality and authentication. To this end, somesession keys are derived from these keys. This is performedby the following procedure.
The elliptic curve approach, allows the sharing of a pointwith two coordinates. Therefore, each key K is a point inE(Fp ) that is composed by abscissa, Kx and an ordinate
Ky . In order to be conform to the rule of separation between
the keys for encryption and authentication, two session keys
denoted as, Kei , and, Kai are derived from the abscissa
and the ordinate of the key K. These two keys are used
respectively for encrypting and generating the MAC of eachmessage exchanged between the sensor node and any otherentity of the network. Here, the key K denotes any kind of
the described keys such as individual key, pairwise key, cluster
key, inter-cluster key, and a network key. New session key is
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 4, July 2014

derived for each session. If i is the current session number,the session keys used securely exchange messages during thissession is given by the following formula
Kei = Hash(Kx , Kei−1 , M ACi−1 )(7)

Kai = Hash(Ky , Kai−1 , M ACi−1 )(8)

M ACi−1 is referred to the message authentication codeof the packets that had been correctly received during thelast session by the entity with witch the key K was shared.
Similarly, Kei−1 and Kai−1 are the session keys used in thelast session for ensuring encryption and authentication during
the last session. As it will be explained later in this paper,
the selected derivation procedure can ensure backward andforward secrecy of the transmitted data and resilience to nodecapture and replication attacks.

C. Keys management procedures
In this subsection, we describe procedure of modifying
the different types of keys due to new nodes deployment or
elimination. Also, we detail the re-keying process that will be
executed to initiate the establishment of new keys when thevalidity of the current keys expires.
1) New nodes deployment: When a new node is deployedin the WSN, it must first create its individual key shared withthe base station using the same procedure as described in theprevious section. The main difference is that the initial key will
be different from the one used in the initial deployment phase.
Indeed, suppose that a new node will be added at the instant t
after the initial deployment. The base station will generate andconfigure the node with an initial key Kt . This procedure will
prevent an adversary, that have access to previous initial keys,
to add its own replicated nodes. Once the individual key isgenerated and the public value is validated, the sensor follows
the previously described steps to establish the other keys.
2) Nodes elimination and revocation: When a compromised node is detected by the CH, it informs the base station
to invalidate its public key and adds it to the revocation list.
The CH will isolate the compromised node and establish a newcluster key by eliminating the public value of the compromised
node. Also, the base station will generate and distribute a new
network key using the new cluster key.
3) Mobility Management: The use of public key cryptography approach in the proposed key distribution mechanism
enables an efficient key update even in case of mobile sensor
nodes. We assume that some sensor nodes can move from one
cluster to another with a moderate frequency. The sensor node
should establish a pairwise key with its new CH and participate
in the generation of a new common cluster key using the sameprocedures as described earlier. However, the new CH shouldverify the validity of the public value of the node that wants
to join the cluster. Also, the old cluster should be informedthat the node has left the cluster to initiate cluster key update.

4864) Re-keying procedure: A global re-keying procedure is
triggered when the number of compromised nodes reaches agiven threshold or the validity period of the generated private
keys expires. New private and public keys should be created
to renew different shared keys. To this end, each sensor node
should reconstruct the key, Kr that will play the same role as
the initial key used in the deployment phase. We have investigated the use of threshold secret sharing techniques to manage
the distribution and the reconstruction of this key. The basic
idea is that every sensor node will possess a partial secret thatcan be used to reconstitute the key Kr . However, this cannot
be achieved unless a minimum number of nodes, denoted by t,collaborate together and assemble their secrets. This approachhas the advantage of maintaining the security of the key if
the number of compromised nodes is less than t − 1. Also,
the re-keying procedure can be initiated if at least t trusted
nodes are still operational in the network. Our proposal uses
the Shamir’s method[29] based on the Lagrange interpolation.
This approach consists in randomly selecting a polynomial
function, f (x) = Kr + a1 x + a2 x2 + ... + at−1 xt−1 mod(Q)by the base station, where Q is a prime number. We cannotice that, Kr = f (0) and all coefficients of f (x) must
belong to ZQ . For i = 1, 2, ..., N , the secret Si of each sensor
node i is calculated as Si = f (idi ), where idi is a unique
identifier of the node i. Each partial secret must be securelytransmitted to the corresponding sensor node. To this end, thebase station will encrypt every secret Si by the individual
shared key Ki . According to the Lagrange interpolation, f (x),can be reconstructed by giving t points (S1 , S2 , ..., St ) using
the following formula

f (x)

=

t
X
i=1


Si 

Y x − idj
i6=j

idi − idj mod(p)(9)

Particularly, the key Kr can be reconstructed by applying theequality


t
X
Y idj
 mod(Q)Kr = f (0) =
Si 
(10)
id
−
id
j
i
i=1
i6=j

IV. S ECURITY A NALYSISThe evaluation of the security schemes intended for WSNs
is significantly different from those used in conventional
networks. Indeed, the evaluation criteria should consider thecharacteristics of the WSNs deployment and their resource
constraints. In this section we analyze the security level offeredby our key distribution mechanism with regard to four proprieties that reflect the specificity of WSNs: (1) the possibility ofproviding backward and forward security for encrypted data,(2) the resilience to node capture, (3) resistance against node
replication, (4) the vulnerability to energy depletion attack.
A. Backward and forward security
The forward security propriety is to prevent the possibility
to an attacker to predict a future key if he captures a currently
used key. On the other hand, backward security is to preclude
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 4, July 2014

an attacker from obtaining information about previously usedkeys when he can capture the currently used key. These two
proprieties are very important in key distribution schemes
to ensure data confidentiality. To ensure the forward andbackward secrecy, our proposed key distribution scheme isbased on public key encryption paradigm where at each rekeying period the private and public keys of any sensor node
are generated independently of any previously used keys.
Therefore, all symmetric keys established between network
entities are not derived from any used key and are recalculated
based on the newly generated public and private key pairs. In
addition, no future keys must be encrypted by currently used
key to be shared. Moreover, all group communication keys are
modified each time a change in the network topology occurs
in the sensor level or in the cluster level.
B. Node capture
In many applications, sensor nodes are usually randomly
deployed by aerial dropping in large areas. Consequently,
sensor nodes can be easily captured by an adversary, who
can access to their memory content. Security schemes should
maximize the network resilience by minimizing the amountof information revealed to attacker on non captured nodes.
A sensor node can be accessed either using soft capture or
physical capture. In the soft capture, the attacker tries toestablish a connection to access to the management console ofthe sensor node. Many techniques can be used to implement
authentication in administrative mode, such as passwords,
RFID technology, and challenge-response approaches.
On the other hand, a sensor node can be physically captured.
In our case, an attacker can capture either a sensor node or acluster head. When an attacker captures a sensor node, he canaccess to the individual keys it shares with the base stationand the cluster head, and the cluster key it shares with all
members of its cluster. The later key can affect security within
the cluster and should be modified by eliminating the public
value of the captured node in the key calculation operation.
In addition, the sensor node stores a single part of the sharedsecret used to reconstruct the network re-initialization key. To
prevent the discover of this key the number of captured nodes
should not exceed the degree of the polynomial function, t.
Besides, getting access to a cluster head is more criticalthan in the case of a sensor node. In this case, the attacker
can access to the individual pairwise key shared between thecluster head and the base station, all individual keys shared
between the cluster head and each sensor node, the cluster key,
and the inter-cluster key shared with all other cluster heads
and the base station. Therefore, all group communication
keys must be changed by recalculating the keys without the
public value of the compromised cluster head. Also, the cluster
member should establish another individual keys with othercluster heads.
In addition, several techniques can be used to prevent thatan adversary can access to the content of a sensor or a cluster
head such as triggering of a physical auto-destruction, or asoft erasing of the content of all memories when an attempt
to access to the sensor is detected. These techniques are487

