Journal Editorial Board Editor-in-ChiefProf. Thomas Byeongnam YOON, PhD.
Founding Editor-in-Chief
ICACT Transactions on the Advanced Communications Technology (TACT) Editors
Prof. Jun-Chul Chun, Kyonggi University, Korea
Dr. JongWon Kim, GIST (Gwangju Institute of Science & Technology), Korea
Dr. Xi Chen, State Grid Corparation of China, China
Prof. Arash Dana, Islamic Azad university , Central Tehran Branch, Iran
Dr. Pasquale Pace, University of Calabria - DEIS - Italy, Italy
Dr. Mitch Haspel, Stochastikos Solutions R&D, Israel
Prof. Shintaro Uno, Aichi University of Technology, Japan
Dr. Tony Tsang, Hong Kong Polytechnic UNiversity, Hong Kong
Prof. Kwang-Hoon Kim, Kyonggi University, Korea
Prof. Rosilah Hassan, Universiti Kebangsaan Malaysia(UKM), Malaysia
Dr. Sung Moon Shin, ETRI, Korea
Dr. Takahiro Matsumoto, Yamaguchi University, Japan
Dr. Christian Esteve Rothenberg, CPqD - R&D Center for. Telecommunications, Brazil
Prof. Lakshmi Prasad Saikia, Assam down town University, India
Prof. Moo Wan Kim, Tokyo University of Information Sciences, Japan
Prof. Yong-Hee Jeon, Catholic Univ. of Daegu, Korea
Dr. E.A.Mary Anita, Prathyusha Institute of Technology and Management, India
Dr. Chun-Hsin Wang, Chung Hua University, Taiwan
Prof. Wilaiporn Lee, King Mongkut’s University of Technology North, Thailand
Dr. Zhi-Qiang Yao, XiangTan University, China
Prof. Bin Shen, Chongqing Univ. of Posts and Telecommunications (CQUPT), China
Prof. Vishal Bharti, Dronacharya College of Engineering, India
Dr. Marsono, Muhammad Nadzir , Universiti Teknologi Malaysia, Malaysia
Mr. Muhammad Yasir Malik, Samsung Electronics, Korea
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Dr. Kyuchang Kang, ETRI, Korea
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Copyright ⓒ 2014 GiRI (Global IT Research Institute)Dr. Youssef SAID, Tunisie Telecom, Tunisia
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Dr. Abu sahman Bin mohd Supa'at, Universiti Teknologi Malaysia, Malaysia
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Dr. Jamshid Sangirov, KAIST, Korea
Prof. Ahmed Dooguy KORA, Ecole Sup. Multinationale des Telecommunications, Senegal
Dr. Se-Jin Oh, Korea Astronomy & Space Science Institute, Korea
Dr. Rajendra Prasad Mahajan, RGPV Bhopal, India
Dr. Woo-Jin Byun, ETRI, Korea
Dr. Mohammed M. Kadhum, School of Computing, Goodwin Hall, Queen's University , Canada
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Prof. Yao-Chung Chang, National Taitung University, Taiwan
Dr. Abdallah Handoura, Engineering school of Gabes - Tunisia, Tunisia
Dr. Gopal Chandra Manna, BSNL, India

Copyright ⓒ 2014 GiRI (Global IT Research Institute)Dr. Il Kwon Cho, National Information Society Agency, Korea
Prof. Jiann-Liang Chen, National Taiwan University of Science and Technology, Taiwan
Prof. Ruay-Shiung Chang, National Dong Hwa University, Taiwan
Dr. Vasaka Visoottiviseth, Mahidol University, Thailand
Prof. Dae-Ki Kang, Dongseo University, Korea
Dr. Yong-Sik Choi, Research Institute, IDLE co., ltd, Korea
Dr. Xuena Peng, Northeastern University, China
Dr. Ming-Shen Jian, National Formosa University, Taiwan
Dr. Soobin Lee, KAIST Institute for IT Convergence, Korea
Prof. Yongpan Liu, Tsinghua University, China
Prof. Chih-Lin HU, National Central University, Taiwan
Prof. Chen-Shie Ho, Oriental Institute of Technology, Taiwan
Dr. Hyoung-Jun Kim, ETRI, Korea
Prof. Bernard Cousin, IRISA/Universite de Rennes 1, France
Prof. Eun-young Lee, Dongduk Woman s University, Korea
Dr. Porkumaran K, NGP institute of technology India, India
Dr. Feng CHENG, Hasso Plattner Institute at University of Potsdam, Germany
Prof. El-Sayed M. El-Alfy, King Fahd University of Petroleum and Minerals, Saudi Arabia
Prof. Lin You, Hangzhou Dianzi Univ, China
Mr. Nicolai Kuntze, Fraunhofer Institute for Secure Information Technology, Germany
Dr. Min-Hong Yun, ETRI, Korea
Dr. Seong Joon Lee, Korea Electrotechnology Research Institute, korea
Dr. Kwihoon Kim, ETRI, Korea
Dr. Jin Woo HONG, Electronics and Telecommunications Research Inst., Korea
Dr. Heeseok Choi, KISTI(Korea Institute of Science and Technology Information), korea
Dr. Somkiat Kitjongthawonkul, Australian Catholic University, St Patrick's Campus, Australia
Dr. Dae Won Kim, ETRI, Korea
Dr. Ho-Jin CHOI, KAIST(Univ), Korea
Dr. Su-Cheng HAW, Multimedia University, Faculty of Information Technology, Malaysia
Dr. Myoung-Jin Kim, Soongsil University, Korea
Dr. Gyu Myoung Lee, Institut Mines-Telecom, Telecom SudParis, France
Dr. Dongkyun Kim, KISTI(Korea Institute of Science and Technology Information), Korea
Prof. Yoonhee Kim, Sookmyung Women s University, Korea
Prof. Li-Der Chou, National Central University, Taiwan
Prof. Young Woong Ko, Hallym University, Korea
Prof. Dimiter G. Velev, UNWE(University of National and World Economy), Bulgaria
Dr. Tadasuke Minagawa, Meiji University, Japan
Prof. Jun-Kyun Choi, KAIST (Univ.), Korea
Dr. Brownson ObaridoaObele, Hyundai Mobis Multimedia R&D Lab , Korea
Prof. Anisha Lal, VIT university, India
Dr. kyeong kang, University of technology sydney, faculty of engineering and IT , Australia
Prof. Chwen-Yea Lin, Tatung Institute of Commerce and Technology, Taiwan
Dr. Ting Peng, Chang'an University, China
Prof. ChaeSoo Kim, Donga University in Korea, Korea
Prof. kirankumar M. joshi, m.s.uni.of baroda, India
Dr. Chin-Feng Lin, National Taiwan Ocean University, Taiwan
Dr. Chang-shin Chung, TTA(Telecommunications Technology Association), Korea

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

Copyright ⓒ 2014 GiRI (Global IT Research Institute)

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Copyright ⓒ 2014 GiRI (Global IT Research Institute)Evalution Procedure

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Copyright ⓒ 2014 GiRI (Global IT Research Institute)Journal Procedure
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Copyright ⓒ 2014 GiRI (Global IT Research Institute)Journal Submission Guide
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Copyright ⓒ 2014 GiRI (Global IT Research Institute)Volume 1, Issue 2

1

Beamforming Design of Decode-and-Forward Cooperation for Improving Wireless Physical Layer Security

41

Hui MA, Piming MA
School of Information Science and Engineering, Shandong University, China

2

ZigBee RF Signal Strength for Indoor Location Sensing – Experiments and Results

50

K Subaashini, G Dhivya, R Pitchiah
Centre for Development of Advanced Computing (C-DAC), Chennai, India

3

Quantum Communication Scheme for Blind Signature with Arbitrary Two-Particle Entangled System

58

Jinjing Shi1, Ronghua Shi1, Xiaoqi Peng2 and Moon Ho Lee3, Senior Member, IEEE
1 School of Information Science & Engineering, Central South University, Changsha 410083, China.
2 Department of Information Science & Engineering, Hunan First Normal University, Changsha 410205, China.
3 Institute of Information and Communication, Chonbuk National University, Chonju 561-756, Korea.

4

A Epidemic Style Super-node Election Method Based on Self-information Theory

63

Zhiwei Gao*, yingxin Hu**Department of Computer Science, Shijiazhuang TieDiao University, Shijiazhuang, 050043, China

5

SFML: Screening Form Markup Language for Healthcare Service
Kyuchang Kang*, Seonguk Heo*, Changseok Bae*
*BigData Software Research Lab. Electronics and Telecommunication Research Institute 218 Gajeongno Yuseong-gu Daejeon Korea

72

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 2, September 201241

Beamforming Design of Decode-and-Forward
Cooperation for Improving Wireless PhysicalLayer Security
Hui MA, Piming MA
School of Information Science and Engineering, Shandong University, China
maphoenix@126.com, mapiming@sdu.edu.cn

Abstract—Physical-layer-based security aims at ensuring thereliability of communication and preventing eavesdropping by
taking advantage of the physical layer’s characteristics rather
than the data encryption in upper layer. Cooperation is a way toachieve this goal with many benefits for wireless communication.
In particular, the cooperation scheme called decode-and-forward(DF) is discussed in this paper and our objective is to design the
beamforming weight of each cooperating node which is one
antenna equipped for maximum achievable secrecy rate.
Considering that individual power constraint is more reasonable
than total power constraint and to set noise power levels at thedestination and the eavesdropper different is more practical than
the same, we get the whole optimization problem which is
unconvex. With the help of perfect global channel state
information (CSI), the problem is solved through a way whereconvex optimization and one-dimensional search are combined
together. And strict proofs are presented for this method. Thenzero-forcing (ZF) based simplification and extension to cope withmulti-antenna case are discussed. Numerical results show that the
proposed design can significantly improve the security
performance of wireless systems.
Index Terms—physical layer security, maximum achievablesecrecy rate, cooperating relays, beamforming, convex analysis.

I. INTRODUCTIONS

ECURE data transmission plays an important role inwireless communication system. However, the open natureof wireless communication makes it vulnerable to wiretapping.
At physical layer, this problem was first studied by Wyner [1]fromaninformation-theoretic
perspective.
Wyner
demonstrated that secure communication is possible without
relying on private (secret) keys if the source-eavesdropper
channel is a degraded version of the main (source-destination)
channel, even though the eavesdropper has unlimited
computation ability and know the coding/decoding scheme. He
used a concept ‘secrecy rate’ to describe a rate at which
Manuscript received May 15, 2012.
H. Ma and P.M. Ma are with the School of Information Science andEngineering, Shandong University, Jinan, China(corresponding author, P.M.
Ma to provide phone:+86-531-88364613; fax:+86-531-88364613; e-mail:mapiming@ sdu.edu.cn).

information can be transmitted reliably in the main channel andcan not be wiretapped by the eavesdropper, and defined‘secrecy capacity’ as the maximal achievable secrecy rate.
Then, Wyner’s result was generalized to the Gaussian channel[2].In [3] secure communications over broadcast channels werestudied by I. Csiszár and J. Körner. In recent years,
considerable efforts have been made to extending this line ofwork to the fading channel like [4] ,[5].
To overcome the problem that the traditional single antenna
system based PHY layer security approaches are infeasible
when Wyner’s condition is not met [1], [2], some recent works
have been proposed to make up for this weakness by using
multiple antenna technique e.g., multiple-input multiple-output(MIMO) [6-10], single-input multiple-output (SIMO) [11] andmultiple-input single-output (MISO) [12-13].
Additionally, another more flexible and practical approach isrelaying cooperation where the source to destinationtransmission is helped by relays. Totally, there are three
cooperative schemes which can be used to provide security, i.e.
decode-and-forward (DF), amplify-and-forward (AF) andcooperative jamming (CJ). And in particular, the security
performance of DF based cooperation system has attracted
much attention in recent years [14-17].
In [14] and [15], a DF based cooperative protocol was
considered and beamforming vector of relays was designed forthe achievable secrecy rate maximization or transmit power
minimization. However these works just took the circumstancewith a total power constraint into account.
Because relays are distributed and independent in many
applications, individual relay power constraints are morereasonable than the total power constraint in these case. As acomplement, Junwei Zhang considered the maximization of thesecrecy rate of DF model with individual relay power
constraints through semidefinite programming (SDP) in [16].
But the optimal value we got through the SDP problem may not
be the maximum secrecy rate of the system, because there is no
proof that can show the existence of rank-one optimal solutionsof the SDP problem in [16].
Figure 1. System model

In this paper, a more practical system model with different
noise power at different nodes than that in [16] is studied.
Copyright ⓒ GiRI (Global IT Research Institute)

ICACT TransactionsRon Advanced Communications E
Technology (TACT) Vol. 1, Issue 2, September 2012

S

R

In Stage II, relay nodes re-encode the decoded message andthen cooperatively transmit weighted versions of there-encoded symbols to the destination and the eavesdropper.
When the re-encoded symbol x%S is transmitted by relays, the

D

R

S
R

D

source

E

relay

42

signal yD which is received at D equals

destination

N -1

yD = å wh
x% + nD
i i S

(2)

i= 0

eavesdropper

where wi (i = 0, 1,.....N - 1) means the beamforming factor
Based on this model where each cooperating node is one
antenna equipped, a new algorithm is proposed by combining
the convex optimization and the one-dimensional search
together to obtain the maximum achievable secrecy rate with
sufficient proofs. Then a simplified problem with zero-forcing(ZF) constraint is discussed. Further more, in the end, theproposed algorithm is generalized to cope with the more
complicate multi-antenna case.
This paper is organized as follows. In Section II, we will
introduce the system model and the DF-based cooperative
protocol. In Section III, we will propose and prove ouralgorithm for the maximum secrecy rate and the corresponding
beamforming vector. Then we discuss the simplified problem
in Section IV and the extension in Section V. Simulation results
are presented in Section VI, and conclusions are given in
Section VII.

at Ri , hi Î £ is the channel between Ri and D , and nD is thenoise at D with variance s D2 .Then the signal yE which is thesignal at E can be expressed as,
N -1

yE = å wg
x% + nE
i i S

(3)

i= 0

where gi Î £ denotes the channel between Ri and E , and nE isthe noise at E with variance s E2 . Without the loss of generality,
all the symbols in the re-encoded message are normalized, i.e.
2

E[x%S ]= 1 where E [g] denotes expectation.
Let’s define w = [w0,...., wN -1 ]T , h = [h0,...., hN -1 ]H ,g = [g0,....., gN -1 ]H

andRh = hhH ,Rg = ggH where

superscripts (g)T and (g)H represent transpose and conjugatetranspose respectively. Then the SNR at D and E can be
II. SYSTEM MODEL AND COOPERATIVE PROTOCOL

2

In this paper, we first consider a scenario in which there is
only one source node S , one eavesdropper node E , one
destination node D and N relay nodes labelled as{R0,....., RN -1} . As Figure 1 illustrates, the source and relays
are in the same cluster, while the destination and eavesdropper
are located far away from this cluster. Each network node isequipped with only an omni-directional antenna. All channels
are flat fading, quasistatic and memoryless. The global CSI isavailable for system design. And thermal noise at all nodes iszero-mean white complex Gaussian. Besides, it is assumed thatthe number of relays is known before optimization.
The system works under a DF-based cooperative protocol.
The protocol is divided into two stages and can be described asfollows. In Stage I, the source transmits a message to othernodes within the cluster, and then the relays receive and decode
it. When transmitting the symbol xS , the received signal at therelay Ri can be expressed as

yR,i = xSli + nR,i(1)where li Î £ denotes the channel between Ri and S and nR ,iis the noise at Ri with variance s R2 ,i . As the distance between
the source and the relays are not too long, the relays can decode
the received signal properly. And the power of the signal
broadcasted by the source would be small so that the faraway
destination and eavesdropper can receive none of it.

expressed

as GD = hH w / s D22

andGE = gH w / s E2

respectively. As discussed in [2], for a given w the secrecy
capacity C S (w) is

1
2
1+ GD
1
= max{ log(
), 0}
2
1+ GE

C S (w) = max{ (log(1+ GD ) - log(1+ GE )), 0}(4)

III. DESIGN FOR ACHIEVABLE SECRECY RATE MAXIMIZATION
Aiming at finding out the maximum achievable secrecy rateof this system which works under the protocol we described, it
is obvious that we should try to maximize C S (w) via the designof the beamforming vector. Considering individual power
constraints is more practical in the relay system, the problem
what we are interested in is formulated as follows,
maximize: CS (w)w(5)
2subject to: wi ? pi , i = 0,..., N - 1
where pi is the power constraint for Ri , " i = 0,..., N - 1 .
Because of the property of function max{?, } and log(g) , inorder to solve (5), we could solve the following problem first,

Copyright ⓒ GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 2, September 2012

1+
maximize:

wH Rh w

Theorem 1: w1* (t1) is the optimal point of (8), and R1(t1) is

s D2
H

w

1+
2

subject to: wi

w Rg w

(6)

2

sE

w

max R1(t) = R1(t1) 납R1(t * )s E2 (s D2 + wH Rh w)
2H

2

s D (s E + w Rg w)
2(7)? pi , i = 0,..., N - 1

maximum point of R1(t) . So we haves E2 + wH Rg w

subject to: wi

H

In addition, when t = t1 , assume that 殞
w1* (t1) Rgw1* (t1)油薏

R1(t2) > R1(t1) , which contradicts with the fact that t1 is thes D2 + wH Rh w
2

(8)

? pi , i = 0,..., N - 1

However, solving (8) is challenging owing to its non-convex
character. Motivated by [18] and [19], our method is to firststudy a subproblem with the denominator of (8)’s objectivefunction fixed, and then use one dimension search to find thesolution. Moreover, the strict proof of this method is presented.

H殞w* (t ) Rgw1* (t1) + s E2 = t1 .
(12)
油
薏1 1
Then in order to obtain f1(t1) and w1* (t1) we could focus on
the following problem,

maximize: wH Rh w
w

subject to: s E2 + wH Rg w = t1

.

