Journal Editorial Board Editor-in-ChiefProf. Thomas Byeongnam YOON, PhD.
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ICACT Transactions on the Advanced Communications Technology (TACT) Editors
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Copyright ⓒ 2017 GiRI (Global IT Research Institute)Dr. Youssef SAID, Tunisie Telecom, Tunisia
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Copyright ⓒ 2017 GiRI (Global IT Research Institute)Dr. Il Kwon Cho, National Information Society Agency, Korea
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Prof. Bernard Cousin, IRISA/Universite de Rennes 1, France
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Dr. Feng CHENG, Hasso Plattner Institute at University of Potsdam, Germany
Prof. El-Sayed M. El-Alfy, King Fahd University of Petroleum and Minerals, Saudi Arabia
Prof. Lin You, Hangzhou Dianzi Univ, China
Mr. Nicolai Kuntze, Fraunhofer Institute for Secure Information Technology, Germany
Dr. Min-Hong Yun, ETRI, Korea
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Dr. Ho-Jin CHOI, KAIST(Univ), Korea
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Dr. Myoung-Jin Kim, Soongsil University, Korea
Dr. Gyu Myoung Lee, Institut Mines-Telecom, Telecom SudParis, France
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Prof. Yoonhee Kim, Sookmyung Women s University, Korea
Prof. Li-Der Chou, National Central University, Taiwan
Prof. Young Woong Ko, Hallym University, Korea
Prof. Dimiter G. Velev, UNWE(University of National and World Economy), Bulgaria
Dr. Tadasuke Minagawa, Meiji University, Japan
Prof. Jun-Kyun Choi, KAIST (Univ.), Korea
Dr. Brownson ObaridoaObele, Hyundai Mobis Multimedia R&D Lab , Korea
Prof. Anisha Lal, VIT university, India
Dr. kyeong kang, University of technology sydney, faculty of engineering and IT , Australia
Prof. Chwen-Yea Lin, Tatung Institute of Commerce and Technology, Taiwan
Dr. Ting Peng, Chang'an University, China
Prof. ChaeSoo Kim, Donga University in Korea, Korea
Prof. kirankumar M. joshi, m.s.uni.of baroda, India
Dr. Chin-Feng Lin, National Taiwan Ocean University, Taiwan
Dr. Chang-shin Chung, TTA(Telecommunications Technology Association), Korea

Copyright ⓒ 2017 GiRI (Global IT Research Institute)Dr. Che-Sheng Chiu, Chunghwa Telecom Laboratories, Taiwan
Dr. Chirawat Kotchasarn, RMUTT, Thailand
Dr. Fateme Khalili, K.N.Toosi. University of Technology, IranDr. Izzeldin Ibrahim Mohamed Abdelaziz, Universiti Teknologi Malaysia , Malaysia
Dr. Kamrul Hasan Talukder, Khulna University, Bangladesh
Prof. HwaSung Kim, Kwangwoon University, Korea
Prof. Jongsub Moon, CIST, Korea University, Korea
Prof. Juinn-Horng Deng, Yuan Ze University, Taiwan
Dr. Yen-Wen Lin, National Taichung University, Taiwan
Prof. Junhui Zhao, Beijing Jiaotong University, China
Dr. JaeGwan Kim, SamsungThales co, Korea
Prof. Davar PISHVA, Ph.D., Asia Pacific University, Japan
Ms. Hela Mliki, National School of Engineers of Sfax, Tunisia
Prof. Amirmansour Nabavinejad, Ph.D., Sepahan Institute of Higher Education, Iran

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Copyright ⓒ 2017 GiRI (Global IT Research Institute)Volume. 6 Issue. 2

6J

1 A Low-complexity Practical Energy Saving Algorithm for Real Dense Wireless Scenario

985

Hsu-Chen CHENG *, Tsuei-Ping KUNG *, Chia-Ming LI *, Yu-Jou SUN **School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing, China**School of Information Science and Engineering, Southeast University, Nanjing, China***Research and Development Center, China Mobile Group Jiangsu Co.,Ltd, Nanjing, China

2 NRIT: Non-Redundant Indirect Trust Search Algorithm for a Cross-Domain based CDNi-P2P

992

Architecture
Shi Li, Inshil Doh, Kijoon Chae
Department of Computer Science and Engineering, Ewha Womans University, Korea

Copyright © 2017 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 6, Issue 2, March 2017

985

A Low-complexity Practical Energy Saving
Algorithm for Real Dense Wireless Scenario
Fei Ding* **, Ye Wang***, En Tong***, Zhi-wen Pan**, Xiao-hu You***School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing, China**School of Information Science and Engineering, Southeast University, Nanjing, China***Research and Development Center, China Mobile Group Jiangsu Co.,Ltd, Nanjing, China
dingfei@njupt.edu.cn; { wangyesgs, tonge} @js.chinamobile.com; { pzw, xhyu}@ seu.edu.cn

Keyword—energy saving, practical, energy efficiency, switch
off/onI. INTRODUCTIONR

ECENTLY, with the gradually commercialization oflong term evolution (LTE) system and the rapid
expansion of smart terminals, a blast growth in mobile dataservice has been taken place. Meanwhile, it also results inmore and more energy consumptions and CO2 emissions.
According to [1], by 2020, mobile cellular networks willcontribute up to 4% of the total world CO2 emission. Hence,
it is urgent to save the energy consumption of mobile
communication systems.

1Normalized Traffic Load

Abstract—In this paper, a low-complexity practical energy
saving algorithm by switching off/on some eNBs in a real dense
urban scenario considering historical and real-time eNB load is
proposed. First, eNBs are ranked according to their loads in an
ascending order and the first eNB in the list with loaddecreasing and smaller than a low threshold is pre-selected astarget switching off cell. Then, the effect of the target switching
off eNB on neighbour eNBs is evaluated. The target eNB
switches-off while the load of neighbour eNBs after the eNB
switches off is smaller than another threshold. Since estimationof the additional load on the neighbour eNBs due to theswitch-off eNB is of high complexity, a fast estimation algorithm
considering the whole eNB load by a traffic load conversion
coefficient is proposed. The traffic load conversion coefficientdeclines slowly with the increasing of site traffic load. Third, theswitching-off eNB can be switched on by the active eNBs in a
distributed way. Based on the load changes in a week period of
the eNB, the cumulative probability distribution of normalized
load is analyzed, and then the threshold value of the eNB in
different periods is evaluated. The energy saving ratio is
obviously related with the interval between the switched on or
off threshold values and the complexity of the algorithm issignificantly reduced. Simulation results show that the proposedenergy saving scheme can save up to 24% energy consumption
and with low system complexity.

Normalized Traffic Load
Weekend period

0.8
0.6
0.4
0.2
0
0

24

48

72

96

120

144

168

Time [h]Fig. 1. The normalized traffic load of an eNB during a week———————————————————————
Manuscript received on June 28th, 2016. This work is a follow up of theinvited journal of the accepted conference paper for the 18th International
Conference on Advanced Communication Technology (ICACT-20150459).
This work is partially supported by National Major Project of China(2013ZX03001032-004, 2010ZX03006-006), National 863 Program ofChina (2014AA01A702), National Natural Science Foundation of China(61571241, 61221002), Jiangsu Province Key Technology R&D Program(BE2012165) and Jiangsu Mobile Key R&D Program (B1323232).
F. Ding is with the School of Internet of Things, Nanjing University ofPosts and Telecommunications, Nanjing 210003, China, and also with the
School of Information Science and Engineering, Southeast University,
Nanjing 210096, China (corresponding author to provide phone:
86-025+83492013; fax: 86+025-83535106; e-mail: dingfei@njupt.edu.cn).
Y. Wang is with the R&D Center, China Mobile Group Jiangsu Co.,Ltd,
Nanjing, 210012, China (e-mail: wangyesgs@js.chinamobile.com).
E. Tong is with the R&D Center, China Mobile Group Jiangsu Co.,Ltd,
Nanjing, 210012, China (e-mail: tonge@js.chinamobile.com).
Z. W. Pan is with the School of Information Science and Engineering,Southeast University, Nanjing, 210096, China (e-mail: pzw@seu.edu.cn).
X. H. You is with the School of Information Science and Engineering,Southeast University, Nanjing, 210096, China (e-mail: xhyu @seu.edu.cn).

