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 ⓒ 2016 GiRI (Global IT Research Institute)Dr. Youssef SAID, Tunisie Telecom, Tunisia
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Copyright ⓒ 2016 GiRI (Global IT Research Institute)Dr. Il Kwon Cho, National Information Society Agency, Korea
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Dr. Ting Peng, Chang'an University, China
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Copyright ⓒ 2016 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 ⓒ 2016 GiRI (Global IT Research Institute)

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Copyright ⓒ 2016 GiRI (Global IT Research Institute)Volume. 5 Issue. 3

1 Renewable Microgrid State Estimation using the Internet of Things Communication Network

6J

823

Md Masud Rana, Li Li
Faculty of Engineering and Information Technology University of Technology Sydney, Broadway, NSW 2007,
Australia

2 A Distributed Cloud based Video Storage System with Privacy Protection

830

Kang Il Choi, Jung Hee Lee, Bhum Cheol Lee
Network Computing Convergence Lab., ETRI, Daejeon, Korea

3 A Flexible FPGA-to-FPGA Communication System

836

An Wu*, Xi Jin*, Xueliang Du**, ShuaiZhi Guo*
*Department of Physics, University of Science and Technology of China, Hefei, Anhui Province, China**Department of System verification, Chinese Academy of Science Institute of Automation, Beijing, Beijing
Province,China

4 CampusSense - A Smart Vehicle Parking Monitoring and Management System using ANPR Cameras

844

and Android Phones
Pongsatorn Sedtheetorn, Tatcha Chulajata
Department of Electrical Engineering, Faculty of Engineering, Mahidol University 25/25 Phuttamonthon 4 Road,
Salaya, Nakornpathom, Thailand

5 Your Neighbors Are My Spies: Location and other Privacy Concerns in GLBT-focused Location-based

851

Dating Applications
Nguyen Phong HOANG, Yasuhito ASANO, Masatoshi YOSHIKAWA
Department of Social Informatics, Graduate School of Informatics, Kyoto University Yoshida-Honmachi, Sakyo-ku,
Kyoto 606-8501, Japan

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 3, May 2016

823

Renewable Microgrid State Estimation using theInternet of Things Communication Network
Md Masud Rana, Li Li
Faculty of Engineering and Information Technology
University of Technology Sydney, Broadway, NSW 2007, Australia
Email: 11766084@student.uts.edu.au, mrana928@yahoo.com

Abstract—Given the huge concerns all over the world regarding carbon emissions from fossil fuels, energy crisis andglobal warming, the renewable distributed energy resources(DERs) are going to be integrated in electricity grids, which will
make the energy supply more reliable and decrease transmission
losses. Regrettably, one of the main practical defies in smart
grid planning, control and operation with DERs is the voltage
regulation at the distribution field level. This problem motivatesthe deployment of sensors and actuators in electricity grids sothat the voltage regulation can be controlled at the desired level.
To do that the measurements from the renewable microgrid state
information is transmitted to an energy management center via
the internet of things (IoT) based communication network. In
other words, the proposed IoT communication infrastructure
provides an opportunity to address the voltage regulation challenge by offering the two-way communication links for microgrid
state information collection and estimation. Based on this smart
grid communication infrastructure, we propose a Kalman filter
based state estimation method for voltage regulation of the
microgrid. Finally, the effectiveness of the Kalman filter basedstate estimation method is illustrated using the linear state spacemodel of a microgrid incorporating DERs.

patterns [3]. Therefore, an unacceptable voltage level may
frequently occur at the point common coupling (PCC). Thiscan lead to over-voltage or under-voltage problems for the
power network, with undesired voltages appearing at busesof the distribution power network [4], [5]. Driven by thisfactors, voltage regulators should be installed at planned
positions of the distributed feeders [2], [5]. Remarkably,
the bidirectional smart grid communication infrastructure
between the microgrid and the energy management centercan be leveraged to facilitate voltage regulation issues [4].
The key concepts of such intelligent energy management
systems are parallel to those of the internet of things (IoT)
which can exploit reasonable security and privacy of DERs
measurements, seamless interoperability and far-reachingconnectivity. To accomplish the goals, the fifth generation(5G) communication network will be the future infrastructureassisting the objectives of the IoT.

Index Terms—Communication network, distributed energy
resource, internet of things, Kalman filter, microgrid, smart grid,state estimation.

A. Related Research

I. I NTRODUCTION
All over the world, the global warming in one of the
major concerns. The key reason behind is the dramaticallyswelling greenhouse gas emissions from burning fossil fuels
and vehicles [1]. In order to diminish this problem, therenewable distributed energy resource (DER) is considered as
one of the future electricity generation units [1], [2]. Basedon the incentives from governments all over the world, thepenetration of DERs is growing promptly. Thus, electricity
consumer are participating in the eco-aware global community
and the excess amount of energy is sell to the smart grid.
Nevertheless, there are significant technical challenges arisein the planning, operation and control of DERs, due to therandomness and weather-dependence in the power generation
Manuscript received Sept. 29, 2015. Part of this work is published in the
12th International Conference on Information Technology-New Generations.
This is an invited paper from the ICACT-TACT Journal.
Md Masud Rana Faculty of Engineering and Information Technology, University of Technology, Sydney, NSW 2007, Australia ( Corresponding author
to provide phone: +61470352998; e-mail: 11766084@student.uts.edu.au)
Li Li are with Faculty of Engineering and Information Technology, University of Technology, Sydney, NSW 2007, Australia.

Based on the information and communication network, thesmart grid can spread the intelligence from the central control
center to the distributed control center, thus enabling accurate
state estimation (SE) and wide-area real-time monitoring of
the renewable energy sources [6] [7]. First of all, powersystem SE often practices the weighted least square methodthat minimizes the sum square of the weighted residuals
errors; however, the gain matrix may be ill-conditioned [8].
Later, a comparison between the extended Kalman filter (EKF)
and nonparametric belief propagation (NBP) has been implemented for distributed dynamic state estimation [9]. Morespecially, a NBP method to compute the power system stateis developed, showing that the performance of the NBP isbetter than that of EKF algorithm. In [10], [11], a factor
graph based message-passing algorithm for power system state
estimation is proposed. Generally, the factor graph entails ofvariable and factor nodes. The factor nodes are the logicalrepresentation of the sensor measurement, whereas the variablenodes do not exists actually [10]. The information can beprocessed and passed between the variable and factor nodes
with definite sum product rules [10], [11]. A BP algorithm
has interesting structural properties corresponding to nonlinear feedback dynamical systems in the context of decoding
the received signal [12]. Generally, the BP based statisticalestimation techniques can provide a better performance if there
Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 3, May 2016

B. Key Contributions

II. A RCHITECTURE AND VISION OF THE I OTThe IoT is a vision that encompasses and surmounts several
technologies at the confluence of power systems, information technology, medicines, nanotechnology and biotechnology[16], [17]. In fact, the application scenarios of the IoT indiverse areas is illustrated in Fig. 1. The IoT has been
considered as the latest revolution in the digital technology
after the invention of computers and the internet [16], [18].
From the aspect of electricity network, it brings major benefits
to the smart grid infrastructure design. Technically, it represents a world-wide network of heterogeneous things such as
smart devices, smart objects, smart sensors, smart actuators,
radio frequency identification (RFID) tags and readers, global
positioning systems (GPS) and embedded computers [18].
Such things can be deployed and exploited in different physicalenvironments to support diversified cyber physical applications such as information collection, information processing,identification, control and actuation [18], [19]. For clarify ofwind power.
PHEV charging,vehiclesmonitoring,railway track
monitoring,
hydrant,
e-payments andnavigation

Manufacturing, security,
disaster prevention,
emergency signal, ventilation,
contamination, water and gas
monitoring

Computing,networking

Smart grid

Smarttransport

Internetof things

E-health

Military

Agriculture

WeatherSpy monitoring, aeroplanesmonitoring, missiles control andnavigation, location andassessment

Fig. 1.

DERs monitoring, smart
meters reading, smart
inverters, substations, EMS,
control systems, transmission
and distribution lines

Industrial
automation

Smart farming, irrigation,water pipe monitoring,
environment monitoring,
sensors based taps andwater supply

Diagnostics(ECG, EEG,
EMG, bloodpressure)assisting topatients,
devicesmonitoring

Weather forecasting,precipitation, flood prevention
and earthquake

The application scenarios of the IoT [16].

understanding, Fig. 2 shows the information flow between the
cyber and physical space using the IoT infrastructure. It can
Cyber space

Information flow
SCADA

Control center
APPs

Data
center

Process
EMS

ServiceWeb

Ubiquitous internet
5G network
S
e
n
s
o
r

Actionable
information

This paper proposes an approach for microgrid state estimation using the IoT networks. First of all, a renewable microgrid
incorporating multiple DERs is modelled as a continuous
linear state space model. This model is transformed to thediscrete linear state space system considering the uncertainty.
Then the smart sensors are positioned around the microgrid toobtain the measurements. Afterward, the measurements fromthe microgrid is transmitted to an energy management centervia the IoT based 5G communication network. This IoT communication technology affords an opportunity to address theestimation challenge by offering the two-way communication
links for microgrid state information collection and estimation.
Based on this smart grid communication infrastructure, we
propose a KF algorithm for state estimation. The effectivenessof the KF method is verified by numerical simulations using
a microgrid incorporating DERs.
The remaining of this manuscript is organized as follows.
The fundamental description of the IoT and its vision is
described in Section II. Section III explores the IEEE 4-bus
distribution system with microgrid model and IoT communication network. In addition, the proposed KF based dynamic SE
scheme is described in Section IV, followed by the simulation
results and discussions in Section V. This is followed by theconclusion in Section VI.
Notation: Bold face lower and upper case letters are used
to represent vectors and matrices, respectively; and I is theidentity matrix.

Smart phones, tablets, PCs,
laptops, electronic health record,
database, IoT server, businessorder and identification

Sensing data

is no cycle in the graph [13]. This method can converge to theactual system states in the tree like configuration. When cycles
are present in the graph, the technique may cause fluctuationand the estimated state may diverge from the actual state[13], [14].Furthermore, the ensemble KF approach uses theprobability distribution function of the system state and thedata likelihood [15]. Due to the use of stochastic measurement
rather than of the whole available data set, it is computationally
faster and performs satisfactorily for highly stochastic systems.

824

Actuator

PhysicalspaceFig. 2. Information flow between the cyber and physical space using the
IoT with 5G networks [18].

be seen that the information produced in the physical spaceis transmitted to the cyber space for interpretation, which inturn affects the physical environment such as plug in hybrid
electric vehicle and smart grid communications [18].
Due to the economic, environmental as well as technicalreasons, the energy sector has a growing awareness of smart
grid technologies to enhance the efficiency and reliability ofelectricity networks [1], [20]. From this perspective, renewable DERs such as solar cells, photovoltaic arrays and windturbines, have been integrated into the grid in the form ofsmart distribution grids. From the aspect of smart devices andsmart metering, they play a vital role for remote monitoringand power systems’ state estimation [21]. The reliable state
estimation is a key technique to fulfil the automation of power
grids. In order to monitor the DER state, the proposed IoT
Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 3, May 2016

based communication network architecture for sensing the
DER states describes in the next section.
III. DER S M ODEL AND I OT C OMMUNICATION N ETWORKThis section illustrates the multiple DERs model that isconnected to the IEEE 4-bus distribution system, observation
model, uniform quantization and IoT communication systems.
A. DERs Connected to the IEEE 4-bus distribution system
Fig. 3 shows IEEE 4-bus distribution test feeders that areinterfaced to the local load through converter [22]. We adopt

where x(t) = vs − vref is the PCC state voltage deviation,
vref is the PCC reference voltage, u(t) = vp − vpref is the
DER control input deviation, vpref is the reference control
effort, n(t) is the zero mean process noise whose covariancematrix is Qn , the state matrix A and input matrix B are given
by:


175.9
176.8
511
103.6
 −350

0
0
0

(4)
A=
−544.2 −474.8 −408.8 −828.8  ,−119.7 −554.6 −968.8 −1077.5


I4

Bus
4

Bus
3

I3

Bus
2

I2

I1

0.8
 −350B= −69.3−434.9

Bus
1

Loads

Infinite bus
V4

V3

V2

V1

825

334.2
0
−66.1
−414.2525.1
0−420.1−108.7

−103.60.
−828.8 −1077.5

The continuous state space model (3) can be written into
the following discrete state space form:x(k + 1) = Ad x(k) + Bd u(k) + nd (k),Fig. 3.

(5)(6)

An illustration of the IEEE 4-bus distribution system.

the model of interconnected DERs from [23], [22], as shownin Fig 4. It is assumed that four DERs are modelled aswhere Ad = I + A∆t, ∆t is the discretization step value,
Bd = B∆t and nd (k) = ∆tn(k) with the variance Qnd [24],[25]. The smart sensors can sense the microgrid states to form
an observer model as follows:

Networky(k) = Cx(k) + w(k),

v1
Lc1

PCC

PCC

DER 1

v2
Lc2

DER 2

v3
Lc3

PCC

DER 3

v4
Lc4

DER 4

vp4

Input

vp1

Input

Fig. 4.

Four DERs are connected to the power network [22].

vp2

Input

vp3

Input

PCC

voltage sources whose input voltages are denoted by vp =(vp1 vp2 vp3 vp4 )T , where vpi is the i-th DER input voltage.
The four DERs are connected to the main power network at
the corresponding Point of Common Coupling (PCCs) whose
voltages are denoted by vs = (v1 v2 v3 v4 )T , where vi is thei-th PCC voltage. In order to maintain the proper operation of
DERs, these PCC voltages need to be kept at their reference
values. A coupling inductor exists between each DER andthe rest of the electricity network. Now applying the Laplacetransformation in this microgrid to obtain the nodal voltage
equations. The nodal voltage equation is given by:1
vp (s),(1)
Y(s)vs (s) = L−1s cwhere Lc = diag(Lc1 , Lc2 , Lc3 , Lc4 ) and Y(s) is theadmittance matrix of the power network. Based on the typicalassumptions of the IEEE 4-bus distribution feeder [22], theadmittance matrix is given in (2). Now we can transform the
Laplacian form into the linear state space model. The brief
conversion can be found in [22]. Normally, the dynamic of
the physical state space system is given by:ẋ(t) = Ax(t) + Bu(t) + n(t),

(3)

(7)

where y(k) is the measurement, C is the measurement matrix
and w(k) is the measurement noise whose variance is Qwd .
The observation noise comes from the distributed wirelesssensors measurements. The observation information by thewireless sensor networks (WSN) powered by 5G technologies
is transmitted to the nearby base station (BS) as shown in
Fig. 5. The uniform quantizer of this base station maps eachmeasurement signals to a sequence of bits.

State estimator
Wireless smart
sensors
Communication
network

Smart university

Fig. 5.

Base station

Snapshot of
the system

Smart house

Interfacing microgrid measurements to the base station.

B. IoT Communication Network
For transmitting the microgrid state information to theenergy management system, we used binary phase shift keying(BPSK) as a modulation technique. The bit sequence b(k) isgoes through a BPSK and get modulated signal s(k). The s(k)goes through the internet and add noise. To demonstration,
Fig. 6 shows the IoT communication network and dynamic
state estimation process. The received signal at the energy
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 3, May 2016

10.1750+0.0005s−1
0.1750+0.0005s
Y(s) = (Lc s)−1 + −10.1750+0.0005s



0
0

826

0

1
1
0.1750+0.0005s + 0.1667+0.0004s−10.1667+0.0004s

0−10.1667+0.0004s
1
10.1667+0.0004s + 0.2187+0.0006s−10.2187+0.0006s



0
0


.


−1
0.2187+0.0006s

110.2187+0.0006s + 12.3413+0.0148s

(2)

and

x(k+1) =Ad(k)x(k)+ Bd(k)u(k)+n(k)Uniform
quantizationx(k) y(k)=Cx(k)+w(k)

y(k)

b(k)

Noise e(k)

s(k)

+

r(k)=s(k)+e(k)State feedback
controllerState estimatoryrd (k)Dequantization

P(k) = P− (k) − K(k)CP− (k).

Modulation(BPSK)

Internet

Measurement
using sensors

DERs

Demodulation(13)

Based on the KF steps, the energy management system canobtain the predicted distribution of the measurement which isa Gaussian distributed with the expectation given by Eq. (9)and the covariance matrix given by Eq. (10). Considering the
aforementioned expressions taking into account, the flow chart
for the KF state estimation algorithm is sketched in Fig. 7.

Fig. 6.
The IoT communication network and microgrid dynamic state
estimation process.

Initialize parameters

management system is given byr(k) = s(k) + e(k),(8)

Obtain yrd from the proposed communication
systems

where e(k) is additive white Gaussian noise AWGN noise.
Then the received sequence is followed by dequantization andfinally KF algorithm is used for this microgrid.

Compute the predicted state estimationand covariance matrix using (9) and (10)

IV. K ALMAN FILTER BASED MICROGRID STATEThis section tries to answer the following question: (i)What is the optimal smart grid SE method for the microgrid
incorporating multiple DERs?
The discrete time KF is a set of recursive mathematicalequations that provides an efficient recursive means to estimate
the state of a process in a way that minimizes the mean squared
error between the measurement and prediction. The KF operates recursively on streams of the noisy measurers to produce
a statistically optimal estimate of the underlying microgrid
system states. This method works in two-steps (prediction andcorrection step). The energy management system computes thefollowing steps [26]:x̂− (k) = Ad x̂(k − 1) + Bd û(k − 1),−

P (k) = Ad P

(k − 1)ATd + Qnd ,Compute Kalman gainusing (12)Update state estimation using (11)Update covariance matrix using (13)(9)

where x̂− (k) is the microgrid estimate states of the earlier
step. The predicted estimate covariance matrix is given by:−k = k+1

ESTIMATION METHOD

Fig. 7. The flow chart for the Kalman filter based microgrid state estimation.

(10)

−

where P (t) is the microgrid estimate covariance matrix of theearlier step. The microgrid estimated update state (correctionstep) is given byx̂(k) = x̂− (k) + K(k)[yrd (k) − Cx̂− (k)],(11)

where yrd (k) is the dequantized and demodulated output bit
sequences and the Kalman gain K(t) is given by:
K(k) = P− (k)CT (CP− (k)CT + Qwd )−1 ,(12)

V. P ERFORMANCE E VALUATIONS AND D ISCUSSIONS
We consider four DERs in which the system state is afour-dimensional vector. Each DER is connected to the IEEE
4-bus distributed systems operated as an island mode [27].
The continuous state space system has been approximated tothe discrete time state space system with a small step size
parameter. The simulation parameters of the IoT networks are
summarized in Table I. The simulation of this proposed KF
Copyright ⓒ 2016 GiRI (Global IT Research Institute)

35

30

30

25

25

State deviation, volt

State deviation, volt

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 3, May 201620
True state ∆v1

15

Estimation state
10

True state ∆v

3

Estimation state
20
15
10
5

5
0
0

827

5

10

15

20

25
30
Time slot k

35

40

45

0
0

50

Fig. 8. ∆v1 comparison between the true and estimated state using 4 sensors.