(13)

2

wi ? pi , i = 0,..., N - 1

A. Subproblem With Fixed s E2 + wH Rgw
Fixing s E2 + wH Rgw in (8) to a scalar t, then our problem

Comparing (13) with (9), it is obvious that f1(t1) = f (t1) so
we can got

transforms into

max R1(t) = R1(t1) = R(t1) ? max R(t)tHmaximize: w Rh w
w

subject to: s E2 + wH Rg w = tt+sE2 = t2 < t1 . Then we have f1(t1) = f1(t2) . Because t2 < t1 ,Because s E / s D is a constant, (7) can be simplified intow

R(t * ) = max R(t) (11)t2maximize:

When t = t * , w* (t * ) is the optimal point of (9) and also isrelation below,

subject to: wi
2

its optimal value.
Proof:
the feasible point of (10). So f1(t * ) ³ f (t * ) . Then we have the? pi , i = 0,..., N - 1

which can be re-expressed as

maximize:

43

.

(9)

t

Combine (11) and (14) together, we havemax R1(t) = max R(t ) .

2twi ? pi , i = 0,..., N - 1
It is shown that the optimal objective value and optimal
solution of (9) are influenced by t, which are defined as f (t) andw* (t) respectively. To indicate the relationship between the
optimal value of (8) and (9), we define a new function
R(t) = (f (t) + sD2 ) / t . Definitely, if t * maximizes R(t) , thenR(t * ) is the optimal value of (8) and w* (t * ) is also the optimal

(15)

t

According to (13) and (15), t1 is also R(t) ’s maximum point,
and w1* (t1) is also (8)’s optimal point.

In the light of Theorem 1, we can find out the maximum
point of R1(t) through solving (10) instead of trying tocalculate the complicate problem (8) directly. However (10) is
also non-convex. In order to solve (10), we define a convex
optimization problem as follows

point of it.
However, (9) is also difficult to tackle because of theexistence of equality constraint. In order to overcome this, wechanges (9) into the following optimization problem,

maximize: wH Rh w

R(t * ) (14)maximize: Re(wH h)w

subject to: s E2 + wH Rg w ? t

,

(16)

2

wi ? pi , i = 0,..., N - 1

Let’s define the optimal value of (10) as f1(t) and thewhere Re(wH h) is the real part of wH h . Then we have thefollowing theorem.
Theorem 2: The optimal solution of problem (16) is also the
optimal solution of (10).
Proof:

corresponding optimal point as w1* (t) . Let R1(t) = j (t) / t

Assuming that wR* (t) is an optimal solution of (16), then we

w

H

2

subject to: s E + w Rg w ? t

(10)

2

wi ? pi , i = 0,..., N - 1

where j (t) = f1(t) + sD2 and denote R1(t) ’s maximum point as

t1 . Then we will have the conclusion stated in theorem 1 asfollows.

have

Re2((wR* (t))H h) = (wR* (t ))H Rh (wR* (t ))(17)

Supposing that the former equation is invalid, then we have

Copyright ⓒ GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 2, September 2012

(wR* (t))H Rh (wR* (t)) > Re2((wR* (t))H h) ?

v ? [ , 2p)

out

N -1

. So we can find+ å l i (Wi 2 +Wi 2+N - pi )Re2((wR* (t)ej v )H h) =

make

i= 0

= W T (-

(wR* (t)ej v )H Rh (wR* (t)ej v ) and Re((wR* (t)ej v )H h) > 0 . So
Re((w*R (t)ej v )H h) > Re((wR* (t))H h) which contradicts that

N -1
i= 0

Then

*

Definitely wR (t) is also a feasible point of (10) so(18)

Now considering the fact that $t ? [ , 2p) which can makeRe2((w1* (t)ej t )H h) = (w1* (t)ej t )H Rh (w1* (t)ej t ) and Re((w1* (t)ej t )H h) ?

. Here we can find that w1* (t)ej t is still the optimal

point of (10) and the feasible point of (16). So
Re2((w*R (t))H h) ³ Re2((w1* (t)ej t )H h)= (w1* (t)ej t )H Rh (w1* (t)ej t ) = f1(t)

the

dual

objectivefunction

is

G (ml
, )=

minimizeL(W, ml, ) which reaches the minimum at W *Wwhich is an optimal solution of (21). So
G (ml
, )=
念
diag(l ) 0N ´ N ÷ *
HHT
ç
÷
ç
(W * )T (+mP+)W (23)÷çg* Tçdiag(l )÷
÷(W ) H曜0N´ NN -1(19)(17), (18), (19) together lead (wR* (t))H Rh (wR* (t)) = f1(t)which means wR* (t) is an optimal point of (10).

念
diag(l ) 0N´ N ÷
ç
÷
ç
+
P
+
)W (22)
m
÷
ç0
g
T
÷
(
)
diag
l
÷
ç
W H
曜 N´ N
HHT+ ms E2 - mt - å l i pi

wR* (t) is an optimal solution of (16). Then we have (17).
(wR* (t))H Rh (wR* (t)) £ f1(t) .

44+ms E2 - mt - å l i pi .
i= 0Through a similar way in [19], (21)’s dual problem can be
written as
N -1

maximize ms E2 - mt - å l i pi
ml
,

i= 0

subject to m ?

B. Search for the Optimal Solution
In order to obtain R1(t) ’s maximum point through which we
could find out w1* (t1) , let’s state some properties of f1(t) andR1(t) .
Theorem 3: j (t) is a concave function of t .
Proof:The proof is similar to the steps performed in [19, section IV],and therefore is sketched.
We convert (16) into an equivalent real case as shown below,

maximize: W T H

(24)

l f 0

念
çdiag(l ) 0N ´ N ÷
÷
f 0
÷
÷
diag(l )÷
ç(W ) H曜0N ´ N
Then (20)’s duality can be got through writing out the
opposite of (24):
T

HH

-

* T

+ mP g + ççN -1

minimize mt + å l i pi - ms E2
ml
,

i= 0

subject to m ?

(25)

l f 0

念
çdiag(l ) 0N ´ N ÷
÷
f 0
÷
* T
÷
diag(l )÷
ç(W ) H曜0N ´ NDue to the convexity of (20), strong duality holds and theTW

subject to: s E2 + W T P gW ? t(20)

Wi2 + Wi2+N ? pi , i = 0,..., N - 1

HH-+ mP g + ççwhere W = [ReT (w), ImT (w)]T , P g = [Re(Rg ), - Im(Rg );optimal value of (25) is (W * )T H . Definitely multiplied by

Im(Rg ), Re(Rg )]. And Im(Rg ) is the imaginary part of matrix(W * )T H , the optimal value of (25) becomes ((W * )T H)2 . From
Theorem 2 we know that the square of optimal value of (20) is
equal to (10)’s optimal value. From all this, the optimal value ofthe following problem is exactly f1(t) :Rg , Wi is the ith element of vector W .
Then considering the following convex optimization
problem,minimize:W- WT HN -1

subject to: s E2 + W T P gW ? t
2

2
i +N

Wi + W(21)? pi , i = 0,..., N - 1

it is obvious that (21)’s optimal value is the opposite to (20)’s
and they have the same optimal solutions. The Lagrangian of(21) is

L(W, ml, ) = - WT H + ms
( E2 + WT P gW - t)minimize (W * )T Hmt + å (W * )T Hl i pi - (W * )T Hms E2
ml,subject to m?
l f 0

i= 0

念
diag(l ) 0N´ N ÷
ç
÷
ç
+
m
P
+
÷f 0
ç
g
* T
ç
diag(l )÷
÷(W ) H曜0N´ N
(26)
* T
'
* T
As there must be (W ) H ? , by defining m=(W ) Hm,-l i' = (W * )T Hl i

HHT

and l ' = (l 0,..., l N -1)T , (26) can be

Copyright ⓒ GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 2, September 2012expressed as,a < b < c < d and C ¹ C 1 . Because of (28) we have

N -1

' 2
minimize m't + å l i' pi - msEml,j (t) = Ct < C 1t on [a,b] . As t > 0 , C < C 1 . Similarly,

i= 0

j (t) = C 1t < Ct on [c, d ] . As t > 0 , C > C 1 . Then

subject to m' ?
(27)

l'f 0
'
念
çdiag(l ) 0N´ N ÷÷f 0- HHT + m'P g + ç
ç
' ÷
÷
ç
0
diag
(
l
)
÷
ç
曜 N´ N

Then j (t) can be expressed ascontradiction appears. As a result, there is at most a single
straight line through the original which partly overlaps withj (t) .
Combining the two parts above, we could easily get that
there’s at most a single interval in Dom(R1(t)) where R1(t) is

N -1

constant.
Suppose j (t) = Ct if and only if t ? [a,b],a' 2minimize m't + å l i' pi - ms
+ s D2
E

ml
,

i= 0
'

subject to m ?
(28)

l'f 0

R1(t2 ) ='j (t2 ) Ct2
.
<
t2
t2

(29)

Through the similar way, for t1 < a , there is(28) is a point-wise minimum of a family of affine functions, soj (t) is concave[20, p.80].

R1(t1) <Theorem 4: R1(t) is a quasiconcave function of t .
Proof:
Suppose p(x) is a concave function and q(x) is a convex

b . Then from(28) we know j (t2 ) < Ct2 for t2 > b . So念
diag(l ' ) 0N´ N ÷ç÷ç- HH + mP g + ç÷f 0çdiag(l ' )÷÷
ç曜0N´ N
T

45

Ct2
.
t2

(30)

Through (29) and (30), we can conclude R1(t) achieves its
maximum for " t ? [a,b] as R1(t) = C on [a,b].



function, with p(x) > 0 and q(x) > 0 on a convex set C. We

Considering Theorem 3-5, we will find that the optimal point

can easily get f (x) = p(x) / q(x) is quasiconcave on C
according to the theorem in [20, p.103]. Then at the base of
Theorem 3, it can be concluded that R1(t) is quasiconcave forand maximum value of R1(t) can be efficiently got using
Golden Section method which is one of the classic one
dimensional search algorithms. Before using this algorithm, weshould find an interval including the optimal point of R1(t) .

Theorem 5: There’s at most a single interval in Dom(R1(t))

Denote [tmin , tmax ] as this interval. Definitely, tmin would be

where R1(t) is invariant and any t belongs to this interval wills E2 , and tmax would be the optimal value of the following

be the maximum point of R1(t) . Here Dom(R1(t)) represents

problem,j (t) > 0 is concave, t > 0 is affine (so convex).

maximize: s E2 + wH Rg w

the domain of R1(t) .

w

subject to: wi

Proof:

only a single interval in Dom(R1(t)) where j (t) is proportional
to t .
Part I:
Assume j (t) = Ct on two separate interval [a,b] and [c, d ]where a < b < c < d . Then we can get j (t) is no bigger than? pi , i = 0,..., N - 1

1: Input: s D2 , s E2 , pi , g , h .
2: begin3:
initialize tmin , tmax , len = tmax - tmin .
4:

while len > e , where e is the threshold.

5:

tleft = tmax - 0.618(tmax - tmin ) .

(1- q)j (c) q ? [ , 1] [20, p. 67]

6:tright = tmin + 0.618(tmax - tmin ) .

which means j (t) is no smaller than Ct on [b, c] .

7:

calculate R1(tleft ), R1(tright ) .

Consequently, we have j (t) = Ct on [b, c]. Therefore, there is8:

if R1(tleft ) < R1(tright ) .

on[b, c] fromj (qb + (1- q)c) ? qj (b)

(28).

As

j (t)

is

only a single interval where j (t) = Ct .
Part II:
Assume j (t) = Ct on [a,b] and j (t) = C 1t on [c, d ] with

(31)

Proposed Algorithm

concave,Ct.

The complete algorithm is summarized as follows.

First, let’s consider the fact that R1(t) = C in an interval if
and only if j (t) = Ct . Then we just need to prove that there’s

2

9:
10:

tmin = tleft .
else if R1(tleft ) > R1(tright )Copyright ⓒ GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 2, September 2012

11:
12:
13:
14:
15:
16:
17:
18:

tmax = tright

46
Ni -1

yR ,i = å xSli , j + nR ,i

else

(35)

j= 0

tmin = tleft .

where li , j means the channel between the source and Ri ’s jthtmax = tright .

antenna, N i means the number of Ri ’s antenna, nR ,i is theendnoise at Ri with variance s R2 ,i .

len = tmax - tmin

In stage II, when symbol x%S is transmitted, the received

end
t1 = (tmax + tmin ) / 2

signal yD at D equals*

19:

take t = t1 into (16) to find out w .

20:

calculate C S (w* ) = max{log sE2 R1(t1) / sD2 / 2, 0} .

N -1 Ni -1(

yD = 檍

)wi , j hi , j x%S + nD

(36)

i= 0 j= 0

where wi, j (i = 0, 1,.....N - 1; j = 0, 1,......Ni - 1) means the

21:end.
22:output: w* , C S (w* ) .

beamforming factor at Ri ’s jth antenna, hi , j Î £ is the channel
between Ri ’s jth antenna and D , and nD is the noise at D

IV. ZF CONSTRAINT BASED SIMPLIFICATIONAs discussed above, maximizing C S (w) under individualwith variance s D2 . The received signal yE at E can be shownas,power constraint is a complicate problem. In this section, we
simplify the problem using a zero-forcing (ZF) constraint on
the receiving signal at the eavesdropper, which is equivalent to

N -1 Ni -1

yE = 檍

wi , j gi , j x%S + nE

(37)

i= 0 j= 0

asking wH Rgw = 0 . It is clear from (5) that the optimal w

where gi , j Î £ is the channel between Ri ’s jth antenna and E .

under ZF constraint is given by

Define wi = [wi ,0,...., wi ,N -1 ]T , w = [wT0 ,....., wTN -1 ]T , hi =iHmaximize: w Rh w
w

subject to: wH Rg w = 0(32)

maximize: Re(wH h)
subject to: w g = 0

(33)

2

wi ? pi , i = 0,..., N - 1
Then the maximum secrecy rate under ZF constraint can be
written as* H*(w ) R ( w )1max{ log(1+ z 2 h z ), 0}
2
sD

T
H
N -1

H

H

] and Rh = hh , Rg = gg . Then we can2

From the analysis similar to that in Theorem 2, we have(32)’s optimal solution can be got through solving the convex
problemHiiTg = [g0 ,....., g2

wi ? pi , i = 0,..., N - 1

w[hi,0,...., hi,N -1 ]T , h = [hT0 ,.....,hTN - 1 H], gi = [gi,0,...., gi ,N -1 ]T ,(34)

where w*z is an optimal solution of (33). Note that this value isjust sub-optimal, because of the existence of the ZF constraint.

V. EXTENSION TO MULTI-ANTENNA CASE
In this section, we will study a more complex scenario as anextension. In this scenario, relay nodes are equipped withmultiple omni-directional antennas and other conditions arestill the same as those in the former scenario. So in stage I,
when the symbol xS is transmitted, the received signal yR ,i atstill express the SNR at D and E as GD = hH w / dD2 and2GE = gH w / dE2 respectively. So the secrecy capacity for agiven w can still be shown as (4).
In order to get the maximum achievable secrecy rate, in thissection, the core optimization problem becomesmaximize:w

s D2 + wH Rh w
s E2 + wH Rg w

subject to: wi

2

(38)

? pi , i = 0,..., N - 1

Here we still obtain a subproblem by fixing the denominatorof (38)’s objective function as t and change the equality
constraint of it by substituting " £ " for " = " to get anotheroptimization problem. And then, we still denote f (t) and f1(t) as
the optimal value of the two optimization problem above
respectively and define w* (t) , R(t) , t * , w1* (t) , R1(t) , j (t) , t1
through the same way in section III. It can be seen that in thissection we could have theorems similar with those stated in
section III. For these theorems, what need to be noted is that (8),(10), (16) should be substituted by their counterpart, i.e. (38),(39), (40) respectively.

Ri is
Copyright ⓒ GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 2, September 2012

maximize: wH Rh w

4w

subject to: s E2 + wH Rg w ? t
2(39)? pi , i = 0,..., N - 1

3

maximize: Re(wH h)w

subject to: s E2 + wH Rg w ? t
wi2

CvxGld-DF
SDP-DF
ZF-DF
DT

3.5

(40)

? pi , i = 0,..., N - 1

So the proposed algorithm can be easily generalized to tackle
the multi-antenna case.

Secrecy rate (bit/s/Hz)

wi

47

2.5
2
1.5
1
0.5

VI. SIMULATION RESULT0

In this section, simulations are carried out to investigate theperformance of the proposed algorithm. For simplicity, we use
a one-dimensional system model, as illustrated in Fig. 2, where
the source, relays, destination and eavesdropper are along a
horizontal line. What’s more, because the source-relay distanceand the distances between relays are very small compared to thesource-destination distance and relay-destination distance, thesource-destination distance and the distances between different
relays and the destination can be considered as the same. So are
the source-eavesdropper distance and the distances between the
different relays and eavesdropper. To emphasize the effect of
distance, a simple line-of-sight channel model which contains
the pass loss and a random phase is used. Generally, we can
express the channels as h = d- c/ 2ej q where d is the distance,c is the path loss exponent chosen as 3.5 and random phase q is
uniformly distributed over [0, 2p) . The number of relays is set
to 6, i.e. N = 6 and the eavesdropper are fixed at 60 m. For
individual power constraints, we assume each relay has thesame power budget: pi = pT / N where pT represents the total
power constraint of the DF based system. And the noise powersD2 = - 55dBm and sE2 = - 65dBm .

R
S

D

0

E
60

R

0

5

10
15
The position of source/relays (m)20

25

Figure 3. Secrecy rate versus the position of source/relays.