mainly appropriate for cluster heads which encompasses alarge quantity of information.
C. Node replication
The node replication attack consists in the possibility that anadversary party can introduce malicious nodes after gathering
information from captured nodes. In this case, the replicatednodes will try to establish connection with other nodes, cluster
head, or even the base station. These nodes should be detected
and isolated from the network. In the sequel, we describe how
the proposed scheme can resist to cloning attacks according
to different situations.
1) Node duplication at the initial deployment phase: In
this situation, the attacker tries to add new nodes to the
network with copied identities at the initial deployment phaseof the network. The inserted nodes will attempt to generate
private and public keys and establish symmetric keys withthe cluster heads and the base station. The proposed scheme
can guarantee resistance against this attack. Indeed, before
establishing connections in the network any new node should
generate a private key based on its identity and the secret initial
key. All initial keys are eliminated from the memory after the
deployment of sensor nodes, the generation of their private
keys, and the validation of the corresponding public keys.
Consequently, these keys cannot be recovered by capturing
already deployed sensor nodes. Furthermore, the base station
tracks all identities of the deployed nodes and validates anddistributes public keys to all entities requesting the establishment of secret keys. Therefore, unused or compromised publickeys can be revoked by the base station. Consequently, before
establishing any secure connection within a cluster, the identityof the node is checked and unauthorized nodes can be detected
and eliminated from the network.
2) The replication of an active node or a sleeping node:In this case the attacker will replicate a number of alreadydeployed nodes that are either in an active state or in a sleeping
state. We suppose that the attacker will get access to theprivate/public key pair of the cloned node. The attacker will
try to affect the copies of the replicated nodes to different
cluster in order to avoid their detection. In fact, if a clone
tries to connect to the same cluster as the original node, it canbe easily detected by the cluster head. This can be performedby identifying traffic patterns. For example, the cluster headcan notice that two packets are transmitted in a very shortperiod from the same node identity using two different routes.
In this case, it can either trigger the revocation of the two
nodes or, based on its history, it can detect the false node. Also
the inserted node will not be able to reconstruct the session
keys which are generated in every session based on the packet
transmission history.
Consequently, the clones will try to establish shared keys
with new clusters. In our scheme, we can detect copies ofnodes even in this situation. As it had been previously detailed,when the new cluster head receives the key establishment
request from the false node. It first consults the base station
to gather information about its original cluster. After that, it
informs the old cluster which verifies the connectivity status
Copyright ⓒ 2014 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 4, July 2014of the node by sending a beacon message to see if the node
had effectively left the cluster. If it detects that the original
node is still connected to its cluster it will communicate thisinformation to the new cluster head which isolates the false
node.
3) The replication of disconnected sensor nodes: In this
scenario, the intruder will try to insert duplicated copies ofa node that is disconnected from the network due to somereason. The replicated nodes are configured with the private
and public keys of the original node. They try to establish
keys with different cluster heads. The main problem in thissituation is that the original cluster head is not able to detect
if the node is still connected to its original cluster or not.
To resolve this problem we use authentication using the last
known session key of the node. In other words, when the false
node wants to establish new secret keys, we will first verify
that the original node is not connected to its original cluster byapplying the procedure described in the last subsection. Then,the new cluster head will challenge the node by requestingthat is encrypts a given packet using its last session key. The
encrypted message is then sent to the original cluster head toverify the genuineness of the node. This procedure allows anefficient detection and isolation of the false nodes even if they
are deployed in many clusters.
D. Energy depletion attack
Sensor nodes are battery based devices with a very limited
life time. Hence, energy management is a very important issue
in wireless sensor networks. An attacker can try a denial ofservice by sending many false key establishment requests withdifferent identities in order to deplete the available energy of asensor node. We can classify situations where this attack canbe performed in three classes:
1) Attacks performed during the identification process:In this situation, a certain number of malicious nodes try tosend an important number of false keying requests to the basestation in order to make intermediate nodes that are relaying
the message, out of energy. One solution to combat this attackis that when the base station receives a number of false keying
requests from a specific source higher than a specific value, itwill send a message to the neighbors of the source to not relay
any packet from it in the future. The attacking node is therefore
detected and isolated. However, this solution generates false
positives and does reduce completely such an attack.
2) Attacks performed during key establishment between
sensor nodes: In this case, a malicious node will send false key
establishment requests to a neighbor that will execute costly
processing operations that deplete its available energy. Our
scheme can prevent this kind of attacks because sensor nodes
will not perform any costly key establishment operation before
validating the identity and receiving the appropriate public key
from the base station. Also, we can set that if a sensor node
receives a number of key establishment requests with invalid
public keys it will isolate the source of these requests.
3) Attacks performed during data transmission: In thiscase, the attacker will try to send an important number offalse encrypted messages where the destination will apply the488

costly power consuming and unnecessary decryption procedure. This can dangerously reduce the available energy of thesensor node. One solution for this problem is that the basestation stores a profile for each sensor node transmission. The
profiling operation can be based, for example, on transmission
frequency and sampling. If a transmission deviates from agiven profile by a certain threshold, the base station will orderthe neighbor nodes of the source to not relay any new packet
from it.
V. P ERFORMANCES A NALYSIS
In this section, we assess the performance of the proposed
key distribution scheme in terms of scalability, key storage
requirement, communication overhead and computation power
cost.
A. Scalability
The scalability is the ability of the scheme to maintainan acceptable security level regardless of the network size.
This is very important is wireless sensor networks that usuallyencompass a very large number of sensor nodes. To be scalable
the number of encryption keys managed by each sensor must
not extensively increases when the number of nodes increases
in the network. This is due to the limited storage capacity of
sensor nodes.
The designed key distribution system is fully scalable
because it is based on public key encryption that provides
an effective security independently of the number of nodes
deployed in the network. In addition, the hierarchical topology
ensures the scalability of the communication process andoptimizes the resource consumption in the network.
B. Key storage requirement
To provide security for data transmission, in any key distribution scheme, each sensor node should store and managea specific number of keys in its memory. Due to the large
size of WSNs and the limited memory capacity of sensors,
the amount of the consumed memory, needed for storing keys
is a very important parameter. In our scheme, every sensor
node should store very limited number of keys. These are theprivate key, individual key shared with the base station, an
intra-cluster pairwise key established with the cluster head, acluster key, and the network key. Also, in case where a sensor
does not have a direct connectivity with the cluster head, itshould share individual keys with the neighbors that are closer
to the cluster head to relay transmitted packet. Although, thenumber of these keys will depend on the connectivity level ofthe cluster, it will be very limited. Typically, a sensor node willneed to set shared keys with two of its neighbors in order toensure communication reliability. Consequently, by using anappropriate clustering model, the storage capacity required to
manage the encryption keys in each sensor node will decrease.
On the other hand, the cluster head will need higher
storage capacity than sensor nodes. Indeed, in addition to theencryption keys managed by normal nodes, this device should
store an individual pairwise key with each sensor belonging
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 4, July 2014

to the cluster. Furthermore, it should set an individual key
with neighbor cluster heads and manage the intra-cluster key.
For this reason, cluster heads should be equipped with higher
storage resources. The amount of needed memory capacity
will depend on average number of sensor that can compose
the cluster.
Besides the few number of keys generated by the proposed
scheme in each sensor, the use of elliptic curve encryption
reduces the keys size. The public keys managed in every node
have a size that is almost similar to that of secret encryption
keys. This contributes also in decreasing the memory occupancy needed for storing keys in each sensor bonging to thewireless sensor network.
C. Communication Overhead
The communication overhead is referred to the number of
exchanged messages needed to achieve keys establishment
between different entities of the network. This parameter isvery important in WSN due to the fact that communication
procedures are the most energy consuming tasks. Therefore,
an efficient key distribution scheme should minimize thecommunications required to share different kinds of keys
while ensuring an acceptable security level and effective data
management procedure by enabling the in-network processing
capability.
In the proposed key distribution scheme, each sensor nodeneeds to exchange two messages with the base station tovalidate its public key and generate the individual pairwise key.
Three other messages and an acknowledgment are required toestablish intra-cluster pairwise key with the cluster head andthe cluster key. A last message and an acknowledgment areexchanged between the sensor node and the cluster head toshare the network key that is sent encrypted with the cluster
key. These communication messages should be exchanged
independently of the network size and connectivity.
Moreover, each sensor that has not direct connectivity withthe cluster head should share keys with neighbors that canrelay its packets. This will generate the need for exchanging
at least two packets per neighbor to retrieve the public keysof the nodes. However the number of these messages will bevery limited and can be significantly reduced if the number ofclusters and their deployment is chosen adequately.
Withal, the mobility of nodes or the deployment of newnodes can require extensive exchange of messages to perform
group keys update between the cluster heads. However, thisis less critical because this devices are supposed to havesufficient energy to sustain these operations.
D. Computation power requirement
Another performance parameter for any key distribution
scheme is the computation power required to perform key
distribution. Indeed, sensor nodes are tiny devices that areendowed with a cheap processor having a very limited processing capability. In public encryption scheme, the mostcomplicated operations are the computation of the public keys
and the establishment of shared keys using the Diffie-Hellman
elliptic curve key exchange procedure. Arithmetic operations