In LTE networks, the energy consumption caused by the
Evolved Node B (eNB) is about 60%-80% of the total energy
consumption [2], and once the eNBs are deployed, it is hard tomodify the network topology for energy saving. Thus,
switching-off/on the eNBs in light traffic conditions becomesa widely used approach. In [3], a cellular network with a large
difference in traffic volume at different time periods isverified. Within a week, the city's communication network
traffic curve shows a quasi-sinusoidal pattern, and the
network load in weekends is significantly lighter than that in
weekdays. Therefore, they designed a fixed traffic change rate
to determine when to switch off/on the base station (BS). But
in the 4G network, urban traffic fluctuations will be more
obvious and irregular, so it is necessary to study an adaptive
threshold selection method for the changes in traffic load atdifferent working stages, and adopt the optimal thresholds toswitch off/on the BS and improve the network energy
Copyright © 2017 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 6, Issue 2, March 2017

obtained by Voronoi diagram through the Perpendicular
Bisector Delaunay (PBD) method [13], [14], as shown in Fig.
2 by solid line.
1000

S32 r3=c

800

Y-distance [m]efficiency. A typical traffic load profile of an eNB in actual
LTE network during one week is shown in Fig. 1. Obviously,
traffic peaks occur in business hours and the traffic ofordinary weekdays is higher than that in weekend.
Nevertheless, the eNBs consume most of their peak power
energy consumption even when they are in low traffic
condition. It is shown that the tendency of traffic load in aperiod is regularity, so these data can be used to design anenergy saving algorithm.
Now the energy saving scheme by switching on/off theeNBs has been attracted much attention recently [4]-[11]. In[4], a centralized entity in the network controls the switching
off of the eNBs by the information pigged with signal
overheads. However, the researchers paid little attention to
the switching on schemes of the eNBs. In [5], an algorithm toswitch on the eNB based on the location information of eNB
and user equipments (UEs) when its load is larger than a fixed
threshold is proposed. Further, in hexagonal and Manhattan
model networks, dynamic BS switching strategies relying on asimple analytical model [6] for saving the energy
consumption have been introduced [7]. And, considering thedifference between traffic load of day-time and night, the load
threshold is set dynamically and a distributed eNBs switchingoff/on algorithm is proposed in [8], and whether an eNB willswitch off/on is decided by its neighbours based on its load
impact factor. In addition, the scheme in [8] has been
employed for macro/macro, macro/micro, macro/femto-cells
are proposed in [9-11]). However, these algorithms areusually with high complexity since the traffic load changes
dynamically and how to make good use of the historical trafficload of eNBs to select the switching-off eNB fast are notconsidered. Meanwhile, how to estimate the load impact on
the adjacent eNBs accurately and quickly considering
distance, number of eNBs, real time load is lacking.
In this paper, a practical algorithm for network energy
saving for the real LTE cellular network in dense urban
commercial area is proposed. Based on the joint analysis ofhistorical and real-time eNB load, a low complexity eNB
switching-off scheme with a centralized entity is adopted andthe switching-on scheme for sleeping eNBs is implemented
by the active eNBs in a distributed way.
The rest of the paper is organized as following: In Section
II, the system model is introduced. In section III, the practicaland fast eNBs switching-off/on energy saving algorithm(PFSES) is proposed. Simulation results are given in Section
IV, and the paper is concluded in Section V.

A. Network Model
A real LTE cellular network in dense urban areas is chosen
and the layout of eNBs in the 1km × 1km area is shown in Fig.
2. Usually, the distance between macro-eNB sites is morethan 500m, and the coverage and capacity for UEs can be wellguaranteed [12]. As can be seen in Fig. 2, there are 13 eNBs in
the 1km2 area, and for each eNB, the neighbour eNBs can befound within 500m. Some eNBs will be switched off for better
energy saving as its coverage and capacity can be guaranteedby its neighbour eNBs. The cell boundary of each eNB is

r4=d
S34

eNBs

S31

r2=b

600

r1=a

400
200
0
0

200

400
600
X-distance [m]

800

1000

Fig. 2. The eNBs layout in a dense urban area and the Voronoi diagrams for
eNBs in solid line and an example for illustrating the effect of switching-off
an eNBB. System Load
According to the Shannon theorem, the achievable rate foruser k in eNB b is:(1)
Rb,k = BW × log 2 (1 + SINRb ,k ) [bps]where BW denotes the system bandwidth; SINRb,k is thereceived signal to interference and noise ratio (SINR) of user
k in eNB b:
Pb Gb, k
SINRb, k =
[dB]
(2)
2
å PG
i i,k + s
iÎBon
nei

where P b is the transmit power of eNB b, and Gb,k is thechannel gain between eNB b and user k including the path loss
and log-normal shadowing, where σ2 is the noise power.
According to [14], traffic load for each user k served by theeNB b with the rate requirement rk is defined asrk = éêrk Rb,k ùú Nb(3)

where êé x úù is the minimum integer lager than x, andéê rk Rb, k ùú is the number of TFRB allocated by eNB to user k,
and Nb is the total number per second in TFRB of eNB.
C. Energy Consumption Model
The total input power Pinb of eNB b isìïP + Dpb Lb Pb
in
Pb = í b
SïîPb , Lb = 0
0

II. SYSTEM MODEL

986

max

, 0 < Lb < 1(4)where P0b represents the minimal RF output power whenthe eNB is idling, P Sb is the minimum system power
consumption when the eNB is sleeping, ᇞ pb refers to thepower amplifier efficiency, Lb is the load of eNB b, and Pmaxb isthe maximum transmit power. Parameters of an energy
consumption model for macro-eNBs are listed in Table I.
In order to formulate the energy efficiency optimization
problem, a variable xb,k is defined to represent whether the kthuser is served by the eNB b.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 6, Issue 2, March 2017

ì1, user k is served by eNB b
xb,k = íotherwiseî0,(5)

An activity indicator ab(t) is introduced to represent
whether the eNB b is sleeping or working.
ì1,
ab (t) = íî0,b is working(6)b is sleeping

Then the RF energy consumption for user k, and circuit
energy consumption of eNB b are:P
P

RF

circuitRF= å å xb ,k Pb ,k(7)

kÎK bÎB0S= å ( ab (t ) Pb + (1 - ab (t )) Pb )

(8)

bÎB

Eb (T) = ò Pbin ab (t )dttÎT= ò ëé( Pb0 + Dpb Lb Pbmax )ab (t ) + PbS (1 - ab (t ))ûùdt

(9)

tÎT

The total energy consumption in this area is computed asヲ
EB (T) = å Eb (T)

(1

bÎB

0)
where B denotes the set of all eNBs.
Therefore, the energy saving ratio is defined as:
EB (T)
RatioES = 1 0
å ò ( Pb + Dpb Lb Pbmax )dt(11)

bÎB tÎTTABLE I
EARTH ENERGY CONSUMPTION MODEL PARAMETERS CONFIGURATION
PMax(W)
40.00

in system load and energy consumption assuming
switching-off it should be evaluated. If the historical load islarger than ρoff, then this eNB b turns to a waiting list Lwait.
The target switching-off eNB b is selected according to thelist Lwait firstly in next time window. The detailed
pre-selection rule is concluded as follow:
Pre-selection rule:
Initializing: an execution order list L is generated;
1: For eNB b
2: If Dtraffic / Dt < 0 ;3:
4:Then for an eNB, the total energy consumption is given by:

ᇞp

14.20

P0(W)

PS(W)

780.00450.00III. PFSES ALGORITHM
In this section, a practical algorithm for eNB
switching-off/on in dense urban commercial area is proposed.
The algorithm consists of three parts: the pre-selection rule
for switching-off eNB based on historical load record and realtime load; the decision for switching-off eNB based on fastload prediction and its effect on system load and energy
consumption; the eNB switching-on by neighbour eNBs.
A. Pre-selection of Switching-off eNB
It is found that the varying tendency of traffic load of each
eNB is similar for all weeks. Hence history data can be used
for designing the algorithm.
Firstly, the eNBs are ranked by the total load from small to
big in a time window, and an execution order list L isgenerated initially in a central control entity. Then the eNB b
for switching-off is selected by:
b = arg min å å rk (t)
bÎB

(12)

tÎT kÎ Ab

Secondly, for an eNB b, if both real-time load andhistorical load in the same period are decreasing and smaller
than the system switching–off load threshold ρoff, the variation

987

If total _ trafficnow < r off ;If total _ traffichistory < r off ;5:

Estimate the variation of system load and energy
consumption assuming switching-off it;
6:
Else the eNB b turns to a waiting list Lwait for the nexttime window, and turn to step 1;
7:
End if8:
Else turn to step 1;
9:End if
10: Else turn to step 1;
11: End if
12: In next time window, the eNB b is selected according to
the list Lwait firstly;
13: If Dtraffic / Dt < 0 ;14:

If total _ trafficnext _ window < r off ;15:
Estimate the variation of system load and energy
consumption after switching-off it;
16:
End if17:
Else turn to step 12;
18:
End if
19: Else turn to step 12;
20: End if
B. Decision for Switching-off eNB
When the eNB b is switched off, as the UEs in it should
handover to the adjacent eNBs, the adjacent eNBs need toserve the UEs in its own coverage area and guarantee the QoS
requirements for the UEs handing over from the eNB b. TheeNB b is bound to have an increasing effect on traffic load ofother eNBs. Another system load threshold, ρswitch, and the
additional load caused by the switching-off eNB Ƹρ areintroduced. If the predicted load of neighbor eNBs assuming
switching off eNB b is less than ρswitch, and the whole energy
consumption predicted by (7) is lowered, eNB b will switch
off. Then eNB b and its adjacent eNBs should be removed
from the execution order list L. This makes sure that only oneof the eNB and its neighbour eNBs could be switched off
simultaneously, preventing the users from the ping-pong
effect caused by switching-off/on the eNBs frequently. The
decision rule is concluded as follow:
Thus how to estimate the ᇞ ρ quickly and accurately
becomes the key point in the decision rule. ᇞ ρ is usuallyestimated by analyzing the traffic load for each UE whichhand over to the other eNB in many practical networks [15].
However, on the one hand, the computational complexity forevery UE is very high and increases with the numbers of UEs.
On the other hand, it is impossible to get the information
including the position and dynamic load of each UE
accurately, and only the load of an eNB can be collected as
Copyright © 2017 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 6, Issue 2, March 2017shown in Fig. 1. So estimation of the ᇞ ρ by the load of eNBs
directly is an effective way.
Decision for switching-off rule:
1: For the eNB b selected by Pre-selection rule;
2: If rb _ nei + Dr < r switch ;3:Ifå U (T) < åU (T)(bÎ B- boff

)

b

b

4:
the eNB b switches-off, update the list L;
5:
Else turn to Pre-selection rule;6:
End if
7: Else turn to Pre-selection rule;
8: End if

3:
4:Firstly, the traffic load of the eNB bi within the coveragearea Si is assumed to be subject to uniform distribution,
namely:1(13)
f ( Lb i ) =Si
Secondly, when the eNB bi is switching off, its coveragearea will be covered by neighbor eNBs with no coverage holedefined as:
Si = å S i ® j(14)
j = bnei

where Si→j describes the additional coverage area caused by
the switching-off eNB bi using PBD method.
Thirdly, a traffic load conversion coefficient, Cload,
describing the ratio of the load caused by the UEs in coverage
Si after switching-off eNB bi to the original load is defined as:
Lbi ®bnei
å
bnei
(15)
Cload =
Lbi
where Lbi→bj is the load of the UEs in Si served by neighbour
eNB bnei.
Then the additional load ᇞ ρ is estimated as:ri ´ Si ® j ´ C load
Dri ® j =(16)
Si
In Fig. 2, a pictorial example is given to illustrate how toestimate the traffic load when an eNB is switched off. As canbe seen, when the eNB b3 with a coverage area S3 and trafficload ρ3=c is selected to switch off, its load may bring positive
impact on the adjacent eNBs b1, b2, b4. Its coverage area will
be separated by b1, b2, b4 by PBD method too, as:(17)

So the additional load for b1 is computed as:

Dr3®1 = c ´ S3-1 ´ C load / S3

more than ρswitch, the last switched off neighbor eNB is
informed to switch on. If the historical load is less than ρswitch,
another system load threshold ρon is introduced. If thereal-time load of an active eNB is increasing over ρon latter,the last switched-off eNB will be waken up. The detailed
decision for switching-on rule is concluded as follow:
Decision for switching-on rule:
1: For the active eNB;
2: If Dtraffic / Dt > 0 ;;bÎB

S3 = S3®1 + S3®2 + S3®4

988

(18)

C. Decision for Switching-on eNB
If the network traffic increases, there would be a need toswitch on some of dormant eNBs. However, the dormant eNB
can’t switch on by itself as it has no information about thecurrent system load. Thus, it should be inspired by active
eNBs. Active BSs exchange traffic load information over theinterface between them (X2 interface in LTE) during normal
network operation in a distributed manner.
Once both real-time load and historical load in the same
period of an active eNB is in an increasing tendency and is

If total _ trafficnow > r switch ;If total _ traffichistory > r switch ;5:
the switching-off neighbor eNB b switches-on;
6:Else if total _ trafficnow > r on in a period of time;7:
the switching-off neighbor eNB b switches-on;
8:
End if9:
End if
10:
End if
11: End if
The complexity of the proposed PESE algorithm is only B2
and it is much less than optimal exhaustive research algorithm
what requires a Bk computational complexity [16].
IV. SIMULATION RESULTS AND ANALYSIS
A. Traffic load conversion coefficientAccording to [17], the path loss model is:

PL = 128.1 + 37.6log10 ( dBS , k /1000)+ S [dB](19)

where dBS,k is the distance between UE and the eNB in meter;
S is the shadow fading in dB.
By using the algorithms in our previous research [18-19]
for the real dense urban area shown in Fig. 2, sets of simulated
data of the total load in coverage Si before and afterswitching-off eNB bi are obtained. Thus, Cload can becomputed as:r kbneiåk Î( b ®bnei )(20)
Cload =
å r kbk Îb

As the eNB load is random in the real network, the Cload isalso a random variable. Fig. 3 depicts the cumulative
distribution function (CDF) of Cload obtained by (17) under
the condition that the traffic load of eNB bi is over 0.2 to 0.25.
Then by using minimum mean square error (MMSE) method
to quantitative analyse the accuracy between the measurement
cumulative probability and the fit value, and the calculated
MSE value for the Nakagami distribution fit depicted in Fig. 3
is just 0.0253. It confirms that Nakagami distribution fits the
Cload well, and the statistic parameters of Cload are given in
Table 2. The mean value 5.98 indicates that it has a significant
impact on the neighbor eNBs in the real dense urban area.

Copyright © 2017 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 6, Issue 2, March 2017traffic changes, Fig. 5 is the cumulative probability
distribution of the base station within a week. It shows that theprobability density is close to 1 while the load of the basestation is within 5%-70% of its peak, but which is close to 7
when the load is less than 5% of its peak, this is basicallyreciprocal with the probability density above 70% peak load
time. The detail values are presented in Table III. From thetable, it is shown that during weekdays, about 33.2 percent of
the time the traffic is less than 10 percent of the peak, while
only 5.1 percent of the time the traffic is more than 70 percentof the peak. Meanwhile during the weekend the low trafficload period increases to 43.7 percent of the time and the hightraffic load period declines to 4.0 percent of the time.

1

Simulated data
Nakagami distribution fit

Cumulative Probability

0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0

3

4

5

6

7

8

9

10

989

11

Cload
Fig.3. CDF and fitting Nakagami distribution of the traffic load switching
factor

0.8

Cumulative Probability

Then in Fig. 4, the histogram describing the simulation
corresponding mean value of Cload for different traffic load ofeNB bi is depicted. It is shown that Cload declines slowly with
the increasing of site traffic load, what is indicating that while
experiencing a larger traffic load, eNB has a smaller impact
on the surrounding traffic after it is switched off. The equation
fitted by using the MMSE method, Cload=Kexp(-aρb),
describes the inverse relationship between Cload and ρb. Fig. 4
also depicts the fit curves. The estimates of K and a are given
in Table II too.

1

0.6

0.4

0.2

Normalized traffic load data
0

0

0.2

0.4

0.6

0.8

1

Normalized traffic load

Fig. 5. CDF of Cload

7
Exponential Fit
rb vs. Cload

TABLE IIIANALYSIS OF TRAFFIC LOAD PROFILES FOR THE TYPICAL ENB DURING A
WEEK
Below x percent traffic load peakOver x percent traffic load peak

C

load

6.5

Threshold
6

5.5

5

0.1

0.2 r
b

0.3

70%

80%

90%

95%

Weekday 31.2% 33.2% 43.5% 53.6% 5.1%

5%

10%

20%

3.4%

2.6%

0.25%

Weekend 39.8% 43.7% 54.5% 64.3% 4.0%

2.6%

1.8%

0.16%

Avarage 33.3% 36.3% 46.4% 56.5% 4.8%

3.2%

2.4%

0.2%

100

0.4

8.1%
energysaving90

Fig. 4. Fit curve of Cload vs. ρb

30%

omega
38.93

Energy Consumption

80

TABLE IISTATISTICAL VALUES OF CLOAD AND FIT CURVE
Meanvariancemu
Nakagami distribution
5.98
3.10
2.99
K
a
Exponential Fit
6.25
0.1746

70
60

19.6%
energysaving504030

B. Energy saving results and analysis
Initially, the values of ρoff, ρswitch and ρon should be set
appropriately in simulations. On one hand, with a low ρoffvalue, eNBs operate in a conservative manner with a low
system load on average. In this case, the UEs would
experience less delay and call dropping probability, as theeNBs are more robust to the burst traffic arrivals. On the other
hand, with ρswitch and ρon value close to one, more energy
saving could be achieved at the cost of slight performancedegradation. Based on the traffic load profiles shown in Fig. 1,
the percentage of time that the traffic is below and overdifferent percent of week peak during weekdays andweekends is acquired. We made statistics on a week of the

20
10
0

Mon

Tue

Wed

Thu

Real Energy Consumption
PFSES
Fri
Sat
Sun

Fig. 6. Energy saving ratio for a week

Hence, in the initial simulation, the threshold values is set
at 0.7, 0.8, and 0.3, for ρswitch , ρon and ρoff. Fig. 6 shows that byusing PFSES algorithm 8.1% energy saving of the network
can be achieved for wednesday when energy consumption is
the most in a week, while about up to 19.6% energy saving forsunday when the energy consumption is the lowest in a week,
by researching all the real traffic profiles of eNBs as shown in
Copyright © 2017 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 6, Issue 2, March 2017

Fig. 2. The overall analysis of weekday and weekend showsthat about 12.8% and 18% energy saving of the network canbe achieved respectively.