We will examine the maximum achievable secrecy rate of
the DF based system calculated by the algorithm proposed in
section III (labelled as CvxGld-DF) and the maximum secrecy
rate under ZF constraint obtained by the simplified method
discussed in section IV (labelled as ZF-DF). For comparison,
we also examine the performance of direct transmission (DT)scheme and the SDP algorithm proposed in [16] (labelled as
SDP-DF).
Firstly, we fix the position of destination at 50m and movethe source/relays from 0 m to 25m. The transmit power is set as
10mdB for DT scheme. And for DF scheme pT is also set as
10dBm. We can observe from Figure 3 that the maximum
achievable secrecy rate always stays at 0 for DT. This isbecause the source-destination channel is always worse than
the source-eavesdropper channel. And for all DF basedalgorithms, the curves coincided. Maximum secrecy rates got
by three DF-based algorithms increase when relays move to thedestination. This can be explained by the fact that even throughthe relay-destination channel and relay-eavesdropper channel
both become better when the relays move from 0 to 25, theimproving trend of the former is more remarkable.
Then we fix the source/relay location at 25m and move thedestination from 40m to 100m with all other parameters
unchanged. Figure 4 illustrate that there is a gap between thesecrecy rate performances of the CvxGld-DF algorithm in thispaper and the SDP-DF algorithm in [16] when the destination
located at 90m and 100m. This means that we cannot get the

Figure 2. Model used for simulation

Copyright ⓒ GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 2, September 2012individual power constraint, the ZF constraint may be a good
choice to simplify the optimization problem without leadingmuch degradation. However, quantifying the impact of the ZF
constraint remains an open problem.

6
CvxGld-DF
SDP-DF
ZF-DF
DT

Secrecy rate (bit/s/Hz)

54VII. CONCLUSIONS
3

2

1

0
40

50

60
70
80
The position of destination (m)

90

100

Figure 4. Secrecy rate versus the position of destiantion.

4.5
CvxGld-DF
SDP-DF
ZF-DF
DT

4
3.5

Secrecy rate (bit/s/Hz)

48

In this paper, we have considered a DF-based cooperative
protocol to improve the physical layer security with one
eavesdropper. Our attention is focused on the design ofbeamforming weight of each cooperating node which is one
antenna equipped to find out the maximum secrecy rate.
However our problem formulation is different from others
because we assume a more practical scenario where thebeamforming vector is subject to individual power constraints
and noise power at different node is different. Under theassistance of perfect CSI, we have solved the optimal problem
by combining convex optimization and one-dimensional search
together and rigorous proof is presented for the correctness of
our method. Further more, a simplified problem withzero-forcing (ZF) constraint and generalization to cope with themore complicate multi-antenna case are considered.

3

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[1]

2
1.5
1
0.5
0

5

10
15
20
Total transmit power/Total transmit power constraint (dBm)

25

Figure 5. Secrecy rate versus total trasmit power/total transmit power
constraint.

optimal beamforming vector through the SDP-DF algorithm in[16] sometimes. This problem comes from the reason that therank of the optimal solution of the SDP optimization problem in[16] may be larger than one under some situation.
In Figure 5, we fix the destination and source/relays at 50m
and 0m respectively and let pT varies from 5dBm to 25dBm.
Correspondingly, the transmit power of DT scheme alsochanges from 5dBm to 25dBm. Figure 5 shows that similar
secrecy rate performances appear for all DF based algorithms
with the increase of pT . It is easy to understand that the secrecy
rate performances become better when more power is allowed
for transmitting. While for DT scheme, the maximum secrecy
rate always stays at 0 even we use more power to transmit
signals. This reveals that just enhancing transmit power ismeaningless when Wyner’s condition is not met for DT
scheme.
In Figure 3, Figure 4 and Figure 5, there exists an interesting
result that ZF-DF can always achieve nearly optimal
performance. Thus we conjecture that, while we want to reachthe maximum secrecy rate of an DF-based system under

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 2, September 201249

[16] J. Zhang and M. C. Gursoy, “Collaborative relay beamforming for
secrecy,”
Dec.
2009[Online].
Available:
http://arxiv.org/abs/0910.4132,submitted for publication[17] L. Dong, Z. Han, A. Petropulu, and H. V. Poor, “Secure wirelesscommunications via cooperation,” Proc. 46th Annu. Allerton Conf.
Commun., Control, Computing., Monticello, IL, Sep.-Oct. 2008.
[18] A.Wiesel, Y. C. Eldar, and A. Beck, “Maximum likelihood estimation in
linear models with a Gaussian model matrix,” IEEE Signal Process. Lett.,
vol. 13, no. 5, pp. 292–295, May 2006.
[19] G. Zheng, L. Choo, and K. Wong, “Optimal Cooperative Jamming toEnhance Physical Layer Security Using Relays,” IEEE Trans. Signal
Process., vol. 59, no. 3, pp. 1317—1322, Mar. 2011.
[20] S. Boyd and L. Vandenberghe, Convex Optimization. Cambridge, U.K.:
Cambridge Univ. Press, 2004.

Hui Ma received the B.S. degree in electricalengineering from Qufu Normal University, Rizhao,
China, in 2010. Currently, he is working toward the M.S.
degree in communication and information system in the
School of Information Science and Engineering,
Shandong University.
His research interests include physical layer security
inmultiple-input-multiple-output
communication
system and collaborative communication system.

Piming Ma received the B.S. degree in electricalengineering, the M.S. degree in signal processing and the
Ph.D. degree in communications and information systemfrom Shandong University, Jinan, China, in 1992, 1997
and 2005, respectively.
She is currently an Associate Professor in the Schoolof Information Science and Engineering, Shandong
University, Jinan, China. From 2008 to 2009, she was a
Postdoctoral Fellow at the Ultra Wide Band WirelessCommunications Research Center, Inha University, Nam-gu, Incheon, South
Korea. She has published more than 20 technical (journal and conference)
papers. Her search interests include LDPC codes, signal processing for wirelesscommunications, software radio, physical layer security.

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ZigBee RF Signal Strength for Indoor Location
Sensing – Experiments and Results
K Subaashini, G Dhivya, R Pitchiah
Centre for Development of Advanced Computing (C-DAC), Chennai, India
subaashinik@cdac.in, dhivyag@cdac.in, rpitchiah@cdac.in
Abstract -This paper discusses about the variation of signal
strength due to the presence of obstacles in an indoor
environment. An experimental analysis of impact of various
obstacles on ZigBee RF signals strength has been done. The
results obtained by the analysis have been used to locate a user
inside a smart home. The parameters like Received Signal
Strength (RSSI), Link Quality Indication (LQI) and Packet
Error Rate (PER) has been measured and analyzed. The
location of the user is an important context, based on which
various controls and services can be rendered. The objective offinding out the location is to provide various services andcontrols like location based luminance, personalized HVAC
systems. In this paper k mean clustering algorithm has been
implemented to predict the location of the user. The results
show that 3 to 5 m of location accuracy has been achieved.
Index Terms — ZigBee, RSSI, Packet Error Rate,
Localization, Fingerprinting

I. INTRODUCTION
This paper discusses in detail about how RF signal strengthis affected due to various obstacles that are generally found inindoor environments. The usual obstacles are materials likeglass, wood, walls in addition to human activity. The analysis
has been done in order to find out if signal strength alone canbe used to find out the location of the user in order to provide
various controls and services in indoor environment likesmart home and buildings. The experiments have been doneusing two different radios RF230 and CC2430. The presenceof human activity also affects the signal strength to a greater
extent. All these effects on the RF signal strength have to betaken.
Manuscript received June 7, 2012. This work is developed under the
project Development of ICT for Smart Buildings with Low Carbon
Emission, funded by Department of Electronics and Information
Technology (DeitY), Government of India.
Subaashini K. is with the Centre for Development of Advanced
Computing, Chennai, India (Email: subaashinik@cdac.in)
Dhivya G is with the Centre for Development of Advanced Computing,
Chennai, India (Email: dhivyag@cdac.in)
Pitchiah R is with the Centre for Development of Advanced Computing,
Chennai, India (Email: rpitchiah@cdac.in)

Received signal strength indicator (RSSI) is a measure of
the signal strength at the receiver expressed in dBm. It is

usually five, eight or ten bit value depending on the hardware
used. This RF signal strength parameter has been widely used
for localization and tracking in indoors as it eliminates therequirement of additional hardware which in turn reduces thecost. Other measurements like Time of Arrival (ToA), Angleof Arrival (AoA) are also used. But time based methods havea disadvantage that line of sight is required for them to give agood level of accuracy which is not possible in indoor
environments because of which RSSI is being preferred.
More over according to our measurements round trip time offlight method did not give accurate results when the distance
was small. Using RSSI means we don‟t need any extra
hardware. But the biggest challenge is mapping of the signal
strength to distance in presence of obstacles such as walls,
human activity etc. The relation between the two has to bemodeled as accurately as possible for developing a precise
localization system. In a densely populated and dynamic
environment where the modeling is not possible
fingerprinting algorithms are used. Fingerprinting has a
disadvantage that it requires a large measurement databasethat needs to be frequently calibrated. Now days to overcome
the difficulty of large database compressive sensing
techniques are being used. The next section discusses therelated work which is followed by other sections that explains
the experiments and results obtained.
II. RELATED WORK
Many location tracking systems have been developed
based on various measurements of the RF signal. Of these
Received signal strength is widely used for location sensing.
There are two different types of localization based on RSSI
measurement. They are range based and fingerprinting
localization. In range based location sensing a relation
between the signal strength and distance is derived. The
relation ship is used for locating the unknown node bymethods like Trilateration and Min-Max. Fingerprinting
based methods have two phases. One is offline or training
and the second is online phase. In this method the nodes arelocated by using algorithms like k nearest neighbor, Support
vector machines etc… The signal strength has to be analyzed

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 2, September 2012

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 2, June 2012using probability distribution and models like kalman filters
for improving the accuracy of location.
RSSI and Link quality indicator (LQI) are the twoparameters well known for link quality estimation. In [2] ithas been mentioned that reflection, scattering and otherphysical properties have an extreme impact on the RSSI
measurement. There are three models for describing the
distance – path loss. They are free space model, two ray
ground model which adds reflection to free space model and
log-normal model. The lognormal model has to be derived
experimentally. The authors of [2] have done experiments on
three different radios and compared the results. They haveconcluded that RSSI is a bad estimator of link quality.
A survey of wireless indoor positioning techniques has
been presented in detail in [3].various measuring principles
and algorithms has been presented in this paper. It has been
mentioned that the time based location methods are notsuitable in Non Line Of Sight (NLOS) conditions and RSSI ispreferred in NLOS conditions. If fingerprinting based
location sensing has to be done algorithms like k Nearest
Neighbors, neural networks, probabilistic methods, Support
vector machines have to be used for location estimation. A
comparison of localization systems such as UWB, GPS,
Bluetooth, WLAN, and GSM has been presented.
Reference [4] proposes a virtual calibration procedure
instead of doing a training phase for fingerprinting based
location sensing methods. This method is not manual andmakes use of measurements between the anchors. In ourpaper though the experiments are manual the measurements
have been done using only the anchors and unknown node.
Paper [5] illustrates a technique to extract an estimate ofvelocity from signal strength. The characteristic footprints
left by the motion of nodes in the network or motion of bodies
external to the network have been exploited for movement
detection. Experimental results have been presented using
Micaz motes. The results are focused how signal strength isaffected by motion of the motes. RSSI has been measured at
different times and at different velocities. Our paper
discusses how RSSI is varying due to obstacles inside a room.
Results of [5] focus more on motion detection but ourexperiments focus on how to improve the location accuracy.
The authors of [6] discuss about the three techniques forautomatic location sensing namely triangulation, scene
analysis and proximity.
The authors of [7] have discussed about the various
measurements, models and algorithms that are commonlyused for device free localization. It has been suggested thatdevice free localization is being developed which can be used
to improve the existing Real Time Location Systems (RTLs).
These methods use the fading characteristics of the RF signal
strength for finding out the presence and location of the user.
These are termed as sensor less localization as the user neednot carry any radio for being localized.
It has been mentioned in [8] that fingerprinting algorithm
cannot be used to track more than one user simultaneously.

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The impact of human presence on RF signal strength due toreflection, diffraction and scattering has been presented.
Different models for establishing the relation between
distance and signal strength has been described.
In the paper [9] experimental results how signal strength isaffected by human presence and sensor node height has beenpresented. The variations in signal strength with and with out
movement has been discussed in detail. Using the results anapproach has been proposed for intrusion detection. A
threshold has been defined to detect the motion. It has beenconcluded that the proposed system can be used along withother surveillance system for better accuracy
It has been proved in [10] that the location can be
estimated in indoor environments up to accuracy of 1m using
RSSI and ToA measurements which means the signal
strength can modeled to get that level of accuracy.
In paper [12] the effect of ZigBee RSSI on crowd in anindoor environment has been discussed. Density, velocity
and disorder are considered as measures to separate crowd
behavior. A 25 pattern of crowd behavior in indoor spacewith the above measures has been defined. Data is analyzed
using both time and frequency series analysis. Average RSSI,
variance and median are considered for time series analysis
and discrete Fourier analysis for frequency series analysis. A
graph plotted between density per experimental area against
velocity and disorder shows that with no crowd the RSSI
value hardly changes and with crowd the RSSI fluctuates to agreater extent. This is due to the difference ofelectromagnetic wave absorption rate on the human body,rate of screening electromagnetic wave path and change inenvironment.
In [13] the RSSI values from WSN nodes are used for the
real-time localization of transceiver-free objects.
Acustomized classification approach based on support vector
machine has been followed to determine location. The
feasibility and effectiveness of the proposed approach has
been assessed by experimental test cases.
The performance evaluation of IEEE802.15.4 wirelessnetworks has been presented in [14]. The effect of direct andindirect data transmissions, CSMA-CA mechanisms, data
payload size and non beacon enabled mode has been
observed through several practical experiments. The data
throughput, delivery ratio, and received signal strength
indication (RSSI) are investigated as the performance metrics.
It has been concluded that IEEE 802.15.4 has better
performance in non-beacon mode. Through experiments theauthors had achieved an average of 153.02 kbps for direct
data transfer and 65.69 kbps for indirect data transfer. It had
been concluded that the decrease in data rate for indirecttransfer is due to the network device‟s polling rate. The
results for the effects of CSMA-CA mechanism conclude thatboth the effective data rate and delivery ratio was decreased
due to the presence of collisions and random back off. It has
been observed that with the increase of payload size, the data
rate also increased since the effect of overhead was reduced

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 2, September 2012

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 2, June 2012leading to a raise of data coding efficiency. From theexperiment, the authors of this paper found that the non
beacon-enabled network would have larger data rate than the
beacon-enabled one.
Statistical Mean Value Model, Distance Between the
Fixed-nodes based Model and Gauss Model are the threeexperimental data processing models that have been
discussed in [15]. In “Statistical Mean Value Model”,
unknown node receives a group of RSSI values and thencomputers their mean value. In “Distance Between the
Fixed-nodes Model”, the distance from unknown node to
fixed node is computed by taking the distance and signal
strength information between fixed nodes as the reference.
Principle of “Gauss model” is that when an unknown node
receives n RSSI values, there must be some values which aresmall probability events. ZigBee-based hardware platform
and MATLAB are used to test the measurement error of thethree methods. This paper concludes that the measurement
error of Gauss model is 2 meters within 20 meters.

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signed 2s complement form of the actual value. Later thesehex values recorded were converted to actual dBm values
according to the datasheets of the CC2420 and RF230 radios.
Every packet transmitted is assigned a packet number tocheck for packet losses at the receiving end. After themeasurements a simple localization system has been tested
by fingerprinting method with a single reference node andalso with multiple reference nodes.
IV. MEASUREMENT RESULTS
In this section the results obtained for various obstacles
have been discussed. The measurements have been taken forboth Line of Sight and Non Line of Sight conditions. The
relationship between distance and RSSI has been obtainedfor each obstacle. The packet error rate has also beenmonitored along with RSSI in order to ensure that PER does
not fall below 2%.

III. EXPERIMENTAL ANALYSISThis section describes the measurement setup that has
been used for taking the RSSI measurements. A set ofmeasurements has been taken separately for each obstacle.
The obstacles such as wood, glass, wall and human presence
have been considered for these measurements.
A. Experimental setup
A set of two Crossbow‟s ZigBee motes (Micaz & IRIS)
has been used. Micaz has an eight bit Received signal
strength indicator (RSSI) and IRIS has a five bit RSSI. The
motes have been programmed using TinyOS 2.x and NesC
language. One node acts as a transmitter and the base station
connected to PC acts a as a receiver which forwards thereceived packets to the serial port of the PC. Further packet
processing is done at the PC using JAVA programming
language. The transmitter and receiver were both kept at aheight of 1m above the ground level. These measurements
were taken inside a 15 m x 3 m x 2.6m C-DAC„s Ubiquitous
Computing Lab.
B. Measurements
One mote was programmed as transmitter that sends apacket every 1 second. The other was programmed in such a
way that it receives the packet sent by the transmitter,appends the RSSI value and its Node Id, then forwards it to
the serial port. The distance between the transmitter andreceiver is known. The received packet is parsed and the
RSSI and distance value is stored in the database. The above
procedure was repeated for different distances between thetransmitter and receiver. The measurements were taken
initially in Line of Sight condition and later repeated byplacing glass, wood and wall obstacle between the transmitterand receiver. The parameters that have been measured are
Received Signal Strength Indication (RSSI), LQI and Packet
Delivery Rate (PDR). The RSSI values are recorded as

Figure 1.a. Distance vs Average RSSI (CC2420)

Figure 1.b. Distance vs Average RSSI (RF230)

A. Line of Sight
These measurements were taken without any obstacle or
human activity between the transmitter and receiver. Fig.1aand 1b represents a plot of the distance and average RSSI for
CC2420 and RF230 radios. At each distance 50 samples of
RSSI have been recorded and an average has been taken.
The relationship between the distance and RSSI was
obtained by interpolating and fitting a logarithmic curveusing the data obtained. Equation (1) gives the relationshipbetween distance and RSSI for RF230 radio.

r  - 2.503* Ln (d) - 56.978(1)
Where r is the RSSI value in dBm and d is distance inmeters. The packet error rate at different distances between
the transmitter and receiver has also been estimated. The
maximum allowed packet error rate is 2%.
β = 1 –ά(2)
Where ά is the packet delivery rate and β is the packet error
rate. Fig. 2 shows the packet count versus average RSSI atdifferent distances.
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 2, September 2012

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 2, June 2012

Figure 2. PacketCount vs Average RSSI at different distance between the
Tx and Rx.