489

Table IS IMULATION PARAMETERS
Parameter
Number of sensors
Packet size
Acknowledgment size
Private, Public keys length
Symmetric key length
Transmitting energy
Receiving energy
ECC private, public key setup energy
MAC computation energy (SHA1)
Encryption/Decryption energy (AES)

Value
100-1000
36 Byte12 Byte160 bits
128 bits
59.2µJ/Byte
28.6 µJ/Byte
22 mJ
5.9µJ/Byte
1.62/2.49 µJ/Bytein the elliptic curve Galois Field are shown to have little
complexity compared to conventional public key encryption.
Furthermore, sensor nodes execute a very limited number ofthese operations which are triggered during initial deployment,
topology changes, and re-keying procedure. This is due to theclustering topology adopted in our scheme where the sensor
node will share keys with only the base station, its cluster head
and a very limited number of its neighbor nodes belonging to
the same cluster.
However, the re-keying procedure can have also an extensive
computation cost. In this case, the sensor should recover the
re-initialization key after collecting t − 1 partial secrets. This
operation depends on the threshold t that must be appropriately
selected to make a trade-off between security and computation
complexity.

VI. P ERFORMANCE E VALUATIONIn this section we assess the performance of the proposed
scheme with regard to the required key storage capacity,communication overhead, and energy consumption. In a first
set of simulations, we compare the performance of the proposed security to LEAP [18], [22]. In the second the part ofthe performance evaluation work we assess the scalability of
our scheme by evaluating its performance with regard to thenumber of clusters for different network sizes. Finally, the last
set of simulations is devoted for evaluating the performanceof the re-keying procedure.
To this end, we developed a simulation model using the
Matlab tool. We consider a clustered topology and we computeperformance parameters by varying the number sensor nodes.
The number of cluster in each topology is taken as : M =d0.05 ∗ N e where N is the number of sensor nodes. In theimplementation of the simulation model we used the values
given by table I.
For each number of sensors we generate 5 topologies,
and we compute the memory occupancy, the communication
overhead, and the energy consumption needed to establish keys
for every network. The final results are obtained by taking the
average on all values measured for all generated topologies.
The maximum number of topologies (5) is selected based on
the observation that this value guarantees a confidence intervalof more than 90%.
Copyright ⓒ 2014 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 4, July 2014

Fig. 2. Memory occupancy

490

Fig. 3. Communication overhead

A. Comparison with the LEAP protocol
We compare the performances of our scheme to thoseof the LEAP scheme[18], [22] which implements the innetwork processing concept but using symmetric pairwise
key pre-distribution paradigm. In each simulation, we executethe proposed elliptic curve public key cryptography based
approach, denoted as ECPKC, and the LEAP scheme, on aset of randomly generated topologies composed of a numberof sensors with one sink node.
Figure 2 depicts the required storage capacity for managing
key distribution in the proposed ECPKC. We can notice that
our scheme has remarkably reduced memory occupancy whencompared to the occupancy of the LEAP protocol. Moreover,the needed storage capacity of our scheme varies almost
linearly with the number of sensor nodes. However, for LEAP,
it increases rapidly with the number of sensor nodes. This isdue to the fact that in our scheme, each sensor node manage alimited number of public/private keys and symmetric keys thatare shared with the base station and the cluster head. Also,
each node shares several symmetric keys with its neighborsthat can not directly reach the cluster head. On the other hand,in LEAP the number of keys that must be stored in each nodedepends on the number of its neighbors, since a one pairwise
key and a cluster key should be shared with each neighbornode. Consequently, the number of needed keys will increase
with the density of the network.
The same observation can be formulated for the communication overhead presented by Figure 3. In our the ECPKC
approach the sensor nodes will initiate key exchange procedure
with the base station, the cluster head, and a limited numberof its neighbor nodes. This, decreases the number of messages
needed to establish shared keys. Also, the proposed group key
establishment procedure requires only the exchange of one
packet and an acknowledgment between the sensor node andits cluster head.
An important parameter for any key distribution scheme
is energy consumption. Figure 4 shows the total energy
consumption of the proposed scheme compared to the energy

Fig. 4. Energy consumption

consumption of the LEAP scheme. It can be observed that
elliptic curve based scheme needs less energy and that it varies
linearly with the number of sensor nodes. This can ensure thescalability of our scheme to large scale networks.
B. Evaluation of scalability
In this subsection, we evaluate the scalability of the proposed key distribution scheme. To this end we measure variation of the memory occupancy, the generated communication
overhead, and the consumed energy in function of the numberof clusters for different network sizes. The simulation results
are depicted by Figures 5,6, and 7. We can observe that theneeded storage capacity, the communication overhead, and theoverall energy consumption of our scheme decreases very fastwith the increasing in the number of clusters that compose the
network. Also, we can notice the existence of an optimal value
for the number of cluster from which the performances become
almost constant. This value is around 5% of the number ofnodes composing the network. This can be explained by the
Copyright ⓒ 2014 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 4, July 2014

491

Fig. 5. Variation of memory occupancy in function of the number of clusters
for different network sizes

Fig. 7. Variation of energy consumption during key establishment process infunction of the number of clusters for different network sizes

Fig. 6. Variation of the communication overhead needed to establish keys infunction of the number of clusters for different network sizes

Fig. 8. Variation of the communication overhead in function of the re-keying
period for different threshold values

fact that when we divide the network in a higher number ofclusters the number of sensor nodes that can connect directly
to its cluster head increases. However, the limit is reachedwhen all nodes of the network can be directly connected toits cluster head without any relay. This corresponds to theaforementioned optimal value of the of clusters.
C. Evaluation of the re-keying mechanism
In this subsection, we present results of simulation work
conducted to the evaluate the re-keying procedure of thepresented scheme that is based on threshold secret key sharing
technique. In these simulations the number of sensor nodes
composing the network is fixed to 1000 nodes which are divided into 50 clusters. We assess the communication overhead
and the energy consumption variation in function of the rekeying period for different values of the threshold t. We execute each simulation during 3600 minutes. At the end of every
re-keying interval every sensor node will share its individual
secret with t − 1 nodes to recover the re-initialization key, Kr

and after that it executes the key establishment procedure of
our scheme. Figures 8 and 9 present the simulation results. We
can notice that increasing the re-keying interval contributes
to decreasing the communication overhead and the energy
consumption. However, for a re-keying period higher that 30
minutes the variation of the performance parameters becomesalmost linear. This values can be considered as an optimalvalues for the re-keying period. We can see also that when
the threshold value increases the communication overhead
increases. Nevertheless, the variation is less important for theenergy consumption parameter.
VII. C ONCLUSION
Key distribution and management in WSNs is much moredifficult to achieve than in classical networks owing to the resource constraints, important number of nodes, and the lack ofinfrastructure support. Consequently, tailored key distribution
schemes need to be developed taking into consideration the
Copyright ⓒ 2014 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 4, July 2014