990

0.24
0.22

0.5

0.2

0.4

roff

0.22
0.2

0.18

0.3

0.16

0.2

0.18

0.14

RatioES

0.1
1

0.16

0.9

1 0.12

0.14

0.8

0.12

ron

0.7

on

r
=0.8,r =0.9
rswitch=0.7,ron=0.8
rswitch=0.6,ron=0.7
0.2

0.3

0.4

roff

0.5

0.6

0.1

0.7
0.6

rswitchFig. 9. Energy saving ratio for varying ρoff , ρswitch and ρon

0.7

Fig. 7. Energy saving ratio for fix interval between ρswitch and ρonThen, the energy saving ratio during a week is studied forcouples of ρswitch and ρon with fixed interval between them,varying the ρoff from 0.1 to 0.8. The results are shown in Fig. 7.
It can be clearly seen that the energy saving ratio increases asρoff increases. The limit and maximum value is about 19.6%
when the ρoff is larger than about 0.5. Moreover, for the sameρoff, with the rising ρswitch and ρon, the energy saving ratio
increased slightly, e.g., the maximum energy saving ratio is
about 18.0% when ρswitch is 0.6, and nearly 19.6% when ρswitch
is 0.8. As only 4.8% of the time is more than 70 percent of thepeak, the energy saving ratio is similar when ρswitch is over 0.8.

Min load of the active eNBs, rminswitch

0.1
0.08
0.1

0.9
0.8

0.8

rswitch=0.6,ron=0.7
rswitch=0.7,ron=0.8
rswitch=0.8,ron=0.9

0.7
0.6
0.5
0.4
0.3
0.2
0.1
0

1

2

3

4

5

6

Max number of switch-off eNBs simultaneously
Fig. 10. The maximum number of switching-off eNBs simultaneously

0.25

RatioES

0.2
0.15

rswitch=0.7,ron=0.80
rswitch=0.7,ron=0.85
rswitch=0.7,ron=0.90
rswitch=0.7,ron=0.95

0.1
0.05
0.1

0.2

0.3

roff

0.4

0.5

Fig. 8. Energy saving ratio for varying intervals between ρswitch and ρon

Further, the energy saving ratio is analysed with variedintervals between ρswitch and ρon. In the research, ρswitch is fixed
at 0.7 and ρon increases from 0.75 to 0.95 stepping by 0.05.
From the results depicted in Fig. 8, on the one hand, theenergy saving ratio is improving with the increasing ρon when
ρoff is the same value. On the other hand, the energy saving
ratio improves obvious when ρoff is higher, (e.g. the energy
saving ratio is 23.52% when ρoff is 0.95 what is about 4.16%
more than the condition when ρoff is 0.7). It concludes that thehigher ρoff is, the more load is charged with the active eNBs
adequately. To join the three parameters ρoff , ρswitch and ρon ,Fig. 9 describes the energy saving effect. It is found that theeffect of energy saving has some limit.

Finally, Fig. 10 shows the maximum number ofswitching-off eNBs simultaneously with the minimum trafficload of the active eNB for the research area shown in Fig. 2. In
this study, the value of ρoff is set as the minimum load of active
eNBs in the area. It is shown that the maximum number of theswitching off eNBs simultaneously increases as the min loadof the active eNB falls down. And when ρswitch rises, the loadof minimum active eNBs increases too, (e.g. when ρswitch is set
at 0.8, the minimum load value for the active eNBs is 0.66).
And in the area, owing to the location deployment of eNBs,
the maximum switching-off eNBS number is 5 among 13
eNBs. It means that even in the night time, in order to ensure
the capacity and coverage of the network, maximum 5 eNBs
can be switched-off at the same time.
V. CONCLUSIONSThis paper is proposed a low-complexity practical energy
saving algorithm by switching off/on some eNBs considering
the historical and real-time load of eNB. The eNBs are ranked
according to its loads in an ascending order with a central
controller and first eNB in the list with load decreasing andsmaller than ρoff is pre-selected as target switching off eNB.
The effect of the target switching off eNB on neighbour eNs isevaluated by Cload conveniently. The eNB switches-off while
the load of neighbour eNBs assuming switching-off an eNB islower than ρswitch. As only 4.8% of the time is more than 70
percent of the peak, the energy saving ratio is similar whenρswitch is over 0.8. The switching-off eNBs is switched on
inspiring by the active eNBs in a distributed way. According
Copyright © 2017 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 6, Issue 2, March 2017

to the changes of eNB load within a week, the cumulative
probability distribution of normalized load is analyzed, andthe eNB load threshold of different periods is evaluated, andby varying different load thresholds, the simulation results
show that the proposed energy saving scheme has a good
performance in the urban commercial area. Simulations also
show that the proposed PFSES algorithm can reduce the
network energy consumption with a low complexity.
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991

Ding Fei was born in Taizhou city, China, on August30, 1981. He received the Ph.D. degree in the Schoolof Instrument Science and Engineering, Southeast
University, China in 2010. He was an Internet ofThings (IoTs) research leader in the R&D Center,
China Mobile Group Jiangsu Co., Ltd., China. He iscurrently an associate professor in the School ofInternet of Things, Nanjing University of Posts andTelecommunications, Nanjing 210003, China, andalso a Research Fellow in the School of Information Science andEngineering, Southeast University. He has long been engaged in wirelessnetworks, Internet of Things (IoTs) and mobile communication related key
technologies, chaired or participated more than 10 National or Provincial
Science and Technology Projects, and chaired more than 20 Enterprise
Projects.
Wang Ye was born in Yangzhou city, China, onJanuary 3, 1987. He received the Ph.D. degree inNanjing University of Posts and Telecommunications,
China, in 2013. He is currently an Internet of things(IoTs) and mobile network Researcher in the R&D
Center, China Mobile Group Jiangsu Co.,Ltd, Nanjing,
China. His research interests include the internet ofthings, mobile communication and key techniques for
integrated
application
betweenmobilecommunication and the internet of things.
Tong En was born in Yangzhou city, China, on March
15, 1971. He received Ph.D degree in School ofInformation Science and Engineering, Southeast
University, Nanjing, China. He is currently a Research
Manager of the R&D center at China Mobile Group
Jiangsu Co.,Ltd, Nanjing, China. He has long been
engaged in the research of mobile communication andthe internet of things (IoTs) related technologies,
chaired or participated more than 50 mobile
communication research projects. He was the winner of China Mobile
innovation awards for many times and published nearly 20 academic papers.
Pan Zhiwen was born in 1970.11. He received Ph.D
degree in Nanjing University, Nanjing, China. He has
been with National Mobile Communications ResearchLaboratory, Southeast University as associate
professor from 2000 and professor after 2004. During
2000 through 2001, he has been involved in the
research and standardization of 3G, and from 2002, hehas been involved in the investigations on key
technologies for IMT-A and 5G. He has publishedover thirty papers recently, and holds over 50 patents. His research interests
include self-organizing networks, wireless networking, and radiotransmission technology for wireless communications.
You Xiaohu was born in 1962.8. Since 1990, he has
been working with National Mobile Communications
Research Laboratory at Southeast University, where
he held the rank of professor, the Chair Professor of
Cheung Kong Scholars Program and served as
Director of the laboratory. His research interests
include mobile communication systems, signal
processing and its applications. He has contributed
over 50 IEEE journal papers and 2 books in the areasof adaptive signal processing, neural networks and their applications tocommunication systems, and holds over 80 patents. He was the PremierFoundation Investigator of the China National Science Foundation. From1999 to 2002, he was the Principal Expert of the C3G Project, responsible fororganizing China’s 3G Mobile Communications R&D Activities. From
2001-2006, he was the Principal Expert of the national 863 FuTURE Project.
He has been Section Chair of IEEE Nanjing Section since 2010 and IEEE
Fellow since 2011. He served as the general co-chair of IEEE WCNC 2014.
His current research interests focus on wireless and mobile communication
systems and modern digital signal processing.