As long as the packet error rate is less than 2% thecommunication between the nodes is acceptable. If it is morethan 2% the communication is no longer reliable. The RSSI
value of the packets also should not fall below the receiversensitivity for reliable communication. The PER was 0 %even when the distance between the transmitter and receiverwas 25m.
The results in Fig. 2 were observed when there was no
obstacle or movement between the transmitter and receiver.
It can be observed that all the packets were received without
any loss when there is no obstacle. The PER was 0% upto a
range of 40m.The next section discusses results obtained forvarious obstacles.

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Figure 3.a. Distance vs Average RSSI (glass-wood partition)

When there is an obstacle the packet error rate is also an
important parameter to be noted. The obstacles attenuate thesignal, so chances for packet loss are more. It is very
important to note that the packet error rate should not be
above 2%.

Figure 3.b. Distance vs Average RSSI (glass partition)

B. Non line of sight
These measurements were taken by placing a glass,glass-wood and partial wall partitions in between thetransmitter and receiver. The properties of the obstacles areshown in table I.
TABLE I PROPERTIES OF OBSTACLES

S.No

Obstacle type

Thickness (mm)1.

Glass ( Glazed Door)

10

2.

Glass + Wood
(Compressed Wood)

Wood: 120
Glass: 6.35

3.

Partial Wall (Brick +Gypsum board)

35

Location estimation has to be done in lab environment
with these obstacles. So to model the lab environment theabove obstacles have been chosen. Fig 3.a, 3.b, 3.c shows themeasurement results obtained when the obstacles whereplaced in between transmitter and receiver.

Figure 3.c. Distance vs Average RSSI (partial wall)Fig 4.a, 4.b and 4.c show the PER results observed for
different obstacles. When the measurement was taken
through walls the range was reduced to 6.4m. If the distance
between the transmitter and receiver was increased beyond
6.4 m, the PER was more than 2 %. So measurements results
up to 6.4m are shown in the figure.

Figure 4.a. Glass wood partition

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54

the experiment up to a distance of 40m.

Figure 5.a. Person count vs. Average RSSI
Figure 4.b. Partial wall

Figure 5.b. Person count vs. Average LQI

Figure 4.c. Glass door

But for the glass and glass wood obstacles the PER wasless than 2% up to a range of 20m.
C. Human PresenceThis section discusses about the impact of human presenceon the received signal strength values. The measurements
have been done for different crowd density by transmitting
1000 packets at the rate of 2 packets per second. The Fig 5.aand 5.b show how the RSSI and LQI values are affected byhuman presence.
The measurements had been taken by increasing thehuman count between the transmitter and receiver. It can benoted from Fig 5.a that the RSSI was having the maximum
value (-75dBm) when no human was present between thetransmitter and receiver.
With increase in human count the RSSI value decreased
due to absorption and fading. When the person count wasmore than 2 the graph (Fig5.a) shows an increase in RSSI
value due to reflection and scattering of RF signal. It wasobserved that the Link Quality remained at 255 through out

V. LOCALIZATION SENSING
After the measurement and analysis a localization
algorithm has been implemented and tested inside the
UBICOMP Lab of C-DAC Chennai. The location was
estimated by fingerprinting method. Since the accuracy wasless when simple fingerprinting was used clustering
algorithm has been implemented to improve the location
accuracy.
A. Location setup
The location sensing algorithm was implemented inside
the UBICOMP Lab as shown in Fig 7. One reference oranchor mote has been placed at a known location inside thelab. AT86RF230 radio of Iris motes has been used. The
transmission power was set at +0.5 dBm.

Figure 7. UBICOMP LAB L1, L2….L8 – Training points, A1 - anchor node

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The reference or anchor mote (A1) has been placed at aheight of 1.4 m above the ground level, so that it can receive
the beacon message packet broadcasted by the unknown fromany location inside the lab. The placement of reference motehas to be determined empirically. The user to be located will
be carrying the unknown mote. The objective is to determine
to which LED fixture the user is closer by. The location
sensing involves two phases. One is offline and the second is55

55

online phase.
B. Offline/Fingerprinting Phase
Location estimation by fingerprinting has two phases. The
first phase is training phase. The different states of the motes
and process involved during the offline phase are shown in
Fig 8a, b, c.

Figure 8a. Offline Phase - Unlnown Mote

Figure 8c. Offline Phase – Base Station (BS)The unknown node is placed at every location of interests(L1 to L8).It broadcasts a beacon packet every 100 ms whichcontains its Node ID and Location at which it is placed. The
packet is received by the anchor mote A1. The mote A1
appends the RSSI value, LQI value and its Node ID to thepacket and forwards to the Base station (BS) mote which isconnected to PC. The BS forwards the packet to the serial
port of the PC and the data is received and parsed. After
parsing the packet, the Location, RSSI value and Anchor-Id
are store in the database. The readings are taken for 1000
packets at every symbolic location in order to find out of the
RSSI value that has the maximum probability at thatparticular location.

Figure 8b. Offline Phase – Anchor Mote (A1)

C. Online Phase
During the online phase, the unknown mote broadcasts a
beacon every 100 ms. The anchor mote (A1) receives thebroadcast packet and appends the RSSI value and forwards it
to base station. At the base station the RSSI value obtained iscompared with the fingerprinted values in the database andthe maximum likely hood location is chosen. But the location
accuracy varied from 1m to 8m. The location obtained has
not been consistent for implementing in a smart homeenvironment for Location based Lighting application.
D. Online Phase with Clustering Algorithm
In order to improve the accuracy of location, k-Mean
Clustering algorithm [16] has been implemented. If a RSSI
value that has not been fingerprinted (in the training phase)

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56

occurs during online phase, the location could not be
identified. To overcome this issue, Nearest Neighboridentification feature has been incorporated along with theclustering.
E. k Mean Clustering
During the offline phase the unknown mote is kept at every
location of interest and the corresponding RSSI values are
stored in the database (in PC) that is connected to the basestation node. The RSSI data stored in the database is divided
into a number of clusters using k mean clustering algorithm.
The number of clusters has to be decided by the user. An
initial set of centroids have to be chosen. The number ofcentroids is equal to the number of clusters into which the
RSSI data set has to be divided. Each centroid belongs todifferent clusters.
According to the k mean clustering the Euclidean distance
between a RSSI value and the each of the centroid is
calculated. After calculating the Euclidean distance between
the centroids and the RSSI value, the minimum distance isfound out. The RSSI value is now added to the cluster whichcontains the closest centroid. This step is repeated for all the
RSSI data set for one time. After the first iteration, a new setof centroid values is calculated by taking the average of eachcluster elements and the same process of finding theminimum distance and clustering is done. The process isrepeated until two consecutive iterations result in clusters
with the same elements. Fig 9 describes the steps involved ink mean clustering algorithm.

Fig 10 Online phase with clustering and Nearest Neighbour Algorithm

If an RSSI value that has not been fingerprinted occurs
then the algorithm first finds its nearest neighbor by directly
finding the RSSI value that is closest to the fingerprinted data
set. After getting the nearest neighbor value the previous
steps of clustering and location prediction is followed.
F. Results
The location accuracy and precision has improved afterusing the clustering algorithm with the existing fingerprinting
location sensing method. Table 2 gives the details of theaccuracy obtained after using clustering and nearest neighboralgorithm.
TABLE II LOCATION ACCURACY

S.No

1.
2.
3.
4.
5.
6.
7.
8.

ActualLocation(Symboli
c)
L1
L2
L3
L4
L5
L6
L7
L8

Obtained
Location(Symbolic)
L2, L4L2, L1
L3
L4, L7
L5, L7
L6, L7
L6, L7
L8, L6, L7

Average
Error
(m)
5
2
0.8
4
2
2
2
3

VI. CONCLUSION AND FUTURE WORK

Figure 9. Clustering Algorithm

During the online phase the average RSSI of 50 packets istaken as and identified to which cluster it belongs to. After
cluster identification, the corresponding locations at which
the RSSI value occurred have been retrieved. The final
location is predicted based on the maximum number of times
the RSSI value occurred for a location. Fig 10 explains thesteps involved in predicting the location.

The results obtained after including a data classification
method, shows that the RSSI based location sensing can beused for indoor environments if the accuracy levels of 3-5 m
are acceptable. The algorithm has been tested to locate
multiple users simultaneously. The location of the user
obtained has been integrated with lighting to control theilluminance of LED Fixtures based on the location of the user.
In this method frequent fingerprinting has to be done to retain
the location accuracy. In future it is proposed to explore
filtering techniques to improve the location accuracy.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 2, September 201257

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 2, June 201257

ACKNOWLEDGMENT
The work is developed under the project Development of
ICT Technologies for Smart Buildings with Low Carbon
Emission, funded by Department of Electronics andInformation Technology (DeitY), Government of India. We
would like to thank Department of Information Technology
for providing us this opportunity. We would also like to thankMrs S. Sridevi, Senior Engineer, C-DAC, Chennai for her
programming support for the clustering algorithm.
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[9] P.W. Lee, W.K Seah, H. Tan and Z. Yao, “Wireless sensing without
sensors – an experimenal approach,” in Proc. Int. Symp. Personal
Indoor Mobile Radio Commun., pp. 62-66, Sep 2009[10] Neal Patwari, Alfred O. Hero, Matt Perkins, Neiyer S. Correal andRobert J.O‟Dea, “Relative Location Estimation in Wireless Sensor
Networks,” IEEE Transactions on Signal Processing, Vol.51, No.8,
August 2003[11] Kannan Srinivasan and Philips Levis, “RSSI is under appreciated,”
Proceedings of third workshop on embedded sensor nodes, 2006[12] Masaya Arai, Hidenori Kawamura, Keiji Suzuki, “Estimation of
ZigBee‟s RSSI fluctuated by Crowd Behavior in Indoor Space”, SICE
Annual Conference 2010, The Grand Hotel, Taipei, Taiwan ,August
18-21, 2010.
[13] F. Viani, L. Lizzi, P. Rocca, M. Benedetti, M. Donelli and A. Massa,“Object tracking through RSSI measurements in wireless sensor
networks”, ELECTRONICS LETTERS 8th May 2008 Vol. 44 No. 10[14] Jin-Shyan Lee, “Performance Evaluation of IEEE 802.15.4 for
Low-Rate Wireless Personal Area Networks”, IEEE Transactions on
Consumer Electronics, Vol. 52, No. 3, AUGUST 2006[15] Zhang Jianwu and Zhang Lu, “Research on Distance Measurement
Based on RSSI of ZigBee”, ISECS International Colloquium on
Computing, Communication, Control, and Management, IEE 2009[16] http://people.revoledu.com/kardi/tutorial/kMean/index.html,
accessed on Feb27th 2012.
[1]

K.Subaashini - This author has been working inCentre for Development of Advanced Computing,
Chennai since November 2008. She has completed her
Masters in Engineering in Embedded Systems from
College of Engineering, Guindy, Anna University,
Chennai in the year 2008. Her area of interests
includes wireless sensor networks using Zigbee andubiquitous computing for smart homes and buildings.
G.Dhivya – This author has been working in Centre
for Development of Advanced Computing, Chennai
since Nov 2006. She has completed her Bachelors
in Engineering in Electrical & Electronics from
Erode Sengunthar Engineering College, Affiliatedto Anna University, Erode. Her area of interests
includes wireless sensor networks using Zigbee andubiquitous computing for smart homes andbuildings.

R.Pitchiah - This author has functioned as Group
Coordinator, Real-time Systems Development
Group at C-DAC, Bangalore from 1995 to 2002, andwas responsible for architecting R&D projects in thearea of Dependable Computing. He had functioned
as Programme Coordinator, in National Ubiquitous
Computing Research Centre at C-DAC Chennai andhas been the Principal Coordinator for many R&D
projects. He is currently working as a scientist in
Department of Electronics and Information technology, India. He has

co-authored more than 10 research
publications
International conferences/workshops.

inNational/

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 2, September 2012

58

Quantum Communication Scheme for Blind
Signature with Arbitrary Two-Particle Entangled
SystemJinjing Shi1 , Ronghua Shi1 , Xiaoqi Peng2 and Moon Ho Lee3 , Senior Member, IEEE
1 School of Information Science & Engineering, Central South University, Changsha 410083, China.
2 Department of Information Science & Engineering, Hunan First Normal University, Changsha 410205, China.
3 Institute of Information and Communication, Chonbuk National University, Chonju 561-756, Korea.

Abstract—A quantum communication scheme for blind signature is proposed based on two-particle entangled quantum system
to create a novel systemetrical quantum cryptosystem. All themessages are encrypted by the private key of the sender Aliceduring the communication and the authenticity verification of
signatures and an arbitrator’s batch efficient proxy signature isapplied. It demonstrates that a large number of blind signatures
can be derived with the characteristics: impossibility of forgery,
impossibility of disavowal by the signatory and impossibility ofdenial by the receiver. The security of our scheme depends on the
two-particle entangled system which cannot be deterministicallyintercepted.
Index Terms—Quantum communication, Blind signature,
Proxy signature, Quantum signature, Quantum cryptography.

I. I NTRODUCTION
A classical digital signature is an electronic signature thatcan be used to authenticate the identity of the sender of amessage or the signer of a document. It ensures that theoriginal content of the message or document is unchanged[1]. Digital signatures are easily transportable, cannot be
imitated by someone else, and can be automatically timestamped. A blind signature introduced by David Chaum [2] istypically employed in privacy-related protocols and realized byusing a number of common public key signing schemes [3].
Fan and Lei [4] proposed a scheme based on the quadratic
residues problem in 1996. Zeng [5]–[7] has introduced aquantum signature scheme based on the correlation of quantum
entanglement states in 2001. Gottesman and Chuang [8] havealso proposed a quantum digital signature scheme based on
quantum one-way function. In 2008, Wen [9] proposed a weakblind signature scheme based on quantum cryptography, Shi
et al. introduced a multiparty quantum proxy group signaturescheme for the entangled-state message [10]–[12] and Lee
also presented two quantum signature schemes with message
recovery [13]. In 2008, Yang and Wen suggested a multiproxy quantum group signature scheme with threshold sharedverification [14], in which only the cooperation of all thesigners in the proxy group can generate the proxy signature
on behalf of the original signer.
In this paper, a quantum communication scheme for blind
signature is proposed to create a new systemetrical quantum

key cryptosystem with two-particle entangled quantum system.
A third fully trusted participant Charlie (the arbitrator andproxy) is involved. The responsibility of Charlie is to help
Alice and Bob trust each other before communication, verify
the legalization and authenticity of the trying blind signatureand provide batch efficient proxy blind signatures to Alice.
During all the communications, two-particle entangled quantum system are applied to create the quantum message strings
and to make distribution of keys. The rest of this paper isorganized as follows. Sect. II proposes the quantum communication scheme for blind signature. The security analysis anddiscussions are made in Sect. III. Finally, the conclusions are
drawn in Sect. IV.
II. Q UANTUM C OMMUNICATION S CHEME FOR B LIND
S IGNATURE
The classical blind signature is described like this: Bob isa notary, Alice expects that Bob can sign the message fromher and she does not let Bob understand the content of themessage. Bob does not care the content of the message andonly testifies he has notated it at some time [3]. The quantum
communication scheme for blind signature utilizes the arbitrary two-particle entangled quantum system [15] which canbe expressed as follows,|ϕi = a00 |00i + a01 |01i + a10 |10i + a11 |11i,(1)

where |a00 |2 + |a01 |2 + |a10 |2 + |a11 |2 = 1.
Suppose that Alice and Bob share a maximally entangledstate:
1(2)|ϕAB i = (|00iAB + |01iAB + |10iAB + |11iAB ),2
Alice and Charlie share a maximally entangled state:
1(|00iAC + |01iAC + |10iAC + |11iAC ),(3)2
and Bob and Charlie share a maximally entangled state:|ϕAC i =1(|00iBC + |01iBC + |10iBC + |11iBC )2
according to Eq. (1).
|ϕBC i =(4)

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 2, September 2012

Fig. 1. The relationship among Alice, Bob and Charlie for the distributionof quantum keys in the quantum communication scheme of blind signature.

