Wireless Technology for Monitoring Site-specific Landslide in Vietnam

International Journal of Electrical and Computer Engineering (IJECE)
Vol.8, No.6, December 2018, pp. 4448~4455
ISSN: 2088-8708, DOI: 10.11591/ijece.v8i6.pp4448-4455  4448
Journal homepage: http://iaescore.com/journals/index.php/IJECE
Wireless Technology for Monitoring Site-specific Landslide
in Vietnam
Gian Quoc-Anh1
, Nguyen Dinh-Chinh2
, Tran Duc-Nghia3
, Tran Duc-Tan4
, Kieu Thi Nguyen5
,
Kumbesan Sandrasegaran6
1,2,4
Electronics and Telecommunication Faculty, VNU, Hanoi-University of Engineering and Technology, Vietnam
1
Department of Electronics, Nam Dinh University of Technology Education, Vietnam
3
Institute of Information Technology, Vietnamese Academy of Science and Technology, Vietnam
5
Faculty of Mechanical, Electrical, and Electronic Engineering, Nguyen Tat Thanh University, Vietnam
6
Faculty of Engineering and IT and CRIN, University of Technology Sydney, Australia
Article Info ABSTRACT
Article history:
Received Feb 2, 2018
Revised Jun 1, 2018
Accepted Jun 14, 2018
Climate change has caused an increasing number of landslides, especially in
the mountainous provinces of Vietnam, resulting in the destruction of vital
transport and other infrastructure. Current monitoring and forecasting
systems of the meteorology department cannot deliver accurate and reliable
forecasts for weather events and issue timely warnings. This paper describes
the development of a simple, low cost, and efficient system for monitoring
and warning landslide in real-time. The authors focus on the use of wireless
and related technologies in the implementation of a technical solution and
some of the problems of the wireless sensor network (WSN) related to power
consumption. Promising compressed sensing (CS) based solution for
landslide monitoring is discussed and evaluated in the paper.
Keyword:
Compressed sensing
Landslide monitoring
Power consumption
Real-time warning system
Sensors
Wireless Sensor Network
Copyright © 2018 Institute of Advanced Engineering and Science.
All rights reserved.
Corresponding Author:
Tran Duc-Tan,
Electronics and Telecommunication Faculty,
VNU, Hanoi-University of Engineering and Technology,
E3 building, 144 Xuan Thuy, Cau Giay, Ha Noi, Vietnam.
Email: tantd@vnu.edu.vn
1. INTRODUCTION
Climate change has caused an increasing number of landslides, especially in the mountainous
regions of Vietnam. These landslides can cause a disastrous effect on the neighboring communities as well as
the local infrastructure and economy [1]. Landslides can be broadly classified into four main types: pre-
existing, rainfall-induced, earthquake-induced, and endogenous landslides. In Vietnam, most landslide events
are triggered by rainfall and their harm is serious [2], [3].
There are two types of monitoring in Landslide Systems, namely short term and long term [4]-[8].
Long-term monitoring uses a combination of remote sensing data from satellites, global positioning system,
geographic information systems, and related mathematical models to predict landslides over large time
intervals. Short-term monitoring identifies the early signs of the landslides using a combination of many
sensors such as acceleration, soil, rain and temperature.
Wireless communication networks form a critical enabling technology of Landslide Monitoring and
Real-time Warning (LMRW) systems. Wireless Sensor Network (WSN) and other wireless technologies are
the most appropriate technology for landslide monitoring due to difficult to access terrain, ease of
maintenance, cheap and quick installation along with the need to satisfy the important real-time requirement
of LMRW system [5], [7], [9], [10]. Most rural communities in Vietnam have at least 2G coverage, which