Copyright © 2017 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 6, Issue 2, March, 2017

992

NRIT: Non-Redundant Indirect Trust Search
Algorithm for a Cross-Domain based
CDNi-P2P Architecture
Shi Li, Inshil Doh, Kijoon Chae
Department of Computer Science and Engineering, Ewha Womans University, Korea
lishi1116@ewhain.net, isdoh1@ewha.ac.kr, kjchae@ewha.ac.kr
Abstract—A content delivery network (CDN), as a distributed
network architecture, enhances efficient delivery of content. Theinterconnection of different CDNs (CDNi) further improves
efficiency and the experience of end users. As another
distributed network with high availability and highperformance, a peer-to-peer (P2P) network can provide efficient
resource sharing. To combine the advantages of the two
networks, we propose a hybrid CDNi-P2P architecture, alongwith trust management models to achieve more efficient content
delivery. In CDNi-P2P architecture, end users can obtain therequested content from the nearest CDN edge server, and canalso share these contents with other users in the same domain asa P2P network. After the transactions, users can rate each otherbased on the reputation evaluation method adopted in thesystem. For some mobile users, they can move among different
domains and share the contents who have with the end users in
different system. In general, different systems adopt different
reputation evaluation standards. This leads to disparate trust
values for mobile users in different systems. Based on thearchitecture, we propose two trust models to solve this problem:
a local trust model and a cross-domain trust model. To evaluate
reputation more effectively and accurately, we also propose asearch algorithm for the trust model called the non-redundant
indirect trust search algorithm (NRIT-SA). Using the proposed
trust models, a mobile user can transform his/her local trust into
mobile trust in a new domain. We thus avoid disparate trust
values for a single user in different domains and improve theavailability of the content possessed by mobile users as they
move among different domains. The result of the performance
analysis shows that when there is a high connectivity degree ofusers in the system, the calculation time of the proposed NRITSA tends to be stable. And depending on the comparison resultwith the full search algorithm, NRIT-SA shows more efficient
calculation performance and more reliable result.
Keyword—CDNi,
Evaluation, Trust

Cross

Domain,Mobile,Reputation

Manuscript received on November 25, 2016. This work was
supported by the National Research Foundation of Korea (NRF) grant
funded by the Korea government (MSIP) (2016R1A2B4015899), and afollow-up the invited journal to the outstanding paper of the 19thInternational Conference on Advanced Communication Technology(ICACT 2017). It is also partially supported by the State Scholarship
Fund organized by the China Scholarship Council (CSC).
Shi Li is with the Computer Science and Engineering Department,
Ewha Womans University, Seoul, 03760 Korea (e-mail: lishi1116@
ewhain.net).
Inshil Doh is with the Computer Science and Engineering
Department, Ewha Womans University, Seoul, 03760 Korea (e-mail:isdoh1@ewha.ac.kr).
Kijoon Chae is with the Computer Science and Engineering
Department, Ewha Womans University, Seoul, 03760 Korea. He is thecorresponding author of this paper. (Corresponding author phone: +822-3277-2370; fax: +82-2-3277-3506; e-mail: kjchae@ewha.ac.kr).

I. INTRODUCTIONT

WO major technologies provide large-scale video
streaming over the Internet: content delivery networks(CDNs) and peer-to-peer (P2P) networks. Both of them candistribute content with high availability and high performance.
With CDNs, an origin server distributes content to cacheservers (edge servers) located close to end users, resulting in
fast, reliable applications and Web services for users [1].
There are many commercial CDN companies: Akamai,
AT&T, NTT Communication, Limelight, Mirror Image,
Level 3, etc. In practice, it is extremely expensive to deploy
and maintain CDN servers, so CDN architectures do notbenefit from high scalability. On the other hand, P2P
networks can be highly scalable because of their low start-up
costs and because they rely on peers instead of dedicated andexpensive servers. The complementary advantages of CDN
and P2P networks allow their combination into a hybrid
CDN-P2P architecture that creates a distribution system withhigher scalability and reliability than either kind of network
alone [2], [3].
Content delivery network interconnection (CDNi) is a newinteractive network infrastructure that allows information andcontent to be transmitted between different CDNs throughspecific interfaces. CDNi provides all of the benefits of CDN
and also has some unique characteristics. In CDNi, end users
do not need to register at all CDN providers to obtain content
from different content service providers (CSPs). Whenrequested content is not cached in any edge server of theregistered CDN, the end-user’s request will be redirected toother CDNs to capture the content through the interfaces
among them. (CDNi requires the specification of interfaces
and mechanisms to address issues such as request routing,
distribution metadata exchange, and log in informationexchange across CDNs [4].) As a result, content can bedelivered via a CDN chain and transmitted to end users by theclosest CDN. CDNi thus uses two categories of CDNs: theone that caches content from a CSP is called the upstream
CDN (uCDN), and the one that delivers content directly to an
end user is called the downstream CDN (dCDN).
To deliver content more efficiently, we combine CDNi andP2P architectures into a hybrid CDNi-P2P network thatcombines the advantages of both, which can be found in ourprevious research as well [11]. Based on the the hybrid CDNiP2P architecture, end-users could receive content from the
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 6, Issue 2, March, 2017

closest edger server of the dCDN with which they are
registered and from peers in the same domain.
In a hybrid CDNi-P2P network, all the end users, as peers,
are both consumers and providers. When an end-user requests
content from a peer, some users might be honest and provide
accurate content received from the edge server of a dCDN,
others might be self-serving and unwilling to provide content
to peers, and still others might be malicious and provide false
or harmful content [5]. A trust model provides a way togenerate trust based on a peer’s history of behavior [6]-[10].
A larger trust value indicates a higher probability of providing
accurate content. Meanwhile, some mobile peers moveamong different systems or domains. From a reputationestimation point of view, they need to develop a trust value ineach P2P system. In general, different P2P systems anddomains adopt different trust models and reputation
evaluation standards that lead to disparate trust values for asingle peer in different domains, even if that user always has
the same performance. Most research on existing reputation
systems focuses on the trust model of a single system or
domain. Creating a cross-domain trust model has never been
considered. Therefore, in this paper, we propose a crossdomain trust model for a hybrid CDNi-P2P network. And
based on this cross-domain trust model, a non-redundant
indirect trust search algorithm is proposed in this paper as
well, which can be used to calculate the local trust degree
more efficiently and more reliably.
The rest of this paper is organized as follows: we introduce
our local trust model in Section 2 and propose our hybrid
CDNi-P2P architecture and cross-domain trust model in
Section 3. And the performance analysis and conclusion are
in Section 4 and Section 5, respectively.
II. PROPOSED LOCAL TRUST MODELA cross-domain trust model presupposes a relative
reputation evaluation for each participant in a local system,
which can indicate the reliability of each individual
participant. In this section, we propose a local trust model togenerate a relative reputation value for each participant
according to the different reputation evaluation standards
currently used by local systems.
A. Trust Value and Trust Degree
Existing online reputation systems and research use twomain approaches to evaluate a participant’s reputation withina specific network. In the first approach, the ratings for boththe service receiver and provider are given via a bi-directionalor one-directional rating after each transaction. The rating
could take the form of reputation scores, feedback ratings,
positive feedback rates, etc. The overall reputation of aparticipant is the sum of those ratings and is called that user’s
trust value, denoted by v. Online reputation systems that use
this approach are the online auction system eBay, Amazon,and Alibaba. Generally, the trust value is an integer equal toor greater than zero that is public to all system participants;
other participants can decide whether to trust a participant
based on this trust value. In the second approach, both theservice receiver and provider can rate each other after atransaction, and they calculate the trust degree to the others[6-10]. The trust degree is a value between 0 and 1, denotedby d. For example, when there are two participants i and j, i993

can rate j after each transaction, and the trust degree of itrusting j (denoted by dij) is the ratio of positive ratings. The
relations among those evaluations can be illustrated by agraph called a reputation evaluation diagram.
B. Local Trust Model
The objective of a local trust model is to calculate therelative reputation degree (local trust degree) of eachparticipant in a local system using a value between 0 and 1.
According to the two approaches explained above, we
propose two methods to calculate the local trust degree of anindividual participant. Fig. 1 illustrates the two types of
reputation systems: the trust value reputation system and thetrust degree reputation system.
B

62

B

0.2

25
49

A

D

A

0.9

0.5

D

0.7

C 70
(a)

C

(b)

Fig. 1. (a) Trust value reputation system. (b) Trust degree reputation system.