The quantum communication scheme for blind signature canbe presented as following aspects: preparation of quantum keys
and messages, trying quantum blind signature, verification andbatch quantum blind signature.
A. Preparation of Quantum Keys and Messages
Step 1. Alice owns a private key Ka which is used toencrypt her messages that are excepted to be signed by Bob.
Secret keys Kab , Kac and Kbc are distributed to Alice, Bob
and Charlie, where Kab is employed in the communication
between Alice and Bob and it only can be used twice for Bob’s
encrypting and Alice’s decrypting in the first communication,then it will be discarded. Kac and Kbc are employed in thecommunications between Alice and the arbitrator Charlie andbetween Bob and Charlie respectively. The relationship among
Alice, Bob and Charlie for the distribution of quantum keys
is given in Fig. 1.
Step 2. Alice prepares quantities of messages which are
expected to be signed by Bob which can be described as amatrix
M1
M2
..
.
M=
Mi
..
.
Mm



m11
 m21

 ..
 .

 mi1

 .
 ..
mm1

m12
m22
..
.
mi2
..
.
mm2

···
···
..
.
···
..
.
···

m1j
m2j
..
.
mij
..
.
mmj

···
···
..
.
···
..
.
···


m1n
m2n 

.. 
. 
,
min 

.. 
. 
mmn

59

Fig. 2. The process of quantum communication scheme for blind signature.
(1) ∼ (7) denote that seven steps of quantum communication scheme forblind signature.

inform Charlie the result of the signature and Charlie verifies
legalization and authenticity of the trying signature. (6) If theverification is successful, Alice sends quantities of messages
to Charlie. (7) Charlie signs quantities of messages from Alicewith the the combination of Bob’s personal information andCharlie’s random checking photons.
B. Trying Quantum Blind Signature
Step 1. Alice creates a qubit string |ψM1 i for the trying
message. She transforms the trying message M1 into a qubit
string |ψM1 i, and there are n qubits in this string such as|ψM1 i, i.e.,|ψM1 i = {|ψ11 i, |ψ12 i, . . . , |ψ1j i, . . . , |ψ1n i},where |ψ1j i is a single qubit in the string |ψM1 i. Any qubit|ψ1j i(j = 1, 2, . . . , n) in |ψM1 i can be expressed as asuperposition of the two eigenstates {|0i, |1i}, ,i.e.,|ψ1j i = α1j |0i + β1j |1i,(5)

there are m messages {M1 , M2 , . . . Mm } and each message
has n bits, for example:£¤
Mi = mi1 mi2 · · · mij · · · min ,(6)
and M1 is chosen to be considered as the trying message forthe first trying quantum blind signature.
The quantum communication scheme for blind signature canbe briefly defined as following seven steps corresponding to
Fig. 2. (1) Alice firstly sends a trying message M1 encryptedby her private key Ka to Bob. (2) Bob adds his personal
information to this secret message and encrypts it by the shared
key Kab with Alice. (3) Bob sends the secret message with his
personal information to Alice which is called the trying blind
signature. (4) Alice receives this trying blind signature anddecrypts it by the shared key Kab with Bob and judge whether
the secret trying message has been falsified, and if falsified
the signature process stops. (5) Alice and Bob separately

(7)

(8)

where α1j and β1j are complex number satisfying |α1j |2 +|β1j |2 = 1. The general quantum message states |ψMi i can be
expressed as the tensor product of the qubits in that message
string, i.e.,|ψMi i = |ψi1 i ⊗ |ψi2 i · · · ⊗ |ψij i · · · ⊗ |ψin in= sum2γ=1 λiγ |µ1iγ µ2iγ · · · µjiγ · · · µniγ i,

(9)

P2n

where γ=1 |λiγ |2 = 1 and µjiγ ∈ {0, 1}.
Step 2. Alice transforms her private key Ka ={|Ka1 i, |Ka2 i, . . . , |Kaj i, . . . , |Kan i} to a sequence of measurement operators Mka ,i.e.,
Mka = {Mk1a1 , Mk2a2 , . . . , Mkjj , . . . , Mknan },a(10)where the operator Mkjj is defined to arise from the key |Kaj iafor j ∈ {1, 2, . . . , n}. A more detailed method is describedin Ref. [5]. After the transformation, Alice measures theinformation string of qubits |ψM1 i with Mka to derive a secretstring|T i = Mka |ψM 1 i = {|t1 i, |t2 i, . . . , |tj i, . . . , |tn i},(11)where |tj i = Mkjj |ψ1j i and it denotes the j-th qubit in thea
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 2, September 2012

0 i matches
Fig. 3. The comparison quantum circuit for verifying whether |ψMi
to |ψMi i.

string of |T i. Thus |T i is the secret state of the trying message
and she directly sends it to Bob and expects Bob to sign it.
Step 3. Bob adds his personal information to the secretmessage |T i though he doesn’t understand the content of it.
(1) Bob creates a qubit string |ψp i of his own personal
information which contains n qubits, i.e.,|ψp i = {|ψp1 i, |ψp2 i, . . . , |ψpj i, . . . , |ψpn i},(12)where |ψpj i is a single qubit in |ψp i. Any qubit |ψpj i(j =1, 2, . . . , n) in |ψp i can be expressed as a superposition of thetwo eigenstates {|0i, |1i} like |ψ1j i in the Step 1.
(2) Bob doesn’t expect Alice know the content of his
personal information either and he encrypts |ψp i with Kbc . Hej1
2nrelates the key Kbc = {|Kbci, |Kbc
i, . . . , |Kbci} toi, . . . , |Kbc
a sequence of measurement operators Mkbc and the modus
is like Eq.(10). Then Bob measures the personal information
string |ψp i with Mkbc and obtains|P i = Mkbc |ψp i = {|P1 i, |P2 i, . . . , |Pj i, . . . , |Pn i},(13)

where |pj i denotes the j-th qubit in the string of |pi and |pj i =Mkjj |ψpj i.
bc(3)Bob utilizes kab , |T i, |P i to provide a quantum blind
signature to the secret trying message, which can be implemented as this way: he encrypts |T i, |P i with kab to drive
Sb = kab (|T i, |P i),(14)

where Sb is a blind signature on Alice’s secret trying message.
Step 4. Bob sends Sb back to Alice and waits for theverification of the signature.
C. Verification
Step 1. Alice receives Sb and uses kab to decrypt Sb toderive |T 0 i and |P 0 i. Then she uses her private key ka to0decrypt |T 0 i to obtain a quantum string |ψMi.
1
Step 2. Alice verifies whether the signature is blindness.
0
She compares |ψM
i to her |ψM1 i which she has reserved
1in the Step 1 of trying quantum blind signature phase. The
comparison quantum circuit is presented in Fig. 3. It im0i ⊕ |ψij i and we can justify whether
plies that |vij i = |ψij
0|ψMi i matches to |ψMi i according to the output qubit string|Vi i = {|vi1 i, |vi2 i, . . . , |vij i, . . . , |vi(n−1) i, |vin i}. Because
0|0i ⊕ |0i = |0i, |1i ⊕ |1i = |0i, |0i ⊕ |1i = |1i, |ψMiimay match to |ψMi i when |Vi i = {|0i, |0i, . . . , |0i, . . . , |0i}60

0
is derived. If |ψM
i 6= |ψM1 i, it means the secret message has
1been misrepresented. Maybe there is somebody has measured
the trying message or intercepted the whole or parts of thecontent, because any measurement may change the state ofquantum photons. Then the protocol should be terminated. If0|ψM
i = |ψM1 i, we can suggest that there is nobody knows
1
the content of the trying message except Alice, thus the blind
signature can be established.
Step 3. Bob transmits |P i to the arbitrator Charlie. |P i isthe encrypted result of |ψp i with Mkbc , and it is secret toanyone except Bob and the arbitrator Charlie. So Bob cansend it directly to Charlie through the quantum channel.
Step 4. Alice sends |P 0 i to Charlie.
Step 5. The arbitrator Charlie receives |P 0 i and |P i, andhe certifies whether the signature is authentic. He firstly
compares if |P 0 i = |P i, and then he uses kbc to decrypt|P 0 i and |P i separately. Charlie obtains |ψp0 i and |ψp i, andthen he compares whether |ψp0 i = |ψp i. If |P 0 i = |P i and|ψp0 i = |ψp i, we can consider this trying blind signature is
successful, then Charlie will inform Alice and Bob this trying
blind signature is authentic and blindness. Charlie can also
apply the comparison quantum circuit in Fig. 3 to implement
the verification procedure. Next step, Charlie can sign alarge number of messages from Alice as a proxy of Bob.
However, when any previous condition is not satisfied, thecommunication should be terminated.

D. Batch Proxy Quantum Blind SignatureCharlie may become a proxy of Bob and sign quantities ofmessages {M2 , M3 , . . . Mm } of Alice with the combinationof Bob’s personal information |P i and his random checkingkphotons |Pchecki.
Step 1. Alice transforms her remaining messages{M2 , M3 , . . . Mm } into m − 1 strings of qubits{|ψM2 i, |ψM3 i . . . , |ψMm i} like the Step 1 ∼ 3 of thetrying quantum blind signature phase. Then she encrypts
them by her private key ka , thus the remaining secretmessages can be expressed as follows,|Mk i = Mka |ψMk i = {|mk1 i, |mk2 i, . . . , |mkj i, . . . , |mkn i},(15)
where k = 2, 3, ..., m.
Step 2. Alice sends {|M2 i, |M3 i, ...|Mm i} to Charlie successively and waits for the signature from Bob’s proxy Charlie.
Step 3. Charlie adds one qubit random checking photonk|Pchecki into |P i which Charlie has obtained in the step 3 ofverification phase, wherekk|Pcheck
i = Mkrbc
r |ψcheck i(16)kand |ψchecki(k = 2, 3, . . . , m) is formed as Eq.(9). r is arandom number in {1, 2, . . . j . . . , n}. It means Charlie maykrandomly choose a Mkjj from Mkbc to measure |Pchecki. Thusbc
the general expression of the combining qubit strings iskk|PBT
i = {|P i, |Pchecki}k= {|P1 i, |P2 i, . . . , |Pj i, . . . , |Pn i, |Pchecki},(17)

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 2, September 2012

where k = 2, 3, . . . m. Each message state |Mk i is correk
sponding to a |PBT
i, and Charlie can randomly use a differentk|PBT i to sign a message from Alice.
Step 4. Charlie separately signs m − 1 secret messages withk
the string |PBT
i(k = 2, 3, . . . , m) which is the combinationof Bob’s personal information and Charlie’s checking photons,
and he encrypts them by kac . Thus the proxy blind signatures
are obtained:k
STk = kac (|Mk i, |PBT
i),
(18)
where k = 2, 3, . . . , m.
Step 5. Charlie sends the secret messages with blind signatures {ST2 , ST3 , . . . , STm } back to Alice successively.
Step 6. Alice receives the blind signatures STk (k =2, 3, . . . , m) and decrypts them by kac to get |Mk i(k =k02, 3, . . . , m) and |PBT
i(k = 2, 3, . . . , m). Then she decrypts |Mk i(k = 2, 3, . . . , m) with ka to obtain |ψk i(k =2, 3, . . . , m). Because Charlie is the fully trusted arbitrator
and the proxy signatures contain his checking photons andBob’s correct personal information, it is not necessary tosuspect the accuracy of the signature. However, Alice canrandomly measure the accuracy of the signatures by justifying
whether they satisfy |ψk i = |ψMk i(k = 2, 3, . . . , m) andk0|PBT
i = |P 0 i(k = 2, 3, . . . , m).
III. S ECURITY A NALYSIS A ND D ISCUSSIONSThe security of this scheme can be analyzed as following
four aspects: impossibility of forgery, impossibility of disavowal by the signatory, impossibility of denial by the receiverand the security of the entangled quantum system.
A. Impossibility of Forgery
If an dishonest participant Eve wants to forge the signatureof Bob, she may sign the illegal messages herself by imitating
Bob’s signature or pretend the legal user Alice to require for
Bob’s signature. Even if Eve succeeds to sign her messages
by forging Bob’s personal information. Denote the spurious
Bob’s personal information is |Ps , she may be detected in theverification phase, and the arbitrator Charlie can judge |Ps idoes not match to the Bob’s correct personal information |P i.
The communication of the signing phase should be terminatedimmediately.
If the attacker Eve expects to forge the signature of Bob,
she may be recognized in the step 3 of initial phase. Even
though she is so lucky to escape identity verification and she
can send her trying secret message to Bob, Bob doesn’t carewhat the content of the message is but only signs it. Because
the signing message is encrypted by kab before she sends itback to Eve, Eve can not decrypt it for lack of kab .
B. Impossibility of Disavowal by the Signatory
Suppose Bob has added his personal information to sign
the trying message, and the signature is obtained by Alice.
Because the arbitrator Charlie can judge whether the signature
is authentic or not, once it is proved to be authentic, the Bob’s
personal information has already denoted for a register fromhim, then the following signing for the last m − 1 messages is61

the responsibility of the arbitrator and proxy Charlie, and hewill utilize the combination of his checking photons and Bob’s
personal information to do this. If Bob disavow it, he will be
discovered by Charlie immediately. In this scheme, as long as
Bob has signed the trying message and the signature has been
proved to be authentic by Charlie, the mechanism of proxysignature make Bob have no chance to disavow the remainingm − 1 signature. Thus the signatory Bob is impossible todisavow the signature.
C. Impossibility of Denial by the ReceiverIn the verification phase, Alice derives Sb and use kab
to decrypt Sb to obtain |T 0 i and |P 0 i. After the arbitrator
Charlie’s authentication, if |P 0 i = |P i and |ψp0 i = |ψp i,
Charlie informs Alice and Bob this trying blind signature isauthentic, otherwise, the trying signature will be considered
incredible and the process of signature is discontinued at once.
It means the two participants Alice and Bob can not deny thesignature any way and Charlie is considered to be a judge.
If one of them deny or disavow the signature, Charlie canunconditionally suspect the identification of them, and they
may be no longer join in this communication.
D. Security of Entangled Quantum System
Suppose an attacker Eve can entangle her ancila system withthe two-particle entangled quantum system |ϕi in Eq. (1) byapplying the strongest collective attack with probabilities. The
combined Eve’s and quantum system state can be expressedas|Eϕi = λ00 a00 |00i|e00 i + λ01 a01 |01i|e01 i+λ10 a10 |10i|e10 i + λ11 a11 |11i|e11 i, (19)where |e00 i, |e01 i, |e10 i and |e11 i are un-normalized states of
Eve and |λ00 i, |λ01 i, |λ10 i and |λ11 i are parameters relative to
her attack probabilities. If Eve is clever enough to implement
single qubit QND measurement on one particle of |ϕi with themeasurement operator ρ = 12 |0ih0| ± 12 |1ih1|, she can derive
the ideal entangled states|Ei =1{(λ00 a00 |0e00 i + λ01 a01 |1e01 i)
2±(λ10 a10 |0e10 i + λ11 a11 |1e11 i)}(20)

according to Eq. (19). Thus the entanglement entropy of |Ei
can be analyzed based on the degree of entanglement [16]to indicate the maximal amount of information which can beintercepted by Eve. The entanglement entropy is√√
1+ 1−ε1+ 1−εSE = −log2√2√2
1− 1−ε1− 1−ε
−
log2
,
(21)
2
2
where ε = 4|λ00 λ11 a00 a11 − λ01 λ10 a01 a10 |2 . Even though
Eve can adjust the parameters |λ00 i, |λ01 i, |λ10 i and |λ11 i tomake SE approach the maximal value 1 (when ε = 1) while
the maximal entropy of the two-particle entangled quantum
system |ϕi is 2. Therefore the entangled quantum system statecannot be deterministically intercepted.
Copyright ⓒ GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 2, September 2012

IV. C ONCLUSIONSA quantum communication scheme for blind signature with
two-particle entangled quantum system is proposed, which is a
new quantum key cryptosystem that combines proxy signatureand blind signature. The two-particle entangled quantum states
are applied to create the strings of qubits for messages andmake distribution of keys. No matter the trying message or
the remaining m − 1 messages, they are encrypted by theprivate key ka of Alice. Moreover, an authenticity verificationof signatures and an arbitrator’s efficient proxy signature areboth applied. The analysis shows that a large number of blind
signatures for quantities of messages can be achieved withthe characteristics: impossibility of forgery, impossibility ofdisavowal by the signatory and impossibility of denial by thereceiver. The security of our scheme depends on the twoparticle entangled system which cannot be deterministicallyintercepted.
ACKNOWLEDGMENT
This work was supported by the National Natural Science
Foundation of China (Grant Nos. 61272495), WCU R322010-000-20014-0 (Fundamental Research2010-0020942NRF,
Korea), the Hunan Provincial Innovation Foundation For
Postgraduate (Grant Nos. CX2011B087) and the Excellent
Doctoral Dissertation Fund of Central South University (Grant
Nos. 2011ybjz030).
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[2] D. Chaum, Advance in Cryptography, Proceedings of Crypto’82 SpringerVerlag, Berlin, 1982, p.267.
[3] B. Schneier, Applied Cryptography:Protocols, Algorithms, and Source
Code in C, John Wiley and Sons, New York, 2nd ed., 1996, p.79.
[4] C. Fan and C. Lei, Efficient blind signature scheme based on quadratic
residues, Electronic Letters., vol. 32, no. 811, 1996.
[5] G. H. Zeng and C. H. Keitel, Arbitrated quantum-signature scheme, Phys.
Rev. A., vol. 65, no. 042312, 2002.
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[7] G. H. Zeng, Reply to ”Comment on ’Arbitrated quantum-signaturescheme’”, Phys. Rev. A., vol. 78, no. 016301, 2008.
[8] D. Gottesman and I. Chuang, Quantum digital signatures, arXiv:quantph/0105032.
[9] X. J. Wen, X. M. Niu, L. P. Ji, and Y. Tian, A weak blind signaturescheme based on quantum cryptography, Optics Communications., vol.
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62

Jinjing Shi is now a joint Ph.D. student of Central
South University, China and Chonbuk National University, Korea, and participating in the World Class
University (WCU) project sponsored by the National
Research Foundation (NRF), Korea. She received
her B. E. degree in the School of Information
Science and Engineering, Central South University,
Changsha, China, in 2008. Her research interests
are quantum communications, quantum cryptography and network security.

Ronghua Shi received the B.S., M.S., and Ph.D.
degrees in electrical engineering from Central South
University (CSU), Changsha, China, in 1986, 1989,
and 2007, respectively. He is presently a Professor
and the Vice Dean of the School of Information
Science and Engineering at Central South University.
His research interests include information security,
quantum cryptography and network security.

Xiaoqi Peng received the B.S., M.S., and Ph.D.
degrees in automation and control from Chongqing
University, Harbin Institute of Technology and Central South University respectively. He is presently
a Professor of the School of Information Scienceand Engineering at Central South University, and thedean of Hunan First Normal University. His researchinterests include intelligent detection of complex
industrial processes, optimization of the decisionmaking and intelligent control.

Moon Ho Lee is a professor in Chonbuk National
University, Korea. He received the Ph.D. degreefrom Chonnam National University, Korea in 1984,
and from the University of Tokyo, Japan in 1990,
both Electrical Engineering, He was in Universityof Minnesota, U.S.A, from 1985 to 1986 as a postdoctor. He was conferred an honorary doctorate fromthe Bulgaria Academy of Sciences in 2010. Dr. Lee
has made significant original contributions in theareas of mobile communication code design, channel
coding, and multidimensional source and channel
coding. He has authored 34 books, 135 SCI papers in international journals,
and 240 papers in domestic journals, and delivered 350 papers at international
conferences. Dr. Lee is a member of the National Academy of Engineeringin Korea and the National Academy of Mathematical Sciences in India, anda Foreign Fellow of the Bulgaria Academy of Sciences. He is the inventor ofJacket Matrix and it in Wikipedia was cited over 49,559 times.