Fig. 9. Variation of energy consumption in function of the re-keying period
for different threshold values

limited computation capability, the little storage capacity andthe finite energy of sensor nodes. In this paper, we addressed
key management problem in WSNs. We proposed an elliptic
curve public key cryptography based key management scheme.
Our scheme is able to ensure secure sharing of many types
of keys in each level of the network topology. Particularly, it
uses elliptic curve Diffie-Hellman like key exchange procedure
to establish pairwise keys between the sensor node, the basestation, and its cluster head. Also, a group key establishment
protocol was proposed to create a cluster key used to securecommunication within each cluster and an inter-cluster key
used to secure message exchange between the cluster heads
and the base station. These keys, enable in-network processing,which improves message transmission efficiency and resources
usage in the WSN. Furthermore, the proposed approach enables re-keying procedure based on the concept of thresholdsecret sharing mechanism. Security analysis and performance
evaluation using simulation works showed that the ECPKC
mechanism ensures an enhanced security level while reducing
the required storage capacity, communication overhead, andenergy consumption which enables an efficient and scalable
implementation of our scheme in large scale WSNs. Finally,
developing a strong authentication method for broadcast traffic
based of the proposed key distribution scheme and ensuring
adaptive security in WSNs can be envisioned in a future work.
ACKNOWLEDGMENTS
This work was partially supported by the National Research Foundation of Korea (NRF) grant funded by the
Korean government and the Tunisian government (No.
NRF2012K1A3A1-A09026959).
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 4, July 2014[22] S. Zhu, S. Setia, and S. Jajodiadd, “LEAP+: Efficient security mechanisms for large-scale distributed sensor networks,” ACM Transactions on
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493

SunshinAn received the B.S. degree from Seoul
National University, Korea in 1973, and the M.S.
degree in Electrical Engineering from KAIST (Korea Advanced Institute of Science and Technology),
Korea in 1975 and the Ph.D. degree in Electricand Information from ENSEEIHT, France in 1979.
He joined the faculty of Korea University in 1982,
where he is currently a Professor of Electronic
and Computer Engineering. Prior to joining Korea
University, Prof. An was Assistant Professor of
Electronic Engineering in Ajou University, Suwon,
Korea. He was with NIST (National Institute of Standards and Technology)
in U.S.A., as a visiting scientist in 1991. His research interests include
the distributed system, communication networks and protocols, information
network, intelligent network, multimedia communication system, wirelesssensor network and mobile RFID network.

Walid Abdallah is an Assistant Professor at theaviation school of Borj Elamri, Tunisia. He received
his PhD in Information and communication technologies and the Diploma of engineer in telecommunications from the School of Communications
Engineering (Sup’Com), Tunisia. He received his
Master Diploma from the National School of Engineer of Tunis (Tunisia). From 2001 to 2005 heworked for the National Digital Certification Agency(NDCA, Tunisia) and from 1997 to 2001 he worked
for the national telecommunication operator (Tunisia
Telecom). Currently, he is a member of the Communication Networks andSecurity Lab, where he is conducting research in optical networks and wirelesssensor networks.

Noureddine Boudriga is an internationally known
scientist/academic. He received his PhD in algebraic
topology from University Paris XI (France) and his
PhD in computer science from the University ofTunis (Tunisia). He is currently a full Professor of
Telecommunications at the University of Carthage,
Tunisia and the Director of the Communication Networks and Security Research Laboratory (CNAS).
He has served as the General Director and founder of
the Tunisian National Digital Certification Agency.
He is the recipient of the Tunisian Presidential award
in Science and Research (2004). He was involved in very active research andauthored and co-authored many journal papers, book chapter, and books on
networks and security.

Daehee Kim received the B.S. degree in Electronics
Engineering from Yonsei University, Korea, in 2003
and M.S. degree in Electronic and Computer Engineering from Korea University, Korea, in 2006. Currently, he is working for Ph.D. degree on Electronic
and Computer Engineering in Korea University, Korea. His research interests include the wireless sensor
network, LTE, and security in wireless networks.

Copyright ⓒ 2014 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 4, July 2014

494

An Efficient LSDM Lighting Control Logic
Design for a Lighting Control System
Sung-IL Hong, Chi-Ho Lin
Schools of Computer, Semyung University, 65- Semyung-ro, Jecheon, Chungbuk, Korea
megadriver@hanmail.net, ich410@semyung.ac.kr
Abstract—in this paper, we propose an efficient LSDM
lighting control logic design for a lighting control system. The
proposed LSDM lighting control logic is designed according tothe operating conditions by dividing them into the signal control
part for the I/O data bus and the timer/counter part for the clock
signal control. Also, the control logic is transmitted to the MCU
through a data bus based on the environmental information
provided by each sensor node. The power dissipation rate of the
proposed LSDM lighting control logic was measured in order todemonstrate the efficiency of the applying the control system. In
addition, it was demonstrated that the proposed design iseffective for the reduction of overall power consumption.
Keyword—Control logic, LSDM, Lighting control, Signal
control, Power dissipation, MCU

I. INTRODUCTION

T

HE field of lighting design in the 21st century is being
developed based on intelligence and automation
technology, because the LED lighting device performance was
improved quickly in recent years. The LED lighting device
needs control logic for LED emitting. The LED lighting iscommonly used in lighting devices by configuring to one
module the multiple LED. The power consumption of LED
affects the life of the LED lighting device by changing theinternal temperature of the LED through the operation of thecontrol logic. Also, the LED lighting control system should be
provides exceptional identification skills and high color
rendering by considering the energy savings [1-6].
The existing lighting device is not made as a complicatedcontrol circuit design, in order to implement the simple on-off
function according to operation of the sensor. But, theexisting lighting control logic for the LSDM (LED streetlight
dot-matrix module) control has problems when operating byusing each different device drivers and control programs. The
lighting control logic for LSDM is sensitive device that———————————————————————
Manuscript received May 30, 2014.
This research was supported by the MSIP (Ministry of Science, ICT and
Future Planning), Korea, under the Human Resource Development Project for
SoC support program (NIPA-2014-H0601-14-1001) supervised by the NIPA
(National IT Industry Promotion Agency).
Sung-IL Hong is with the School of Computer, Semyung University, 65
Semyung-ro,
Jecheon,
Chungbuk,390-711,
Korea.
e-mail:(megadriver@hanmail.net).
Chi-Ho Lin is with the School of Computer, Semyung University, 65
Semyung-ro,
Jecheon,
Chungbuk,390-711,
Korea.
e-mail:(ich410@semyung.ac.kr).

directly affects the light output of LED, reliability, efficiency,
and life. Also, the LED lighting device is required to be
designed by using MCU, because many features can becontrolled and operated [7-8].
The current flowing through the LED determines thebrightness, and the internal power consumption will change
the internal temperature of the LED. Therefore, the lighting
control logic is a sensitive device that is related to LED's
brightness, reliability, efficiency, long-lasting life, etc.,
because the operating temperature affects the life of the LED.
There have been various studies performed on LED lighting
control logic to develop optimal performance of the system
configuration or the circuit. However, these researches areincomplete when considering the technology needed to reflect
optimal design with modeling of the LSDM lighting control
logic.
The existing street lighting control system has been
operated by design providing the ability to distinguish objects
only to pedestrians or motorists than the light functionality. As
a result, the lighting device control system needs to be
designed to ensure the safety of pedestrians via a signal placed
above the road, using a variety of add-ons. For
troubleshooting in this study, we propose the design of an
efficient LSDM lighting control logic for a lighting control
system using the MCU by considering the compatibility of thedevices and the scalability.

II. LSDM LIGHTING CONTROL LOGIC
A. LSDM Control Logic
The ATmega128 is downloaded to your system bycompiling the lighting control program created for the controlof LSDM with the kernel, the device drivers, and the filesystems. The LSDM lighting control logic is used to control
the LSDM by configuring the decoders, the drivers, thelatches, and the shift registers.
Figure 1 shows the configuration of the LSDM lighting
control systems. In this paper, efficient control logic for the
LSDM operation is designed by configuring the LSDM as
signal controller, timer, and counter parts. The LSDM control
signal can be sent to the LSDM lighting control by operating
the control logic with the program execution. The LSDM
lighting control logic was designed by dividing the controls
into the signal control part for the I/O data bus and thetimer/counter part for the clock signal control according tooperating conditions.