1) Trust Value Reputation System
We assume there are n participants in the system, and eachparticipant i, here i∈(1, n), has a trust value vi which is derived
from the ratings given by other participants after thetransactions in a trust value based reputation system. Thus,
the local trust degree of i can be calculated as follows:𝑣𝑖(1)
𝑙𝑜𝑐𝑎𝑙_𝑑 𝑖 =𝑚𝑎𝑥𝑗∈(1,𝑛) 𝑣𝑗
Here, local_di is a value between 0 and 1 that indicates the
ranking of i's trust value in the system. Thus, as shown in Fig.
1 (a), the local trust degree of D local_dD is 0.7 according toequation (1).
2) Trust Degree Reputation System
To calculate the local trust degree of participant j in trust
degree reputation system, we first need to obtain the trust
degrees of all the other participants trusting in j, i.e., we need
all dij, i ∈ (1, n), which can be calculated as follows:
𝑛

𝑑𝑖𝑗 =

∑

𝑑𝑖𝑘 𝑑𝑘𝑗(2)

𝑘=1,𝑘≠𝑖,𝑘≠𝑗Equation (2) is a recursive equation used in computing dik
or dkj. In Fig. 1 (b), dAD can be calculated as dAB×dBD +dAC×dCD, and dCD can be calculated as dCB×dBD, i.e., dAD =dAB×dBD + dAC×dCB×dBD = dAB×dBD + dAB×dBD. In other words,
there are two ways to calculate dAB: one results in the directtrust degree, and the other in the indirect trust degree.
However, in a reputation evaluation system, direct trust will
be more reliable than indirect trust. Thus, if a trust degree canbe calculated both ways (direct and indirect), the direct way
will be selected. In this case, we use only the direct trust
degree to calculate dAD, whose result is 0.1. Similarly, we cancalculate dBD and dCD as 0.5 and 0.45 respectively.
However, in a realistic reputation system, a large numberof peers can communicate and rate one another; thus, arealistic reputation evaluation diagram is huge andcomplicated. There are many different paths from one peer to
Copyright © 2017 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 6, Issue 2, March, 2017

A

P1=P-{A}P2=P1-{B,C,D}

994

NA=P1∩PA'

B

C

D

NB=P2∩PB'

NC=P2∩PC'

ND=P2∩PD'J

P3=P2-{E,F}
P4=P2-{E,G}P5=P3-{H,I}
P6=P3-{G,J}P7=P4-{H,I}
P8=P4-{F,H,I}

E

F

E

G

NE=P3∩PE'

NF=P3∩PF'

NE=P4∩PE'

NG=P4∩PG'

G

H

H

I

×

NH=P5∩PH' NI=P5∩PI'G

P9=P5-{G}P10=P5-{G,J}

G

NG=P6∩PG'
NG∩J=Ø

J

× ×

J

×

I

F

H

×

I

NF=P8∩PF'
NI=P8∩PI'
NH=P7∩PH' NI=P7∩PI'
NH=P8∩PH'
NH=Ø
NH=Ø

J

J

J

NG=P9∩PG' NG=P10∩PG'
NG=Ø NG∩J=Ø
Fig. 3. The calculation process of the trust degree from pA to pJ using NRIT-SA.

another, which makes it difficult to select a direct trust way
among them. Moreover, the reputation evaluation diagram
will contain some cycles, as shown in Fig. 2, which is adifficult issue for the indirect trust degree calculation, andmost research has excluded it from consideration. In thispaper, we propose an algorithm to calculate the indirect trust
degree by considering the cycles and excluding redundancies
when there is direct trust. We call it the non-redundant
indirect trust search algorithm (NRIT-SA).

C
0.32
0.1

D

0.6
0.5

A

G
F

E

0.1
0.12

H
I

B

J
Fig. 2. Reputation evaluation diagram.

We assume that a P2P evaluation system contains n peers,and each peer k can be considered as a point in the reputation
evaluation diagram, which is denoted pk, k ∈ (1, n). If peer kevaluates peer l after a transaction between them, an arrow is
generated from k to l in the diagram, denoted arrk,j. Thus, thereputation evaluation diagram composed of the point set P ={pk}, k ∈ (1, n), and the arrow set ARR = {arrk,j | k, j ∈ (1, n)
and k ≠ j}. ∀ pk ∈ P, ∃ Pk’, which is a set of points that pk can
indicate in the reputation evaluation diagram. As shown in
Fig. 2, PC’ = {pA, pE, pF}. Moreover, if Pk’ = Ø , there is no
point that pk can indicate.
NRIT-SA is a breadth first search algorithm that does notsearch for the shortest path from source to destination. Its
objective is to calculate the indirect trust degree using as
much direct evaluation of others as possible. The reputation

evaluation diagram shown in Fig. 2 is based on a trust degree
reputation system. To calculate the indirect trust degree frompA to pJ based on equation (2) and exclude redundant andcyclic evaluations, the NRIT-SA algorithm can search theindirect evaluation paths, as shown in Fig. 3.
Step 1: First, as a source point, pA should select a set ofpoints as its next step, denoted NA. As shown in Fig. 2, a point
set PA’ contains all the points that pA can indicate, here PA’ ={pB, pC, pD}. To avoid a redundant path, pA should remove
itself from the available point set P and generate a new point
set P1 = P-{pA} that can be used instead of P in the next step.
Then, the next step point set of pA can be calculated as NA =P1∩PA’ = {pB, pC, pD}. NRIT-SA will move to each of thenext step points of pA to continue. If there is a pi ∈ NA as thedestination point, then there is a direct evaluation from thesource to the destination peer, and the algorithm is complete
because there is no need to calculate the indirect trust degree.
Step 2: To avoid a redundant path, the available point set
P1 should remove each point pi that belongs to set NA (i.e., pi∈ NA) to generate the newly available point set P2. In this case,P2 = P1-{pB, pC, pD}. ∀ pi ∈ NA will calculate its next step
point set by Ni = P2∩Pi’. If Ni = Ø , there is no point that pi
can indicate, and the search from pi will be stopped. And if
Ni∩pJ = Ø (here pJ indicates the destination point), a directtrust way has already been found in the previous step pi to thedestination point, so the search from pi will be stopped. If Ni= pJ, then the path from the source to the destination should
be recorded as one of the NRIT paths. For the other cases of
Ni, NRIT-SA will move to each of the next step points of pi
and repeat the similar process in Step 2.
After finding all of the NRIT paths from a source point toa specific destination using NRIT-SA, the trust degree fromthe source point to the destination can be calculated using
Equation (2). In the example shown in Fig. 3, the trust degree
dAJ can be calculated using all the NRIT paths: dAB×dBJ +dAC×dCE×dEI×dIJ + dAC×dCF×dFJ + dAD×dDE×dEI×dIJ +dAD×dDG×dGF×dFJ + dAD×dDG×dGI×dIJ. Similarly, we can also
calculate the trust degrees from all the other points to thisdestination. According to the trust degrees of j given by all
the other participants in the system, the local trust degree of
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 6, Issue 2, March, 2017

participant j can be calculated as follows:∑𝑛𝑖=1,𝑖≠𝑗 𝑑𝑖𝑗
𝑙𝑜𝑐𝑎𝑙_𝑑 𝑗 =
𝑛−1

(3)

Here, n indicates the number of participant peers in thesystem, and local_dj is a value between 0 and 1 that represents
the average trust ranking for peer j based on the ratings given
by all the other participants in the system.
III. PROPOSED CROSS-DOMAIN TRUST MODEL FOR HYBRID
CDNI-P2P NETWORKFrom a reputation estimation point of view, mobile peers
who move among different systems or domains need trust
values in each P2P system. In general, different P2P systems
or domains adopt different trust models and reputation
evaluation standards. That leads to disparate trust values fora single peer in different domains, even if the user always
offers the same performance. In this section, we propose across-domain trust model for a mobile peer in a hybrid CDNiP2P network.
A. Hybrid CDNi-P2P Network Architecture
Our proposed hybrid CDNi-P2P network architecture isshown in Fig. 4. The architecture contains two types of CDNs:
uCDNs and dCDNs. Content provided by CSPs is stored onlyin the edge servers of the uCDNs. For an end user u1 who can
only obtain service directly from dCDN-A (i.e., the end user
u1 registers at dCDN-A), if u1 sends a content request to theorigin server, the content will be delivered from the uCDN todCDN-A and then transmitted to the end-user through thecloset edge server of dCDN-A. If another end-user from thesame domain also wants to obtain this content, s/he can get
the content directly from u1. In this situation, each CDN can
act as an uCDN and dCDN simultaneously based on thecontent requested by the end-user.