Int J Elec& Comp Eng ISSN: 2088-8708 
Wireless Technology for Monitoring Site-specific … (Tran Duc-Tan)
4449
will be upgraded to 3G and 4G networks in the near future. Furthermore, these systems will provide vital data
for national weather monitoring systems.
A number of LMRW have been reported in the literature today. In Idukki, India, a complex and
elaborate LMRW deployment of 50 sensors and 20 WSN nodes was described in [5]. Another
deployment [9] uses sensor node buried in the slope to detect movement signal and combines with soil
parameters for predicting time of landslide. A low-cost solution to detect landslide in [10] utilizes
accelerometers to evaluate the landslide risk, but the effect of other parameters such as rainfall has not been
considered. Other researchers [11], [12] have used saturated hydraulic conductivity and infiltration of
rainwater into slopes along with computer models and simulations to predict landslide risk but their ability
for real-time and robust predictions is questionable. Moreover, power consumption for WSN is also an
important issue.
This paper describes the use of wireless technology for a simple, low cost, and efficient landslide
monitoring and real-time warning system with an integrated rain gauge which provides the rain data. We
focused on an innovative development of Compressed Sensing (CS) based algorithm for landslide risk
monitoring. The algorithm finds the properties of data acquired in the time domain. Also, the algorithm is
specially developed to overcome the problem of power consumption. Wireless data transmission module
ZigBee uses the 802.15.4 standard for wireless communication. ZigBee modules also provide the sleep mode
to save power consumption. Data is then transmitted to a server using a 3G/2G mobile network. At the server
information received from the remote equipment is compared with the predetermined thresholds established
by experts in the field to predict likelihood of landslide. The data received from sensor nodes can be
monitored on the project website and warning messages are sent to registered users mobile phones.
2. SYSTEM IMPLEMENTATION
Figure 1 shows the implementation of LMRW where the terrain is divided into two areas: safe areas
(for rain gauge) and potential slide areas where sensing and transmitting nodes are placed.
Figure 1. Landslide monitoring system
2.1. System Topology
Figure 2 shows the topology of the system. This network has several sensor nodes that communicate
using a Zigbee protocol with a sink node that is part of the data logger. The rain gauge is connected to the
data logger through a wired communication link. The logger transmits data to a database through the Internet.
The information in the database is used to update the website and send SMS alerts to the community.

 ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 8, No. 6, December 2018 : 4448 - 4455
4450
Figure 2. System topology
2.2. Sensor Implementation
The sensor column consists of major components: sensors, microprocessors, transmitter and
rechargeable battery (see Figure 3(a,b)). Three types of sensors are used in this implementation, namely soil
moisture, temperature and accelerometer (tilt meter and geophone) sensors. The microprocessor receives
sensor data as input and executes a number of processes, for example, filtering noise, calibrating sensors, etc.
Thereafter, the wireless module, XBeePRO [13] based on 802.15.4, transmits the processed data to a central
computer at speeds 250 kbps on the 2.4 GHz band with 50mW transmitted power. The power supply is
provided through a battery with sufficient capacity to operate for one rainy season. Figure 3(b) shows the
photo of a sensor column.
Figure 3. (a)Block diagram of sensor system; (b)Sensor implementation
The Weather Station WS-3000 was used in the project to collect weather data such as wind
direction, wind speed, and rainfall. The accuracy of the WS-3000 was tested against other weather stations
and the results show the device to be accurate and reliable, but inexpensive.
2.3. Embedded Computing Module
In this paper, the Waspmote using ATmega1281 microcontroller was chosen to connect and process
information received from sensors. The Atmega1281 are suitable for engineering a sensor node, which has
battery constraint, due to advantageous characteristics such as high performance, low power, etc. The data
which sensor nodes gathered on the slope is transmitted wirelessly to the sink node, and subsequently
delivered to the central computer where the data is transferred to a MySQL database installed on the web
server. The users monitor remotely information through the web application as shown in Figure 4.