Copyright ⓒ 2014 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 4, July 2014495

selection signal for lighting of LSDM. In this case, the LSDM
input data signal control part will light up the LSDM byshifting the lighting data value using the shift register. The
anode and the cathode driver will output to LSDM, thelighting data signal of the rows and columns through the shift
register and the latch. Figure 3 shows the control flow of
LSDM I/O data signal.
Fig. 1. LSDM Lighting control system

Figure 2 shows the configuration of the LSDM lighting
control logic. The control logic is used as a decoder for
selected lighting line by considering expansion of the LSDM.
The I/O of the lighting control data uses the latch for control.
Also, the control logic outputs the lighting data as LSDM uses
the driver vertically and horizontally. The oscillator isdesigned to provide clock synchronization on all circuits using
the internal oscillator circuit of ATmega128. To enable thisthe signal is received from ATmega128 for data output by
repeating the latch process after a certain period of time. The
R-Data and G-Data are used with the data for lighting of the
LSDM. The A0 to A2 of 3-bit address lines are designed to be
the line selection of LSDM by serial connection of up to
maximum of eight. It outputs the lighting line selection signalof LSDM, because the LSDM I/O data signal controller canbe controlled by selecting the LSDM connected as serial byadding a 3-bit decoder. In this case, the LSDM will light up by
shifting the lighting data values using a shift register. The
anode and the cathode driver are output as LSDM byreceiving the lighting data signal of the rows and columns.

Fig. 2. LSDM lighting control Logic

B. I/O Data Signal Control
The I/O data signal flow of the control logic for LSDM
control is entered through LSDM control logic, and the data
output pin is determined depending on the address of theATmega128. It uses address from 0x2500 to 0x2800 for the
LSDM control, and stores in the internal memory by applying
simultaneously to the data, address, and clock. If the data areread in the control logic of LSDM, the signal and data from
ATmega128 is entered into the LSDM, according to the clock
and latch signal. When the data is output into the control logicof LSDM, it is entered into the LSDM data when RW output
signal is 1. The LSDM input data signal control part is selected
and the control is used as the serial connecting LSDM byadding the 3-bit decoder that can be extended, because I/O
pins are not simply I/O functionality. It outputs the line

Fig. 3. LSDM control signal flow

Figure 4 shows the configuration default value of the
optimization register. The control logic is used the output port
C from port A on ATmega128 for data processing. It uses
port G for controlling using address latch signal and R/W
strobe signal for memory. The signal of the R/W signalroughly accesses the memory to the I/O buffer, and theaddress latch etc. in order to light up the LSDM. A port that isused at the I/O of the lighting data signals for LSDM lighting
control can be the individual bits control when used as
general-purpose I/O ports. Therefore, it is to be read/written
as lighting data of LSDM using the PORTx data register (Data
Register) that corresponds to the output and the DDRx (Data
Direction Register) that sets the direction of I/O.

Fig. 4. LSDM lighting signal control registers

The data for controlling the flow of input and output signalsof the LSDM is set to the direction and output of the input andoutput data, depending on the settings of the DDRx and the
PORTx. In the DDRx register sets the input or output wheneach bit value is 0 or 1 using the 8-bit from 0 to 7. The PORTx
register stores the value of the logical output port and verifies
DDRxn bit setting contents of the PORTx register. Also, it isdesigned to load the status value of the corresponding port pin
using the PINxn (Port Input Pins Register) bit register. In thiscase, the PINxn register bits are synchronized in order to
optimize the input and output values in order to avoid theintermediate situation lasting phenomenon, whether the bit
value is 0 or 1. Table 1 shows the values that the PORTx dataregister are used for processing of data signals for the lighting
control on LSDM.
Copyright ⓒ 2014 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 4, July 2014

TABLE 1. PORTX DATA REGISTER VALUE

DDRx

0xff

~row[1]
0xff
0x01
0xff
0x80
0xff
0x01
0xff
0x80

PORTx
~row[2]~row[3]
0xdd
0x01
0x55
0xff
0x55
0x80
0x55
0xff
0x55
0x01
0x55
0xff
0x55
0x80
0x77
0xff

~row[4]
0xee
0xaa
0xaa
0xaa
0xaa
0xaa
0xaa
0xbb

The DDRx register value is set to 0xff to be used as the
row-by-row lighting control data, with the PORTx register
output used as bit-by-bit. It was designed to allow controlling
according to the periodic pulse outputs by specifying an
output data value of PORTx register.
C. The Timer/Counter for Controlling of LSDM Clock
SignalThe LSDM lighting control system requires a periodic pulse
output in order to control the LSDM and ATmega128 due tothe synchronization. The LSDM control logic behavior cuts
off the clock supply using sleep mode individually, providing aseparate clock to each part to reduce the power consumption
in the timer/counter part. The LSDM control logic behavior
avoid fast-paced change of frequency for stable operation of
the control logic when you use a separate clock. The
timer/counter initial state is designed to be used by changing
to other content appropriate for the user environment using
the ISP or parallel programmer. The LSDM control logic uses
the XTAL divide control register (XDIV) by setting the initialvalue to 0x90 for the continued use of the same frequency andto reduce the power consumption. Figure 5 shows the default
setting values of the XDIV for the control of demultiplying.

Fig. 5. The XDIV default setting value

The clock source used in the LSDM lighting control logic is
16MHz. Therefore, a clock source has a set value to activate
clock frequency, which is set to match the control program
during behavior of the control logic. A frequency is set
dividing 128 for the use of the control logic.
D. LSDM Control Algorithm
In this paper, the LSDM control algorithm to be used in the
LSDM lighting control logic is designed to control theappropriate use of the LSDM to ensure low cost. The LSDM
control algorithm is converted to the digital signal of theanalog signal using the illuminance sensor, and it can be apattern control according to the changes in the brightness of
the light. . If the convert value of the ambient light is less thanthe default value, LSDM is continuously illuminated. If theconverted value is large than the default value, LSDM will496

turn off by shutting down all processes automatically to avoid
unnecessary energy consumption. This method will improvethe energy efficiency of LED lighting device.
Figure 6 shows the control algorithm for the behavior stateof the LSDM, controlled by a pre-set time schedule. The
control algorithm flow for the LSDM control is defined by thetype of I/O data, and transmitted to the MCU using the data
bus by starting illuminance measurement and motion detection
through installed sensor on lighting device. The control
system from MCU can calculate the illuminated level
depending on the set control method by correction of the I/O
data, and decide whether to the level is maintained bycomparing the results. The LSDM control algorithm
determines whether to adjust level by comparing with theexisting measurement values with the data according to theillumination measurement and motion detection. Therefore,
the lighting control system was applied to the control
algorithm for an efficient LSDM control.

Fig. 6. LSDM control algorithmIII. THE EXPERIMENTAL RESULTIn this paper, a proposed LSDM lighting control logic
measured the power of dissipation rate of the control logic on
the lighting status by checking the control status, using LSDM
lighting control data value of input and output from lighting
control system. The Lighting control data confirmed data
input for LSDM control that applies to the changes at any timeby user needs.
The existing LSDM lighting control logic replaces thestreet lighting device from the lighting method using sodium
lamps and metal halide lamps to LSDM. In addition, the used
LED module does not require an MCU, because to light up byplacing as the line forms or an array form. The lighting control
method is simply implemented with an on/off function bymeasuring the illuminance. For this reason, the street lighting
will be used in the case where lighting shows excessive
brightness in comparison to the illuminance of the surrounding,
avoiding unnecessary power consumption. The proposed
LSDM lighting control logic is lighting control based on
factors such as the temperature, humidity, illuminance, vehicle,
and pedestrian traffic around streetlights, because the MCU isused so that we can adjust the brightness using PWM (Pulse
Width Modulation) or change the lighting pattern by placing
the LED module in the form of a matrix.
Copyright ⓒ 2014 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 4, July 2014

In this paper, we designed control logic for controlling the
LSDM of efficient lighting control system, and the control
program was written to operate in device driver and LSDM.
The drivers and written programs were preferences using
minicom and RS232C communication through the serial port
in order to download to the lighting control system. In
addition, an environment was set up of the host PC and trivial
file transfer protocol (tftp), and transmits. We used this for the
cross compiler, because the compiler should behave
differently in the host PC and the generated executable file
behavior system. The boot loader was compiled for
configuring the lighting control system, and the download.
Also, we were compiling with the kernel, the file system, andthe device drivers and we created a lighting control program.
The host PC checked the status of lighting control bydownloading the program into the lighting control systemthrough ethernet or serial.
Figure 7 shows the experimental environment of the host
PC and the lighting control system had serial connection. It
was configured so that it can control behavior of LSDM
through the network on client PC in other places. The LSDM
controlled lighting by applying the dynamic driving method
constructed as 8x8 dot type.