995

accumulate trust in the new domain.
We assume that the uCDN and dCDN can also rate eachother after each transaction between them, and that theirevaluation method is based on the trust degree reputation
system described in Section 2. We denote the two CDNs as
CDNx and CDNy respectively. The trust degree that CDNx
𝐶𝐷𝑁
gives CDNy is 𝑑𝑥𝑦, and the trust degree that CDNy gives
𝐶𝐷𝑁
CDNx is 𝑑𝑦𝑥 . When a mobile user um would like to movefrom CDNx to CDNy, um will send a mobile request message
to CDNx in order to obtain the individual local trust degreefrom CDNx where um registered before. Because CDNx could
collect the trust information of all users located in its domain
periodically, it can calculate a fair and credible local trust
degree for each user. After receiving the mobile request
message, CDNx will calculate the local trust degree of um
𝐶𝐷𝑁
indicated as 𝑙𝑜𝑐𝑎𝑙_𝑑𝑢𝑚 𝑥 by using the local trust model
𝐶𝐷𝑁

proposed above, and deliver 𝑙𝑜𝑐𝑎𝑙_𝑑𝑢𝑚 𝑥 to CDNy. If it is thefirst time for mobile user um to move from CDNx to CDNy
domain, the mobile trust degree of um can be calculated by
CDNy as follows:
𝐶𝐷𝑁 →𝐶𝐷𝑁𝑦

𝑚𝑜𝑏𝑖𝑙𝑒_𝑑𝑢𝑚 𝑥

𝐶𝐷𝑁

𝐶𝐷𝑁
= 𝑙𝑜𝑐𝑎𝑙_𝑑𝑢𝑚 𝑥 × 𝑑𝑦𝑥(4)Here, it should be noted that the trust degree between
CDNs indicates how CDNy rates CDNx. If um already has atrust value (or trust degree) in CDNy, it is used continuously.
If CDNy is a trust value reputation system and the numberof total participants is t, the mobile trust degree of um can betransformed to the trust value in CDNy as follows.
𝐶𝐷𝑁

𝐶𝐷𝑁 →𝐶𝐷𝑁𝑦

𝑣𝑢𝑚 𝑦 = 𝑚𝑜𝑏𝑖𝑙𝑒_𝑑𝑢𝑚 𝑥

𝐶𝐷𝑁𝑦

× 𝑚𝑎𝑥𝑗∈(1,𝑡) 𝑣𝑗(5)

And if CDNy is a trust degree reputation system, the mobile
trust degree of um can be the trust degree in CDNy for all otherparticipants.

B. Cross-Domain Trust Model
Movement
Communication and evaluation
End user peer

Movement
Communication & evaluation
End user peerContent Service Provider(Origin server)

Edge serverdCDN(CDNy)CDNx
uCDN(CDNx)CDNy
CDNz
End user P2P network
Fig. 4. Hybrid CDNi-P2P network architecture.
Fig. 5. A mobile user movement scenario.

In Fig. 4, a mobile end user peer (the red solid point) canmove between uCDN and dCDN domains. Some mobile
peers with high trust values in one P2P domain could havetheir trust values initialized based on the trust model used ina new domain when they move because they are new-comers
to the new domain. This will lead to a waste of resources,
because other peers are unwilling to obtain content from a
new peer. The mobile peer must wait a long time to

In the new domain CDNy, the mobile user um can registerat CDNy and receive contents from CDNy, then share thecontents with other users in the new domain. And thecalculated trust value or trust degree which is derived from
𝐶𝐷𝑁 →𝐶𝐷𝑁𝑦𝑚𝑜𝑏𝑖𝑙𝑒_𝑑𝑢𝑚 𝑥
will be used as the initial evaluation
value to all the other users located in CDNy. And it can be

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 6, Issue 2, March, 2017

updated depending on the ratings given by the other users
after transactions.
Moreover, there are some special cases during the mobile
user moves among CDNs in this CDNi-P2P hybrid network,
which are listed as follows. Depending on these cases, we will
discuss the different applications of proposed cross-domain
trust model. First, we need to define some terminologies.
Take the figure shown in Fig. 5 as a scenario, a mobile user
located in CDNx moves to CDNy domain, later, this user
leaves CDNy and moves to another CDNz domain. In this
scenario, CDNx, CDNy, CDNz are called original CDN,
intermediate CDN and destination CDN, respectively.
1) No transaction with destination CDN
In this case, the destination CDN has never exchanged
contents with the original/intermediate CDN before, thus
there is no direct evaluation between these two CDNs. In
order to calculate the mobile trust degree of user as shown inEquation (4), the destination CDN will ask its internet service
provider (ISP), which can be considered as the manager ofCDN, for the indirect evaluation to the original/intermediate
CDN. And by using the proposed NRIT search algorithm, the
ISP can generate the indirect trust degree that destination
CDN trusts in other CDNs. Depending on it, the destination
CDN can calculate the mobile trust degree of the mobile user.
2) No transaction with other users in intermediate CDN
If the mobile user does not communicate with any otherusers in the new domain, its trust value or trust degree will
not be changed. When s/he moves to another domain called
CDNz, s/he does not need to calculate the new local trust
degree in the intermediate CDNy domain. Instead, the local
trust degree calculated in the original CDNx domain can beused, because there is not any new evaluation given by theusers in intermediate CDNy, and we believe that the local trust
degree calculated in the original CDNx is more credible which
is based on the actual transaction of this mobile user. Then,the mobile trust degree of this user can be calculated𝐶𝐷𝑁
depending on the Equation (4), here 𝑑𝑧𝑥
is used as the trust
degree that CDNz trust in CDNx. And if there is no directtransactions between CDNx and CDNz, the method introducedin the first case can be referred.
3) Trust value or Trust degree updated in intermediate CDN
If the mobile user communicates with other users and beevaluated in the intermediate CDNy domain, the trust value ortrust degree will be updated. When s/he would like to moveto another domain called CDNz, a new local trust degree in
CDNy needs to be calculated depending on the local trustmodel proposed in this paper. And based on the Equation (4)and this new local trust degree, another mobile trust degreefrom CDNy to CDNz can be computed, which will be used by
CDNz to generate the initial evaluation value of this mobile
user in the CDNz domain.
IV. PERFORMANCE ANALYSIS
In this section, we will analyse performance of the
proposed NRIT search algorithm, and compare it with one of
the most well-known search algorithms called full searchalgorithm.

996

A. Calculation Time of NRIT-SA
Based on the proposed algorithm NRIT-SA, mobile users
can calculate their local trust degree which is mainly
dependent on the direct evaluations among users in the local
system. The proposed NRIT-SA can eliminate all the
redundant indirect connections between two users, if there is
the direct evaluation between them. Because we believe thefact that the direct evaluation is more trustworthy than theindirect one. Moreover, for users who cannot provide thedirect evaluation to a specific user, i.e. these users have neverconduct a transaction before, the indirect evaluation fromothers can also be used to generate the trust degree to thespecific user. Thus, the connectivity rate among users becomea significant determinant of the performance in NRIT-SA.
In this section, we will discuss the relationship between theconnectivity rate of users and the calculation time of the
proposed algorithm. First, we will give the definition of theconnectivity rate, here it is called connectivity degree, as
follows.
Definition 1 (Connectivity degree)
For a local system with n users, the connectivity degree canbe calculated by∑𝑛𝑖=1 ∑𝑛𝑗=1,𝑗≠𝑖 ⌈𝑑𝑖𝑟𝑒𝑐𝑡_𝑑𝑖𝑗 ⌉(6)𝑑𝑐𝑜𝑛𝑛𝑒𝑐𝑡𝑖𝑏𝑖𝑡𝑦 =𝑛(𝑛 − 1)
Here, direct_dij indicates the direct trust degree between
user ui and uj, which can be considered as the directconnection between node ui and uj in Fig. 2. From thedefinition, we know that the connectivity degree indicates thepercentage of real connections among users in a local system.
According to this definition, we will analyze the relationshipbetween the calculation time of proposed algorithm NRITSA and the connectivity degree of users in a local system.
The tool we use to implement NRIT-SA is JDK 1.7.0_80,
and 3.60GHz processor with 64-bit OS. The connections
among users are randomly selected based on the connectivity
degree. For two specific user ui and uj, the time to calculate
the trust degree dij based on NRIT-SA is shown in Fig. 6.
In Fig. 6, the calculation time indicates the average valueof processing time based on different connection topologies
which is randomly generated. From the figure, we can see thatthe processing time of NRIT-SA increases along with thegrowth of the number of users in the system. And when there
are 50 users, the longest calculation time is around 30s which
can be tolerant by an evaluation system, because thecalculation of trust degree dij is used for generating the local
trust degree of mobile user uj which can be considered as anoffline value during a short period of time. The reason is that,for user uj, its local trust degree local_dj can be influenced by
the change of other trust degree between any two users in thesystem, however, according to equation (2) and (3), thisinfluence is negligible small when there are large numbers ofusers in the local system.
Moreover, from Fig. 6, we can see that, for different
number of users, the peak values of calculation time typicallyappear around 20% of connectivity degree. And along with
the increase of connectivity degree, NRIT-SA processing
time decreases exponentially. When the connectivity degreeincreases to 40%, all the calculation time is around 1s.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 6, Issue 2, March, 2017997

According to a well-known fact that the number of systemusers will tend to be stabilized, the connectivity degree will
increase with time, thus the calculation time of proposed
algorithm can also tend to a small and stable value.

algorithm is almost around 23s, which is much more than thetime taken by the proposed NRIT-SA. Thus, depending on
the result shown above, our NRIT-SA is much more efficientthan the well-known full search algorithm.