5

10

15

20

25
30
Time slot k

35

40

45

50

∆v3 comparison between the true and estimated state using 4

Fig. 10.
sensors.

20

15
State deviation, volt

State deviation, volt

40

True state ∆v2

10

Estimation state
5

0
0

35

True state ∆v

30

Estimation state

4

25
20
15
10
5

5

10

15

20

25
30
Time slot k

35

40

45

50
0
0

5

10

15

20

25
30
Time slot k

35

40

45

50

Fig. 9. ∆v2 comparison between the true and estimated state using 4 sensors.
∆v4 comparison between the true and estimated state using 4

Fig. 11.
sensors.

based microgrid SE for the IoT communication network is
carried out for two different sensing scenarios.

35
30

T HE SYSTEM PARAMETERS AND ASSUMPTIONS USING M ATLAB .
Parameters
Step size ∆tModulation
Time slots

Values0.00001
BPSK
50

Parameters
Quantization
ChannelQuantization

Values
Uniform
AWGN
Uniform 16 bits

State deviation, volt

TABLE I

25
20

Estimation state
10
5
0
0

First of all, it assumes that the four voltage sensors cansense the four PCC voltage states directly. From the simulation
results as shown in Figs. 8 to 11, it can be seen that the
proposed KF is able to estimate the PCC state voltages
properly and it needs few iterations to track the original states.
However, in practical scenario there are the possibility thatthe smart sensors battery gets low so that they cannot sense
the system state properly.

10

15

20

25
30
Time slot k

35

40

45

50

20

B. Number of sensors less than states
When the smart sensors battery gets low and it cannot sense
the system state properly. In this case, one assumes that thetwo voltage sensors are out of order among four sensors tosense the four PCC voltage states. From the simulation results
as shown in Figs. 12 to 15, it can be seen that the proposed
KF is able to estimate system states properly and it needs
more iterations to track the original states. Due the two sensors
sensing problems, the PCC state voltages v2 and v3 are not

5∆v1 comparison between the true and estimated state using 2

Fig. 12.
sensors.

State deviation, volt

A. Number of sensors equal to states

True state ∆v1

15

15

10

5

0
0

Fig. 13.
sensors.

True state ∆v2
Estimation state

5

10

15

20

25
30
Time slot k

35

40

45

50

∆v2 comparison between the true and estimated state using 2

able to be sensed directly by the corresponding sensors. But
the KF is able to track these states with small considerable
errors. However, there are the possibility that the number of
Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 3, May 2016

30

35
True state ∆v

25

3

30

Estimation state
20

25
15

State deviation, volt

State deviation, volt

828

10
5
0
0

5

10

15

20

25
30
Time slot k

35

40

45

20

Estimation state

10

50

5

∆v3 comparison between the true and estimated state using 2

Fig. 14.
sensors.

True state ∆v1

15

0

0

10

20

30

40

50

Time slot k
40State deviation, volt35

∆v1 comparison between the true and estimated state using 8

Fig. 16.
sensors.

True state ∆v4

30

Estimation state

25
20

20

15

18
10

16
5

Fig. 15.
sensors.

14
5

10

15

20

25
30
Time slot k

35

40

45

State deviation, volt

0
0

50

∆v4 comparison between the true and estimated state using 2

12
True state ∆v2

10

Estimation state

8
6
4

smart sensors is greater than the system states.

20

C. Number of sensors greater than states

10

20

30

40

50

Time slot k∆v2 comparison between the true and estimated state using 2

Fig. 17.
sensors.

40
True state ∆v

35

4

Estimation state30

State deviation, volt

Finally, we assume that the number of sensors is greaterthan the system states. This simulation it is assumed thatthe observation matrix has eight sensors. From the simulation
results as shown in Fig. 16 to 19, it can be seen that the
proposed KF is able to estimate system states properly andit needs few iterations to track the original states. Due themore sensors, the PCC voltage are not able to sense by thethe sensors correctly. But using the proposed KF is able to
track these states with very small errors. Therefore, it is better
to use same number of sensors and states to properly estimate
system states in the IoT communication network.

0

25
20
15

VI. C ONCLUSIONS

10This paper addresses the voltage regulation issue from thecommunication perspective. To do so, wireless sensor network
components such as sensors and actuators have been applied
into the microgrid to coordinate DER states regulation. In
order to transmit the sensing information to the observer,
the proposed innovative communication systems have beenutilized. Based on this infrastructure, this paper proposes a
KF algorithm for centralized DER state estimation. Finally,
the effectiveness of the developed approaches is verified bynumerical simulations. In the future, we will use least square
based Kalman filter in order to obtain the better initial statevalue.

5
0

0

10

20

30

40

50

Time slot k

Fig. 18.
sensors.

∆v3 comparison between the true and estimated state using 8

R EFERENCES[1] X. Zhang, W. Pei, W. Deng, Y. Du, Z. Qi, and Z. Dong, “Emerging smart
grid technology for mitigating global warming,” International Journalof Energy Research, vol. 39, no. 13, pp. 1742–1756, 2015.

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 3, May 201630
True state ∆v3
Estimation stateState deviation, volt

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∆v4 comparison between the true and estimated state using 8[2] R. Mao and H. Li, “Nobody but you: Sensor selection for voltage
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of the INFOCOM. IEEE, 2014, pp. 2652–2660.
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[6] N. Kayastha, D. Niyato, E. Hossain, and Z. Han, “Smart grid sensor
data collection, communication, and networking: A tutorial,” WirelessCommunications and Mobile Computing, 2012.
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Y. Lee, and X. Yu, “Smart grid technologies for autonomous operationand control,” IEEE Transactions on Smart Grid, vol. 2, no. 1, pp. 1–10,
2011.
[8] Y. Wang, P. Yemula, and A. Bose, “Decentralized communication andcontrol systems for power system operation,” IEEE Transactions on
Smart Grid, December 2014.
[9] P. Chavali and A. Nehorai, “Distributed power system state estimation
using factor graphs,” IEEE Transactions on Signal Processing, vol. 63,
no. 11, pp. 2864–2876, 2015.
[10] Y. Li, “Fully distributed state estimation of smart grids,” in Proc. of theInternational Conference on Communications, 2012, pp. 6580–6585.
[11] Y. Weng, R. Negi, and M. D. Ilic, “Graphical model for state estimationin electric power systems,” in Proc. of the International Conference on
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[12] B. Ruffer, C. M. Kellett, P. M. Dower, and S. R. Weller, “Belief
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IET Control Theory and Applications, vol. 4, no. 7, pp. 1188–1200,
2010.
[13] H.-A. Loeliger, J. Dauwels, J. Hu, S. Korl, L. Ping, and F. R. Kschischang, “The factor graph approach to model-based signal processing,”
Proceedings of the IEEE, vol. 95, no. 6, pp. 1295–1322, 2007.
[14] Y. Hu, A. Kuh, A. Kavcic, and D. Nakafuji, “Real-time state estimation
on micro-grids,” in Proc. of the International Joint Conference on Neural
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[15] P. K. Ray and B. Subudhi, “Ensemble Kalman filter based powersystem harmonic estimation,” IEEE Transactions on Instrumentation andMeasurement, vol. 61, no. 12, pp. 3216–3224, 2012.
[16] M. Yun and B. Yuxin, “Research on the architecture and key technology
of internet of things (IoT) applied on smart grid,” in Proc. of theInternational Conference on Advances in Energy Engineering, 2010,
pp. 69–72.
[17] Q. Chi, H. Yan, C. Zhang, Z. Pang, and L. Da Xu, “A reconfigurable
smart sensor interface for industrial WSN in IoT environment,” IEEE
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[18] I. Bojanova, G. Hurlburt, and J. Voas, “Imagineering an internet ofanything,” Computer, no. 6, pp. 72–77, 2014.
[19] J. Huang, Y. Meng, X. Gong, Y. Liu, and Q. Duan, “A novel deployment829

scheme for green internet of things,” Internet of Things Journal, vol. 1,
no. 2, pp. 196–205, 2014.
[20] J. Gaoa, Y. Xiao, J. Liu, W. Liang, and P. Chenc, “A survey ofcommunication and networking in smart grids,” Future Generation
Computer Systems, vol. 28, pp. 391–404, 2012.
[21] A. G. Exposito, A. Abur, A. D. L. V. Jaen, and C. G. Quiles, “A
multilevel state estimation paradigm for smart grids,” Proc. of the IEEE,
vol. 99, no. 6, pp. 952–976, 2011.
[22] H. Li, L. Lai, and H. V. Poor, “Multicast routing for decentralized
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1097–1107, 2012.
[23] H. Li, F. Li, Y. Xu, D. T. Rizy, and J. D. Kueck, “Adaptive voltage
control with distributed energy resources: Algorithm, theoretical analysis, simulation, and field test verification,” IEEE Transactions on Power
Systems, vol. 25, no. 3, pp. 1638–1647, 2010.
[24] E. Ghahremani and I. Kamwa, “Online state estimation of a synchronous
generator using unscented Kalman filter from phasor measurements
units,” IEEE Transactions on Energy Conversion, vol. 26, no. 4, pp.
1099–1108, 2011.
[25] D. Buchstaller, E. C. Kerrigan, and G. A. Constantinides, “Sampling
and controlling faster than the computational delay,” IET control theory
and applications, vol. 6, no. 8, pp. 1071–1079, 2012.
[26] D. Simon, Optimal state estimation: Kalman, H infinity, and nonlinear
approaches. New Jersey: John Wiley and Sons, 2006.
[27] M. M. Rana and L. Li, “Kalman filter based microgrid state estimation
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Md Masud Rana is in school ofElectrical, Mechanical and Mechatronic
Systems at University of Technology
Sydney, Australia. His research interests
are in the theoretical and algorithmic
studies in signal processing andoptimizations, statistical learning andinferences for high dimensional data,
distributed optimizations and adaptivealgorithms, as well as their applications
in communications, networked systems and smart grid.
Li Li received Ph.D degree from the
University of California, USA. He is servicing as a senior lecturer in school ofElectrical, Mechanical and Mechatronic
Systems at University of Technology Sydney, Australia. His current research interests are robust control systems, distributedmodel predictive control of power systems, model reduction of power systems,
control on microgrids, vehicle-to-grid coordination method and smart grid market.

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 3, May 2016

830

A Distributed Cloud based Video Storage
System with Privacy Protection
Kang Il Choi, Jung Hee Lee, Bhum Cheol Lee
Network Computing Convergence Lab., ETRI, Daejeon, Korea
forerunner@etri.re.kr, jhlee@etri.re.kr, bclee@etri.re.kr
Abstract— In this paper, we present a novel method to protect
privacy information for the Cloud based Video Storage System(CVSS), which not only distribute the CVSS geographically, butalso use different privacy protection key algorithm for different
CVSS to encode/decode the actual video data itself stored in thestorage with the subscriber key for the privacy protection sothat the system even under the hacking of the CVSS systems,
still provide the privacy protection of the video data. We present
how this system stores the video stream data transferred fromthe network connected camera such as IP CCTV. As the system
receives the video stream, it masks the privacy related part, suchas facial information or car plate number and so on, of the video
stream data with a scrambling key and it also encrypts thescrambled video stream data with an encryption key. In thispaper, we also present how the system retrieves the privacy
information protected video image requested by either the
network control centre or end users. When the network control
centre or end user requests the networked video, it retrieves thecorresponding video image from the video storage first. Then, it
decrypts the image with the decryption key and unscrambles thedecrypted image with the unscrambling key. Then it transfers
the network video back to the network control center or to theend-user. We present the architecture of the distributed CVSS
with the privacy protection for the Cloud based Network
Function Virtualization System. We also provide flowchart forreceive/transmit operation of the DCVSS. Finally, we present a
POC implementation of the distributed CVSS in the Cloudbased Network Function Virtualization System.
Keyword— Cloud Video Storage System, Privacy Protection

I. INTRODUCTIONI

n general, components of a video data storage system, acamera, a monitor, a video recording device, are connectedto each other over a closed network to monitor the facility
such as a predetermined building or convenience facility.
In this case, a user agent is present in a user device toprovide video data to the user device. Accordingly, a camera,———————————————————————
Manuscript received January 29, 2016. This work was supported by
Institute for Information & communications Technology Promotion(IITP)
grant funded by the Korea government(MSIP) (B0101-15-233, Smart
Networking Core Technology Development)
Kang Il Choi is with the Electronics Telecommunication ResearchInstitute, Daejeon, South Korea (phone: +82-42-860-1704; mobile:+82-10-3861-7758; fax: +82-42-860-5213; e-mail: forerunner@etri.re.kr).
Jung Hee Lee is with the Electronics Telecommunication ResearchInstitute, Daejeon, South Korea (e-mail: jhlee@etri.re.kr).
Bhum Chul Lee is with the Electronics Telecommunication ResearchInstitute, Daejeon, South Korea (e-mail: bclee@etri.re.kr).

a server, and the user device control the camera throughmutual interface communication.
Also, video data photographed from the camera is recorded
in a storage device within the server and is also transferred toa viewer of the user device.
The above system is generally configured as a closed
network among the camera, the server, and the user device forcontrolling the camera and storing the video data.
In the closed network, the user agent operating in the user
device obtains state information of the camera by transmitting
a command to the camera through the server, and the cameratransfers current state information to the user agent throughthe server.
A camera server provides interface communication
between the user agent and the camera. Also, the camera
server enables a user to store an image through the user agent
or enables the server to execute a command, such as
displaying the image on the viewer in lieu of the user.
When the user desires the image to be displayed on theviewer of the user device, the image transferred to the serveris transferred to the user device.
When the user desires to store the image in the storage
device, the image transferred to the server is stored in thestorage device within the server.
However, a camera monitoring system connected using theclosed network has constraints in extensibility. In addition,
when the user is not directly connected to the closed network,
it is difficult to provide a function capable of controlling thecamera through a general network (for example, the Internet)
or at least viewing or storing an image.
In this environment, providing the privacy protection is oneof the social issues because of several hacking accident ends
up with huge privacy information leaking. There are several
different approaches to cope with this privacy protection for
the video data storage system. However, almost all
approaches focus on the IP camera side to provide securetransmit of the video data collected by the IP camera [3-12].
In this paper, we propose a novel privacy protection
approach in the video data storage server side, which
encoding/decoding the actual video data itself stored in thestorage with the subscriber key for the privacy protection sothat the system even under the hacking of the CVSS system,
still provide the privacy protection of the video data.
The paper is structured as follows. Section II describes an
overview of the Cloud based Video Storage System (CVSS)
which supports Privacy Protection. We also describe theoperation of the CVSS with flowcharts so that how the system

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 3, May 2016

supports the privacy protection for the CVSS. In Section III,
we describe the architecture of the distributed CVSS(DCVSS) with the Privacy Protection for the Cloud based
Network Function Virtualization System. We also describe
the operation of the DCVSS with flowcharts so that how thesystem supports the privacy protection for the DCVSS.
Finally, In Section IV, we describe the POC implementationof the DCVSS with the Privacy Protection in the Cloud based
Network Function Virtualization System.
II. CLOUD BASED VIDEO STORAGE SYSTEM
Fig. 1 is a control block diagram illustrating a control
configuration of a Cloud based Video Storage System(CVSS) [1,2].
End Users

IP camera
IP cameraInternet...
IP camera

Network Camera
Control Center

Incoming
Incoming Video
Video Data
Data Privacy
Privacy Protection
Protection
Video
Privacy
Masking

Video Data
Encryption

Image
Storage

Privacy
Privacy Protection
Protection
Video
Privacy
UnMasking

Video
Data
Decryption

Outgoing
Outgoing Video
Video Data
Data Privacy
Privacy Protection
Protection

Fig. 1 Cloud Based Video Storage System

In Fig. 1, the CVSS system consists of an “Incoming Video
Data Privacy Protection (IVDPP)”, an “Outgoing Video Data
Privacy Protection (OVDPP)” block and Image Storage. The
IVDPP block creates a protected image by applying a privacy
protection algorithm to an original image received in the
IVDPP block. The IVDPP block stores the protected image
into the Image Storage. The OVDPP block retrieves a
protected image from the Image Storage and creates theoriginal image by applying a privacy release algorithm to theprotected image.
In Fig. 1, the CVSS may be included in a device capable ofphotographing an image, for example, an imaging device suchas a network camera, a mobile communication terminal, and aclosed-circuit television (CCTV), or may be stored in astorage server, for example, a network server and a cloud
configured to store an image photographed by the imaging
device.
In Fig. 1, in the incoming direction, the IVDPP blockincludes “Video Privacy Masking (VPM)” block and “Video
Data Encryption (VDE)” block.
In Fig. 1, the VPM block detects the privacy image fromthe original image which is received from the remote video
capturing system. The VPM block detects the privacy image
based on at least one of the edge information of the original
image, shape information, color analysis information, andlearning information about a previous privacy image.