Copyright ⓒ GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 2, September 2012

63

A Epidemic Style Super-node Election Method
Based on Self-information Theory
Zhiwei Gao*, yingxin Hu**Department of Computer Science, Shijiazhuang TieDiao University, Shijiazhuang, 050043, China
gao_zhiwei@163.com, huyinxin@163.com
Abstract—Many distributed applications such as cloudcomputings, grids use peer-to-peer (P2P) paradigm as the lower
service. In P2P technology, the super-node paradigm can lead to
improved efficiency, without compromising the decentralized
nature of P2P networks. So the above applications adopt supernode paradigm to provide services. However, due to inherent
dynamism, decentralisation, scale and complexity of P2P
environments, self-managing super-node selection is a challenging
problem. This paper present a super-node election protocol basedon self-information theory and gossiping technology(SPSI). In
SPSI, every node has a information vector (VI), and SPSI uses aweighted mean mechanism based on VI to promote the “best”
nodes to super-node status. As we know we are the first to use
self-information theory to select super-node. The paper also
includes extensive simulation experiments to prove the efficiency,
scalability and robustness of SPSI.

algorithm, potentially require communication with all peers in
the network and thus do not scale to large networks. Other
solutions such as manual or static configuration of super-nodes
are inappropriate due to a lack of global knowledge ofapplication characteristics.
II. RELATED WORKIn this section, we briefly review some related work. We
start with P2P based on super-node technology, and thenpresent the related work on super-node selection problem.
The super-node approach to organize a P2P overlay is atrade-off solution that merges the client-server model relative
simplicity and the P2P autonomy and resilience to crashes. The
need for a super-node network is mainly motivated by the fact
to overcome the heterogeneity of peers deployed on the
Keywords—self-information quantity, super-node, scalability,
Internet.
SPSI
Meirong Liu[1] et al. present a super-peer-based
coordinated service provision framework (SCSP) to coordinate
the service groups to work collaboratively and share their
I. INTRODUCTION
service peers. The SCSP is made up of an S-labor-market
any distributed applications such as cloud computing, grids
model, a recruiting protocol based on a weighting mechanism,
use super-node paradigm as the lower service. Super-nodes and an optimal dispatch algorithm.
allow these applications to run more efficiently by exploiting
KaZaA [8] and Gnutella [9], [10] have explored using
heterogeneity and distributing load to machines that can handle heterogeneity of peers to improve search performance. Thesethe burden. On the other hand, because this architecture allows systems have efficient peers hold more neighbors and process
multiple, separate points of failure, increasing the health of the more queries. An efficient peer (super-peer) acts as a server in
distributed network, it does not inherit the flaws of the a local area, builds an index of the shared files provided byclient/server model. The use of P2P protocols is expected to those peers connected to it and offers a searching index service
improve the efficiency and scalability of information services for those who have connected to it by flooding queries to otherin these systems [1],[4],[5].
super-peers. These systems mainly explored improving
However, due to inherent decentralisations, scale, dynamism, performance by decreasing the number of transmitted
and complexity of P2P environments, self-managing super- messages and latency hops.
node selection is a challenging problem.
Yang and Garcia Molina [12] proposed some design
A number of P2P systems address the heterogeneity of P2P
guidelines. A mechanism to split node clusters is proposed andenvironments by electing super-nodes and assigning them evaluated analytically, but no experimental results are
extra responsibilities [6],[7],[10],[11]. Solutions based on presented.
flooding, random walking or other traditional election
Garces-Erice[13] et al. studied hierarchical DHTs, in whichpeers are organized into groups, and each group has its
Manuscript received May 21, 2012. This work was supported by theautonomous intra-group overlay network and lookup service.
National Natural Science Fund of China (No: 50975185).
Z.W is a Associate Professor of Information Science and Technology, The groups themselves are organized in a top-level overlay
Shijiazhuang TieDao University. Gao's research interests center around peer- network. To find a peer that is responsible for a key, the top
to-peer computing, PKI and certificate chain, PKI based on P2P technology.
level overlay first determines the group responsible for the(phone: 086-0311-87939496; fax: 086-0311-8795288; e-mail: gao_zhiwei@
key; the responsible group then uses its intra-group overlay to
163.com).
Y.X is a Associate Professor of Information Science and Technology, determine the specific peer that is responsible for the key.
Shijiazhuang TieDao University. (Email: huyinxin@163.com )They concluded that hierarchical organization could improve aM

Copyright ⓒ GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 2, September 2012

system’s scalability. A hierarchical system demonstrates betterstability due to selection of peers who are more reliable as themembers of the upper overlay, generates fewer messages in awide area and can significantly improve the lookup
performance by transmitting queries through the upper overlay.
Nejdl et al. proposed a design organizing super-peers with ahypercube structure in [14]. In their approach, every superpeer serves a subset of peers and all super-peers are arranged
in a hypercube topology. Because the topology is vertexsymmetric, it features inherent load balancing among superpeers. When a super-peer wants to transmit a query, according
to the spanning tree algorithm, it forwards the query to its
neighbors instead of flooding the system with queries. Eachsuper-peer wants to maintain at most d neighbors’ informationand it takes at most d logic hops for a query from any superpeer to the farthest super-peer, where d is the dimension of thehypercube.
Mizrak [15] et al. proposed a design based on the Chord
ring. In their approach, there are two rings, named the innerring and outer-ring respectively. Each peer is placed on acircular identifier space in the “outer-ring”, using a DHT
algorithm such as Chord. Of all the peers, m peers who joined
the system first are selected as super-peers to create a smaller
core “innerring”. The outer-ring is divided into m equal arcs
and each arc is assigned to one super-peer. Each super-peer is
responsible for maintaining two pieces of information: theaddresses of the peers contained within its arc and the mapping
between arcs and their responsible super-peers. Each peer
registers in only one super-peer, and requests searching
services from its super-node. Each super-peer offers searching
services for its registered peers and the other super-peers. The
lookup is performed using super-peers in constant time. Whena super-peer’s load approaches its capability, it may share partof its load with its neighbors if they have sufficient excess
capacity or with a new super-peer selected from the volunteer
peers. In either case the super-peer splits its arc appropriately
and reassigns pieces of this range to the neighbors accepting
the load.
In [16], the authors propose a socio-economic inspiration
based on Shelling’s model to create a variation of the supernode topology. Such variation allows ordinary peers to beconnected with each other and to be clients of more than one
super-node at the same time. This topology focuses on
efficient search. As in our case, the super-nodes are connectedto each other to form a network of hubs and both solutions are
suited for unstructured networks. However, they do notaddress the problem of the super-node election.
In [18], a mechanism for the construction and themaintenance of overlay topologies based on super-nodes SG-1
was proposed. This mechanism is based on the well-known
gossip paradigm, with nodes exchanging information withrandomly selected peers and re-arranging the topology
according to the requirements of the particular P2P application.
In [19], the author presents SG-2, a protocol for building andmaintaining proximity-aware super-node topologies. Like SG1, SG-2 also uses a gossip-based protocol to spread messages
to nearby nodes and a biology-inspired task allocation

64

mechanism to promote the “best” nodes to super-node status.
Unlike all these studies, our implementation is based on
information theory, and as we know we are the first to
introduce information theory to super-node selection. Ourcontribution is as follows: (1) Propose a super-node election
protocol SPSI based on self-information. (2) Give the relation
between node’s capacity and online time through experiments.
(3) Propose a weighted mean algorithm to describe node’s
properties. Our model’s efficiency is equal to SG-1 or SG-2,
but the super-nodes we elected are more stable, so the costs of
network maintenance are lower than them.
III. BACKGROUND THEORY AND TERMINOLOGY
The framework of SPSI can be considered as a natural
evolution of Rigorous binary tree model. We propose aframework based on our own efficient and scalable Rigorous
binary tree model and its theorem[6,17]. If the size of networkis small, file lookups are resolved with only one hop. As thesystem’s scale become larger, it can expand automatically
based on the super-node’s capability and suit for large scale
system. In the worst case, file lookups are resolved with only
three hops. But this model did not discuss super-node selection
problem, and this is the main concern of this thesis.
Here, we first provide the definition of a rigorous binary tree
and the other relevant theory to give readers a better
understanding of our model.
A. Rigorous binary tree and its mapping theorem
Definition 1: Rigorous binary tree
For a random node of a binary tree, if it has at least one
child node, its left child node and right child node must exist at
the same time. If this condition is satisfied, the binary tree isdefined as a rigorous binary tree.
Definition 2: Rigorous binary tree extension
After a random leaf of a rigorous binary tree produces twochild nodes, the original rigorous binary tree becomes a new
rigorous binary tree. This is called rigorous binary tree
extension.
Definition 3: Rigorous binary tree code algorithm: The
letter T represents a rigorous binary tree, “A” represents a
random node in T, ha represents the depth of node A, and Na
represents its code. The code of T’s root node was set as 0.
The code of A’s left child is equal to Na. The code of A’s righthchild is equal to (Na + 2 a ), The depth A’s child is ha+1.
Theorem 1: Rigorous binary tree mapping theorem: For
any one integer I (I >=0), there is one and only one leaf node X
hwhose code (Nx) and depth (hx) can accord with Nx =I % 2 x ,among all leaf nodes in a fixed rigorous binary tree. (Here %denotes modular arithmetic.) the proof process is included in
our prior work[6], [17].
Figure.1 illustrates a rigorous binary tree. When we extend
its leaf node G in Figure.1(1) by adding two children nodes toG, the rigorous binary tree becomes that described in
Figure.1(2). According to the rigorous binary tree code
algorithm, node A is the root node, so its code and depth are (0,0). Node B is A’s left child, so B’s code is equal to A’s code (0)Copyright ⓒ GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 2, September 2012

and B’s depth is A’s depth plus one (0+1=1). Node C is A’s
right child, so C’s code is equal to (0+20=1) and C’s depth isequal to B’s depth (1). In the same way, we can compute theremaining nodes’ code and depth as described in Figure. 1(3).

Figure 1. The extending and coding of a rigorous binary tree

Based on Rigorous binary tree extension and mapping
theorem, we design RBTree model. In the model, when thenumber of peers registered in a super-peer reaches the quantity
limit, in order to balance the load and avoid a bottleneck, thesuper-peer will use SPSI protocol to select a high-powered
peer from its registered-peer table as a new super-peer, code itwith the rigorous binary tree code algorithm and share one partof its load with the new super-peer.
B. Self-information
Definition 4: Let E be an event belonging to a given event
space and having probability Pr(E ) = p E , Let I(E) –called the self-information of E – represent the amount of
information one gains when learning that E has occurred (orequivalently, the amount of uncertainty one had about E prior
to learning that it has happened).

65

Theorem 2: The only function defined over p ∈ [0, 1] andsatisfying• I(p) is monotonically decreasing in p;• I(p) is a continuous function of p for0 ≤ p ≤ 1;
• I(p1 × p2) = I(p1) + I(p2);where I( p ) = −c • log b ( p ) , c is a positive constantand the base b of the logarithm is any number larger than one.
In SPSI, every node has a information vector (VI), and SPSI
uses a weighted mean mechanism based on VI to promote the“best” nodes to super-node status.
C. Weighted Arithmetic Mean
In calculation of arithmetic mean, the importance of all theitems was considered to be equal. However, there may besituations in which all the items under considerations are notequal importance. For example, we want to find average
number of marks per subject who appeared in different
subjects like Mathematics, Statistics, Physics and Biology.
These subjects do not have equal importance. If we findarithmetic mean by giving Mean. For example, A student
obtained 70, 80, 80, 70, and 65 marks in the subjects of Math,
Statistics, Physics, Chemistry and Biology respectively. And
we assume weights 5, 4, 2, 3, and 1 respectively for the above
mentioned subjects. The solution was listed in table 1.
TABLE 1.

SOLUTION OF WEIGHTED ARITHMETIC MEAN
Subjects

Marks Obtained

Weight(w)

wx

Math

70

5

350

Statistics

80

4

320

Physics

80

2

160

Chemistry

70

3

210

Biology65

165∑ w = 15∑ wx = 1105

Total

Defination 5: arithmetic mean computed by considering
relative importance of each items is called weighted arithmetic
mean. To give due importance to each item underconsideration, we assign number called weight to each item inproportion to its relative importance. Weighted Arithmetic
Mean is computed by using following formula:
Xw = ∑

wx∑w

Where:
X w : Stands for weighted arithmetic mean.
x : Stands for values of the items andw : Stands for weight of the item.

IV. SYSTEM MODEL AND ALGORITHM

Generally speaking, our goal is to create a topology where
the most powerful nodes (in terms of capacity) and the enoughstable nodes are promoted to the role of super-nodes.
The main topology features of the SPSI protocol algorithm
are that each client just connected to a super node, and supernode of each other connected together by random. This
protocol can find a smaller number of nodes and super nodes
set which has a longer online time, and these super nodes canserve as client nodes to cover the rest nodes. Such a topology
structure can be easily used to implement file sharing, also can
reduce the traffic caused by the application program.
To build a topology with such characteristics, we propose amechanism based on NEWCAST [19]. Topology informationsuch as identifier, capacity, online time, current role andneighborhood of participating nodes are disseminated throughperiodic gossip messages between randomly selected nodes.
Based on the received information, nodes update their
Copyright ⓒ GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 2, September 2012neighborhoods in order to obtain a better approximation of thetarget topology.
In NEWSCAST, the state of a node is called partial view
and it is constituted of a fixed-size set of peer descriptors. A
peer descriptor contains the address of the node, along with alogical timestamp identifying the time when the descriptor was
created. The size of a partial view is denoted by s. Generally,
we chose the maximal view size to c = 30 and can get enoughrobust target topology[19].
A. Node Capacity
Apparently, Nodes are heterogenous: they may differ intheir computational and storage capabilities, and also (andmore importantly) on the bandwidth of its network connection.
In order to distinguish nodes that are capable to act as supernodes from nodes that can join just as clients, we associateeach node n with a parameter Cn representing its capacity, i.e.
the number of clients that can be handled by n. In other words,
we use the concept of capacity to abstract in a single quantity
all the characteristics listed above. In order to simplify our
simulations, we assume that each node knows its capacity
parameter; in a real implementation, this value could becomputed on the fly, by performing on-line measurements; theresult is strongly dependent on the particular application to be
implemented. The techniques used to perform this computation
are outside the scope of this paper.
In [20], through measurements done over existing P2P
networks, the author concluded that most of the nodes have
low capacity, while very few of them are able to support alarge number of clients and the node’s capacity obeys powerlaw distribution. So we have node n has a capacity of x withthe probability P(Cn = x ) = x −α , In which x ∈ [1, C max ] , α is the
distribution parameters (usually the parameter α = 2). The
node capacity does not necessarily follow a strict power
distribution, but it provides a reasonable distribution close tothat.
B. Online time of nodes
In any super nodes based peer-to-peer network, super nodes
take charge of both data block index and overlay organization.
When a super node logouts from the system or a super nodedecides to alleviate its load, it has to transfer the corresponding
block index and child nodes to another super node. This
process will bring about considerable communication cost, so
it is necessary to select a highly stable peer to act as a supernode.
Many research papers such as [2], [3] study the online timeof nodes through measurement method. They observed thatsession times (in minutes) with a mean=266, standard
deviation=671. Network nodes joining or leaving is considereda Poisson distribution, and online time of nodes is subject tothe negative exponential distribution of λ. So its probability
density function is:f ( x ) = λe

− λx

x>0

(1)
We use maximum likelihood estimation method to estimate
the parameter λ, assuming that x1, x2, x3 ... is a set of random

66

sample values, representing the node’s Online time, Then
Likelihood function
n

n

i =1

i =1

L(λ ) = λn ∏ exp(−λxi ) = λn exp(−λ ∑ xi )(2)d ln L(λ ) n n= − ∑ xi = 0dλλ i =1then

We obtain

λˆ =

n
n

∑x
i =1

=

1
x

(3)

i

As long as we estimate the average online time roughly, we
can figure out the estimated value to the equation of (3). Fromˆ[20] we set x = 266 , then the parameter is λ ≈ 0.004 . When the
distribution parameter is determined, it will generate therandom exponential distribution number as the node’s online
time, specific methods are:F( xi ) = 1 − exp(−λxi )Online Timexi =ln(1 − F( xi ))
−λ

(4)

(5)

Depending on the fundamental theorem of random variablesampling, there is R = F (x), where R is a uniformly distributed
random variable among [0,1]. As the 1-R and R have the same
distribution, so (5) can be written

x=−

ln R

λ

(6)

C. Weighted Arithmetic Mean in SPSIOur goal in SPSI is to select “best” nodes as super-nodes.
And we formalize the problem as follows: We are given a set S
of n nodes(vi,wi), where vi denotes the one of a node’s
attribute value such as computational, storage capabilities,
bandwidth of its network connection, online time etc. and widenotes the weight of the value.
In peer-to-peer systems, different application emphasizes
different capacity of super-nodes. Here our emphasis is therelation of node’s online time and the other capacity. In order
to simplify discussion, we use the concept of capacity toabstract in a single quantity all the characteristics such as
computational, storage capabilities, bandwidth of its network
connection etc. we associate each node n with a parameter Cnrepresenting its capacity, i.e. the number of clients that can behandled by n, and the node’s online time is Tn. The easiest
way is a linear combination of the two parameters, then toarrive at a parameter:δ (C n , Tn ) = ξC n + η Tn

(7)

where coefficient ξ is a value between 0 and 1,

ξ + η = 1 ,denoting the importance of node’s online time inrelation with node’s capacity.

Copyright ⓒ GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 2, September 2012

Apparently, There are some questions we must resolve. One
is Cn and Tn has different dimensions, and the other is Cn andTn has different quantity scale. If Cn or Tn is very large, andTn or Cn is very small, so δ (Cn , Tn ) is large. This selected node
is not our expected super-node. In order to resolve thesequestions, we introduce the information quantity theory toresolve it.