Fig. 7. Experiment environment

The experimental environment of figure 7 confirmed thelighting control status by efficiently controlling the LSDM
control logic through I/O LSDM lighting signal data values by
operation of LSDM lighting control program.
The lighting control data was confirmed by the data input
for controlling the LSDM, because it was applied so that it canbe efficiently changed at any time according to the user needs.
Figure 8 shows the timing diagram of the lighting control dataof the LSDM lighting control logic entered through PORTx of
the ATmega128 with application of the LSDM lighting
control algorithm. The lighting control data represents thevalue of the ~row[1] in the register-values for the control
signals of Table 1, and the data were entered sequentially from~row[1] to ~row[4].

Fig. 8. LSDM control data

497

Figure 9 shows the time chart of the experimental results on
the LSDM lighting control logic operation state of lighting
control system that based on the control data entered from theATmega128 by the LSDM control algorithm.

Fig. 9. LSDM operation timing chart

The ATmega128 enters the clock signal to the LSDM fordata input and display, and enters the data through the PORTx
for LSDM lighting. In addition, the reset signal is entered as"H" in order to counter the value initialized. In this case, theLSDM is not deleting the stored data even though the reset
signal is input as "L". If the selected input signal is "H", thedata input control is displayed with the data input. If the data
input is "L", it is displayed by the set that disables the stored
data. The brightness of the LSDM are controlled bydetermining the pulse width of the “tckc” when the selectedsignal for lighting control is entered as "H", because it cancontrol the brightness by adjusting the pulse width on the
PWM. The LSDM control logic receives a signal to check theon/off status using a dynamically-driven, and it could obtainoff results of LSDM while the signal is being input.
Figure 10 is a graph showing measured results of the power
dissipation rate by comparing the footprint and the efficiency.
For the measured result of the average value of power
dissipation rate, IC power dissipation (IC Pd) were increased
0.086W, but LED power dissipation (LED Pd) weredecreased 12.80W as 63.34W from 76.15W. The total power
dissipation (Total Pd) were decreased 4.01W as 2.53W froman average of 6.54W. The result of comparison to the power
dissipation rate of control logic obtained effective results ofpower dissipation reduction and the power dissipation rate of
the proposed control logic was reduced by 61.19% whencompared with the conventional control logic.

(a) Power dissipation existing lighting control logic

Copyright ⓒ 2014 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 4, July 2014(b) Power dissipation proposed lighting control logic
Fig. 10. Power dissipation

Figure 11 shows the measured results of efficiency and the
duty ratio for LSDM operation state by comparing the input
voltage when applying the LSDM lighting control algorithm.

498

measured to decreasing 14.64% that it measured the 58.78%
when the minimum input power is 20V, and measured at50.171% when the maximum input power is 28V. In figure11(b), the efficiency of the proposed control logic was
obtained at an average efficiency of 92.03% by measured toincreasing 1.14%, it measured at 91.389% when the minimum
input power is 20V, and at 92.436% when the maximum input
power is 28V. The duty cycle was obtained at an average duty
cycle of 54.18% by measured to decreasing 14.65%, it wasmeasured at 58.71% when the minimum input power is 20V,
and at 50.109% when the maximum input power is 28V.
Figure 12 shows the comparative results of development
costs for efficiency by comparing the area occupied of theexisting control logic and the proposed control logic. The
BOM (Bill of Materials) cost of the control logic wasestimated by the primarily considering efficiency than the area
occupied.

Fig. 12. Total BOM cost of system(a) Existing lighting control logic

IV. CONCLUSION(b) Proposed lighting control logic
Fig. 11. Efficiency and duty cycle

In Figure 11(a), the efficiency of the existing control logic
could be obtained at an average efficiency of 91.79% bymeasured to increasing 1.08% that it measured the 91.124%,
when the minimum input power is 20V; and it is measured at
92.206% when the maximum input power is 28V. The duty
cycle could obtain an average duty cycle of 54.25% by

In this paper, we proposed an efficient LSDM lighting
control logic design for a lighting control system using the
LSDM control algorithm. In this paper, a proposed LSDM
lighting control logic was measured to power dissipation rateof the control logic on LSDM lighting status, by downloading
the LSDM control algorithm into the control logic throughserial port. LSDM control algorithm is able to efficiently
control the lighting control system by the lighting control
signal data value of input and output into LSDM lighting
control logic.
As a result of checking the lighting control status, weanalyzed the results by measuring the efficiency and the duty
ratio for the input power. The efficiency of the proposed
LSDM lighting control logic was obtained at an average
efficiency of 92.03% by measured to increasing 1.14%. The
duty cycle was obtained at an average duty cycle of 54.18% bymeasured to decreasing 14.65%. As a result, the proposed
LSDM lighting control logic was proved to be more effective
than the existing control logic for improving the overall
efficiency of the lighting control system. The LSDM lighting
control logic based on MCU makes it possible to utilize
lighting control with real-time monitoring when configuring
the sensor network using zigbee communication method.
Copyright ⓒ 2014 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 4, July 2014

499

In the future, LED street lighting control system must be
applied to the LSDM lighting control logic to increase theutilization of the proposed LSDM lighting control logic, andmore research must be carried out for the establishment of an
efficient street lighting management system.

ACKNOWLEDGMENT
This research was supported by the MSIP (Ministry of
Science, ICT and Future Planning), Korea, under the Human
Resource Development Project for SoC support program(NIPA-2014-H0601-14-1001) supervised by the NIPA
(National IT Industry Promotion Agency).

REFERENCES[1][2]

Seoul Semiconductor, Z-POWER LE D Series Technical Data sheet for
W4218X, www.zled.com, 2007.
Sang-Bin Song, In-Seon Yeo, “The Thermal and Circuits Design of an
LED Bulb Considering Temperature Property”, Institute of ElectricalEngineers, Vol 56, No 7, pp.1261-1267, 2007.

[3]

[4]

[5]

[6]

[7]

[8]

Mi-Young Lee, Woo-Hee Lee, Jun-Ha Lee, Hoong-Joo Lee, “Design of
LED Driving Circuits to Detect Degradation Characteristics”, The
Korea Institute of Power Electronics, Power Electronics Conference,
pp.81-83, July. 2005.
Jung Kwang-Sung, Kim Chang-Beom, Moon Cheol-Hong, “LEDControl Board Design using Xilinx Spartan3 FPGA Module”, KIEE,CICS '09, pp. 331-332, 2009.
H. Broeck, G. Sauerlander, and M. Vendt, “Power Driver Topologies
and Control Schemes for LEDs”, Proc. IEEE. APEC, pp. 1319–1325,
2007.
Y. Hu and M. Jovanovic, “LED Driver With Self-Adaptive Drive
Voltage”, IEEE, Trans. on Power Elec, vol 23, no. 6, pp. 3116-3125,
2008.
Soo-Bin Han, Suck-In Park, Eugine Song, Hak-Guen Jeoung,
Bong-Man Jung, Gue-Duk Kim, “Overview of Classification andCharacteristics of Recent LED drive IC”, KIEE
AutumnAnnualConference, pp. 105-107, 2008.
Borbély, A., A. Sámson, and J. Schanda. “The Concept of CorrelatedColor Temperature Revisited”, Color Research & Application, vol 26,
no. 6, pp.450–457, 2001.

Sung-IL Hong received B.S and M.S. degrees insciences and education from Semyung University in2007 and 2009, respectively. Since August 2009, heenters to the Ph.D course. His current researchinterests include SoC CAD, ASIC Design, CAD
Algorithms, SoC Design, RTOS and Embedded
Systems. Now, he studies the lighting control
systems and remote control & management system.

Chi-Ho Lin received B.S and M.S. degrees inEngineering from Hanyang University in 1985 and1987, respectively. He also received Ph.D degree inEngineering from Hanyang University in 1996.
Since March 1992, he has been with the school ofcomputer in the semyung university as the professor.
His current research interests include SoC CAD,
ASIC Design, CAD Algorithms, SoC Design, RTOS
and Embedded Systems. Now, he studies thelighting control systems and remote control & management system.