831

In Fig. 1, the VPM block obtains the edge information by
analyzing the original image and detects a boundary betweena face and a portion excluding the face using the edge
information.
It determines whether the face is present within the original
image by comparing the results of analyzing the original
image and shape information about a facial shape. Also, theVPM block compares the color analysis information withunique color distribution information of the face and thus,
may more accurately detect the face.
Additionally, the VPM block detects a facial area using
learning information about the previous privacy image,including an Adaboost learning scheme.
In Fig. 1, the VPM block performs masking by scrambling
the privacy image detected by the VPM block to a privacy
protection image using a set of scrambling keys. The VPM
block scrambles the privacy protection image to be expressedusing a predetermined color and a predetermined figure.
In Fig. 1, the VDE block creates a protected image in which
the privacy protection image input from the VPM block andthe general image is encrypted using an encryption key.
In Fig. 1, the VDE block may increase security about theprivacy protection image by encrypting the privacy protection
image and the general image. Then, the VDE block transfers
the protected image to the image storage.
The image storage block may assign a unique number
capable of recognizing the protected image input from the
VDE block and thereby stores the protected image.
The image storage transfers the protected image to the
OVDPP block in response to a control command of the
Network Camera Control Center or the End Users.
In Fig. 1, the OVDPP block operates relatively inversely to
the IVDPP block. In Fig. 1, in the outgoing direction, theOVDPP block includes a Video Data Decryption (VDD)
block and a Video Privacy Unmasking (VPU) block.
In Fig. 1, the VDD block decrypts the privacy protection
image and the general image from the protected image using adecryption key when the protected image is input from theimage storage. Here, the decryption key may be identical tothe encryption key, or may be another key capable ofperforming decryption and corresponding to the encryption
key.
In Fig. 1, the VPU block detects the privacy protection
image from the privacy protection image and the generalimage decrypted by the VDD block. The VPU block detects,
from the privacy protection image and the general image, theprivacy protection image that is expressed using at least oneof the predetermined color and the predetermined figure.
The VPU block detects the privacy protection image basedon at least one of the edge information of the original image
including the privacy protection image and the general image,shape information, color analysis information, and learning
information about a previous privacy image. In addition, the
VPU block detects the privacy protection image as an image
expressed using at least one of the predetermined colors andthe predetermined figure of the privacy protection image.
In Fig. 1, the VDD block unscrambles the privacy
protection image detected by the VPU block to the privacy
image using an unscrambling key. The unscrambling key may
be identical to the scrambling key, or may be another key
corresponding to the scrambling key.
In response to a control command of the Network Camera
Control Center or the End Users, the VDS block transmits the
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 3, May 2016

original image including the privacy image and the generalimage, or may transfer the original image to the Network
Camera Control Center or the End Users.
A. Receive Operation of the CVSS
Fig. 2 is a flowchart illustrating a receive operation methodof a Cloud Based Video Storage System (CVSS).
In Fig. 2, the CVSS determines whether a privacy image is
detected from an original image when the original image is
input, and scrambles the privacy image to a privacy protection
image using a scrambling key when the privacy image is
detected.
The original image may include at least one of the privacy
image including user information, for example, at least one of
facial information, license plate information, and privacy
information, and a general image excluding the privacy image.
Specifically describing, the CVSS determines whether theprivacy image is detected from the original image when theoriginal image is input.
In Fig. 2, the CVSS detects the private image based on atleast one of the edge information of the original image, shapeinformation, color analysis information, and learning
information about a previous privacy image.
In Fig.2, the CVSS obtains the edge information by
analyzing the original image M1, may detect a boundary
between a face and a portion excluding the face using the edge
information, and may determine whether the face is present
within the original image by comparing a result of analyzing
the original image and shape information about a facial shape.

When the privacy image is detected, the CVSS performs
masking by scrambling the detected privacy image to theprivacy protection image using a set of scrambling key.
After this operation, the CVSS creates a protected image inwhich the privacy protection image and the general image are
encrypted using an encryption key, and transfers the protected
image to the image storage.
When the privacy image is not detected from the original
image, the CVSS performs encryption operation on theoriginal image.
B. Transmit Operation of the CVSS
Fig. 3 is a flowchart illustrating an operation method of a
CVSS when an image is output from the CVSS.
In Fig. 3, when an image request signal requesting an
original image is input, the CVSS receives a protected image
stored in the image storage, and the CVSS decrypts theprotected image to an original image including a privacy
protection image and a general image using a decryption key
and detects the privacy protection image.
Network Camera Data Request by
Network Camera Control Center or
End Users
Retrieve Video DataNO

Privacy Protection
Enabled ?
YESReceive Network Camera
Video Data

NO

YES
NO

832

Video Encryption
Enabled ?
YESPrivacy Masking
Enabled?

Retrieve Decryption Key

YESRetrieve Scrambling Key

Video Data Decryption with
Decryption Key

Privacy Masking with
Scrambling Key

Privacy Masking
Enabled?

NO

YESVideo Encryption
Enabled ?

NO

Retrieve Unscrambling Key

YESRetrieve Encryption Key

Video Data Privacy UnMasking with
Unscrambling Key

Video Data Encryption with
Encryption Key

Send Video Data
to Network Camera Control Center

Store Video Data into Video Storage

Fig. 2 Receive Operation of the Cloud based Video Storage System

Also, the CVSS compares the color analysis information
with unique color distribution information of the face and thus,
may more accurately detect the face. Additionally, the CVSS
may detect a facial area using learning information about theprevious privacy image, including an Adaboost learning
scheme.

Fig. 3 Transmit Operation of the Cloud based Video Storage System

In response to a control command of the Network Camera
Control Center or the End Users and the image request signal
requesting the original image, the CVSS receives, from theimage storage, the protected image to which a privacy
protection algorithm corresponding to the original image isapplied among images stored in the image storage.
Next, the CVSS decrypts the protected image to theoriginal image which is including the privacy protection
image and the general image using a set decryption key.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 3, May 2016

The CVSS detects, from the original image, the privacy
protection image that is expressed using at least one of apredetermined color and a predetermined figure.
After this operation, the CVSS unscrambles the privacy
protection image to the privacy image using an unscrambling
key, and creates the original image including the privacy
protection image and the general image.
The CVSS creates the privacy image by unscrambling,using the unscrambling key, the privacy protection image
scrambled using a scrambling key.
Next, the CVSS creates the original image including theprivacy protection image and the general image, and transfers
the original image to the Network Camera Control Center orthe End Users or a predetermined device having input theimage request signal.

833

we can also provide separation of privacy protection in caseof hacking, so that hacking on the one DCVSS doesn’t affectthe other DCVSS.
A. Receive Operation of the DCVSS
Fig. 5 is a flowchart illustrating a receive operation methodof a DCVSS. In Fig. 5, if privacy protection and privacy
masking is enabled, then the DCVSS retrieve both personalscrambling key and site dependent scrambling key. And the
DCVSS do privacy masking the video data with combined(personal + site) scrambling key. If video encryption isenabled, the DCVSS retrieve both personal encryption key
and site dependent encryption key. And the DCVSS encrypt
the video data with combined (personal + site) encryption
key.
Receive Network Camera
Video Data

III. DISTRIBUTED CLOUD BASED VIDEO STORAGE SYSTEM
Fig. 4 is an architecture diagram illustrating a distributed
Cloud based Video Storage System (DCVSS).
NOEnd Users

YES

Network Camera
Control CenterNO
IP camera

IP camera...
IP camera...
IP cameraInternet...
IP camera...
IP camera

Privacy Protection
Enabled ?

Privacy Masking
Enabled?
YES
Retrieve (Personal + Site)
Scrambling Key
Privacy Masking with
(Personal + Site) Key

Privacy
Protection

CVSS with Privacy Protection...

Privacy
Protection

CVSS with Privacy Protection

Fig. 4 An architecture of the Distributed CVSSIn Fig. 4, each individual DCVSS process the video images
transmitted by a group of IP cameras, which is geographicallyclose to the DCVSS, so that it can provide privacy protection
for the video images generated by the group of the IP cameras.
In Fig.4, the Network Camera Control Center configure
which DCVSS will handle which IP cameras in terms of the
CVSS configuration and IP camera’s configuration, whichmeans the Network Camera Control Center knows who
process the specific IP cameras video image and provide theprivacy protection for the IP cameras.
When an end user request their own image, the network
camera control center re-route the end-users request to the
DCVSS who actually processed the image so that the DCVSS,
who actually process the end user’s IP camera, respond to theend-user’s request.
In Fig. 4, by distributing the DCVSS system
geographically so that the DCVSS process the video images
transmitted by a group of IP cameras which is geographicallyclose to the DCVSS, we can provide the Quality of Service
requirement from the end-user, such as low latency for thevideo image processing.
In Fig. 4, by applying separate privacy protection key
algorithm (which arithmetically identical but generate
different key output) per geographically separated DCVSS,

Video Encryption
Enabled ?

NO

YESRetrieve (Personal + Site)
Encryption Key
Video Data Encryption with
(Personal + Site) Encryption Key

Store Video Data into Video Storage
Fig. 5 Receive Operation of the DCVSS

B. Transmit Operation of the DCVSS
Fig. 6 is a flowchart illustrating transmit operation methodof a DCVSS. In Fig. 6, if privacy protection and video
encryption is enabled, then the DCVSS retrieve both personal
decryption key and site dependent decryption key. And the
DCVSS decrypt the video data with combined (personal + site)
decryption key. If privacy masking is enabled, the DCVSS
retrieve both personal unscrambling key and site dependent
unscrambling key. And the DCVSS unmasking the video datawith combined (personal + site) unscrambling key.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 3, May 2016

Network Camera Data Request from
Network Camera Control Center

Retrieve Video DataNO

Privacy Protection
Enabled ?
YESNOVideo Encryption
Enabled ?
YESRetrieve (Personal + Site)
Decryption Key

Video Data Decryption with
(Personal + Site) Decryption Key

Privacy Masking
Enabled?

NO

YES
Retrieve (Personal + Site)
Unscrambling Key

Send Video Data
to Network Camera Control Center
Fig. 6 Transmit Operation of the Cloud based Video Storage System

IV. AN POC IMPLEMENTATION OF THE DISTRIBUTED CVSS
IN THE CLOUD BASED NETWORK FUNCTION VIRTUALIZATIONSYSTEM

Fig. 7 is a POC implementation of the distributed Cloudbased Video Storage System (DCVSS) in the Cloud based
Network Function Virtualization System, which consist of a
DCVSS Control Center, Daejeon DCVSS and Seoul CVSS.
Seoul

DaejeonVideo
Imge
StorageSeoul DCVSS
with Privacy
Protection

Module, which running on the Servers, encrypts and decrypts
the IP camera images for privacy protection.
In Fig. 7, each DCVSS also apply separate key system forthe privacy protection algorithms. End users from Daejeon
areas access their video image through Daejeon DCVSS withprivacy protection, and the End users from Seoul area accesstheir video image through the Seoul CVSS.
In Fig. 7, there are two DCVSS system with privacy
protection, Daejeon DCVSS, which provide privacy
protection for the video images generated by the group of the
IP cameras located in the Daejeon area, and Seoul DCVSS,
which provide privacy protection for the Seoul areas
In Fig. 7, the DCVSS Control Center control the setup
process for each DCVSS and configure each DCVSS so thateach DCVSS not only process the video images from thegroup of IP cameras located geographically close to the
DCVSS but also use different privacy protection key
algorithm. The Daejeon DCVSS configured to process thegroup of IP cameras from the Daejeon area with privacy
protection key algorithm A, and the Seoul DCVSS configured
to process the group of IP cameras from the Seoul area withprivacy protection key algorithm B. With this configuration,even the Daejeon DCVSS hacked so that the video image of it
hacked, the video images in the Seoul DCVSS are safe andprovide privacy protection because these two DCVSS use
different privacy protection key algorithm.
V. CONCLUSION

Video Data Privacy UnMasking with
(Personal + Site) Unscrambling Key

Servers

834

DCVSS
Control Center

Daejeon DCVSS
with Privacy
Protection

In this paper, we presented a novel method to protect
privacy information for the Cloud based Video Storage
System (CVSS), which encoding/decoding the actual video
data itself stored in the storage with the subscriber key for theprivacy protection so that the system even under the hackingof the CVSS system, still provide the privacy protection of thevideo data. On the top of this, by distributing the CVSS
system geographically and by applying separate privacy
protection key algorithm, we can prevent huge privacy
information leaking and localize the privacy leaking. We
presented how this system stores/retrieves the video stream
data transferred from the network connected camera such as
IP CCTV to/from the cloud based video storage system. In thepaper, we presented the detailed procedures and algorithms
for these processes. In this paper, we masked the privacy
related part and encrypted the scrambled video with an
encryption key so that we protected the privacy information of
the networked video stream, which is stored in the cloud
based video storage system. We presented the architecture of
the distributed CVSS with the privacy protection for the
Cloud based Network Function Virtualization System. We
also provided flowchart for receive/transmit operation of the
DCVSS. Finally, we presented the implementation of the
distributed CVSS in the Cloud based Network Function
Virtualization System
REFERENCES

Fig. 7 An implementation of the Distributed CVSS in the Cloud
Network Function Virtualization System[1]

In Fig. 7, each DCVSS consists of Servers, which process
the IP camera network image, Video Image Storage, which
stores the IP camera network images, and Privacy Protection[2]

Kang Il Choi, Jung Hee Lee and Bhum Cheol Lee, “Cloud based Video
Storage System with Privacy Protection”, ICACT2015, 2015, pp.
448–451.
Kang Il Choi, Bhum Cheol Lee, Seung Woo Lee, Young Ho Park, Jung
Hee Lee and Sang Min Lee, “Image processing apparatus andoperation method thereof”, US20150055775 A1, Aug 20,2013

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 3, May 2016

835

[3]

DJ Neal and Syed Shawon Rahman, “VIDEO SURVEILLANCE IN
THE CLOUD?”, International Journal on Cryptography andInformation Security (IJCIS),Vol.2, No.3, September 2012[4] Yong-Hua
Xiong,
Shao-Yun
Wan,
Yong
He
and Dan Su, “Design and Implementation of a Prototype Cloud Video
Surveillance System”, Journal of Advanced Computational
Intelligence and Intelligent Informatics, Vol.18, No.1 pp. 40-47, 2014[5] Biao Song, Yuan Tian and Bingyin Zhou, “Design and evaluation of
remote video surveillance system on private cloud”, 2014 4thInternational Symposium on Biometrics and Security Technologies,
26 August 2014
[6] Chia-Feng Lin, Shyan-Ming Yuan, Muh-Chyi Leu and Ching-Tsorng
Tsai, “A framework for scalable cloud video recorder system insurveillance environment”, IEEE 9th International Conference on
Ubiquitous Intelligence and Computing and IEEE 9th International
Conference on Autonomic and Trusted Computing, UIC-ATC 2012,
Article number 6332062, Pages 655-660[7] Räty, T.D., “Survey on contemporary remote surveillance systems for
public safety”, IEEE Transactions on Systems, Man andCybernetics, art. no. 5422679, pp. 493-515[8] Kim, I.S., Choi, H.S., Yi, K.M., Choi, J.Y., Kong, S.G., “Intelligent
visual surveillance - A survey”, International Journal of Control,
Automation and Systems, 8 (5), pp. 926-939[9] Zhao, Z., Cui, X., Zhang, H., “Cloud storage technology in video
surveillance”, Advanced Materials Research 2012, 532-533, pp.
1334-1338[10] Rodríguez-Silva, D.A., Adkinson-Orellana, L., González-Castaño,
F.J., Armiño-Franco, I., González-Martínez, D., “Video surveillance
based on cloud storage”, 2012 IEEE 5th International Conference on
Cloud Computing, CLOUD 2012, art. no. 6253615, pp. 991-992.
[11] Hossain, M.S., Hassan, M.M., Qurishi, M.A., Alghamdi, A.,“Resource allocation for service composition in cloud-based video
surveillance platform”, Proceedings of the 2012 IEEE International
Conference on Multimedia and Expo Workshops, ICMEW 2012, art.
no. 6266418, pp. 408-412[12] Pearson, S., “Taking account of privacy when designing cloudcomputing services”, 2011 IEEE 3rd International Conference on
Communication Software and Networks, ICCSN 2011, art. no.
6014715, pp. 245-249
Kang Il Choi received B.S. degree in Computer
Science from KAIST, Korea and M.S. degree in
Computer Science from Sogang University in 1992
and 1994, respectively. He is currently seniorresearcher of Electronics and Telecommunications
Research Institute (ETRI), Korea. His researchinterests are Multicore Parallel Processing,Distributed Cloud Data Center, Data Plane
Acceleration
Technology
andNetwork
Virtualization.
Jung Hee Lee received B.E. and M.S. in Electronic
Engineering at Kyungpok National University in1984 and 1991, respectively. She is currently
principal
researcher
ofElectronics
andTelecommunications Research Institute (ETRI),
Korea. Her research interests are Flow based
Network Processor, Multicore Parallel Processing,High Speed Parallel Switching and Network
Virtualization.
Bhum Cheol Lee received M.S. and Ph.D. degree
in Electric Engineering from Yonsei University,
Korea in 1983 and 1997, respectively. He iscurrently Manager of Networking Computing
ConvergenceLab.
inElectronics
andTelecommunications Research Institute (ETRI),
Korea. His research interests are Smart Network,
Parallel
Flow
Processing
andNetwork
Virtualization.

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 3, May 2016

836

A Flexible FPGA-to-FPGA Communication
System
An Wu*, Xi Jin*, Xueliang Du**, ShuaiZhi Guo*
*Department of Physics, University of Science and Technology of China, Hefei, Anhui Province, China**Department of System verification, Chinese Academy of Science Institute of Automation, Beijing, Beijing
Province,China
wuan@mail.ustc.edu.cn, jinxi@ustc.edu.cn, xueliang.du@ia.ac.cn

Abstract—In high-performance computing systems, eachcomputing node communicates via a high-speed serial bus toensure sufficient data transfer bandwidth. However, eachcomputing node of different bus protocols is very difficult tocommunicate directly, which is not conducive to theextensibility of HPC (High performance computing) clusters. In
this paper, we propose UPI, a inter-node communicationinterface based on FPGA, which can transmit different bus
protocols (PCIe protocol and Ethernet protocol) simultaneously.
More importantly, many different bus-supported computing
nodes can be connected to the same HPC system. We
implemented our UPI system on “Gemini” prototype
verification board with two Xilinx Virtex-6 FPGAs. The results
show that the transmission speed of the UPI can reach
11.04Gpbs (PCIe Gen2 X4) and 4.32Gpbs (Gigabit Ethernet)
when DMA payload sizes is greater than 260KB and 80KB,
respectively.
Keyword—FPGA-based SoCs, PCIE, Gigabit Ethernet, HPC

W

I. INTRODUCTION

ith the rapid development of high-performance
FPGA-based devices, high-performance computing
system performance bottleneck has shifted from the ability ofsingle node to the architecture of the HPC clusters. In thesystem-level or board-level interconnect system, high-speed
serial bus technology with its enormous advantage is rapidly
replacing traditional parallel bus technology and becoming
the main technology of high-speed FPGA-based SoC design.
As the applications of the high-speed serial bus technology
gradually expand to all research area, more and more articles
focus on the technology, especially the multi-nodeinterconnect technology and the high-speed I/O interface
technology.
———————————————————————
Manuscript received February 24, 2016. This work is a follow up of
the invited journal of the accepted conference paper for the 18th
International Conference on Advanced Communication Technology. This
research was supported by the “Strategic Priority Research Program” of theChinese Academy of Sciences, Grant No.XDA06010402-4.
An Wu is with the Department of Physics, University of Science andTechnology of China, Hefei, Anhui Province, China (corresponding author
to provide phone: +86-159-5510-2092; e-mail: wuan@ mail.ustc.edu.cn).
Xi Jin is with the Department of Physics, University of Science andTechnology of China, China (e-mil:jinxi@ustc.edu.cn).
XueLiang Du is with the Department of System verification, Chinese
Academy of Science Institute of Automation, China (e-mil:xueliang.du@ia.ac.cn).
Shuaizhi Guo is with the Department of Physics, University of Scienceand Technology of China, China (e-mil:dybjxmg@mail.ustc.edu.cn).