Fig. 6. The calculation time of NRIT-SA based on different connectivity
degree of the n users.

Fig. 7. The comparison of calculation time between full search algorithm
and NRIT-SA when connectivity degree is 40%.

B. Comparison with Full Search Algorithm
Moreover, as a well-known search algorithm, full search is
widely used in the search mechanism. And it is also employedin some famous reputation evaluation systems. Then, we willcompare our NRIT-SA with the full search algorithm, and thedefinition of the full search is as follows.
Definition 2 (Full search)
For each individual user j located in a system, the trust
degree of other users trust in user j is calculated only by the
Equation (2), i.e. the trust degree is contributed from both
direct trust degree and indirect trust degree.
The definition of full search implies that all users in thesystem needs to participant in the calculation of trust degree
between any two users. In order to generate a new trust degree,
full search algorithm should refer to more indirect trust
degree. Thus, the full search algorithm will generate a larger
trust degree than our NRIT-SA, which cannot be regarded asa more accurate result when compare with NRIT-SA, because
we believe that the direct trust degree is more trustworthy
than the indirect one during P2P communications. Meanwhile,by using full search algorithm, it will take much morecalculation time to search for all paths from the source node
to the destination node. The comparison of the calculation
time between full search algorithm and the proposed NRITSA is illustrated in Fig. 7 as follows.
Depending on the result shown in Fig. 6, we know that if
the connectivity degree is more than 40%, the calculation
time of NRIT-SA tend to be stable. Thus, the result illustrated
in Fig. 7 is based on the 40% connectivity degree of users in
the domain. From Fig. 7, we can see that the calculation timeof NRIT-SA is less and be stable along with the increase of
the number of users. However, the calculation time of fullsearch algorithm exponentially increase when the number ofusers increases linearly. And even when there are only 15
users in the domain, the calculation time of full search

Moreover, because full search algorithm refers to moretrust degrees existed in the system than NRIT-SA, any changeof direct trust degree between two users will influence thecalculation result of indirect trust degree among other users
in the system. As an example shown in Fig. 8, we would liketo calculate the indirect trust degree that user A trust in user
D which is indicated as dAD, and the trust degree between user
B and C is different in Fig. 8 (a) and (b). Thus, based on the
topology and the different values of dBC shown in Fig. 8 (a)and (b), we will compare the influence on the calculation
result of dAD by using full search algorithm and NRIT-SA
separately.
B

0.2

A

D

0.9
0.7

B

0.2

0.5

A

0.5

D

0.2
0.7

0.1

C

(a)

0.1

C

(b)

Fig. 8. An example of trust degree changes between two users.

First, depending on the full search algorithm, the trust
𝑓𝑢𝑙𝑙
degree that A trust in D, indicated as 𝑑𝐴𝐷 , can be calculated
𝑓𝑢𝑙𝑙
as 𝑑𝐴𝐷 = dAB×dBD + dAC×dCB×dBD + dAC×dCD. And based on
𝑓𝑢𝑙𝑙
this formula, the result of 𝑑𝐴𝐷 is 0.485 and 0.24 in Fig. 8 (a)and (b) respectively. From these results, we know that thechange of trust degree dCB can lead to a distinct change of𝑓𝑢𝑙𝑙
𝑑𝐴𝐷 . Second, according to the proposed NRIT-SA, the trust
𝑁𝑅𝐼𝑇
degree that user A trust in user D is indicated as 𝑑𝐴𝐷, which𝑁𝑅𝐼𝑇
can be calculated as 𝑑𝐴𝐷 = dAB×dBD + dAC×dCD. And
𝑁𝑅𝐼𝑇
depending on this formula, the values of 𝑑𝐴𝐷
are the samein Fig. 8 (a) and (b), which equal to 0.17. From the results
shown above, we can see that the change of trust degree dCB
𝑁𝑅𝐼𝑇
will not impact the calculation result of 𝑑𝐴𝐷. In other words,
𝑁𝑅𝐼𝑇
for the calculation of indirect trust degree 𝑑𝐴𝐷, the trust
degree between user B and C is a uncorrelated value, which

Copyright © 2017 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 6, Issue 2, March, 2017
𝑁𝑅𝐼𝑇
changes cannot influence the result of 𝑑𝐴𝐷. In reality, thetrust between two users should not be affected greatly by thetrust among any other users. In other words, it is undesirable
that the trust between two users is strongly correlated to someuncorrelated trust values of others. As a result, depending on
the analysis above, it shows that the proposed NRIT-SA ismore conform to the reality than the full search algorithm.

998

reputation. Proc.2nd Int. Joint Conf. on Autonomous Agents andMultiagent Systems (AAMAS), 2003, pp. 69-75.
[10] Y. Gil and V. Ratnakar, Trusting information sources one citizen at atime. Proc. of the first International Semantic Web Conference. 2000,
pp. 162-176.
[11] S. Li, I. Doh, K. Chae, Non-Redundant Indirect Trust Search Algorithm
Based on a Cross-Domain Trust Model in Content Delivery Network.
Proc. of the 19th International Conference on Advanced
Communications Technology (ICACT). 2017.

V. CONCLUSION
In this paper, we propose a hybrid CDNi-P2P architecture,
an NRIT search algorithm, and two trust models: a local trustmodel and a cross-domain trust model. Based on the proposed
NRIT-SA and trust models, a user can calculate his/her local
trust more effectively and accurately, and a mobile user cantransform his/her local trust into mobile trust that can be taken
to and used in a new domain. The proposed models can avoid
disparate trust values for a single user in different domains
and improve the availability of content possessed by mobile
users as they move among different domains. And from theperformance result, we know that the peak value of thecalculation time appears around 20% of connectivity degree,
and along with the increase of the connectivity degree, thecalculation time will decreases exponentially. And when theconnectivity degree is more than 40%, the calculation time
tends to be stable, which value is around 1s. From thecomparison result with the full search algorithm, we can seethat our NRIT-SA shows more efficient calculation
performance and more reliable indirect trust result. In the
future, we will research more available cross-domain trustmodels for different network architectures.
ACKNOWLEDGMENT
This work was supported by the National Research
Foundation of Korea (NRF) grant funded by the Korea
government (MSIP) (2016R1A2B4015899). It is also
partially supported by the State Scholarship Fund organizedby the China Scholarship Council (CSC). Kijoon Chae is thecorresponding author of this paper.

Shi Li received the B.S. degree in the Department ofcomputer science and engineering from Harbin Instituteof Technology, China in 2010. She is currently a Ph.D.
candidate in the Department of computer science andengineering at Ewha Womans University, Seoul, Korea.
Her research interests include sensor network security,
smart grid security and content delivery network
security.
Inshil Doh received the B.S. and M.S. degrees in
Computer Science at Ewha Womans University, Korea,
in 1993 and 1995, respectively, and received the Ph.D.
degree in Computer Science and Engineering from
Ewha Womans University in 2007. From 1995-1998,
she worked in Samsung SDS of Korea to develop amarketing system. She was a research professor of
Ewha Womans University in 2009~2010 and of
Sungkyunkwan University in 2011. She is currently an
assistant professor of Computer Science and Engineering at Ewha Womans
University, Seoul. Her research interests include wireless network, sensor
network security, and M2M network security.
Kijoon Chae received the B.S. degree in mathematics
from Yonsei University in 1982, an M.S. degree incomputer science from Syracuse University in 1984,
and a Ph.D. degree in electrical and computerengineering from North Carolina State University in1990. He is currently a professor in Department ofComputer Science and Engineering at Ewha Womans
University, Seoul, Korea. His research interests are
network security including sensor network, smart grid,
CDN, SDN and IoT, and network protocol design and performance
evaluation.

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