67

Defination 6: In joint probability space [XY,P(xy)], on thecondition of event y ∈ Y, Event x ∈ X ’s conditional
information quantity is:

I ( x | y ) = − log p ( x | y )(8)

For the same reason,

I ( y | x) = − log p ( y | x)

(9)

8

100
80

6

60

4
40

2

20

0
60

0
60
40
20
0

0

10

30

20

40

50

20
0

Defination 7: In joint probability space [XY,P(xy)], any joint
event xy, the joint information quantity of (x∈X，y∈Y) is:

50

(15)

When a node has a capacity of Cn, and online time Tn, we
assume that Cn, Tn is independent, then from (13) the
combined amount of information is

I (C nTn ) = I (C n ) + I (Tn ) = − log Pc − log Pt(16)In order to avoid the appearance of Cn , Ts in the formula,
the above equation becomes

For the same reason,(12)

When the event X and Y independently of each other, thenI (C nTn ) = − log
Let

Cn
T
− log nCs
Ts

δ (Cn , Tn ) = log Cn + log Tn(13)Assuming that the total number of node in the network is m,
and node n has a capacity of Cn and online time Tn. The total
capacity of all nodes in the network ismThen

I (CnTn ) = −δ (Cn , Tn ) + log(Cn * Ts )(17)

Since log(C n * Ts ) is a constant，I (Cn,Tn) changes only with

C s = ∑ Cn
n =1

and Online time of all nodes ism

Ts = ∑ Tn
n =1

Apparently, the probability of node n with capacity Cn
become super-node ispCn = C n / C s

40

pT n = Tn / TsI ( xy ) = − log p( xy )= − log( p ( x) p ( y | x))= − log p ( x) − log p ( y | x)
= I ( x) + I ( y | x)
(11)

I ( xy ) = I ( x) + I ( y )1030

The greater Cn is the more probability to be a super-node.
Similarly, the node’s probability to become super-node is(10)

Based on the definition of joint information quantity,
conditional information quantity, thenI ( xy ) = I ( y ) + I ( x | y )020

Figure 3. δCn ,Tn)=logCn+logTn

Figure 2. δCn ,Tn)=Cn+Tn

I ( xy ) = − log p ( xy )

40

(14)

δ(Cn,Tn), So we can use δ(Cn,Tn) as conditions for selecting a
super node 。 The difference is that if the amount of
information based on self-selected super-node, then I(Cn,Tn)
the smaller the better, If bases on δ(Cn,Tn), then δ(Cn,Tn) the
bigger the better. Since δ(Cn,Tn) only connected with Cn, Tn,
not with Cs , Ts, equation of (17) is more feasible than (16). So
the selection of super-nodes problem becomes the selection of
δ(Cn,Tn).
D. Super-node Selection Algorithm
Our goal is to produce a super-node topology characterized
by a about minimum number of super-nodes and the stability
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 2, September 2012of every super-nodes is taken into account. In order to do that,
we adopt a classification criteria based on the measure
introduced above: nodes with higher δ(Cn ,Tn) are consideredbetter candidates as superpeers. At each time, the target
topology is the one composed by the about minimum set ofnodes whose total capacity is sufficient to cover all other nodes
as clients, moreover the super-nodes are stable as far as
possible. Clearly, only in a static network the target topology
may be obtained; in the presence of dynamism, the real
topology will just approximate it.
The epidemic style algorithm for establishing the supernode and client relationships of the target topology isillustrated in Figure 4. The algorithm is executed only bysuper-nodes: being more powerful, they can more easily pay
the cost of their selection protocol.
The rationale behind function RANDOMGET is thefollowing: all super-nodes try to push clients towards more
powerful nodes that are willing to accept more load. To dothat, RANDOMGET performs a random selection among
those superpeers that are underloaded and whose capacity islarger or equal than the capacity of the local node. Since
UNDERLOADED may contain obsolete information,
multiple selections are made until a node is found whose
capacity is effectively larger than the current number ofclients. Ties (nodes with the same capacity) are broken by
selecting the node with the larger number of clients. The
process continues until such a node is found or no othernodes can be probed.
RANDOMGET ()Define ξ 0.6

ξ =1- ηS←{r|(log(cr)* ξ + log(tr)* η )≥(log(Cp)* ξ + log(Tp)* η )∧r∈UNDERLOADED}q←nullwhil(S≠Φ∧q=null)r←<pick a random node from S>
S=S-{r}lr←<request load from r>
if (lr<cr∧((log(Cp)* ξ +log(tp)* η )<(log(Cr)* ξ +log(Tr)* η )∨lr>lp))q←rreturn q
UPDATE(C,p)
CLIENTS←CLIENTS∪C
if (lp == 0∧lp<cp)
CLIENTS←CLIENTS∪{ q}<q becomes a client>else if (∃r∈CLIENTS：(log(cr)* ξ + log(tr)* η )> (log(cp)* ξ + log(tp)* η ))<transfer clients of q to r>
CLIENTS←CLIENTS∪{q}-{r}<q become a client，r becomes a server>

68

V. EXPERIMENTAL EVALUATION
To validate our framework, we have performed numerous
experiments based on simulation. Three main questions wereinterested in by us: first, what is the behavior of the protocol
with respect to its parameters; second, what are thecommunication costs and time consumed associated with its
execution; and third, how robust the protocol is.
TABLE 2.
INITIAL PARAMETERS IN EXPERIMENTS

Parameters

Values

SIZE

40000

MAXCAPACITY

180

MAXTIME

4000

DEGREE

30

REDUCED_DEG

30

ATTEMPTS

30

RATIO

1

LIMIT

0.95

WHEN

30

CRASH

0.90

LAMD

0.02

ALPHA

1.8

All experiments are performed using Peersim and its rounddriven Style. In all figures, 20 independent experiments havebeen performed. Unless stated otherwise, most of theparameters are fixed in all experiments: the maximum capacityof a peer is 500; and the size s of partial views used in
NEWSCAST[19] is 30. All these values can be reasonably
adopted or measured in realistic settings; yet, the behavior of
the algorithm observed under variations of these parameters
are analyzed in the following. The initial parameter settings are
showed in Table 1.
Value of weighted arithmetic mean coefficient From (7), There
are some questions we must resolve. One is C and T hasnndifferent dimensions, and the other is C and T has differentnnquantity scale. If Cn or Tn is very large, and Tn or Cn isvery small, so δ(C n ,Tn ) is large. Here the selected node is not our
expected super-node. In order to resolve these questions, we
introduce the information quantity theory to resolve it. The

value of ξ is important. We get the value of ξ fromexperiments. From [20] we set x = 266 , then the parameterˆ
is λ ≈ 0.004 . When the distribution parameter is determined, it
will generate the random exponential distribution number as
the node’s online time, and the value of C is generated from
[18].

n

Figure 4. Super nodes selection algorithm in SPSI

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 2, September 2012

69

Figure 5. Values of coefficient Test

For a given online time weight Tn , 0.2,0.3,……,0.8, and thecorresponding node content weight Cn as 0.8,0.7,……,0.2.
The experiments are carried out, and the results are shown in
Figure 5.
To illustrate easily, we divide the figure into four region.

Tn ∈ [0,500 ] are defined region I, Tn ∈ [500 ,1000 ] is region
II, and the nodes of the region are more active than the nodesof region I. C ∈ [0,30 ] are defined region III, the nodesnbelong to this region, their capacity are lower, andCn ∈ [0,30] are defined region IV. If Tn > 1000 and

Cn > 500 , that is to say that the nodes belong to this region

Figure 6. Network convergence Test

have longer online time and higher capacity, these nodes arethose we try to select as super-nodes.
From Figure 5 we can see that when the weight of T is 0.2,

A. The Convergence Speed of Network Construction
The goal of convergence experiment is to measure the speedof convergence, it’s important in overlay network construction.
In the experiments we also make our protocol with SG-1, afamous super-node construction protocol based on
NEWSCAST[19] to compare the convergence speed. The
results are showed in Figure 6. In the picture, dashed line
indicates the number of super nodes with SPSI protocol, while
Solid line represents the number of super nodes with SG-1. It
can be seen that the convergence speed of SPSI and SG-1 arebasically consistent. The time needed to reach such utilizationnthere are too many nodes in region I, that is to say the supernodes we selected are too active, and the network have toreselect when the super-node is left. When the weight of T isn

0.6 or 0.7, and too much nodes are located in region III, region
IV, that is to say the super-nodes we selected have lower
capacity. So it is better to set the weight of T 0.4 or 0.5 and itn

is better to set the weight of C is 0.6.
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 2, September 2012

thresholds is independent from network size and around 10
and 13 rounds, respectively. As initial configuration, we
selected a topology that is the farthest from the target: arandom topology where all nodes behave as super-nodes,
although none of them is responsible for any client. The curves
represent the number of super-nodes contained in the network
after the specified number of rounds, averaged over 20
experiments. Individual experiments are shown; their xcoordinates have been shuffled with a small random increment
to separate similar results. The algorithm proves to be
extremely fast, independently from the distribution considered:after less than 15 rounds, the resulting topologies approximate
extremely well the target.
B. The Selected Super-nodes

Figure 7 and Figure 8 shows the online timeparameter’s impact on the number of selected supernodes. The horizontal axis is the uptime remaining (inminutes) of nodes, the vertical axis is the capacity ofnode. There are 1000 nodes in the network being tested.
Each "fork" represents client nodes (shown using "+")
and a box indicates the super node (shown using ).

70

Figure 8. Super nodes selection with online time parameter

C. Communication Costs
In order to verify the effect of the protocol to lower
communication costs, we conduct experiments 9. Twocommunication costs are to be considered: the total number ofprobes sent in protocol to discover the load of other supernodes, and the total number of client transfers performed.
Super nodes take more responsibility in a super nodes based
peer-to-peer network. When a super node logouts from thesystem or a super node decides to alleviate its load, it willbring about considerable communication cost. The main
purpose of the SPSI protocol is to select a relatively stablenode as super-node and save part of the network overhead. In
Figure 9, the solid line represents the results of SPSI protocol,
while the dotted line shows only the case of the node capacity.
The SPSI protocol can select out relatively stable nodes whenselects super-nodes, thus it has fewer reconfiguration. Only
using the node capacity as selection method, the super nodes
will have higher activity, and the network is not stable enough,thus it has more reconfiguration than the self-information
algorithm.

From Figure 7, we can see that some nodes with low
remaining uptimes are selected as super-nodes, and as thesenodes leave the system, the system has to select another nodein the network to take over its work. Figure 8 shows that mostsuper nodes are located at the center of the graph, explainingthat the selected super-node has a certain capacity and a longer
time line. It can be seen that the SPSI protocol can effectively
avoid selecting active nodes as super nodes, thus increasing thestability of the target topology.

Figure 9. Communication Costs

Figure 7. Super nodes selection without online time parameter

Figure 9 shows the difference between the capacity and theself-information amount algorithm in the overhead costs. The
overhead costs include exchange of messages between nodes
and conversion cost between client nodes. It can be seen fromthe figure, the cost has not been decreased with the selfinformation amount algorithm in the early time of the network
construction, but after a long period, when there are nodes
exiting the network, the cost of the self-information amountalgorithm is significantly less than that of capacity algorithm.
It can be seen that the algorithm can effectively limit the
number of active nodes as super nodes, thereby reducing theoverhead for building the network.
D. Roubst Test
In order to demonstrate the robustness of our protocol, we
hypothesis a catastrophic scenario and the test result is shownin Fig.10.: at round 30, 50% of the super-peers are removed.
After the initial period when all clients whose super-peer has
crashed become super-peer by themselves, the protocol
behaves as usual and repair the overlay topology by selecting
new super-peers among the remaining nodes.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 2, September 2012

71

[6]

Figure 10. Roubst Test

VI. CONCLUSIONSThis paper presented SPSI, a fully decentralized, selforganizing general protocol for the construction of super-nodebased overlay topologies. To our best knowledge, we are thefirst to introduce information theory to super-node selection.
The number of generated super-nodes is a little more than SG1 but small with respect to the network size (only 3-5%), andit’s important that the more stable peers are promoted as supernodes, so the communication costs are degraded and the target
topology is more stable. The protocol shows also an acceptablerobustness to churn.
REFERENCES
[1]

[2]

[3]
[4]
[5]

L. Meirong, K. Timo, O. Zhonghong, Z. Jiehan, R. Jukka, Y. Mika,“Superpeer-based coordinated service provision”, Journal of Network
and Computer Applications,Vol 34, pp. 1210–1224, July 2011.
S. Moritz, E.N. Taoufik, W.B. Ernst, “Long term study of peer behavior
in the KAD DHT”. IEEE/ACM Trans. Netw. Vol. 17, pp. 1371-1384,
October 2009.
Z. Liu, C. Wu, B. Li, S. Zhao, “Distilling Superior Peers in Large-Scale
P2P Streaming Systems”. In Proc. of INFOCOM'2009. pp.82~90.
D.T. Talia, P.Trunfio, “Towards a Synergy between P2P and Grids”,
IEEE Internet Computing, vol4, pp. 94-96, July.2003.
A.I. Iamnitchi, I. Foster, J.Weglarz, J. Nabrzyski, “A Peer-to-Peer
Approach to Resource Location in Grid Environments”, In: Proceedingsof the 11th Symposium on High Performance Distributed Computing,
Edinburgh, UK, August 2002.

Z. W. Gao, Z. M. Gu, P. Luo, “RBTree: a new and scalable p2p model
based on gossiping”. In:Proceedings of the IEEE International
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October, 2008.
[7] J. P. Gian, M. Alberto, and B. Ozalp, “Proximity-aware Superpeer
Overlay Topologies”. IEEE Transactions on Network and Service
Management (TNSM), Vol. 4, pp:74-83, September 2007.
[8] KaZaA, [Online]. Available: http://www.kazaa.com/.
[9] K. Truelove, Gnutella and the transient web, Whitepaper, 2002.
[10] S. Q. Lv, Ratnasamy, S. Shenker, “Can heterogeneity make gnutella
scalable?”. in: Proc. IPTPS, March 2002.
[11] B. Yang, H. Garcia-Molina, “Designing a superpeer network”. In:
Proceedings of the 19th International Conference on Data Engineering.
(2003) 49–60.
[12] A.T. Mizrak, Y. Cheng, V. Kumar, S. Savage, “Structured superpeers:
Leveraging heterogeneity to provide constant-time lookup”. In:
Proceedings of the 3rd IEEE Workshop on Internet Applications. (2003)
104–111.
[13] L. Garces-Erice, E. Biersack, P. Felber, K. Ross, G. UrvoyKeller,“Hierarchical peer-to-peer systems”, in: Proceedings of EuroPar,
Klagenfurt, Austria, 2003.
[14] W. Nejdl, M. Wolpers, W. Siberski, C. Schmitz, M. Schlosser, I.
Brunkhorst, A. L¨oser, “Super-peer-based routing and clustering
strategies for RDF-based peer-to-peer networks”, in: Proceedings of the
12th International World Wide Web Conference, Budapest, Hungary,
2003.
[15] A. Mizrak, Y. Cheng, V. Kumar, S. Savage, “Structured super-peers:
Leveraging heterogeneity to provide constant-time lookup”, in: IEEE
Workshop on Internet Applications, 2003.
[16] A. Singh and M. Haahr, “Creating an adaptive network of hubs using
Schelling’s model,” Commun. ACM, vol. 49, no. 3, pp. 69–73, 2006.
[17] H.J. Liu, P. Luo et.al, “A structured hierarchical P2P model based on arigorous binary tree code algorithm”. Future Generation Computer
Systems-The International Journal of Grid Computing Theory Methods
and Applications 23 (2): 201-208 Feb 2007.
[18] A. Montresor, “A robust protocol for building superpeer overlay
topologies,” In Proc. of the 4th Int. Conf. on Peer-to-Peer Computing.
Zurich, Switzerland: IEEE, August 2004.
[19] J. Márk, V. Spyros, G. Rachid, K. Anne-Marie, and S. Maarten, “Gossipbased peer sampling”. ACM Transactions on Computer Systems, 25(3):8,
August 2007.
[20] S. Saroiu, P. K. Gummadi, and S. D. Gribble, “A measurement study ofpeer-to-peer file sharing systems”. In Proc of Multimedia Computing and
Networking 2002 (MMCN ’02), San Jose, CA, USA, January 2002.

Zhiwei Gao is an associate professor at the Departmentof Computer Science, ShiJiaZhuang TieDao University.
He is a Ph.D. student in the Department of Computer
Science at Beijing Institute of Technology, Beijing,
China. He received her Master degree from school of
information and computer technology,Beijing Jiao Tong
University.His current research interests are in network
security, distributed computing and peer-to-peer systems.
Yingxin Hu is a lecture at the Department of Computer
Science, ShiJiaZhuang Railway Institute. His current
research interests are in e-commerce, distributed
computing and peer-topeer systems. He received his
master’s degree in computer science from ShiJiaZhuang
Railway Institute, China.