Effective inter-node communication is receiving
significant attention due to its increasingly important
applications in high-performance computing, thus inter-nodeinterconnection technology is drawing lots of attractions
from more and more researchers. The interconnection
methods are based on three main protocols: PCIe, Ethernet,
and Serial RapidIO (SRIO). However, they have their own
using domain. They can't communicate each other directly,
and for the same reason, each computing node can't becompatible on the means of communication.
In order to achieve a good compatibility between these
protocols, many problems cannot be neglected. For example,
different bus communication requires bridging conversion,
the process is complicated and performance loss is hard to
avoid. The problem appears in these bus controllers'
hardware design. In FPGA-based SoCs, both of Ethernet
controller and PCIe controller have an external physical layer(PHY) chip. The controllers' PHY chip of mentioned three
types are different, mainly due to its PHY's interface signals
and communication speeds are not identical. Ethernet PHY
chip mainly realizes 64b/66b encoding (Gigabit Ethernet),
and PCIe PHY chip completes the 8b/10b encoding (PCIe 2.0
and below). However, they still have a lot in common, e.g.,scrambling and parallel-to-serial conversion all need to be
implemented in PHY chip.
The simplest way to solve the compatibility issue is to add
all these bus protocols to a computing node, but it requires
more PHY chips, differential pairs and hardware resources. A
better approach is to communicate with these computing
nodes through the same interface without changing theinterface types. Generally, the physical layer of PCIe,
Ethernet and SRIO cannot be shared. We try to merge thephysical layer of three bus protocols, finally, we built a
unified physical layer interface which is actually the samefunctions for their upper layer protocols.
The benefits of a unified physical layer design are as
follows:
 A unified physical layer can provide a compatible
interconnection bus with three protocols.
 Either PCIe, Ethernet or SRIO devices can be plugged
into the same interface for communication.
 The problem of performance loss in bridging process
can be settled.
In this paper, we proposes UPI (Unified PHY Interface)system, a flexible interconnection system of inter-nodecommunications based on FPGA devices. Our design shares

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 3, May 2016

837

Fig.1. Hardware structure of UPI system

the same portion of PCIe 2.0 and Gigabit Ethernet's physicallayers, and merges their different parts. The UPI can transmit
PCIe and Ethernet packets with one physical layer chip. The
computing node of HPC (High performance computing)clusters using different protocols can connect with each other,meanwhile, the bridging delay and loss in performance canbe eliminated, and through the interface design we canimplement a more flexible and efficient FPGA-based
computing clusters.
The remainder of this paper is organized as follows:background and related work are discussed in section II. We
describe the design of UPI in section III. The experiments andresults are discussed in Section IV. Finally, we present a
conclusion in Section V.
II. RELATED WORK
A. Background on PCIe and Gigabit Ethernet
PCIe is a high-speed serial bus includes transaction layer,
data link layer and physical layer. The Transaction layer
contains TLP (Transaction Layer Packets) control
mechanism. The Data Link layer primary responsibility is toprovide a reliable mechanism for exchanging TLPs between
the two components on a link [1]. At the physical layer(PHY), the PCIe bus provides a serial high throughput
interconnect medium between two devices. PCIe PHY
contains two sub-layer: Physical Coding Sub-layer (PCS) andPhysical Media Attachment (PMA)[2]. There have been
three versions of the PCIe bus. For a single lane, data transfer
rate for versions 1.x, 2.x and 3.x are 2,4 and 8Gbps [3].
There are two layers in hardware of Gigabit Ethernet,
including physical layer and data link layer (DLL). The mainfunction of DLL is to complete the frame transmission andframe reception. Gigabit Ethernet PHY has three mainfunctions: First, It's provide transferring path of data to data
terminal equipment; Second, to be a proper entities for datatransmission, not only to ensure that data transfers properly
on it, but also to provide sufficient bandwidth and reducechannel congestion; third, complete management of PHY.
Upper layer and PHY interconnect with each other via a
MII/GMII/RGMII/SGMII interface, through the manageinterface in MII, the upper layer can control and monitor
PHY [4].
B. Xilinx GTX
Xilinx GTX is a programmable high-speed serialtransceiver capable of speeds from 500Mbps to 12.5Gbps.

Fig.2. Software structure of UPI system

GTX module in Xilinx FPGA can be realized different serialinterconnect protocols, such as SATA, PCIe, EMAC and
SRIO. Dynamic Reconfiguration Port (DRP) is an interface
module which allows the dynamic change of parameters of
the GTX. Through the DRP interface, we can realize thedynamic changes of each interconnect protocol, making it
possible for system to adapt to the protocol change. QPLL
(Quad PLL) and CPLL (Channel PLL) are two kinds of PLL
circuit with different clock rates embedded in GTX module[5].
C. Existing Work
Ethernet is a widely used protocol in HPC computing
sysems, which key is its inter-node routing policy. Most
system designers make use of the Ethernet protocol toconstitute multi-node communication network, thetransmission rules follows the Ethernet protocol. However, as
more and more high-performance embedded devices appear,
researchers are turning their attentions to the direct communication of GPU, FPGA, DSP and other processing units. As ahigh-speed communication interface, the performance andbandwidth of PCIe meets our design requirements.
Many technologies based on PCIe protocol are proposed,such as InfiniBand and Hypertransport. The InfiniBand
Architecture (IBA) is an industry-standard fabric designed toprovide high bandwidth/low-latency computing, scalability
to ten-thousand nodes and multiple CPU cores per server
platform, and efficient utilization of compute processing
resources. InfiniBand adapters and switches deliver 56Gb/s
bandwidth today and are expected to deliver 100Gb/s by
Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 3, May 20162016 [6].
For chip-to-chip communications, AMD HyperTransport(HT) are used to connect between CPUs, as well as between
CPU and memory. It provides the integral interconnect
backbone structure that links all of the core functional units(processor, memory and I/O elements) in a board-levelsystem. As an optimized board-level architecture,
HyperTransport provides the lowest possible latency,
harmonizes interfaces and supports scalable performance [7].
BlueLink is custom interconnect toolkit for commodity
FPGA clusters. Traditional standard protocols such as
Ethernet and Interlaken are a boon for FPGA-to-other-systeminterconnect, they are inefficient and unnecessary for
FPGA-to-FPGA interconnect. BlueLink can use all thetransceivers available on an FPGA board, over any physicalmedium. Comparing to 10G Ethernet, 10G BlueLink uses
65% of the logic and registers of 10G Ethernet and uses 15%of the memory of 10G Ethernet. To consider throughput,
BlueLink's latency is about equivalent to Ethernet in the
fully-loaded case.
III. SYSTEM DESIGNIn this section we illustrate UPI hardware architecture andsoftware API, as well as explaining the flexibility andcompatibility of UPI architecture.
A. Overall System Architecture
Classic HPC cluster structure is shown in Figure 1. The
letter M indicates the the computing machine, and letter R
indicates the routing policy. We implemented this structure
on FPGA-based devices as shown in the middle of Figure 1.
In our UPI system, the computing machine and routing policy
are integrated into FPGA's user logics. Each FPGA-based
computing node is communicate with each other through our
high-speed UPI bus. The UPI interface is responsible forconnecting each FPGA-based node between user logics and
UPI bus.
The UPI interface consists of a hardware component and asoftware component as shown in the right of Figure 1 andFigure 2. The hardware component mainly consists of two
different types of bus controllers, the unified FIFO interface,
Interface Convertor and the DRP control module. Interface
Convertor is used to convert two different physical layerinterface signals into a standard GTX interface signals. The
DRP control module is used to dynamically reconfigure theparameters of GTX.
Software component is divided into two parts, including
standalone board driver and testing code. Our standaloneboard driver consists of PCIe and Ethernet device driver,
DRP control module driver and DMA driver. Test code
consists of PCIe TLP packets reading and writing tests,
Ethernet TCP/UDP packets reading and writing tests,
equipment switching test. To make our architecture more
flexible, the software API provides the simplest and effective
way to call the underlying driver functions, as well as
shielding the details of low-level operations. By switching
the functions of high-speed serial transceiver, user logics caneasily achieve the mutual communication between the two
protocols.

838

B. DRP Control Module
DRP control module (DCM) needs to complete link speed
selection task and link training control task. Thus, twoimportant parameters for DCM to consider are link width andlink data rate. After system boot up, PCIe requires link
training process to negotiate the link width and link speed
between two sides of PCIe controller. Ethernet also has asimilar link training process. The key function of DCM is alink training state machine, which stores the link state of two
protocols. When the link is switched on, DCM stores current
link state and jump to the next state without need to retrain
link. The switching time of the two controller is CPLL reset
to CPLL locked. The DCM state machine is shown in Figure
3.
At the beginning of link initialization process, both sides
are in Silent mode. Host side starts to seek Device side bysending training sequence in Seek mode. If device side sends
its training sequence back, the state will jump to the
Discovery mode. In this state, each side sends current link
width and link rate for handshaking.
UPI has three configuration modes for link width:
1) If both sides are four lanes, DCM will jump to
ISM_4X_MODE state;2) Theoretically, two lanes have six kinds of interconnect
methods. Considering that each channel sends the same
packets data, DCM will jump to ISM_2X_MODE state;
3) In
this
mode,
DCM
will
jump
to
ISM_1X_MODE_LANE3,
ISM_1X_MODE_LANE2,
ISM_1-X_MODE_LANE1 or ISM_1X_MODE_LANE0 state,
the details is as follows.
The configuration modes can be dynamically changed by
ISM_2X_RECOVERY,
ISM_1X_RECOVERYor
Discovery state. We build a 32-bit width data interface totransmit packet and connect it to Interface Convertor module.
Each channel has different negotiation methods
corresponding to UPI channel features in mode three.

Fig.3. DRP control module link training state machine

The link speed can be changed by modifying QPLL or
CPLL multiplication factor and crossover factor. GTX
supports 16, 32, 64 and other data width, higher data width
can be achieved by stitching several GTXs. In order tocomplete the link speed and width adjustment, DRP control

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 3, May 2016

module generates different interface signals in different states.
For example, when the bus controller is PCIe Gen2 X4, theCPLL reference clock can be adjusted to 125MHz, CPLL
output rate 2.5GHz and data link width can be adjusted to 32
bit data with 4bit k symbol indicator.
C. Interface Convertor
PCIe PHY interface is called PIPE interface, and Ethernet
PHY interface is called RGMII In order to achieve a mutuallycompatible interfaces, we need to make PCIe and Ethernet
physical layer interface convert to the same GTX interface.
This conversion process is completed in Interface Converter.
Table 1 shows the interface signals associated with the GTX,
including indicators sending and receiving 32-bit words with
4-bit k symbol and link status control signals.
TABLE IINTERFACE CONVERTOR RELATED SIGNAL

mac_phy_rxdata[31:0]

I/
OI

mac_phy_rxdatak[3:0]I

mac_phy_txdata[31:0]

O

mac_phy_txdatak[3:0]O

phy_mac_rxvaild[3:0]O

phy_mac_rxelecidle[3:0]O

phy_mac_phystate[3:0]
mac_phy_txdetectrx_loopba
ck[3:0]O

mac_phy_txelecidle[3:0]I

mac_phy_txcompliance[3:0]I

macphy txpolarity[3:0]I

mac_phy_powerdown[2:0]I

bus_state_write[5:0]O

bus_state_read[5:0]I

bus_switch_en
bus_link_speed[3:0]bus_link_width[15:0]

OO
I

Signal

GTX

DRP

I

Description
receive data
K character
indicationtransmit data
K character
indication
receive data is valid
receive electricalidle
PHY functions
enable receiver
detec-tion sequencetransmit electrical
idle
Compliancesequence
Invert the received
data when asserted
PHY power down
current controller
statewrite to DRP
current controller
stateread to DRP
bus switch enable
bus link speed
bus link width

The Interface Convertor mainly includes the following
features: A set of dual-port RAM, the amount of which is equal
to the converted upper interfaces.
 Combining and scattering data to meet UPI interface
data width.
 Generating PHY's control signals. If the control signals
provided by GTX interface, it can be directly
connected to GTX. Signals that GTX does not provide
will be generated in Interface Converter according to
the control signals' relations or be set to a constantvalue. For example, the PCIe PIPE interface signal
mac_phy_blockaligncontrol is only used in PCIe 3.0,
which isn't used in our design, so we give it a constantvalue in Interface Converter.
 Transmitting link information to the DCM module,
completing real-time parameter changes for GTX. The
link status signals will change into the DCM signals.

839

Interface Convertor signals are illustrated in Table 1.
Some signals, such as clock and reset, are omitted in thistable.
D. Data structures
There are three kinds of data structure in our UPI system:
Primary data with DMA descriptors, PCIe and Ethernet
packets data with their own protocols, GTX data with Control
symbol. The Primary data has been stored into DDR withsome DMA descriptors. When PCIe or Ethernet controller
obtains a writing command from CPU, a segment of primarydata will be sent to the controller. PCIe controller transfers
primary data to Transaction Layer Packets(TLP). Data Link
Layer Packets(DLLP) are in charge of link maintenance. TLP
packets will be assembled with a sequence number and a
LCRC code in Data Link Layer. Each TLP can accommodate4096 Bytes data payload. In Ethernet controller, related
packets are TCP, UDP, etc., which have 256 Byte's data
payload.
When GTX receives packets data form controllers, eachkind of packets is marked with a identifier and two kinds ofcontrol symbols (CON symbol and END symbol). We
distinguish PCIe and Ethernet packets with binary code ''01''
and ''10'', respectively. Different packets have different CON
symbol. For example, STP symbol is added to the head of
TLP packets corresponding to the identifier. The details of
CON symbols are listed in table 2. In our design, the packets
with identifier is only generated and digested in UPI layer.
TABLE IICONTROL SYMBOLS IN DCM
Standard
identifier
Control symbol

TLP
DLLP
TCP
UDP

01
01
10
10

K27.7
K28.2
K28.0
K28.4

E. Hardware Interface and Software API
Our UPI system interface includes the following
functions: FIFO-based DMA interface.
 DCM interface.
 System interrupt interface.
We use FIFO as the DMA interface for three reasons. First,
FIFO can be used between two clock domains. Our PCIe
system clock is 125MHz while Ethernet is 100MHz. Twoclock domains can be isolated by FIFO interface. Second,
FIFO is a standard interface. It can shield the low-level
details of UPI hardware, so the transmission process in UPI istransparent for user logic. Third, the width and depth of FIFO
can be configured by circumstances of the design and it's
convenient for developers to comprehend packet structures.
FIFO-based DMA interface is used to transmit data fromsystem's memory to system's I/O controller.
The DCM interface uses a simple DCM bus to read andwrite DCM registers. We provide DCM interface to make oursystem easier to operate. For some customized HPC clusters,
the DCM interface provides a function to change the bus
protocol to meet developers' requirements. Through the
DCM Interface, developers even can change the type ofcommunication protocol, which makes the system more
flexible of protocol switching.
System interrupt interface is a standard vectored interrupt
interface. The main functions provided by the systeminterrupt interface are software interrupt generating, interrupt

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 3, May 2016source searching, interrupt number generating and interrupt
priority setting. Different interrupt numbers mean thatdifferent interrupt exceptions and different CPU response
process.

Fig.4. Structure of FPGA-based verification system

Fig.5. Flow chart of UPI testing program

Therefore, a interrupt-supported CPU is required to handle
PCIe and Ethernet interrupts. We set these two kinds ofinterrupts to the same priority, and provide the appropriate
interrupt number and interrupt type functions, make CPU topoll interrupt easily after the interrupt exceptions isgenerated.
Corresponding to the hardware modules, software API is
also provides three functions: 1: DMA data read and write. 2:
DCM read and write. 3: software interrupts. These APIs arelisted as follows, some basic functions such as system resetare omitted in this list:
unsigned int drp_write(unsigned char addr, unsigned int data);
unsigned int drp_read(unsigned char addr);
int PCIe_dma(int len, int *ddr_data_1, int *ddr_data_2);
int GMAC_dma(int len, int *ddr_data_1, int *ddr_data_2);
void PCIe_Interrupt(unsigned int ictl_prio,int ictl_number);void GMAC_Interrupt(unsigned int ictl_prio,int ictl_number);

840

IV. EVALUATIONIn this section we implemented UPI system on “Gemini”
prototype verification board. Then we evaluate UPI's
performance and compare it with other I/O technologies.
Finally, we show UPI's flexibility by presenting threepractical applications that employ UPI as theircommunication interface.
A. System Verification Platform
Our system verification platform called “Gemini” is shownin Figure 6. The main hardware components provided by“Gemini” board are two PCIe slots, two Xilinx
xc6vlx365tff1156-1 FPGAs, a SODIMM (Small Outline
Dual In-line Memory Module) DDR3 and a CF Card's slot.
The UPI with a DMA-oriented Synopsys PCIe controller anda Synopsys Ethernet controller is integrated into each FPGA.
we also implemented a SoC (system on chip) with a
Microblaze CPU. The structure of FPGA-based verification
system is shown in Figure 4. The Microblaze CPU
communicate with UPI by ARM AXI (Advanced Extensible
Interface) bus.
Figure 5 shows the whole process of UPI verification test.
Primary data has been stored into DDR with some DMA
descriptors. When UPI obtaining a writing command from
CPU, a data segment will be sent to UPI system. UPI
systemtransfers primary data to physical layer. A typicalprogram for UPI test is as follows:
1) Download bit and elf file to the board.
2) Initialize the CF card, DDR controllers, UPI system.
3) Configure DMA descriptors, move data from CF card
into DDR, configure GTX to PCIe gen2.0 X4 mode.
4) Transfer data from FPGA A to FPGA B.
5) Store data in another DDR address in FPGA B.
6) Calculate complete time.
7) Compare the two data segments, calculate error amountif they are different.
8) Switch GTX to Ethernet mode. Repeat steps 3-5.
9) Reconfigure switching speed, and repeat steps 3-6.
B. System test
The UPI simulation process uses standard functional test to
simulate UPI system as shwn in Figure 7. The simulation
model used in our test is described as follows: 1: UPI_top is
the top-level module of UPI system. The gmac, pcie and gpio
are some sub-modules in UPI_top module. 2: Our UPI test
system have two differential input clock. The host provide
100MHz reference clock and transmit the clock to Gemini
board. We use an extra BUF module to receive reference
clock before clock is divided in PLL module. After clock is
divided in PLL, it connected with GTX input clock, auxiliary
clock and AXI interface clock. 3: The test system has only
one global reset clock, which comes form Gemini board resetpin. The GTX reset process is faster than controllers, so webuild a 10us delay circuit to make the GTX and controllers
reset at the same time.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 3, May 2016

841

System test uses Vivado 14.3 ISIM simulation and VCS

Fig.7. Structure of UPI system test

2014.09-3 simulation tools to verify our design. After
functional simulation pass, system test uses Synplify PremierH-2013.03 for RTL implementation and Vivado Chipscope
for testing signals observation. The simulation result isshown in Figure 7.
The signal uli_lane_oe_3 available after the other threechannels' signals because of the UPI system treat lane four asa special channel. Lane four is used to send control signal andsome special communication signals.