4451
Figure 4. Web interface for LMRW
2.4. Warning SMS to any Phone
To broadcasting an alert message to any phone, the central computer is connected to a GSM/GPRS
module. In the warning state, an alert message is issued and automatically sent to the responsible people as
shown in Figure 5.
Figure 5. Phone alerts
3. SYSTEM DESIGN
In the early work [14], the environmental measurements were sent in the discrete-time without any
compression. Data recorded from sensors in LMRW is primarily low frequency data. In the current system,
the authors redesigned the system to reduce the amount of transmitted data and save power. In this paper,
compressed sensing (CS) technique was used to reduce the data transmission [15] by using the Fourier
Transform to convert data to the frequency domain from the time domain and sending it along with the
corresponding Fourier coefficients. By receiving the transmitted data, a nonlinear algorithm would be applied
to reconstruct the original data.
CS is an efficient technique that employs a compact number of samples to reconstruct a sparse
signal through using of nonlinear algorithms, such as Orthogonal Matching Pursuit or l1 norm [16], [17].
Some published works applied this technique: information systems [15], biomedical systems [18-20],
networked systems [21], communication systems [22]-[24], robotic systems [25]. This technique consists of
two main processes: 1) random or chaotic undersampling and 2) nonlinear reconstruction. In many
applications, undersampling will help to reduce a large number of measurements, and thus, reduce the power
consumption. Nonlinear reconstruction often consumes more times compared to the reconstruction without
CS. However, it is not such a disadvantage in many applications.

 ISSN: 2088-8708
4452
However, the requirement of the signal of interest x is that it must be sparse in the form of a linear
representation Φ. After that, the undersampling process is made where the equivalent measurement matrix is
denoted by Ψ. Consequently, the measurements are given by y=Θs, where Θ=ΨΦ. The target of this work is
clear that it is needed to reconstruct x from y (or s from y).
4. RESULTS AND DISCUSSION
In this work, a deterministic basis created by a sequence of pseudo-random is proposed to substitute
for pure random basis. In comparison with CS, the strength of this solution is that this sequence can be easily
installed into the microcontrollers before assembling the sensor nodes in the field site. Therefore, the paper
considers a dynamic deterministic system whose characteristic is deterministic nonlinear. The deterministic
compressed sampling technique is equivalent to the random one on the accuracy of outcome [26]. The
authors used a logistic map based dynamic structure which is transformed into a sequence that would have a
Gaussian-like behavior:
( 1) ( )(1 ( ))q n q n q n   (1)
where ρ is the control parameter [27]; the initial condition q(0) seriously effects the dynamic of
Equation 1. q(0) changes a small value will quickly result a big change in the value of q(n). The sparse signal
can be reconstructed by using the l1-regularized least squares method [20]. The solving problem is
(2)
where λ is a consistency tuning constant,and Fu is the under sampled Fourier operator. Data of temperature,
acceleration, and moisture are reconstructed at the receiver which are shown in Figures 6-8. The data is
reduced by a factor of 25%.
It can be seen from Figure 6 that the temperature increases from 30o
C to 33o
C and it can be noted
that the average difference between the reproduced data and the original one is only 0.58%. In the second
scenario as shown in Figure 7, the data of moisture can be reconstructed with an average error of 1.51% in
comparison with the original one.
Figure 6. Data from temperature sensor, r=0.25 Figure 7. Data from moisture sensor for r=0.25
The Figure 8 shows the variation between the reconstructed and original data from the acceleration
sensor for a low compression ratio of r=0.25. It can be observed that there is considerable error between the
actual and sensed data for this value of compression ratio. Next, the authors investigate the best compression
ratio.
 2
2 1
2
argmin u
x
u
F x y x
subject to F x y


  
 

4453
Figure 8. Data from the acceleration sensor for r=0.25
Figure 9. Effect of compression ratio on relative
error.
To analyze the performance of the reconstruction system and the effect of compression ratio, a
parameter called the relative reconstructed error (e) is proposed:
1
ˆ| |1
100%
ˆ| |
L
i i
i i
x x
e
L x