Copyright ⓒ 2014 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 4, July 2014

500

A Study on the Performance Evaluation ofContainer Tracking Device based on M2M
Eun Kyu Lee*, Hyung Rim Choi*, Jae Joong Kim*, Chae Soo Kim*
*Intelligent Container R&D Center of Dong-A university, 37 Nakdong-Daero 550 beon-gil Saha-gu, Busan, Korea
604-714jabanora@dau.ac.kr, hrchoi@dau.ac.kr, jjkgb@dau.ac.kr, cskim@dau.ac.kr,

Abstract—A M2M-based container tracking device is a device
installed inside container to detect the open/shut status ofcontainer door. In terms of the main features of the device
developed in this study, it can not only detect the open/shut statusof container door but also perform inquiries on the status of insidecontainer environment and shocks received by container during its
transportation upon installing temperature, humidity and shock
sensors. This paper focused on the performance evaluation via
trial operational test of container safe transportation surveillance& tracking, that monitors in real-time the security status of freightfrom departure to arrival.

Keywords— Container, Tracking, M2M, Performance evaluation

I. INTRODUCTION
According to the Container Tracking and Security Market
2012-2022 analysis, companies and organizations in many areas
have a need to track and ensure security of their freight assets
being transported around the world. In addition to increasing
terrorists’ threats, freight thefts and biological weapon terror
threats, many containers become lost during transportation withincreasing cost of goods that go bad. Accordingly, protection ofcontainers through tracking and ensuring security is effectively
being utilized in the commercial field, and an analysis forecaststhat the container tracking and security market will see a strong
growth in the next decade [1]. In particular, as the container
tracking market is being rapidly growing since 2000, there is apossibility that it could see a significant scale of revenue. The
world is engulfed in the wave of economic recession and there
still are regions that are facing a crisis. However, the world’s
trade volumes are increasing exponentially. The containertransportation industry might be experiencing difficulties but
the number of containers (TEU) has been increasing in the last———————————————————————
Manuscript received April 25, 2014. This work was supported by the
Ministry of Land, Infrastructure and Transport, Korea.(KAIA-2013).
Eun Kyu Lee Author is with the Intelligent Container R&D Center, Dong-A
university, Busan Korea 604-714 (e-mail: jabanora@dau.ac.kr).
Hyung Rim Choi Author is with the Intelligent Container R&D Center,Dong-A university, Busan Korea 604-714 (e-mail: hrchoi@ dau.ac.kr).
Jae Joong Kim Author is with the Intelligent Container R&D Center,Dong-A university, Busan Korea 604-714 (e-mail: jjkgb@ dau.ac.kr).
Chae Soo Kim Author is with the Intelligent Container R&D Center,(phone: +82-51-200-7690; fax: +82-51-200-7697; e-mail: cskim@ dau.ac.kr)

several years, which will continue on as it become a catalyst of
the growth container tracking and security market. In 2012, theUS announced an act that mandates all container freights
coming into the US to be installed with security devices certified
by the US Customs to verify that containers have not been
opened during transportation. In addition to the US, the EU
reinforced its logistics security by enacting ‘marine & port
facilities security regulations’ that even mandated therecommendations in the ISPS Code provisions of theInternational Maritime Organization (IMO). It also legalized
logistic security regulations that focus on fulfilling the corporate
logistics security system (SAFE Framework) of the World
Customs Organization (WCO) with continuous efforts toreinforce logistics security by developing and operating animport freight scanning system. Advanced nations including the
US and the EU are developing electronic seal and container
security devices and standardizing technologies to prevent any
bottleneck in logistics flow while reinforcing security measures.
They are moving quickly to preoccupy the market in addition ofincreasing R&D investment. Representative electronic security
devices of freight container include electronic seal (eSeal) using
active RFID (Radio Frequency Identification) technology andcontainer security device (CSD) of IEEE 802. 15.4b. Active
RFID with longer recognition distance compared to passive
RFID tag that can be easily applied to metal object is being used
for port & inland logistics transportation management systemand container protection for metal containers. The eSeal thatuses the technology in this paper is installed on the outside door
handle of freight container. Upon detecting any abnormal
opening of container door by an outsider or any attempt to open
door abnormally, it notifies nearby reader while maintaining alog.
The CSD is installed inside freight container to detect any loss
or theft of container, as well as any attempt to infiltrate container.
While there isn’t any international standard for container
security device currently, the US Department of Homeland
Security (DHS) has announced a technical specification
required by the US Customs and Border Protection (CBP) [2].
Accordingly, various companies centering on GE (US)established Commerce Guard and Savi (US) and CIMC (China)
are actively carrying out related R&D activities. However, inthe midst of a situation where companies across the world arecompeting fiercely, there isn’t any competitive product in Korea
that could compete with foreign products as of now.

Copyright ⓒ 2014 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 4, July 2014

Accordingly, this paper explains the Advanced Container
Security Device (ACSD). ACSD satisfies the user requirementof real-time identification of container position by applying
M2M (Machine-to-Machine) technology that uses mobile
communication technology. And to evaluate the performance of
ACSD and system, we introduces the trial test result which has
been applied to container freight being transported between
Korea and Poland, while also verifying the reliability of thepaper [5].
The composition of this paper is as follows. Chapter II
examines the future direction of development and advancementof electronic security devices through related researches andcomparatively analyzes the features of container security
products. Chapter III explains the CSD system using the ACSD
developed in this paper. Chapter IV evaluates the system
performance through a trial services domestically as well as
between Korea and Poland. Chapter V will conclude this paper.

II. RELATED RESEARCHThis chapter examines the future direction of advancement ofcontainer security devices and comparatively analyzes thefeatures of a competitive companies’ products and ACSD.
Since the early years of logistics using container, plastic or
metal “seal” of conventional bolt barrier was used through one
or more door hasp mechanisms for container security. Since
2000 in which the importance of container security wasemphasize along with IT advancement, eSeal was developed
applying active RFID technology, which detects any abnormal
opening/shutting of container door upon installing. However,
eSeal has the weaknesses of easily breaking down as it is
attached outside container door and not being able to be reused.
Improving the weakness of eSeal, the development of CSD
became active according to the CSD requirement document
presented in 2007 by the US DHS for detecting any illegal
opening/shutting of container door. As it is installed insidecontainer, CSD can be reused. Since then, ACSD (Advanced
Container Security Device) is currently being developed withimproved performance, which not only detects any illegal
opening/shutting of container door but also monitors inside thecontainer and detects illegal immigrants. In terms of future
direction, it is expected that intelligent container will be
developed with devices that perform ACSD function are
preinstalled inside container rather than separately attaching
eSeal device to container. Table 1 compares and analyzes thefeatures of M2M-based container tracking and security devices
in commercialization.
TABLE 1FEATURE COMPARISON

Starcom

KirsenTRITON

CMD500-S

De/attachment •Left wall

501

•Center

Weight•150g•1500gSize•195×96×40(㎜)•200×150×100(㎜)•Zigbee•GSM

Communication•CDMA, GSM option •GPS, RFID option

Battery•5000mAh Li-lon•3 years (2 per day)Position•GPS•GPS + Cellular

Status•Door, illumination

temperature, humidity,•Illegal infiltration,

shock, vibration, slope
Temperature•-40℃-60℃•-30℃-85℃

The two devices can be used in container security related areas
such as the UD DHS CSI, SAFE Port Act, C-TPAT, 10+2
system, etc. Using a clamp type (C-Clamp) auxiliary device,
Kirsen’s CMD500-S is attached outside container door at thecenter and detects opening/shutting status of door using breach[3]. It also provides a log of changes in container status duringtransportation by installing temperature/humidity and shock
detection sensors. Using magnet between container wall anddoor, Starcom Systems’ TRITON is attached to detect
opening/shutting status of container door by using proximity
sensor [4]. It also provides a log of container status duringtransportation by attaching temperature/humidity sensor.
However, CSD that does not include shock sensor cannot detect
any shock received by container.

III. INTRODUCTION TO ACSD
The shape and framework of ACSD prototype and thedefinition of payload for data communication are as follow.
A. About the Prototype
Fig. 2 shows the parts of container security device. This device
has been developed according to the requirements of the US
DHS, marine shipping companies and shipper. The device is
attached on crack between outside wall of container door andthe door at an appropriate height at which freight will not bedamaged. Using magnetic sensor, it detects container door
opening/shutting status and provides a log of the status ofcontainer during transportation through temperature/humidity
and shock sensors installed. As it is installed inside container,
there is a low risk of damage for ACSD that can also be reused.
It has been developed to allow communication at any parts of
the world with communication stations as the device is
M2M-based.