Fig.8. UPI system simulation test

Figure 9 shows the FIFO state when UPI system starts towork.

Fig.9. FIFO state of UPI system

Using chipscope to observe the signals come form DCM
and Interface convertor, we can clearly see that the current
link is in the negotiation state before time of 124us as shownin Figure 10.
After time of 124us, DCM state machine will jump into thenext state for exchange link information and link width.
The final placement and layout of the UPI system areshown in Figure 11. Under the control of the PCIe andEthernet function can be switched by DCM. It uses fewer
resources to complete switching function and improves theflexibility of UPI system. This process needs cost some extra
area. However, it would increase the system running time
significantly for low data throughput or real time
applications.

Fig.10. DCM and Interface Convertor states of UPI system

Fig.11. UPI system layout diagram

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 3, May 2016

The design was constrained to the right hand corner of thedevice where the PCIe and Ethernet blocks resides, as shownin Figure 2. The place with red marked is our UPI wrapper.
The maximum clock rate of UPI system is limited only by thedesign, build tools, and FPGA placing and routing methods.

842

rate of PCIe still increasing. The data rate of PCIe becomessaturated at 2.8Gbps when payload sizes is larger than
260KB. Ethernet data rate entering saturation more quickly
than PCIe because of the maximum packet sizes of Ethernet.
Although the data rate of Ethernet is slower than PCIe, theEthernet physical layer uses 64b/66b encoding, makes
Ethernet more effective than PCIe in terms of transmission
efficiency.
TABLE IIIRESULTS OF DEVICE SWITCHING TEST

bit error

Fig.12. Performance of UPI system

C. Experimental Results
Maximum data rates of PCIe and Ethernet are 20Gbps and4Gbps, respectively. Effective data rate(MB/s) =serial bus
clock frequency * 1 Byte(bit/8)* number of ports * encoding
format * half-duplex/ full-duplex.
For our design, using X4 lanes, 8/10encoding, full-duplex
mode and 2.5GHz(PCIe)/1.25G(GMAC) serial bus clock,effective data rate is 16Gbps/8Gpbs, i.e., 4Gpbs/2Gpbs perlane. System performance can't reach the maximum data rate
because of some limits. The highest data rate in test is about
74% of the maximum data rate, calculated from the clock
frequency of PLL.
We repeat our test more than 200 times. With the DMA
payload sizes keep growing, the performance of UPI also
keeps increasing. In DMA payload test, the data rate of bothcontrollers increases as the amount of data increase, as shownin Figure 12. When the DMA payload sizes is larger than
80KB, data rate of Ethernet hold steady at 1.1Gbps while data

Resource

LUTs
I/O
Flip-Flops
BRAM

SystemBluelink[8]Infiniband[9]1000 based-X Ethernet MAC
PCIe soft IP(stratix IV)
UPI

Switching time(us)full_duplex_PCIe

full_duplex_Ethernet

2.58
1.85
1.43
1.04
0.61

100%
92.6%
46.71%
18.5%
0.037%

100%
93.72%
72.7%
29.35%
0.045%In Device switching test, we recorded the relationshipbetween bit error and switching time as shown in table 3. UPI
can normally work at 2.58us. But as the switching speed
accelerated to 1.85us, the bit error increase significantly.
When the switching time is less than 500ns, no data entered
FPGA B. There are three reasons why the system appears thisphenomenon: 1) The switching time is close to the GTX's
required reset time, when switching time is less than 500ns,
GTX's CPLL is always in the unlocked state, and GTX is
unable to complete the transfer task. 2) Some transferring
data are stored in FIFO, the data will be flushed after GTX's
CPLL reset. In this situation, all data already transmitted are
no error. 3) High speed switching causes analog circuits
crosstalk especially differential pairs.
When the switching time is more than 2.58us, and thetransmission data is greater than 260KB, all transmitted datecan be received with no bit errors, except for the write data
errors generated by DDR itself.
The resource consumption of UPI system is listed in table
4. We also compare the performance of UPI system withother interconnect I/O technologies as shown in table 4. The
results showed that despite the fact that our systems takemore resources, UPI system achieves a better flexibility andcompatibility. When performance and resources are able tomeet the demand of bandwidth between nodes, we madecomputing nodes of two different protocols compatible.

TABLE IVRESOURCE CONSUMPTION OVERVIEWOur Design in FPGA A
Our Design in FPGA B
UPI system

94528(41.5%)
5(0.4%)
65372(14.4%)38(9.1%)101064(44.4%)
5(0.4%)
84432(18.6%)45(10.8%)

Resource Available

11853(5.2%)
N/A
8224(1.8%)6(1.4%)TABLE VSYSTEM PERFORMANCE OF DIFFERENT INTERCONNECT I/O TECHNOLOGIES
Link Rate
Configuration
Link Rate
PCI support

10G, 40G
40G
1G
5G
11.04G/4.32G

1x, 4x
LLC QDR 4x1x
1x Gen2
1x, 2x, 3x, 4x

10G
10G
1.25G
5G
2.76G/1.08G

No
Yes
Yes
No
Yes

2275201156
455040416

Ethernet support

LUTs

Yes
No
No
Yes
Yes

2009
64105
11853
1805
5500

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 3, May 2016

D. Practical Application of UPI
UPI has been used in three practical applications in various
institutes. These applications include: MaPU (Mathematics
Process Unit) commodity FPGA clusters; HDR (HighDynamic Range) vedio clouding system; MOND (Modified
Newtonian Dynamics) hardware accelerator for astronomical
data. All these applications require real-time communication
with multiple computing nodes. UPI can be used in theapplication of a good compatibility with the traditional single
protocol HPC clusters, as well as adding new UPI-based
computing node into computing system. UPI provides abetter system compatibility and interface flexibity as shownin table 5.
V. CONCLUSIONS AND FUTURE WORK
A flexible and compatible interconnect interface has been
proposed for FPGA-based multi-node communication. We
completed a multi-node communication interface with thebenefits of high flexibility and good compatibility. We
implemented our design on “Gemini” prototype verification
board with two Xilinx Virtex-6 FPGAs. The experimental
results show that both two bus protocols can be received andtransmitted without error when DMA payload size is greaterthan 260KB (PCIe) and 80KB (Ethernet) and switching timeis greater than 2.58us. Through the interface we can easily
connect two different HPC clusters. The performance loss
caused by traditional bridge equipment is eliminated.

843

An Wu received his B.S. degree in 2011 from Schoolof Anhui University, Anhui province, China, and he iscurrently a Ph.D. student in Department of Physics inUniversity of Science and Technology of China,
Anhui, China, under the supervision of Prof. Xi Jin.
His current research work is mainly on SoC design
technology, VLSI design and FPGA-based Hardware
Accelerator design.
Xi Jin received the B.S. degree from University of
Science and Technology of China, Anhui, China, andhe is currently an associate professor in Department of
Physics in University of Science and Technology ofChina, Anhui, China. His research interests include
SOC
designtechnology,
VLSI
design,computer-aided design methodologies for SoC system
integration and FPGA-based Hardware structure
design.
Xueliang Du received the Ph.D degree from
University of Science and Technology of China,
Anhui, China, and he is currently an associate
professor in Institute of Automation Chinese Academyof Sciences, Beijing, China. His research interests
include High-Performance SoC Design, DSP Design
and FPGA-based prototyping.

Shuaizhi Guo received his B.S. degree from
University of Science and Technology of China, andhe is currently a M.S. student in Department of
Physics in University of Science and Technology ofChina, Anhui, China, under the supervision of Prof.
Xi Jin. His current research work is mainly on
FPGA-based Hardware Accelerator design.

ACKNOWLEDGMENT
This research is supported by the “Strategic Priority
Research Program” of the Chinese Academy of Sciences,
Grant No.XDA06010402-4.
REFERENCES
[1]
[2]
[3]
[4]
[5]
[6]

[7]
[8]

[9]

Budruk, Ravi, Don Anderson, and Tom Shanley., “PCI express systemarchitecture,” Addison-Wesley Professional, 2004.
Intel Corporation’s, PHY Interface for the PCI Express(TM)Architecture, Specification Version 0.5. pp. 1-15, Aug. 16, 2002.
Gong, Jian, et al., “An efficient and flexible host-fpga pcie
communication library,” Field Programmable Logic and Applications(FPL), 2014 24th International Conference on. IEEE, 2014.
Koch D, Beckhoff C.,“Hierarchical reconfiguration of FPGAs,” Field
Programmable Logic and Applications (FPL), 2014 24th International
Conference on. IEEE, 2014: 1-8.
Xilinx Inc., 7 Series FPGAs GTX/GTH Transceivers User Guide, April
22,2013.
Islam, Nusrat S., et al.,“High performance RDMA-based design of
HDFS over InfiniBand.,” Proceedings of the International Conference
on High Performance Computing, Networking, Storage and Analysis.
IEEE Computer Society Press, 2012.
Shainer, Gilad, et al.,“Maximizing application performance in a
multi-core, NUMA-aware compute cluster by multi-level tuning.,”
Supercom-puting. Springer Berlin Heidelberg, 2013.
Theodore Markettos, “A Interconnect for commodity FPGA clusters:
standardized or customized?,” Field Programmable Logic andAp-plications (FPL), 2014 24th International Conference on. IEEE,
2014.
TPolybus
Systems
Corporation,“InfiniBandcores.
,”
http://www.polybus.com/iblink layer website/ibcores brochure alt.pdf

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 3, May 2016844

Accurate Spectral Efficiency Analysis for Non
Orthogonal Multiple AccessPongsatorn Sedtheetorn, Tatcha Chulajata
Department of Electrical Engineering, Faculty of Engineering, Mahidol University
25/25 Phuttamonthon 4 Road, Salaya, Nakornpathom, Thailand
pongsatorn.sed@mahidol.ac.th, tatcha.chu@mahidol.ac.th
Abstract— This paper presents theoretical analysis on
non-orthogonal multiple access (NOMA) for both downlink anduplink communications. Based on accurate evaluation, we
propose closed-form expressions of NOMA downlink and uplink
spectral efficiency. In terms of channel modelling, we map thechannel conditions with Nakagami and Rayleigh models inwhich both line-of-sight and non-line-of-sight are included. For
uplink transmission, we extend our work to the case of random
active users. The random nature is matched to appropriate
probability models. These make our presented expressions very
comprehensive and benefit us to study the impacts of channel
conditions, random number of users, and other key systemparameters on the NOMA spectral efficiency.
Keyword— Non orthogonal multiple access, 5G mobile
communication, uplink, downlink, spectral efficiency

I. NTRODUCTION

R

ecently, the growth of mobile communication is
rapidly rising. In the next generation, the entries tomobile networks are not only from human but also frommachines. This makes the demand go even greater. It is
important noting that radio access technology plays animportant role. The current radio access technology,
orthogonal frequency multiple access (OFDMA), might not
be the best option due to the issues of spectral efficiency andpower utilization [1].
As mentioned above, the new radio access technology,
namely non-orthogonal multiple access (NOMA), has been
introduced [2]-[7]. Based on NOMA, every users share thewhole spectrum and each individual is multiplexed to one
another via power domain. On the receiver end, the known
successive interference cancellation (SIC) method [8] isemployed to extract the desired signal from stronger
interferences by cancelling (subtracting) them with———————————————————————
Manuscript received February 25, 2016. This work is a follow-up of theinvited journal to the accepted out-standing conference paper of the 18th
International Conference on Advanced Communication Technology(ICACT2016).
P. Sedtheetorn is with the Department of Electrical Engineering, Facultyof Engineering, Mahidol University, Nakornpathom, 73170, Thailand(corresponding author, phone: 66-2889-2225 ext. 6501-3; fax:
66-2889-2225 ext. 6529; e-mail: pongsatorn.sed@mahidol.ac.th).
T. Chulajata is with the Department of Electrical Engineering, Faculty ofEngineering, Mahidol University, Nakornpathom, 73170, Thailand (e-mail:tatcha.chu@mahidol.ac.th).

superposition coding. From the results therein [5], NOMA
provides 30% more throughput than the traditional orthogonal
multiple access (OMA), which is OFDMA.
In terms of research challenges, there are many aspects toconcern. The area on spectral efficiency analysis gains a hugeinterest, e.g. [3]-[7]. For instance, [4]-[5] focus on the spectral
efficiency estimation in which all parameters are however set
constantly. The work in [6] is on the analysis of outage
probability which is directly related to the spectral efficiency.
Others are on the throughput maximization problems such as[3], [7].
Nevertheless, the analysis in the literature still leaves sometasks unfinished. Especially some random-nature parameterssuch as channel gains and the number of active users are fixed
or conditioned owing to the sake of simplicity.
In contrast to the literature, here we present accurate
original analysis on NOMA spectral efficiency inclusively on
both downlink and uplink transmissions. Furthermore, theimpact of random channel gains is mathematically mapped by
the famous Nakagami model in which line-of-sight andnon-line-of-sight scenarios are taken into account. Also, on
the uplink, we model the random number of active users withcorresponding probability processes. This makes our work far
more practical and outstandingly distinguished from theothers.
This paper is organized as follows. Section II illustrates thesystem model that includes all assumptions and parameters
used in this paper. Section III shows the original analysis on
NOMA downlink and uplink spectral efficiency.
Sequentially, the exact closed forms are presented. Section IV
demonstrates the numerical and simulation results. Section V
draws the conclusion of this work.

II. SYSTEM MODEL
In this section, the scenario of the future radio accesstechnology, namely NOMA, is explained in terms of both
downlink and uplink transmissions. Moreover, allassumptions, key system parameters and performance aredefined. Since the transmissions are on wireless channels, themagnitudes of channel gains at a receiver end are inevitably
random. Here, the models of channel gains are also classified
to cover all possible characteristics of signal propagation.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 3, May 2016

Based on SIC process, UE n, n  {1,2,.., N} , can remove the

A. Downlink Communication
Figure 1 describes the scenario of NOMA downlink
communication. The base station, called eNodeB, serves
multiple user equipments (UEs). It is known that NOMA
multiplexes individuals in power domain, each of whichuses the SIC technique and is able to perfectly decode thesignals from the weakest ones.
To demonstrate this, Figure 2 shows the spectrum
allocation of NOMA and OMA (e.g. OFDMA). In the figure,the downlink powers of individual UEs are assigned
unequally based on their distances from the serving
eNodeB. Without the loss of generality, UE1 is assumed tobe the closet one with the lowest dedicated power, while
UE2, UE3, …, UE N stay further with more dedicatedpowers. This technique is called rank adaption [2] in which
PUE1 < PUE2 <…< PUEN and PUE1+PUE2+…+PUEN =P. Next,
the eNodeB sends every UE a downlink signal with the total
power P. Then, each UE uses its SIC receiver to extract thedesired signal. For example, UE1 uses SIC receiver to filterout stronger signals of UE2, UE3, …, UE N.
In this work, the power spectral of zero-mean additive
Gaussian white noise (AWGN) is assumed as N₀. The power
gains of UE1, UE2,..,UE N can be defined by222

h1 , h2 ,.., hN . Recall the technique rank adaptation, we

have
2

845

2

2

h1 / N0,1  h2 / N0, 2  ..  hN / N0, N(1)2

where hn / N 0 , n is defined as signal to noise ratio (SNR) of
UE n.
Obviously, the signal power at the receiver end of UE n is2

Sn  hn P  N0, n .

inter-user interference from UE n+1 whose SNR level issmaller, |hn+1|2/N0,n+1<|hn|2/N0,n. On the assumption of
band-limited waveforms in AWGN channel, the spectral
efficiency of UE n can be declared as [4]




2
P
h


n n
(3)
C n  log 2 1  n 12
Pi hi  N 0, n i 1which is in bps/Hz and for n {1,2,..,N} . Now, we can definethesignal-to-interference-plus-noise

Pn hn

2

 P h Nn 1

i 1 i

2

i

ratio

(SINR)=.

0,n

B. Uplink Communication
Consider uplink transmissions in Figure 3. Every UE isable to transmit their information through multiple
subcarriers, which is called multi-carrier transmissions. Notethat the multi-carrier technique, so called carrier aggregation,is preferably used in the future mobile communication inorder to increase the overall throughput.
Based on NOMA technique, UEs are allowed to share thesame resource simultaneously both in the aspects of frequency
spectrum and time. Therefore, the receiver at the eNodeB is
required to operate multi-user detecttion (MUD) in order todistinguish signals of individual UEs. One of favorite MUD
techniques is SIC in which the desired signal is recoveredfrom the subtraction (cancellation) of interferences.

(2)
UE3

P

UE1

P

P

P

UE2

eNodeB
UE2

UE3

UE N

Fig. 3. Uplink multi-carrier NOMA

eNodeBNo SICSIC of UE 2,..,N SIC of UE 3,..,N SIC of UE 4,..,N

To this point, the signal received at the eNodeB can be
expressed as

SNR level
Low

High

Sn 

Fig. 1. Downlink NOMA with SIC technique P h
i ,l

2

i ,l

 N0

(4)

UE2
UE3

UE N
UE N

NOMA

Fig. 2. NOMA vs OMA (OFDMA)

2

are the power and the gain of the signal

from UE i on the particular subcarrier l.
Similarly, the uplink spectral efficiency of UE n is [3]

UE1

OMA

L

i 1 l 1

UE. Pi ,l and hi ,lEach UE takes thewhole spectrum.

UE1 UE2 UE3N

where L is the maximum number of subcarriers of individual

Frequency spectrum
separated for each UE

Power
level

UE1

Power domain
multiplexing





2
Pn,l hn ,lCn 
log 2 1  N

2
l 1


P
h

N
i ,l i ,l0  i 1,i  nLwhere the term





N

P hi ,li 1, i  n i , lthe desired signal Pn ,l hn ,luplink SINR = Pn,l hn,l

2

2

2

(5)

acts as the interferences ofon subcarrier l. Likewise, theNi 1,i  n

2

Pi ,l hi ,l  N 0 .

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 3, May 2016

C. Channel Modelling
Owing to the fact that the power gains are random
throughout wireless channel, appropriate probability models
are required to complete the analysis. In this work, we apply
two famous probability models, namely Rayleigh fading and
Nakagami fading.
It is known that Nakagami fading is a comprehensivemodel that covers both line-of-sight (LOS) andnon-line-of-sight (NLOS) scenarios via the index m asshown in Table 1 and the mathematical model is illustrated
in Section II-C2.
Table I.