Copyright ⓒ GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 2, September 201272

SFML: Screening Form Markup Language for
Healthcare Service
Kyuchang Kang*, Seonguk Heo*, Changseok Bae*
*BigData Software Research Lab. Electronics and Telecommunication Research Institute218 Gajeongno Yuseong-gu Daejeon Korea{k2kang, h7530, csbae}@etri.re.kr

Abstract— This paper proposes a markup language to describe
and deliver the contents of health screening form and a case study
for data transfer. In this paper, we define elements and schema
needed to generate a health screening form based on personal
lifelogs including data from daily, health and medical domain. In
our proposal, we allow for three categories of data. First, the daily
domain includes lifestyle data represented as activity, a sleeping
pattern and eating habits. Second, the health domain includes
height, weight, blood pressure, glucose and so on. Third, themedical domain includes the result of medical treatment
information from a medical institute. This information is
structured as SFML and can be exchanged with participant ofhealth service entities.
Index Terms—Healthcare, lifelog, markup language, screening
form

I. INTRODUCTIONM

AJOR challenges to modern healthcare is to find solutions
to cope with aging population, rising of inpatient andambulatory costs, lack access to facilities and personal of rural
residents. Due to the aging population, chronic disease began toemerge as the central healthcare issue. Chronic disease is the
major cause of disability, the principle reason why patients visit
hospitals [1]. Especially, South Korea is expected to become
the most aged country in the world in 2050, raising worries it
could erode the economy’s growth potential.
To prevent or monitor chronic diseases, we need continuous
monitoring of lifestyle and connection with healthcare
professionals. A health screening in everyday life can be one ofsolution for these objects.
Screening, in medicine, is a strategy used in population to
detect a disease in individual without signs or symptoms of that

Manuscript received June 30, 2012. This work was supported in part by the
National Research Foundation of Korea (NRF) grant funded by the Korea
government (MEST)(No. 2012-0001002).
Kyuchang Kang is with Electronics and Telecommunication Research
institute, 218 Gajeongno Yuseong-gu Deajeon 305-700 Korea (phone:+82-42-860-1695; fax: +82-42-860-5545; e-mail: k2kang@ etri.re.kr).
Seonguk Heo is with Electronics and Telecommunication Research institute,
218 Gajeongno Yuseong-gu Deajeon 305-700 Korea (e-mail: h75304@
etri.re.kr).
Changseok Bae is Electronics and Telecommunication Research institute,
218 Gajeongno Yuseong-gu Deajeon 305-700 Korea (e-mail: csbae@
etri.re.kr).

disease. Unlike what generally happens in medicine, screening
tests are performed on persons without any clinical sign or
disease [2].
Todays, almost all the people have chance of a regular healthscreening supported by a company or the government every
year. As preparing the checkup, people may generally fill up
the health screening questionnaire to describe theirhealth-related condition. From the point of personal
information, an individual's lifelog of everyday life is a goodrecord of behavior. Therefore, using the personal lifelogs isgood approach to provide personalized healthcare service after
the analysis of individual’s behavior.
In addition to individual’s behavior, we can consider
health-oriented
information
toconfigurescreeningquestionnaire. As one solution to describe individual’s
condition and current status of body, we describe a healthscreening form leveraging personal lifestyles, biometric
information, and medical treatments. By the health screening
form, individual’s health-related data can be commonly utilized
in private and public medical institutes. To this end, users cangenerate and leverage their screening form. As the result, users
can provide their screening form with medical staff on demandwhen they visit a medical center.
On the other hand, the health screening form is melting pot ofvarious personal data and maybe used various entities ofhealthcare service. Therefore, we also consider the cooperationand compatibility of them. To meet this need, we propose
SFML (screening form markup language) to embrace various
data and to exchange them with associated entities.
In this work, we define and specify XML elements forcontrol and data transmission and describe case study for
personal lifelog-based screening form, as a part of screening
system architecture.
The remainder of this paper is organized as follows. In
section II, we present related background of this work. Section
III shows the concept of the health screening form, a prototype
implementation and related data specifications. In section IV,
we present SFML scheme and a case study including datatransfer. Section V discusses related studies and the needs forstandardization. Finally, we conclude this paper in Section V.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 2, September 201273

II. BACKGROUND

III. HEALTH SCREENING FORM

This section presents the background environment of thiswork.
In the medical field, it is necessary to gather as muchpersonal information as possible about patients in order toachieve high-quality diagnosis and treatment. Until now, thepersonal information used in diagnosis and treatment hasbasically been gathered and used only within medical facilities.
It consists of clinical records, test results, medical images, andother such information. However, the medical information thatcan be gathered within a medical facility is very limited. It
would seem that there is a large amount of data that would be
useful for medical purposes within the voluminous and varied
data gathered in daily life, most of which is spent outside
medical facilities, but such lifelog data has gone unused in mostcases [3].
In Korea, SBI (systems biomedical informatics) research
center aims to create personalized ‘health avatar’, representing
individuals genomic through phenomic reality (or ‘digital self’)using multi-scale modeling and data driven semantics for the
purpose of personalizing healthcare [4].
‘The health avatar platform’ will be created as an agent spaceand health data integration pipeline. ‘Health avatar platform’
will create a space for interacting plug-in intelligent healthagents and data analysis toolkits and provide a data and accessgrid for heterogeneous clinical and genomic data. The healthavatar platform will function as an infra-structure for thedevelopment and evaluation of intelligent health applications
for personalized medicine.
Fig. 1 shows the conceptual diagram of health avatar and thiswork is conjunction with the ‘Connected Self’ of health avatar
project supporting lifelogs and stream-type data mining for
health protection.

Basically, the health screening form should reflect
individual’s lifestyles, health-related information and medical
treatments. And it is also compatible with a conventional
paper-based screening questionnaire.
In order to satisfy these requirements, we can summarize
components of the health screening form as three categories ofdata as followings:• Lifestyle logs: representing user’s lifestyles such asactivity, sleeping patterns, and eating habits• Health logs: representing biometric information ofindividuals such as height, weight, blood pressure, heartrate, blood glucose, total cholesterol, high-density
lipoprotein (HDL), low-density lipoprotein (LDL),
triglycerides and so on• Medical logs: representing medical treatments such as
genetic disease history, previous drug reaction and so on
Additionally, we can also consider interactive question &answer tool on behalf of paper-based screening questionnaire.
Fig. 2 shows the conceptual model of a health screening form
and operational flow.

Fig. 2. Basic concept of a health screening form and its operational flow

Fig. 1. Conceptual diagram of health avatar

Basically, the health screening form consists of user’s datafrom daily, health and medical domain. Then, these data are
mixed and mined with knowledge from various expertises.
From these processing, we can summarize the user’s lifelogs as‘rule’, ‘pattern’, and ‘history’. These elaborated data can beseen to user through a smart device and transferred to a caregiver for an advanced care.
Fig. 3 shows the screenshot of prototype implementation for
health screening form in current project [5-6]. Current versionof the health screening form focuses on gathering anddisplaying data.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 2, September 201274

screening form, we can leverage data used in home healthcare
or mobile healthcare which are usually gathering data withdedicated devices from a user. In general, the device collects
data associated with user’s biometric information such asheight, weight, blood pressure, glucose, heart rate and so on.
These data are formalized as ‘healthlog descriptor’ in XML
document. Fig. 5 shows the example of the healthlog
descriptor.

Fig. 3. The screenshot of prototype health screening form

As data items considered in daily domain for generating
screening form, we can leverage data such as activity, sleeping
pattern, and eating habits associated with lifestyle. The dataassociated with lifestyle in daily domain can be collected by
wearable or portable life logging device or a smartphoneembedding various sensors.
These data are formalized as ‘lifestylelog descriptor’ in
XML document. Fig. 4 shows the example of the lifestylelog
descriptor.

Fig. 5. Example of the healthlog descriptor

As data items considered in medical domain for generating
screening form, we can leverage data generated by medical
institute such as disease history, genetic disease history, totalcholesterol, high-density lipoprotein (HDL), low-density
lipoprotein (LDL) and so on. These data associated withmedical field should be linked with the database of medical
institute. Therefore, we define the element for the healthlog
descriptor but do not specify the consisting sub-elements. We
use just link of the medical data sources instead of defining
sub-elements because the medical data allow only very
constrained access.
IV. SFML: SCREENING FORM MARKUP LANGUAGE

Fig. 4. Example of the lifestylelog descriptor

As data items considered in health domain for generatingThis section describes SFML (screening form markup
language) defining ‘action’ and ‘data’ elements and case study
for data transfer.
The components that comprise a health screening form havea hierarchical structure. The top layer consists of ‘action’element and ‘data’ element. The ‘action’ element defines
control action messages including ‘request’ element and‘response’ element. The ‘data’ element defines three categories
of items such as ‘lifestylelog’, ‘healthlog’ and ‘medicallog’.
Each category item has its own sub-items. Each item is
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 2, September 201275

represented by XML elements and includes formatted data
respectively.

logmanager module of SFML recipient may process thisrequestA. Action and Data Elements
Fig. 6 describes XML structure, named as SFML, including‘action’ and ‘data’ elements.
The root element is ‘sfml’ representing ‘screening form
markup language’. The root element has ‘action’ element forcontrol action message and ‘data’ element for data presentation.
As a sub-element, ‘data’ element has ‘items’ element
containing three ‘category’ properties such as ‘lifestylelog’,‘healthlog”, and ‘medicallog’.

• The value of the ‘command’ element indicates that the‘getWeight’ method of the logmanager may be executed• The values of the ‘parameter’ element indicates; the related
data owner is ‘gd_hong’, the response data should startfrom November 22, 2011, and the data is needed two days
The description of ‘session’ element of the response action
shown in Fig. 7 is as following.
• The ‘true’ value of the ‘result’ element indicates that therequest action is successful• The ‘results’ element indicates that the ‘gd_hong’ has 70kg
in November 22, 2011 and 71kg in November 23, 2011
The XML document of Fig. 7 is just an example so that we
describe the ‘request’ and ‘response’ elements together. In
practical case, the request action includes only ‘request’
element and the response action contains only ‘response’
element respectively.

Fig. 6. XML structure of SFML

Fig. 7 shows the ‘action’ element of SFML in detail. The‘action’ element consists of ‘request’ and ‘response’ elements.
The ‘request’ element is a request form for any action and canhave multiple messages encapsulated by ‘message’ element.
The number of each ‘message’ element is mapped with thenumber of ‘session’ element in ‘response’ element.
Each ‘message’ element is composed of three sub-elements
such as ‘target’, ‘command’ and ‘parameter’ elements. The‘target’ element indicates the action executor such as a module
name of a message recipient. The ‘command’ element indicatesthe method name of the action executor. The ‘parameter’element contains parameter values needed to run the method of
the action executor. The sub-elements of ‘parameter’ element
such as ‘id’ and ‘interval’ depend on the value of command.
The ‘response’ element is a response form for any action
execution. The ‘response’ element can have multiple ‘session’
elements for each control action respectively. The number of
each ‘session’ element is mapped with the number of ‘message’element in ‘request’ element.
Each ‘session’ element is composed of two sub-elements
such as ‘result’ and ‘results’. The ‘result’ element only
indicates success or failure for the request action. If the action is
performed successfully, the ‘results’ element is filled with theresults of the action execution. The sub-elements of the‘results’ element depend on the executed command.
The description of the ‘message’ element of request action
shown in Fig. 7 is as following.
• The value of the ‘target’ element indicates that theFig. 7. Example for the ‘action’ element of SFML

Fig. 8 shows the ‘data’ element of SFML in detail. The ‘data’element can have three items which have category properties
such as ‘lifestylelog’, ‘healthlog’ and ‘medicallog’. Each
‘items’ element has ‘id’ and ‘item’ elements. The ‘id’ element
indicates the owner of the item data and the ‘item’ element
contains real data values. The ‘data’ property of the ‘item’
element indicates what kind of data.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 2, September 201276

healthlog descriptors for gathered data in Lifelogging
application. However, this module can be extended or modified
according to the standardization activities described next
section.

Fig. 8. Example for the ‘data’ element of SFML

Generally, the ‘data’ element of SFML can contains
lifestylelog descriptor, healthlog descriptor and medicallog
descriptor in a manner of full text. In a case of just simple
querying data, the ‘response’ element of SFML can support theaction. However, in case of the request action requires full dataof the user, the ‘data’ element is used by embracing mentioned
three descriptors.
The XML document example of Fig. 8 contains threedescriptors of ‘gd_hong’. The ‘items’ element with‘lifestylelog’ category property contains the ‘item’ elements
following the lifestylelog descriptor. And the ‘items’ elementwith ‘healthlog’ category property includes the ‘item’ elements
following the healthlog descriptor. On the other hand, the‘items’ element with ‘medicallog’ category property, the link tothe medical information is used.
B. Case Study for Data Transfer
This section describes cases of data transfer from data
sources to Screening Form Generator, functional module ofhealth screening form.
As the device of data sources, we can use smartphones,
tablets or 3rd party’s devices. In this case, for gathering lifelogs,
we can use built-in sensors of the smart devices such as
accelerometer and GPS or connected 3rd party’s devices such as
blood pressure meter, weighting scale and blood glucose meter.
Fig. 9 shows the data transfer example of daily and healthdomain. In this configuration, each Collector Driver isgathering data from built-in sensors of the smart device and 3rdparty’s devices as a proprietary format. Then, Convertor
transforms and formalizes the data sent from Collector Driverby referencing Schema such as lifelog descriptor or healthlog
descriptor according to the data sources respectively.
In this case study, we only consider lifestylelog and

Fig. 9. Data transfer for daily and health domain

Data Manager can store the transformed data in Local DB,
which can be used as data cache. The transformed data can bealso sent to Screen Form Generator. Then, Data manager
module of Screening Form Generator refers Schema
description and can extract data values. Consequently, the data
values can be displayed in Viewer user interface.
Generally, Screening Form Generator can be running insmart devices or conventional personal computers.

Fig. 10. Data synchronization and its utilization for daily and health domain

On the other hand, the transformed data by Convertor can be
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 1, Issue 2, September 2012synchronized with the DB of Health Portal through Sync.
Manager. In this case, the synchronized data can be used inanother screening form generator or independent otherapplication shown in Fig. 10.
In the result, data structured by SFML can be exchanged andtransferred to SFML viewer of a care giver for an advancedcare.
In order to define the data sets for transferring medical
treatment documents, we can use CDA (Clinical Document
Architecture) [7] of HL7 (Health Level Seven), CCR(Continuity of Care Record) [8] of ASTM (American Societyof Testing and Materials), or CCD (Continuity of Care
Document) [9], combined model of CDA and CCR.
As the data transfer specification in health screening form,
we can use those international standards. Therefore, we only
parse and translate the data, encapsulated with standardized
XML format, instead of defining new ‘medical descriptor’.
This operational flow is shown in Fig. 11.

77

standardize the transmission of data and control messages. For
this needs, we are participating in standardization activities of
TTA (Telecommunication Technology Association) in Korea.
Currently, two standard documents are released with respect
to digital health screening form. TTAK.KO-10.0516 [18] is astandard document for a reference model for personal lifelog
based system. TTAK.KO-10.0517 [19] is a standard document
for link data specifications based on personal lifelog. However,these standardization activities are now on the initial phase ofwork. Therefore, we also need to do additional work to update
and specify standard documents in detail.
Furthermore, from the perspective of user interfaces, theViewer of Screening Form Generator may need to be changed
into HTML5-support. To support the concept of “Write Once,
Run Anywhere” by providing compatibility of heterogeneous
devices such as Android-based devices and iOS-based devices,
we need to do more work by keeping pace with standardization
activities in [18-19].
VI. CONCLUSION

Fig. 11. Data transfer for medical domain

V. DISCUSSION
From the perspective of a health screening questionnaire,
Chris et al [10] proposed an adaptable health screening
questionnaire that is computer-based lifestyle questionnaire
allowing individual doctors to modify the questionnaire to theirrequirement. K. D. Akan et al [11] developed electronic
screening tool providing a graphical user interface with audio
outputs for users who may be functionally or computer illiterate.
However, these previous trials are only subsidiary function of
the health screening form described in this paper and does notsuggest how to transfer data and control messages.
From the lifelog utilization point of view, NTT have studied
several subjects [12-13] enabling lifelogs to be used in thepractical service implementation. In case of these NTT’s
previous work, we can coordinate these results as a lifestylecategory of the health screening form.
From a device point of view, there are several commercial
life logging devices [14-17]. These devices can provide a healthscreening form with the lifestyle data.
As increasing of multiple vendors’ devices, we need toThis paper proposes a markup language, SFML, to describe
and deliver the contents of health screening form and case study
for data transfer.
In this paper, we define elements and schema needed togenerate health screening form based on personal lifelogs
including data from daily, health and medical domain.
SFML defines ‘action’ element and ‘data’ element. The‘action’ element is used to control any action through ‘request’and ‘response’ elements. The ‘data’ element is used to present
data including lifestylelog, healthlog and medicallog.
Currently, because this work is an initial phase to make ahealth screening form, we lack of an elaborate algorithm or abreakthrough idea. However, we think this work will contributeto make a personalized and mobilized health screening form
reflecting personal lifestyles, health information and medical
histories.
In the future, we plan to continue our research efforts in thisfield with the aim of making an intelligent screening form. So
we need to allow for an interpretation of relationship between
data by means of data mining algorithm.
REFERENCES
[1]
[2]
[3]
[4]
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517
Kyuchang Kang (M’06) received his B.S. and M.S.
degrees in electronic engineering from Kyungpook
National University, Korea, in 1994 and 1997
respectively. From 1997 to 2000, he worked on Test and
Evaluation Center at Agency for Defense Development
as a member of engineering staff, where he developed
Doppler signal analyzer and measurement system. Since
2001, he is working on BigData software laboratory andpost-computer research division at Electronics and Telecommunications
Research Institute, where he is developing open service platform for thehealthcare service. He is also interested in mobile applications, distributed
computing and network management.
Seonguk Heo received his B.S. degrees in computerengineering from Korea University of Technology and
Education, Korea, in 2011. He is currently working
toward the M.D. in computer engineering from the
University of Science and Technology, Korea. He isinterested in mobile applications, embedded computing,and lifelog data mining.
Changseok Bae (M’03) received his B.S. and M.S.
degrees in electronic engineering from Kyungpook
National University, Korea, in 1987 and 1989
respectively. He also received his Ph.D. degree inelectrical and electronic engineering from Yonsei
University, Korea, in 2003. From 1989 to 1996, he was asenior researcher at Systems Engineering ResearchInstitute, where he worked on image processing andpattern recognition. From 1997 to 1999, he worked with Korea Ministry ofInformation and Communication, where he participated in establishing national
software research and development policy. Since 2000, he has been a principal
research staff of Post-PC Platform Research Team and the team leader of
Personal Computing Research Team at Electronics and Telecommunications
Research Institute (ETRI). From 2004-2005, he was a Research Fellow at
School of Information Technologies, University of Sydney, Australia. His
research interests include image processing, multimedia codec, information
hiding, personal life-log and stream type data mining.

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