Shape

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 4, July 2014

502

Table 2. DATA DEFINITIONClassification

2byte

Protocol ID

ID of corresponding protocol

1byte

Unique number for distinguishingdevices

Date & Time
Position Data

Fig.2. CSD Framework

C. Definition for Payload Data
As shown in Table 1, data being transmitted to/from container
tracking device included device ID, status info such as position,
temperature, humidity and shock, as well as battery andtransmission cycle information. Such information was definedaccording to the features that can be provided by hardware andprotocol rules.

Display of event occurrence and info
transmission time
Display of current position
Value of temperature measured in

Temp

sensor module
Value of humidity measured in sensor

Humid

B. Framework
As shown in Fig. 2, the M2M-based container tracking systemframework consists of container tracking device, interface andcontainer tracking control information system. The container
tracking device should collect information (position,
temperature, humidity, shock, door status) required by global
supply chain constituents and be usable even in poor logistics
environment. The real-time communication interface needs toconsist of interface with mobile carrier’s communication stationand protocol required for data collected from container tracking
device to be sent to container tracking control information
system. The container tracking control information system issoftware for displaying in real-time visual information to
constituents of global supply chain once the data collected fromcontainer tracking device has been sent via real-timecommunication interface. The middleware of container tracking
control information system consists of middleware that sends
only required data to monitoring system by collecting andfiltering data from container tracking device, and web-based
monitoring system that displays in real-time via electronic map
to users the data refined by middleware, and mobile-based
system that can send information even via smart phone.

LengthIndex of log stored in the device

Device ID

Fig. 1. The shape of ACSD

Content

Index

moduleVector sum of the X, Y, Z axis ofShockacceleration sensor

Door Status

Opened or closed

Remaining Battery
Power
Device On/Off

8byte

6byte
11byte
2byte

2byte

2byte
1byte

Display in % remaining battery power 1byte
Display Device On, In-Process, Offstatus1byte

Transmission

Cycle of transmitting information to

Cycle

server

RSSI

Display of signal reception sensitivity 1byte

1byte

Storage of Error Code of error
Error Status

and malfunction of

1byte

transmitted information

IV. DEMONSTRATION TESTThis chapter introduces a result of overseas trial test for
demonstration and reliability check between Korea and Poland
which was conducted to verify the performance of ACSD
system developed in this research.
A. Demonstration Test
The ACSD system was applied to a container filled with freight
being transported from Paju to Busan Port to Poland, as shownin Fig. 3, with the assistance from Company L to conduct aninternational trial service between Korea and Poland. The
objective of the trial service was to verify the features duringcontainer marine transportation and it checked the status ofcomplete transmission of data collected from the device toserver to operating server DCP when the container installedwith the device passes through the following base points.

Fig.3. Test Section

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 4, July 2014

TABLE 3. DATA TRANSMISSION AND RECEPTION ANALYSIS
Trans. Frequency
OrderTrans.
Section

Set(Server Reception)

Cycle Interval
(min)

(min)

Omission
Cycle
Trans.

1

Domestic

Trans.

Interrupt

Total

Inland →Reception

10

20

2

-

2

0

60

360

6

10

16

0

180

34,560

191

22

213

1

60

1,320

22

-

22

0

36,260

221

34

255

-

Incheon Port
3

Incheon Port
→ Belarus
Belarus →4

Destination(Poland)
Subtotalreal-time information communication network infrastructure(communication station).
It was confirmed through such analysis of data transmission
and reception frequencies according to informationtransmission cycle that the main firmware features of container
tracking device used for the trial operation operated normally.

Frequency

Domestic
2503

B. Performance Evaluation
The main firmware features of the container tracking device,
which are information collection, real-time informationtransmission/reception and memory storage features, arefeatures that can be performed on the premise that the main
hardware features explained earlier operate normally. This
indicates that when the information collection, real-time
information transmission/reception and memory storage
features used during the trial operation operated normally, themain hardware features of the container tracking device also
operated normally. In regards to the main firmware features of
the container tracking device, which are information collection,
real-time information transmission/reception and memorystorage features, number of data transmitted according to
information transmission cycle and number of data received will
be analyzed for verification. Since the real-time informationtransmission/reception feature can operate with the prerequisite
of information collection and memory storage features, normal
operation of information collection and memory storage
features can be also verified by analyzing the number oftransmitted data and the number of received data.
It is expected that the total number of data transmitted
according to the information transmission cycle set as shown in
Table 3 would be 222 but the actual number of information
received by server was 255 based on trial operation results. Thisis a sum of 221 times of transmission according to set cycle and34 times of interrupted transmission that occurred by detecting
through shock sensor and door opening/shutting. Except for thenumber of interruption from the exceptions that occur duringtransportation, the final number of reception was 221 that isshort by one session from the initially expected number oftransmission of 222. This is a result one session of informationtransmission that was omitted in the section transporting fromIncheon Port to Belarus and it is estimated that one session of
information transmission was omitted due to reasons of

V. CONCLUSION
The M2M-based container tracking system frameworkpresented and the system developed in this paper were
evaluated for its performance through trial operation. The CSD
system that has been verified through performance evaluation
enables shipper and forwarders that need real-time tracking ofcontainer the most among the constituents of global supply
chain to effectively establish their global supply chain plan. It isdifficult to collect in real-time information required by users
through conventional container tracking system. However, inthe case of container tracking system presented in this paper,
effective supply chain management can be expected such as
inventory control and production management by collecting andutilizing in real-time container information (position,
temperature, humidity, shock, door opening/shutting) requiredby users upon utilizing M2M real-time communication
technology.
In addition, logistics companies such as forwarder,transportation and shipping companies directly verified freightarrival/delivery by utilizing their or partner’s manpower at eachlogistics base point. Among the container tracking control
information systems presented in this paper, the main
middleware feature of Geo-fencing enables real-timeverification of freight arrival/delivery in office. This featureallows logistics companies to save time and cost involved inverifying freight arrival/delivery status with the expected
effectiveness of improving their credibility by notifying
shippers that their freight is being transported safely.
Upon the logistics paradigm shift mentioned earlier andbackground of study, logistics security is being reinforced
across the world through regulations and policies. The container
tracking system according to the M2M-based container tracking
system framework presented in this paper can satisfy
safety/security regulations required in the US, Europe and Asia.
Accordingly, it has the expected effectiveness of simplificationof customs procedure and provision of swift clearance benefits
at the level of global supply chain management.
REFERENCES[1] Visiongain Ltd, The Container Tracking and security Market
2012-2022.
[2] U.S Department of Homeland Security Customs and Border
Protection, Conveyance Security Device(CSD) Requirements,
Version 1.2, December, 10, 2007
[3] Kirsen Ltd, Available: https://kirsenglobalsecurity.com/[4] STARCOM Ltd, http://www.starcomsystems.com/[5] Su Jin Kim, Guofeng Deng, Sandeep K. S. Gupta and Mary
Murphy-Hoye, “Intelligent Networked Containers for Enhancing
Global Supply Chain Security and Enabling New Commercial
Value,” in the 3rd Int’l Conf. on Communication System
Software and Middleware (COM-SWARE’08), 2008.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 4, July 2014

504

Eun Kyu Lee is a Senior Researcher of Dong-A
University. He received the B.S degree in Information
Communication from Young Dong University, Korea in1999 and M.S degree in Electronic Information
Communication from Kunkok University, Seoul, Korea
in 2001. He is currently a Ph.D. candidate in Electronic
Information Communication from Kunkok University.
His main research topics are active RFID and container
security Device.

Hyung Rim Choi is a professor of Dong-A University.
He received his Ph.D. degree in Management Sciencefrom KAIST in 1993. His main research topics are a
RFID/USN application and Port and Logistics
Systems.

Jae Joong Kim is a professor of Dong-A University. He
received his Ph.D. degree in Civil Engineering fromSeoul National University in 1989. His main research
topics are a RFID/USN application and Port andLogistics Systems.

Chae Soo Kim is a professor of Dong-A University. He
received his Ph.D. degree in Industrial Engineering from
KAIST in 1999. His main research topics are a
RFID/USN application and design & development of Port
Logistics Systems.

Copyright ⓒ 2014 GiRI (Global IT Research Institute)