NAKAGAMI FADING INDEX [9]Nakagami fading index
m
1
2-4
5
6

846

unit-mean gamma random variable with probability density
function [10]z m 1 m  mz
(7)
f ( z) m e
 ( m)hn2

where 0.5  m   is Nakagami fading index and(m)  (m  1)! is the gamma function. When m  1 , theprobability density function is exponentially distributed
which models Rayleigh fading (NLOS fading).
At this point, it is seen that Nakagami fading model covers
both NLOS (Rayleigh) and LOS cases. Moreover, thedominant signal (LOS signal) turns to be stronger when mgoes larger.
III. NOMA SPECTRAL EFFICIENCY ANALYSIS

Propagation
environments
NLOS (urban areas)
obstructed areas
suburban areas
open plain

In terms of Rayleigh fading, this model is considered as a
special case in which a pair of transmitter and receiver stay
NLOS. Rayleigh model is regularly used in research work toanalyze the extreme case of signal propagation.
Furthermore, its mathematical model is expressed by anegative exponential random variable which is quite simple
in comparison to the gamma-distributed Nakagami fading.
1) The special case: Rayleigh fading
In the case of NLOS, there is no dominant signal. The
received signals scatter from all around directions. A channel
gain (voltage gain), h(t), is depicted in terms of imaginary
number h(t) = hi(t)+j hj(t) in which each component is a
Gaussian random variable due to the center limit theory.
Then, the magnitude, h(t ) , stands for Rayleigh processThis section illustrates the analysis of NOMA spectral
efficiency both for downlink and uplink. The analysis starts
from the special case, Rayleigh fading, to the general case,
Nakagami fading, which has more mathematicalcomplexity. As a result, we propose new accurate
closed-form expressions of NOMA uplink and downlink
spectral efficiency. Furthermore, for uplink, we extend ourwork to consider the case when the number of active UEs is
random.
A. Downlink Analysis
The analysis can be categorized to Rayleigh andNakagami fading as follows;
1) Rayleigh fading environment
On the condition of Rayleigh fading, the power gains are
exponentially distributed random variables. Consider thesystem model in Section II-A. Now the average spectral
efficiency, assumed successful decoding and no error
propagation, of UE n can be presented as

Cn, avg  Elog 2 (1  SINR)  log 2 (1  z ) fSINR ( z )dz . (8)02

and its square, h(t ) , becomes a power gain representing aunit-mean negative exponential random variable withprobability density function

f

h(t )

2

( z )  e z .

(6)

When apply power gains to (3) either or (5), the spectral
efficiency becomes a function of multi random variable.
In the literature, the spectral efficiency can be determinedby simulating all possible power gain values and thenaveraging out the result. In some past work e.g. [4]-[5], thegains remain fixed for simplicity.
Alternatively, the average of spectral efficiency can becalculated directly via the multiple integrations of theinterferences’ density functions. This consumes huge
computation time and yields complexity.
In this work, we propose efficient method to accurately
calculate the average spectral efficiency of NOMA. Thanks
to the properties of exponential distribution, we can use
these properties to solve this mathematical difficulty (seeSection III).
2) The general case: Nakagami fading
Assume that the signal (channel) gain hn; models Nakagami
fading. The corresponding power gain hn

2

becomes a

We can see that there is some complexity on the integration of
the probability density function of SINR, fSINR ( z) . To tackle
such the problem, a new efficient method to calculate thespectral efficiency is introduced as below.
Rearrange (8) with the change of logarithmic base, then we
have

dz
C n ,avg  log 2 e  P(SINR  z )
(9)
1 z
0
where fSINR ( z)dz  dP(SINR  z) . Here we use the propertyof an exponential random variable X, P( X   )  e   , when μ
is a constant.
2

Owing to the fact that hn is also exponentially distributed,
therefore

P(SINR  z )  e



z 
Pn 

n 1

i 1 Pi hi 2  N0,n 


.

(10)

To compute the average spectral efficiency, one needs tofind the average of cumulative function P(SINR  z ) .
Fortunately, the average value of the cumulative function canbe determined by calculating the moment generating function
2(MGF) of hn . It is known that the MGF of any exponential

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 3, May 2016847

random variable X is Ee  X   1 /(1   ) for a constant  .

Replace MGF(mz) in (15) and (14) respectively. As a result,Then,the average spectral efficiency of downlink NOMA in
Nakagami fading is P h  N   zN / P n 1 

1


e
E e
 1  ( P / P ) z  . (11)i 1 
inReplace the MGF derived in (11) into (9). As a result, theclosed-form expression of the downlink spectral efficiency of
NOMA is
z

Pn

n 1

i 1

2

i

i

0 ,n

0 ,n



Cn, avg  log 2 een zN 0 , n / Pn



n 1





1

 1  z   1  ( P / P ) z dz.
i 1

0

i

(12)

n

Hint that this closed form presents the exact average of thespectral efficiency without any loss of generality in Rayleighfading environment. Moreover, the closed form can be used in
OMA case by simply adding the orthogonal multiplexing
factor α.
Denote α1, α2, …, αN by the orthogonal multiplexing factorsof UE1, UE2, …, UE N and i 1  i  1 . The power allocationNfor each UE is identical to one another, P1 = P2= …= PN = P,
as a result the spectral efficiency of UE n is


 zN

/P

e 0,n nC n ,OMA   n log 2 e 
dz.
(13)
n
0 1  z 2) Nakagami fading environment
Recall (8) with the change of logarithm. Then, we haveCn,avg  log 2 e   ln(1  z ) fSINR ( z )dz 
 0


(14)

Now we cannot use the method as in Section III-1A because
the density function of SINR is the combination of multiple
gamma random variables. Fortunately, we sort this problem
by the following Lemma 1.
Lemma 1: Let u be a unit-mean gamma random variable withm 1
a probability density function f (u )  u m m e mu and v be
 ( m)
any non-negative random variable and independent from u.
n 1
1
2
2
Let z=u/v where u  hn , v 
P h  N 0 , and2 i 1 i i
Pn hn







  1z 1  (1 1z) MGF(mz )dz .

E ln(1  z ) v 

0

m





(15)

Proof: we know that ln(1  z )  z 2 F1 1;1;2; z  where

2 F1 a; b; c; z  is a Gauss hyper geometric function [11]. Next
we get1
d m m 1z ln(1  z )m  1! dz m

1
dm m
z 2 F1 1;1;2; z m  1! dz m m2 F1 1  m;1;2; z  m  1  tz  ( m 1)

dt 

0

(16)

1
1

.
z z 1  z m

n1

0

n

i 1

1

P 
1  i z  Pn 

.

B. Uplink Analysis
Likewise, the closed-form spectral efficiency of uplink
can be derived by the same procedures as those of downlink.
However, another issue on the uplink is the randomness ofactive UEs. In practice, the number of active UEs (duringvoice either or data transmission) is unknown to theeNodeB. Here we investigate the effect of random numberof UEs and extend our closed forms to include this case.
1) Fixed number of users
Recall (5) and the procedure to achieve (15). We derive
the closed form of uplink NOMA spectral efficiency inRayleigh fading as
N 
L   zN / P
 
1
 e

dz 
Cn, avg  log 2 e . (19)


 
1

z
1

(
P
/
P
)
z
l 1 
i 1, i  n 
i ,l
n,l
 
0

Similarly, recall the same procedure to accomplish (18).
We have the uplink spectral efficiency in Nakagami fading
as
0

n ,l

Cn,avg  log 2 e

  zmN 0 / Pn ,l


1 
 e

1 
m
z
l 1 
 (1  z ) 
0
L



1



1

 dz .
i 1,i  n  1  ( Pi ,l / Pn ,l ) z 



(17)

(20)

N



Let all UEs be active simultaneously, then the total
spectral efficiency is as Ctot  NC n . In practice, the numberof active UEs is however random and distributed from 0, 1,
2, …, N. In the next section, we therefore sort this problem
and put the effect of random UEs in the proposed
expressions.
2) Random number of users
In this paper, we can model the randomness of active UEs
by Poisson and binomial distributions. The Poisson modelseems reasonable when the total number of both active andinactive UEs is unknown at the eNodeB. Then the amount ofactive UEs is estimated by statistically average value. For
binomial model, the total number of UEs is determined andthe number of active UEs is defined by a certain on-off(active-inactive) probability. These assumptions (Poissonand binomial) could be available in practice.
a)
Poisson distribution
In terms of Poisson process, the number of active users
is approximated by the average value λ. Then, the probability
mass function (pmf) can be written as [10]Pr( N  k ) Also, the MGF can be solved in a simpler form,
MGF(mz )  e  zmN / P   zmN 0 ,n / Pn

n1

e
1 
1
 . (18)
Cn,avg  log 2 e  
1

dz

m 


z
(
1

z
)


P

 i1 1  i z  0 P  

n

 

k

k!

e 

(21)

where k is the number of active UEs at an instance. Hint that,when the number of interfering UEs is random, we need torecalculate Cn,avg. For example, recall (19) and assume alltransmitted uplink powers are identical. Without the loss ofgenerality, the spectral efficiency becomes
Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 3, May 2016N 1
 Pzm   P h  N   1 
  e  
(22)
E e
 .


1 z Lemma 2: Let V(⋅) be any function and Y be a Poisson randomvariable with parameter (λ). Then, we have(23)
E V Y   e  V ()1 .
Proof: Let t be a constant. The moment generating function of
Y is E e tY ,n ,lNi 1 ,i  n i ,l

2

i ,l



 

0



   e k!  e  e k! . (24)

MGF(Y )  E e tY 



k

tk

k 0





t

k

k 0

We know that the summation of the pmf is equal to 1; i.e.




k 0

k 0

 e  k / k!  1 , then  k / k!  e  . Apply this to MGF(Y),thus it becomes
MGF(Y )  e   e e   e  ( e 1) .
t

t

(25)

Replace e with function V(⋅). Finally we have the proof
of Lemma 2.
Apply Lemma 2 to (19). Then, we have the spectral
efficiency of individual UE. As a result, the total spectral
efficiency becomes(26)
Ctot  Cn .
Note that we can apply Lemma 2 to (20) for the spectral
efficiency in Nakagami fading as well.
t

proposed from our expressions, with the simulation. In
terms of simulation results, we apply Monte Carlosimulation in which the random power gains are generated
and the spectral efficiency is computed by the original
Shannon formula (see equation (3) and (5) for downlink anduplink respectively). Then, we repeat the iterations over
2,000,000 times to make sure the results reliable.
A. Downlink Communication
Consider a single-cell environment with three UEs,
namely UE1, UE2, and UE3, respectively. UE1 is thenearest one to the eNodeB whereas UE2 stays further awayfrom the eNodeB and UE3 is the furthest. Then, the power
allocations are P1=1/6, P2=1/3, P3=1/2 for UE1, UE2, andUE3, respectively. Let SNR = P/N₀ where P= P1+ P2+ P3.
Assume that the fading index m=1 represents
non-line-of-sight propagation which models Rayleighfading.
In Figure 4, our numerical results, generated from (12), is
positively matched to those of the simulation. This validates
the accuracy of our proposed expression. In the figure, theexpression is used to evaluate the NOMA spectral efficiencyof each UE against the SNR in dB. Obviously, the spectral
efficiency of UE1 (the nearest one) is higher than others
because it has the highest rank adaptation (highest SINR)
which supports the principle of NOMA with SIC receivers.

Binomial distribution

Recall (22). Now N is binomially distributed. Let us
declare Lemma 3.
Lemma 3: Let V(⋅) be any function and Y be a binomial
random variable with parameters (N, p). Then, we haveEV Y ()  1  p  pV ()

N

(27)

Proof: if Y is a binomial random variable with parameters (N,p). The MGF of Y is EetY  in which t is a constant. Then,NNEe tY    e tk   p k (1  p) N k
k 0
k
N N
N
   ( pe t ) k (1  p) N k  1  p  pe t  .
k 0  k (28)

Replace et with function V(⋅), thus we yield the same result
as in (27).
Apply (27) to (19). The exact average uplink spectral
efficiency isN 1
 zN / P e 0 n ,l 
 1 
Cn ,avg  log 2 e 
1  p  p dz . (29)l 1  0 1  z  1 z As a result, the overall spectral efficiency is(30)
Ctot  pNCn
L

where pN is the average value of a binomial random variablewith parameter (N, p). Note that the spectral efficiency in thecase of Nakagami fading can be derived by the same manner.
IV. NUMERICAL RESULTS AND DISCUSSIONSIn this section, selected numerical results and simulation
results are shown. The results are categorized to uplink anddownlink aspects each which represents different scenariosof NOMA spectral efficiency versus key system parameters.
Furthermore, we also validate our numerical results,

1.2
Numerical
Simulation

1.1

Spectral Efficiency (bps/Hz)

b)

848

UE1
1.0
0.9
0.8

UE2

0.7
UE3

0.6
10

11

12

13

14

15

16 17 18 19 20

SNR (dB)

Fig. 4. NOMA spectral efficiency: validation of the proposed expression(numerical result) with the simulation

In addition, we can extend our work to compute thespectral efficiency of OMA (OFDMA) (see equation (13)).
From Figure 5, the overall spectral efficiency of NOMA is
up to 30% higher than those of OMA which is identical to
the results in [5].
Figure 5 demonstrates four different power allocationplans, i.e. A, B, C, and D with various power proportions forindividual UEs. Note that the spectral efficiency plotted inthis figure is the summed value, i.e. C= C1+ C2+ C3. It can beseen that the power proportion of far UE should be greater togain better overall spectral efficiency (plan A). With thisallocation plan, the spectral efficiency however rapidly
drops when SNR goes lower than 17 dB. Thus, the power
allocation plan B seems the most optimal solution in this
scenario.

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 3, May 2016acts as Rayleigh model in which there is no LOS between theeNodeB and its UEs. On the other hand, when m is larger, thedominant line-of-sight becomes more obvious and thespectral efficiency increases.
Again, the solid lines, representing numerical results, arematched to the cross symbols of the simulation. This canconfirm the validation of our proposed expression in (20).

2.6
2.4
2.2
2.0

m=6
3.0

1.8

OMA
NOMA A: P1=1/18,P2=1/3,P3=11/18
NOMA B: P1=1/9,P2=1/3,P3=5/9
NOMA C: P1=1/6,P2=1/3,P3=1/2
NOMA D: P1=P2=P3=1/3

1.6
1.4
10

12

14

16

18

20

SNR (dB)

Fig. 5. NOMA spectral efficiency with different power allocation plans(plan A, B, C, and D) versus OMA (OFDMA)

m=4
2.5

2.0
m=2
1.5
m=1
1.0
10

4.5
4.0
m = 1,2,3,5
3.5
3.0
2.5

12

14

16

18

20

SNR (dB)

Fig. 7. Total uplink spectral efficiency at different fading index

In Figure 8, we plot the uplink spectral efficiency (equation(26)) versus SNR (m=1) in the case that number of active UEs
are random and Poisson distributed. Assume that all UEs use
3 subcarriers to convey their information (L=3). λ varies from2, 3, 5 and 10. It is seen that increasing number of active UEs
reflects on interferences to one another and the decrease of thespectral efficiency.
9

Total Spectral Efficiency (bps/Hz)

Next, the evaluation of NOMA spectral efficiency in
Nakagami fading is taken into account (see equation (18)).
Figure 6 illustrates the impact of channel conditions on the
spectral efficiency. Here the channel conditions are
represented by the integer fading index m. It is known that,when m=1, the channel acts as Rayleigh model in which theline-of-sight between transmitters and receivers disappears.
Otherwise, when m is larger, the dominant line-of-sightbecomes more obvious and the spectral efficiency increases.
From Figure 6, it is worth saying that the impact of fading
index m on the spectral efficiency is greater than that of
SNR. We can observe that, when m =1,2 (highly obstructedchannel), the impact of SNR, considered as backgroundnoise, is unobvious. On the other hand, when the channel isclear (high m), SNR plays a role in the degradation ofspectral efficiency.

Total Spectral Efficiency (bps/Hz)

Overall Spectral Efficiency (bps/Hz)

2.8

Overall Spectral Efficiency (bps/Hz)

849

8

=3
=2

7

6

=5

5

= 10
10

12

14

16

18

20

SNR (dB)

2.0
10

12

14

16

18

20

SNR (dB)

Fig. 6. NOMA spectral efficiency with different fading indicesB. Uplink Communication
In this section, we focus on uplink communication. Assume
that there are 5 active UEs, each of which occupies 3
subcarriers, in the cell and their power transmissions are in thesame level. Hint that signal-to-noise ratio (SNR) is defined as
SNR = Pn,l/N0 in decibel (dB).
Figure 7 illustrates the impact of channel conditions on the
spectral efficiency. It is known that, when m  1 , the channel

Fig. 8. Total uplink spectral efficiency at varying arrival rate

In Figure 9, the total spectral efficiency versus SNR withvarying active probability in (30) is concerned. Here thenumber of active UEs is binomially distributed. The total
number of UEs is N=10. All UEs use 3 subcarriers to convey
their information (L=3). In the figure, the active probability(p) varies from 0.1, 0.3, 0.5, 0.7, and 0.9.
It is seen that high value of active probability activates
UEs to transmit their signals and thus interfere one another.
This leads to the drop of the overall spectral efficiency.
Moreover, we find that the appropriate value is around 0.3 or

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 3, May 2016

3 active users at an instance which is equal to the optimal
Poisson arrival rate (=3) in Figure 8.
To have more demonstration on this issue, let see Figure 10.
SNR is set as 10 dB and all UEs use 3 subcarriers to convey
their information while the total number of UEs (N) varies
from 5, 7, 15, and 20. From the figure, we can get the optimal
condition when the total number of UEs is around 3.

Total Spectral Efficiency (bps/Hz)[1]p =0.187
p =0.36
p =0.5p =0.7p =0.9

5
10

11

12

13

14

15

16 17 18 19 20

SNR (dB)

Fig. 9. Total uplink spectral efficiency at different active probabilities

10

SNR=10 (dB)
SNR=13 (dB)
SNR=15 (dB)
SNR=20 (dB)9Total Spectral Efficiency (bps/Hz)

However, there is an optimal number of active users toachieve the highest spectral efficiency at a certain provided
bandwidth. In this paper, the optimal point of binomial
distributed model is equal to that of Poisson distributed. This
also reasonably confirms and validates our proposed
expressions.
REFERENCES

9

8
7
6
5
4
3
2
1
0
0

850

3

6

9

12

15

18

21

24

27

30

Total Number of UEs

Fig. 10. Total uplink spectral efficiency at different values of SNR

V. CONCLUSION
This paper has presented original analysis on NOMA
spectral efficiency for both downlink and uplink. This has
introduced new exact closed forms of NOMA downlink anduplink spectral efficiency. The closed forms also reflect thechannel condition via Nakagami fading index. Furthermore,
the effect of random users on uplink is taken into accountand characterized with different probability models. This
benefits us to accurately investigate the impacts of channel
conditions, random active users, and key system parameterson the NOMA spectral efficiency.
From the results, we have found that the impact of channel
blockage on the spectral efficiency is far more obvious thanthat of the background noise (represented by SNR).
Furthermore, especially on the uplink, the number of active
users directly reflects the amount of interferences. When thearrival rate (for Poisson model) or active probability (forbinomial) is greater, the overall spectral efficiency decreases.