  (3)
where L is the total number of data used for calculation, x denotes the original data and ˆx denotes the
reconstructed one.
Figure 9 indicates the influence of the compression ratio on the relative error. It is observed that the
error is larger for low range compression ratios between 0.25 and 0.5. If the compression ratio is greater than
or equal to 0.55, the error is quickly reduced to zero. These results suggest that the k-space data is decent
enough for reconstruction of the original data. For the future work, a compression ratio of r=0.55 is selected
based on this result.The algorithm finds the properties of data acquired in the time domain. It is specially
developed to overcome the problem of power consumption. Wireless data transmission module ZigBee uses
the 802.15.4 standard for wireless communication. ZigBee modules also provide the sleep mode to save
power consumption.
5. CONCLUSION
This paper described a successfully implemented LMRW with an operational scheme for
transmitting compressed data that were acquired from three different sensors. Properties of data acquired in
the time domain are exploited toapply Compressed Sensing technique for power saving. A number of
wireless technologies were used in the real-time system design based on system, power and data rate
requirements. A finite number of Fourier coefficients of time-domain data were transmitted and the amount
of data transmitted was halved thus reducing the power consumption. This prototype can form the basis of
solving real world problems related to natural disasters and to assist communities throughout the globe.
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BIOGRAPHIES OF AUTHORS
Gian Quoc-Anh was born in 1981. He received the B.S. degree in Physics from VNU, Hanoi -
University of Science in 2003 and M.S. degree in Electronics and Telecommunication
technology from VNU, Hanoi -University of Engineering and Technology (UET) in 2010. He
is currently working towards the Ph.D. degree in Electronic Engineering at VNU-UET. His
research interests are applications of digital signal processing and embedded systems.

4455
Nguyen Dinh-Chinh received the B.S. degree in Electronic and Telecommunication from
Vietnam National University, Hanoi – University of Engineering and Technology in 2014 and
M.S. degree in Electronic and Communication from the same university in 2017. His research
interests are digital signal processing, machine learning and embedded systems
Tran Duc-Nghia was born in 1986. He is a scientist at Institute of Information Technology
(IOIT), Vietnam Academy of Science and Technology (VAST). He is currently a PHD student
of 'Drug, Toxicology, Chemistry, Imageries' (MTCI ED 563) doctoral school of Sorbonne Paris
Cité (France). His research interests are mathematics and signal processing, Electron
Paramagnetic Resonance (EPR), parameter estimation, data analysis. In his thesis, he focuses
on signal processing of EPR spectra for in vivo experiments. He did his master in 'Science in
Information Technology' at University of Engineering and Technology, VNU.
Tran Duc-Tan was born in 1980. He received his B.Sc, M.Sc, and PhD. degrees respectively in
2002, 2005, and 2010 at the University of Engineering and Technology (UET), Vietnam
National University – Hanoi, Vietnam (VNUH), where he has been a lecturer since 2006. He
was the recipient of the Vietnam National University, Hanoi, Vietnam Young Scientific Award
in 2008. He is currently an Associate Professor with the Faculty of Electronics and
Telecommunications, University of Engineering and Technology, Vietnam National
University, Hanoi, Vietnam. He is the author and coauthor of 30 papers on MEMS based
sensors and their application. His present research interest is in DSP applications.
Kieu Thi Nguyen was born in 1983. She is a head department of mechanical, Faculty of
Mechanical, Electrical, and Electronic Engineering, Nguyen Tat Thanh University, Vietnam.
She did her master at HCMC University of Technology and Education, Vietnam. Her research
interests are network and signal processing.
Kumbesan Sandrasegaran is an Associate Professor at UTS and Centre for Real-Time
Information Networks (CRIN). He holds a Ph.D. in Electrical Engineering from McGill
University (Canada)(1994), a Master of Science Degree in Telecommunication Engineering
from Essex University (1988) and a Bachelor of Science (Honours) Degree in Electrical
Engineering (First Class) (1985). His current research work focuses on two main areas (a) radio
resource management in mobile networks, (b) engineering of remote monitoring systems for
novel applications with industry through the use of embedded systems, sensors and
communications systems. He has published over 100 refereed publications and 20 consultancy
reports spanning telecommunication and computing systems.

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