Jeffrey G. Andrews et al., “What will 5G be?,” IEEE Journal on
Selected Areas in Commun., vol. 32, no. 6, pp. 1065-1082, June 2014.
[2]
Docomo 5G white paper, “5G radio access: requirements, concept
and technologies,” NTT Docomo Inc., 2014.
[3]
Mohammed Al-Imari et al., “Uplink non-orthogonal multiple accessfor 5G wireless networks,” in IEEE Proceeding of ISWCS, vol. 1,
August 2014, pp. 781-785.
[4]
Yuya Saito et al., “Non-orthogonal multiple access (NOMA) for
cellular future radio access,” in IEEE Proceeding of VTC Spring, vol.
1, June 2013, pp. 1-5.
[5]Anass Benjebbour et al., “Concept and practical considerations of
non-orthogonal multiple access (NOMA) for future radio access,” inIEEE Proceeding of ISPACS, vol. 1, November 2013, pp. 770-774.
[6]Zhiquo Ding et al., “On the performance of non-orthgonal multiple
access in 5G systems with randomly deployed users,” IEEE Trans. on
Signal Processing Lett., vol. 21, no. 12, pp. 1501-1505, December
2014.
[7]
Stelios Timotheou et al., “Fairness for non-orthogonal multiple accessin 5G systems,” IEEE Trans. on Signal Processing Lett., vol. 22, no.
10, pp. 1647-1651, October 2015.
[8]
Mazen O. Hasna et al., “Performance analysis of mobile cellular
systems with successive co-channel interference cancellation,” IEEE
Trans. on Wireless Commun., vol. 2, issue 1, pp. 29-40, February
2003.
[9]
John G. Proakis et al., Digital Communications, 5th edition,
Mc-GrawHill, 2008.
[10] Sheldon M. Ross, Introduction to probability models, 7th edition,
Harcourt Academic Press, 2000.
[11] S.M. Abramowitz, Handbook of mathematical functions withformulas, graphs, and mathematical tables, U.S. Department ofCommerce, 1972.
Pongsatorn Sedtheetorn (M’03) received theB.Eng. and M.Eng. degrees from Chulalongkorn
University, Thailand, in 1998 and 2001, and the
Ph.D. degree from the University of Manchester,
United Kindom, in 2007. He is currently an
Associate Professor with the Department ofElectrical Engineering, Mahidol University,
Thailand. His research interests are in the areas ofwireless communications, information theory, as
well as enterprise architecture.
Tatcha Chulajata (M’97) received the B.Eng. from
Kasetsart University, Thailand, in 1992. He received
the M.S and the Ph.D. degrees from Wichita State
University, USA, in 1996 and 2003, respectively. He
is currently a Senior Lecturer with the Department ofElectrical Engineering, Mahidol University,
Thailand. His research interests are in the areas ofwireless communications, communication network,and enterprise architecture.

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 3, May 2016

851

Your Neighbors Are My Spies: Location andother Privacy Concerns in GLBT-focused
Location-based Dating Applications
Nguyen Phong HOANG, Yasuhito ASANO, Masatoshi YOSHIKAWA
Department of Social Informatics, Graduate School of Informatics, Kyoto University
Yoshida-Honmachi, Sakyo-ku, Kyoto 606-8501, Japanhoang.nguyenphong.jp@ieee.org, asano@i.kyoto-u.ac.jp, yoshikawa@i.kyoto-u.ac.jp
Abstract—Trilateration is one of the well-known threat
models to the user's location privacy in location-based apps;
especially those contain highly sensitive information such as
dating apps. The threat model mainly bases on the publicly
shown distance from a targeted victim to the adversary to
pinpoint the victim's location. As a countermeasure, most of
location-based apps have already implemented the "hidedistance" function, or added noise to the publicly shown
distance in order to protect their user's location privacy. The
effectiveness of such approaches however is still questionable.
Therefore, in this paper, we investigate how the popularlocation-based dating apps are currently protecting their user's
privacy by testing three popular GLBT-focused apps: Grindr,
Jack’d, and Hornet. We found that Jack’d has the most privacy
issues among the three apps. As one of our findings, we also
show how the adversary can still figure out the location of atargeted victim even when the "show distance" function isdisabled in Grindr. Without using sophisticated hacking
techniques, our proposed model (called colluding-trilateration)is still very effective and efficient at locating the targeted victim,
and of course in a so-called "legal" manner, because we only
utilize the information that can be obtained just as same as anyother ordinary user. In case of Hornet, although it has adopted
location obfuscation in its system, we were not only able todiscover its noise-adding pattern by conducting empiricalanalysis, but also able to apply the colluding trilateration usedin Grindr to locate the targeted victim regardless of the location
obfuscation. Our study thus raises an urgent alarm to the usersof those location-based apps in general and GLBT-focuseddating apps in particular about their privacy. Finally, the paper
concludes by suggesting some possible solutions from theviewpoints of both the LBS provider and the user considering
the implementation cost and the trade-off of utility.
Keyword—Location-based
Application,
GLBT-focused
Applications, Location Privacy, User Privacy, Trilateration,
Colluding-trilateration, Grindr, Jack'd, Hornet—————————————————————
Manuscript received on March 11th, 2016. This work was supported by
JSPS KAKENHI Grant Number 15K00423 and the Kayamori Foundation ofInformational Science Advancement. The paper is a follow-up of [21],which was presented at the 18th IEEE International Conference on Advanced
Communication Technology, and received the Outstanding Paper Award.
Nguyen Phong HOANG is currently a graduate student at the Department
of Social Informatics, Graduate School of Informatics, Kyoto University,
Japan. (corresponding author: +81-75-753-5375, fax: +81-75-753-5375,
e-mail: hoang.nguyenphong.jp@ieee.org)
Yasuhito ASANO is an associate professor at the Graduate School ofInformatics, Kyoto University (e-mail: asano@i.kyoto-u.ac.jp).
Masatoshi YOSHIKAWA is a professor at the Graduate School ofInformatics, Kyoto University (e-mail: yoshikawa@i.kyoto-u.ac.jp).

N

I. INTRODUCTIONOWADAYS, thanks to the advancement of the Global
Positioning System (GPS), most of the smart phones
have a built-in GPS receiver, which assists to estimate thelocation information with accuracy up to just a few meters.
Taking this advantage, location-based applications (aka:location-based services or LBS) are getting more dominant in
the smart phone application market. Just a decade ago, one
still had to use paper map or ask for direction when going toan unfamiliar area; while young people were surfing around
online chat rooms to look for friends at that time. However,
the introduction of LBS has changed our lifestyle and the waythat people interact with each other thanks to its undeniable
convenience. For instance, one can easily find the nearest
restaurant, convenience store or shopping mall by using
application like Google Map; or hang out with friends byusing application like Find My Friends, etc.
A. Privacy in General
Nevertheless, in the era of Information andCommunications Technology, along with censorship andmassive surveillance in cyberspace, the problem of
information leakage has also become more and more severe.
Tim Cook, the CEO of Apple Inc., used to say at the WhiteHouse Cyber Security Summit in early 2015 that: "Privacy isa matter of life and death" [1]. As people increasingly keep
more sensitive personal information in their phone, big
agencies and companies like Apple have been working hardto provide the best protection to their customer‘s private
information. However, an absolute privacy and a completely
perfect countermeasure to prevent future data breaches still
remain as headache matters. According to the Tenth Annual
Cost of Data Breach Study published by IBM in 2015, theaverage consolidated total cost of a data breach is $3.8
million, increasing 23% since 2013. The report also points
out that the cost incurred for each lost or stolen record thathas sensitive and confidential information increased 6% froma consolidated average of $145 to $154 [2].
Among personally identifiable information, location isconsidered as one of the most essential factors since the leakof location information can consequently lead to thedisclosure of other sensitive private information such as
occupations, hobbies, daily routines, and social relationships[3]. In spite of many attack techniques [4], [5] that have been
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 3, May 2016

studied by the research community since then, the protectionof location privacy from both LBS provider and user has notbeen sufficiently and appropriately taken into account. Thus,in this study, we investigate the current status of location
privacy preserving in popular GLBT-focused dating
applications to have a clearer view on the issue, and observe
how it is being protected in the real-life practice under bothalready-known threat (i.e. trilateration) and its enhanced
version (i.e. colluding trilateration) proposed by our group.
B. Privacy Concerns in GLBT-focused ApplicationsFirst of all, it is important to emphasize that it is not
because of hatred or discrimination that makes us opt forinvestigating GLBT-focused applications like Grindr, Jack'dand Hornet. But, because of their popularity, possession ofhighly-sensitive information, and the huge number of users1
that make these applications highly vulnerable tocyber-attack like the case of Ashley Madison [6]. In addition,
it is also because GLBT-focused dating applications like
Grindr, Jack‘d, and Hornet are location-sensitive, and theirusers depend on the publicly shown distance information tolook for nearby people to meet up right away for hookup(most of the time), thus potentially exposed to the risk ofbeing located.
As stated in [7], there are still many Islamic nations where
homosexuality carries the death penalty. Most recently, there
were several gay men in Syria lured by ISIS terrorists to goout on dates, and later executed publicly by stoning asreported in [8]. Even in those regions like North America andWestern Countries, which are thought to be more
open-minded, the GLBT community is still not widely
accepted. More or less, people belong to the GLBT
community are still facing the problem of being attacked,
harassed or discriminated [9]. Such cases show that
protecting privacy of the user of GLBT-focused application
is a nontrivial task, and should not be neglected by the LBS
providers. Because the location information together withother information such as height, weight, age, and hobby canbe used to accurately disclose the targeted individuals. Later,
the compromised information from those victims such as
occupation, address, or frequently visiting places, daily
routines and social relationships can be used to intimidate formoney, or even lead to physical harassment. At this point, it
is understandable why Tim Cook says: "Privacy is a matter oflife and death" since he also came out as a member of the
GLBT community in October 2014 [10].
C. Organization
The rest of this paper is organized as follows. We will
introduce our experimental environment in the last part ofthis section. In Section II, Jack‘d, Grindr and Hornet areinvestigated in terms of location privacy. By employing our
proposed colluding-trilateration, we will demonstrate how
the user's location still can be accurately discovered evenwhen countermeasures like location anonymization and1
According to [22], both Grindr and Jack‘d currently have more than 5
million active users, while there are more than 4 million active users in
Hornet. To examine that, at the time of writing this paper, we tried inputting
the terms such as ―gay dating‖ or ―gay hookup‖ in to Appcrawlr, which is asemantic mobile application discovery powered by Softonic. The searchengine indeed returned Grindr, Jack‘d and Hornet on the top of the result list
sorted in the order of number of downloads.

852

location obfuscation have been implemented. In Section III,other privacy concerns are discussed with real life
experiments. In this section, we will also introduce aside-channel attack fashion that can be conducted due to thecurrent design of Jack'd. Finally, from the viewpoints of both
LBS provider and user, we then give some possible solutions,and wrap up the paper in Section IV.
D. Experiment Setup
The trilateration threat model actually can be conducted ina physical way that the adversary carries his device around to
three different places and notes down the distances shown
from his position to the victim. However, in order to have an
easily manageable experimental environment, we employ
three virtual machines that host Android OS to play the roleof adversaries. Each machine is then set to be in positions
around our institute as follows:•
Victim is an account run on a real iPhone 5, locates at
Science Frontier Laboratory, Kyoto University withcoordinates (35.02350485, 135.77687703).
•
Adversary A1 is located at Demachi-yanagi Station
with coordinates (35.03051251, 135.77327415).
•
Adversary A2 is located at Heian Shrine withcoordinates (35.01598257, 135.78242585).
•
Adversary A3 is located at Kyoto Imperial Palacewith coordinates (35.02258561, 135.76493382).
Each Android machine is then equipped with Fake-GPS2
so that their positions can be freely set to any corner of theworld. At the time of writing this paper, we did ourexperiments with Grindr (version 2.2.8), Jack‘d (version
3.3.2), and Hornet (version 2.7.1). Next, to capture packets in
Subsection III.A, we set up a proxy machine, and use
Microsoft Network Monitor (version 3.4) to monitor network
traffic passing through that proxy machine. All of the maps
used in this study are sketched using a map tool available at:
http://obeattie.github.io/gmaps-radius/.
II. LOCATION PRIVACY CONCERN
To initially test whether an application adopts location
obfuscation to obscure the publicly shown distance of its user
or not, we move around two accounts run in the virtualenvironment mentioned in Subsection I.D. The distanceshown on each account is then recorded and compared withthe real distance. From some preliminary results, we foundthat Grindr and Jack‘d (in default setting) do not adopt any
location randomization, but show the exact physical distanceof the user. As a consequence, the real location of the user isvulnerable to the trilateration threat model, which will be
discussed in more detail in Subsection II.A. To prevent therisk of being located for its user, Grindr has already
implemented a function which allows the user to hide thedistance from being viewed by other users, while Jack‘d has
not implemented any effective countermeasure to alleviate
this risk. Nonetheless, in Subsection II.B, by deploying our
proposed colluding-trilateration method, we will demonstratethat disabling the ―show distance‖ function still cannot
effectively mitigate the risk. In contrast with Grindr andJack‘d, Hornet seems to be better in protecting its user‘slocation privacy by adopting location obfuscation in its
system. As a result, we always get the distance shown on the2

https://play.google.com/store/apps/details?id=com.lexa.fakegps

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 3, May 2016

application different from the real distance. However, in
Subsection II.C, we will show how our colluding-trilateration
model can still be applied to precisely locate Hornet user
regardless of whether location anonymization and location
obfuscation are enabled at a same time in Hornet.
A. Trilateration Model
As far as we are aware, the trilateration threat model (aka:triangulation) is said to be first reported to Grindr in 2014[11], and discussed in recent studies [4] and [5]. The mainidea of this attack model bases on the distance from the user
to the adversary, which is publicly shown to other users. With
privilege no more than an ordinary user, an adversary justneeds to move around the victim to three different places.
Distances from the victim to the adversary at those threepositions are then used to pinpoint the exact location of thevictim. As shown in Figure 1, Grindr and Jack‘d users, who
keep the default setting, are facing a high risk of being
located since the adversary can obtain an accurate location up
to the victim‘s building as highlighted in the red rectangle.
With this threat model, we could not locate the victim in
Hornet because the real distance is obscured, and the publicly
shown distance is changed to new value every time were-query it. Let us revisit Hornet in Subsection II.C.

Fig. 1 Testing Trilateration Threat Model in Grindr and Jack‘d.

From the geometry point of view, the location of the victim
is nothing else but the coordinates of V, which is the solution(x, y) of a system of simultaneous circle equations.
{
Where:
,andare latitudes
and longitudes of the adversary at three different
positions
respectively.
 D1, D2, and D3 are distances from V to A1, A2, andA3 respectively.
In response to this type of threat, Grindr has adopted afunction in which the user can opt to hide the distance sinceAugust 2014 [12]. Thus, the trilateration model is no longer
able to locate those users who already disabled the ―show
distance‖ function. As we revisited [11] at the time of writing853

this paper, the map is no longer able to pinpoint Grindr users
as shown in Figure 2.

Fig. 2 Previous Grindr's flaw had been fixed.

For Jack‘d, it does not adopt the location anonymization
policy to protect its user. Instead, it creates a function whichallows its user to adjust the accuracy of the distance to threelevels: close, near, and far (in iOS); or street, neighborhood,and city (in Android) as shown in Figure 3.

Fig. 3 Jack'd Privacy Setting.

Notwithstanding these setting options, the publicly shown
distance between two of our fake accounts does not changeeven when we restart the application to load the new privacy
setting. As a result, this new function of Jack‘d does notguarantee the location privacy of its user at all.
B. Your Neighbors are My Spies – Colluding Trilateration
Despite of the fact that the best solution to protect thelocation information is not to publish it; in this part, as a key
point of this paper, we will illustrate an enhanced version of
the trilateration threat model that current approach likelocation anonymization implemented by disabling the "showdistance" function still cannot effectively counter to. The
primary factor in the success of this threat model bases on theway that Grindr arranges its users on the screen. Perhaps, inorder to provide a high utility for the application, users are
displayed left-to-right and top-to-down in an ascending orderof their distances regardless of whether they have already

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 3, May 2016

disabled the "show distance" function or not. By exploiting
this fact, the two neighbors appear just before and just behindthe victim on the application‘s screen unintentionally become
the upper and lower bounds of the distance from the victim to
the adversary. As a result, the region in which the victim is
locating is easily obtained by employing the trilateration
model again, but with two circles drawing from the adversary
to the two nearest neighbors as shown in Figure 4.

Fig. 4 Enhanced version of Trilateration Threat Model.

By using this model, the adversary even does not need toview the victim profile three times to record the distance as
done in the original trilateration model, but still very effective
at locating the victim's region as marked in Figure 5.

854

the victim from three different points, once for each. As aresult, no distance query from the adversary to the victim isissued in this enhanced model. Therefore, approach like
limiting the number of queries issued to get the distanceinformation from one user to another user is not adequate inthis case.
Moreover, a lesson learned from Figure 4 and Figure 5 isthat the adversary gains more location information about thevictim at A2 and A3 than A1, because the bounds set by thepairs of victim's neighbors from the viewpoints of A2 and A3
are narrower than from A1. Therefore, even without drawing
the pair circles from A1, the adversary can still confidently
infer the possible region of the victim, because one of the twointersection areas is a river space, thus the probability that thevictim is locating in that region is relatively low. Since
discussing about the probability of possible activity region ofsmart phone user is beyond the scope of this paper; for more
information, the reader can refer to [13], in which theprobability of possible locations of smart phone user has
already been discussed.
So far, one may think that this type of attack model is not
valid in case the victim locates in low-density area, because
there are not so many neighbors around him for this attack
model to take place. However, as far as we are concerned,
using the term "victim's neighbors" actually is not always
correct, because they may not physically locate near to thevictim in real world. In fact, this attack model is still valid aslong as the following condition holds:
AN1 < AV < AN2
Where:• AV is distance from the adversary to the victim.
• AN1 and AN2 are distances from the adversary to thepair of the so-called victim's nearest neighbors.

Fig. 6 Victim's neighbors are not necessarily close to him.

Fig. 5 The victim‘s region is smoothly bounded.

Hence, instead of querying the distance information of thevictim several times, the adversary just needs to query it fromthe two nearest neighbors (appear on the screen of Grindr) of

In real life attack, the adversary can apply this model toattack the victim in remote area by placing himself in the
middle of high-density areas and the victim's region as shownin Figure 6. The more crowded the urban areas are, the moreresources that the adversary can obtain to precisely explore
the victim's location. Or, in a more active attack fashion, theadversary can create two colluding accounts and move themaround until he can satisfactorily compromise the victim's
location. The key idea is to gradually reduce the subtraction

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 3, May 2016

value of |AN1-AN2| such that V is still sandwiched by N1
and N2 on the application‘s screen of the adversary. The
smaller the subtraction value becomes, the more accurate
location of the victim can be revealed. Up to this point, it isobvious that obtaining victim's real location from Figure 5
becomes a trivial task. With this enhanced model, even when
all local members hide their distances, the adversary can still
make completely use of his colluding fake accounts to infer,thus be able to narrow down the possible region in which thevictim is locating. That is where the name ―colluding
trilateration‖ of our proposed method originates from. The
idea is demonstrated in Figure 7.

855

that Hornet does not use a one-to-one function to obscure thereal distance. In addition to this feature, Horner user can alsochoose to hide his obfuscated distance from other users. As aresult, Hornet seems to be better in protecting its user's
location from the attack fashion of trilateration model.
Indeed, Hornet was first released in 2011 3 , later thanJack‘d (2010)4 and Grindr (2009)5, thus carefully designedwith the concern of privacy and security issues [14].
Because Grindr is one of the first GLBT-focused
applications introduced to the society, and widely used in
North America, it has become a typical object for manystudies ranging from privacy, psychology, gender tosexuality health and so on; while Hornet is new and has notbeen thoroughly studied, especially in the aspect of user
privacy and information security. For instance, Hornet wasrecently discussed in [15], but the authors did not accurately
study the distance obfuscation pattern of Hornet. Instead,they stated that Hornet sends the distance with 10 m accuracy.
However, it is not true, since Hornet does carefully obfuscate
the distance of its user as stated on its homepage [16].
Therefore, to the best of our knowledge, we are the first groupthat empirically studies the location privacy aspect of Hornet.
In order to discover the location obfuscation pattern ofHornet, we repeatedly move around all of our adversary
accounts to about 3000 different locations within 3 Km from
our institute, and record both real distances estimated byourselves and obfuscated distances shown in our Hornet
accounts. Next, we scatter the data and obtain the graph in
Figure 8.
3000

Obfuscated Distance (m)2500

Fig. 7 Colluding Trilateration in Grindr.

As illustrated, the adversary, whose account is marked
with 1, can create two colluding accounts 2 and 3, thenpositions 2 and 3 in a way that the victim (Vic) is sandwiched
between 2 and 3. Later, the adversary can gradually move 2
and 3 so that the value of the distance between them becomes
smaller while the victim remains in between of 2 and 3 on thescreen, thus be able to precisely figure out the distance of thevictim basing on the distances shown in the profile of thecolluding accounts 2 and 3.
C. Location Obfuscation is not enough
While Grindr adopts location anonymization approach toprotect its users from the trilateration threat model, Hornet
adopts both location anonymization and location obfuscation.
In other words, all of the publicly shown distances in Hornet
are obfuscated as mentioned in our preliminary result above.
Even from a same location, we keep receiving different
distance values every time we issue a new query to reload the
profile page of the victim. Therefore, we can initially confirm

2000
1500
1000
500
0
0

1000

2000

3000

Real Distance (m)Fig. 8 Hornet Location Obfuscation Pattern.

As we already noticed that Hornet does not simply use a
one-to-one function to obscure the distance, we cannot justeasily employ the regression analysis to figure out thelocation obfuscation pattern used in Hornet. By observing therecorded data, we found that Hornet is very careful indesigning its location obfuscation scheme with three different
strategies for three different ranges of distance, which are
0~100 m, 100~1000 m, and above 1 km. Within the range of3

https://www.appannie.com/apps/all-stores/app/hornet/
https://www.appannie.com/apps/all-stores/app/jackd/5
https://www.appannie.com/apps/all-stores/app/grindr/
4

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 3, May 2016

0~100 m, Hornet makes the publicly shown distance equal to
80 m for all of the distances which are shorter than 80 m. For
distance longer than 80 m and shorter than 100 m, Hornet
arbitrarily adds some noise to the real distance such that the
obscured distance varies within 80 m and 100 m. We break
down the Figure 8 to have a clearer view on the distanceobfuscation pattern in the range of 0~100 m.
100
80
70
60
50
40
30
20
10
0
0

20

40

60

80

100

Real Distance (m)Fig. 9 Hornet Location Obfuscation Pattern – Range 0~100m.

Next, within the range of 100~1000 m, Hornet adopts amore complex obfuscation pattern that changes the publicly
shown distance every time we refresh the victim profile to get
the distance information. As a result, although the positionsof our fake accounts did not change, we always got different
values shown in our fake accounts. To deeply analyze thisobfuscation pattern, we issue 30 queries from every single
position and note down all the possible obfuscated results
returned by Hornet. Breaking down the Figure 8 in the rangeof 100~1000 m gives us a clearer view.
1000
900
Obfuscated Distance (m)which is longer than 100 m and shorter than 1000 m, is firstrounded to the nearest hundred. It is then obfuscated byadding an arbitrary number ranging from 0 to 100 with thestep of 10. For example, if the real distance is 321 m, then theobfuscated distance is 300 plus any random value in the set of{0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100}. Thus, the publicly
shown value can be any number between 300 m and 400 m
with the step of 10.
Finally, for distance longer than 1 km, Hornet applies
another obfuscation pattern in which the real distance isrounded to the nearest one in the unit of km. For instance, 1.2
km is rounded to 1 km, while 1.6 km is rounded to 2 km.

800
700
600
500
400
300
200
100
0
0

200

400

600

800

1000

Real Distance (m)Fig. 10 Hornet Location Obfuscation Pattern – Range 100~1000 m.

From Figure 10, we can conclude that in the range of100~1000 m, Hornet evenly randomizes the real distancewith amplitude of 100 m. In more detail, a real distance,

Obfuscated Distance (km)Obfuscated Distance (m)

90

856

3
2.8
2.6
2.4
2.2
2
1.8
1.6
1.4
1.2
1
1

1.5

2

2.5

3

Real Distance (km)Fig. 11 Hornet Location Obfuscation Pattern – Range 1 km ~.

From the patterns discussed above, it is not an
exaggeration to say that the obfuscation used in Hornet isvery reasonable with respect to the privacy preserving forHornet user. Distance of every user in the range of 0~1000 m
is complexly obfuscated because this range is considered tobe very sensitive to the user‘s location privacy; while
distance longer than 1 km is simply obscured by rounding
method because it is far enough to protect the user privacy.
Also, showing the approximated distance for those users,
whose distance is longer 1 km, does not have any bad impact
on the utility of other local users. Therefore, the obfuscation
does not only provide Hornet user with a better protection,
but also makes it more difficult for the adversary to carry out
the trilateration attack. That is the reason why we could not
find out the exact location of the victim in Hornet when
applying the trilateration model because three circles
sketched from three different locations of our adversary
accounts do not converge on one point.
Nevertheless, we still have the colluding-trilateration
method proposed in Subsection II.B to test in Hornet;
because, same as Grindr, Hornet also arranges the users on
the screen from left-to-right and top-to-down in an ascendingorder of their distances regardless of whether they havedisabled the "show distance" function, and the publicly
shown distance has already been obfuscated. Therefore, wereuse the colluding-trilateration to examine whether we candiscover the real location of the victim or not. This time, we
just move accounts 2 and 3 to obtain a same result as shownin Figure 7, but do not use the distances shown on these two
fake accounts because they are already obfuscated by Hornet,
thus not correct. However, because those fake accounts

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 3, May 2016

belong to us, we can always measure the real distances
between them as side-channel knowledge without relying on
the one shown in Hornet.
Surprisingly, Hornet perhaps might have alreadyenvisioned about this type of attack and took a step ahead.
What Hornet does is to randomly remove some users fromthe screen of other local users. In other words, at sometpositions, on the screenof account 1, we can see all of thevictim (Vic) and accounts 2 and 3; but at some other positions
we cannot. That means Hornet does not always show all
surrounding local users on one‘s screen. Instead, it arbitrarily
drops some users. As a consequence, we cannot set the upperand lower bounds for the victim‘s distance to conduct thecolluding-trilateration.
In order to bypass this countermeasure, we makecompletely use of the Favorites List of Hornet. Similar to
most of other Social Network platforms, Hornet also has afeature in which a user can add other users to his favorite list
like the ―follow‖ function in Twitter. Then, there is a separate
tab for the user to view his Favorites List in which thefavorite users in this list are also arranged from left-to-rightand top-to-down in an ascending order of their distances
regardless of whether they have disabled the "show distance"
function, the publicly shown distance are obfuscated, or they
are not shown on the local screen of other users. The trick isdemonstrated in Figure 12. (Two accounts at the top-leftcorner are added by default for all users in Hornet topromote sexuality health.)857

position of the victim (Vic) in Hornet although all the
distances are obfuscated and the victim already disabled the―show distance‖ function in his application.
III. OTHER PRIVACY CONCERNS
A. Vulnerabilities from Third-Party Advertisement
Banner and Misconduct in Handling User's Personal
Information
In the age of online marketing, one's privacy is often
threatened by the very advertisements popping up in his
device as mentioned in [17] and [18]. In order to investigate
this issue in Jack‘d and Grindr, we analyze packets captured
while using these two applications. The experiment results
are shown in Figure 13.

Fig. 13 : Information Leak through Third-Party Ads.

Fig. 12 Colluding Trilateration in Hornet bases on Favorites List.

Taking advantage of this Favorites List, the adversary firstneeds to add the other two fake accounts 2 and 3 to his
Favorites List, and then also adds the targeted victim into thislist at the first time he sees the targeted victim on the public
local screen. That is how the adversary can anchor the victim
even when the victim does not appear on his local screen.
Finally, the locating problem becomes a same problem asshown in Figure 4. As a result, we were able to locate theSurprisingly, in both applications, the third-party
advertisements leak many important information of the user
including name of Telecommunications Service Provider (i.e.
KDDI), device's model information (i.e. iPhone 5), countrycode (i.e. JP), and last but not least: the name of theapplications (i.e. Grindr and Jack‘d) which is the mostsensitive information that no any straight-acting person wants
the others to know the existence of such applications in his
phone. It may have no problem if the packets are sent directly
to the ads provider's server. However, what is worth
mentioning here is that the packets are sent in an unencryptedfashion, thus widely open to an attack type known as
man-in-the-middle attack, in which the hacker taps theInternet connection of the victim to eavesdrop the packets.
By analyzing all the captured packets, we were further
shocked by the fact that all the packets containing members'
profile pictures of Grindr are also sent in the air without
encryption, thus being captured and recovered back to theoriginal image files as shown in Figure 14.

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 3, May 2016

858

Fig. 14 Pictures recovered from unencrypted packets in Grindr.

Concerning ethical issue, we only recover image files thatappear on the first screen page of Grindr as evidence, and theusers' avatars are intentionally censored. For Jack‘d, it isvery careless in handling its user‘s private photo, becauseeven when a photo is sent in a private message, Jack‘d does
not use any secure connection to protect the photo. Instead,Jack‘d sends it via HTTP, which is an unsecure transmission
protocol as shown in Figure 15.

Number of Viewers (Upper)
Number of Clicks (Lower)

exploited to perform the linkage attack to retrieve otherpersonal information. To have a clearer view on how thisgimmick is really effective at luring innocent users, we place
our Jack'd accounts in three big cities of Japan which areTokyo, Osaka and Kyoto within 12 hours (from 6PM to 6AMof the following day) to estimate how many innocent andcurious victims could be lured. We choose this time period
because it is the most active usage time according to [20]. To
conduct this task, we had to reboot the virtual machines every
two hours so that our accounts will not disappear from thescreen of other local users, as we found that Jack'd only keeps
an account displayed on other user‘s screen for two hours
since the latest login. The result is illustrated in Figure 16.
25
20
15
10
5
0

18～21 21～00 00～03 03～06

Tokyo

10

10

14

9

Osaka

10

21

8

4

Kyoto

10

24

8

3

Tokyo-click

1

2

2

2

Osaka-click

9

8

3

3

Kyoto-click

3

6

2

0

Fig. 16 Analysis from IP-spy Intrusion Gimmick.

In total, we got 131 viewers from three accounts with 41
viewers clicked through the IP-spy URL we put in the profile.

Fig. 15 Unsecure http protocol used in Jack'd to send private photos.

Taking into account of the above findings, it is obviousthat the user's privacy is not guaranteed at all although thevendor has been alerted to these issues by a security firm
before [11].
As for Hornet, it is not necessary to do this experiment,
because it has been already confirmed in [14] and [15] that
Hornet carefully employs SSL certificates and HTTPS
protocol for its connection.
B. Together with IP Spy and Linkage Attack
Next, as human being is born curious, social engineering
intrusion techniques like phishing is always the easiest but
effective way to compromise people. Since Jack'd provides itusers with a feature to see who viewed his profile withtimestamp, an adversary can put an IP-spy URL into his
profile to promote his appearance, thus being able to obtainthe victim's IP address if the victim feels curious and clicks
on that URL. Nevertheless, as it was discussed in [19] that IP
address is also important personal information which can be

Fig. 17 Linkage Attack with IP-spy and Jack'd timestamp.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 3, May 2016

Among these 41 clicks, we were able to perform linkage
attack to 26 users with high confidence by matching thetimestamp between Jack‘d and our IP-spy server to further
reveal other information including their IP address, ISP,
display language and platform of their devices as shown in
Figure 17.
IV. DISCUSSION AND CONCLUSION
Through this study, we would like to particularly alert theusers of Grindr, Jack'd, and Hornet as well as the users ofother LBS in general about the risk of being located easily
regardless of whether the recent location anonymization andlocation obfuscation approaches have been adopted. By
investigating these three applications, we found a paradoxthat although there have been many attack models proposedby the privacy-preserving researchers, the user‘s location
privacy has not been seriously taken in to consideration by
the LBS provider and the user themselves. As far as we areconcerned, the reason of this negligence derives from bothsides. From the viewpoint of the LBS provider, it might cause
overhead to implement those sophisticated solutions
proposed by the research community, while the utility of theapplication is not really guaranteed, thus probably lead to theloss of its customer. From the viewpoint of the user, it ismaybe because of two reasons. Perhaps, the first one is also
due to the trade-off of utility. The other one is because ofunawareness. For that reason, in this paper, instead of using
complicated mathematical equations and complex algorithms
to show the threat models, we opt for visualizing it on maps
and figures so that even those non-technical readers canunderstand how easily their privacy can be compromised in
the current security condition.
In order to alleviate the risks of man-in-the-middle attack,
IP spy and other side channel attacks as mentioned in Section
III, we urge the LBS providers and involved third-parties tocarefully encrypt the connection from their servers to theusers. For the user, we suggest not opening any URL out of
curiosity. If it is really necessary to open an unknown URL,
the user should turn on VPN at first to prevent the leak of
their real IP address.
For those threat models discussed in Section II, let us argue
that privacy preserving policy is different from person toperson. Especially in GLBT community, some already cameout, thus have no concern about privacy; while some arestraight-acting, thus do not want to be disclosed. Therefore, acentralized solution is not really suitable, and users are thevery ones who need to make decision whether to protect theirown privacy. For the meantime, while waiting for the experts
and the LBS providers to discover a perfect solution forlocation privacy protection without trading off the utility, wesuggest that the user should take a step ahead to protect theirown privacy from those vulnerabilities mentioned in thisstudy. That is to use Fake-GPS applications like the one that
we use in this study (probably also used by most of theadversaries) to hide the real location to an acceptable extent
so that the user can still gain the convenience provided by the
LBS. How far the fake location should be shifted from thereal one depends on how much utility and convenience that auser is willing to trade off with his privacy, thus different
from case by case. We strongly believe that this user-centric
solution not only suits all type of users, but also helps to save859

the vendors from overhead investment in implementing
sophisticated solutions and infrastructures.
Apart from technical methods aforementioned, humanfactor is also important in protecting oneself in the
cyberspace. In order to avoid troublesome problems in the
future when the vendors get hacked as the case of Ashley
Madison [6], the user should not register account to thosehighly sensitive applications under his real name or even apart of his real name. Instead, the user should use informationthat could not be used to link the account with his real-life
personally identifiable information.
Last but not least, in this paper, we of course could have
utilized more complicated techniques to extract and test theaccuracy of the threat models with more users of Grindr,
Jack‘d, and Hornet in bulk. However, as far as we areconcerned with the ethical issue that those compromised
users also have their right to be undisclosed, and there may be
our acquaintances among them. We thus did not go beyond
those accounts created by ourselves.
ACKNOWLEDGMENT
This work was supported by JSPS KAKENHI Grant
Number 15K00423 and the Kayamori Foundation ofInformational Science Advancement.
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860

Nguyen Phong HOANG was born in Tien Giang
Province, Vietnam in 1992. He received his
undergraduate degree in Business Administration
majoring in Information & Communications
technology (ICT) from Ritsumeikan Asia Pacific
University, Japan. He is presently pursuing his
graduate studies at the Graduate School ofInformatics, Kyoto University, Japan. His researchinterests include information security, privacy andanonymous communication. He hopes to advance his
research on Tor (The Onion Router), one of the most robust anonymous
tools, during his graduate studies. He has participated in annual IEEE
International Conference on Advanced Communication Technology(ICACT) since 2014. He also received the Outstanding Paper Award fromthe Technical Program Committee of the conference in the 16th and 18th
ICACT. He has been an IEEE member since 2013, and DBSJ since 2014.
Yasuhito ASANO received the BS, MS, and DS
degrees in information science from the University ofTokyo in 1998, 2000, and 2003 respectively. In
2003-2005, he was a research associate in the
Graduate School of Information Sciences, Tohoku
University. In 2006-2007, he was an assistantprofessor in the Department of Information Sciences,
Tokyo Denki University. He joined Kyoto University
in 2008. He currently serves as an associate professor
in the Graduate School of Informatics. His research interests include web
mining, network algorithms. He is a member of the IEICE, IPSJ, DBSJ, andOR Soc. Japan.
Masatoshi YOSHIKAWA received the BE, ME,
and PhD degrees from the Department of Information
Science, Kyoto University in 1980, 1982, and 1985,
respectively. From 1985 to 1993, he was with Kyoto
Sangyo University. In 1993, he joined the Nara
Institute of Science and Technology as an associate
professor in the Graduate School of Information
Science. From April 1996 to January 1997, he was in
the Department of Computer Science, University ofWaterloo as a visiting associate professor. From June 2002 to March 2006,
he served as a professor at Nagoya University. From April 2006, he has beena professor at Kyoto University. His current research interests encompass
database technologies and their application to medical healthcare domains.
He is a member of the ACM, IEICE, IPSJ and DBSJ.

Copyright ⓒ 2016 GiRI (Global IT Research Institute)