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Performance evaluation of a Volcano MonitoringSystem Using
Wireless Sensor Networks
Roman Lara-Cueva, Member, IEEE, Antonio Caamano, Member, IEEE,
Marco Zennaro, Member, IEEEand Jose Luis Rojo-Alvarez, Senior
Member, IEEE
AbstractWireless Sensor Network have become critical inthe
evolution of Telecommunications. Our interest is related tomonitor
an Active Volcano using wireless sensor networks wherethe
requirement of real-time is mandatory, due to the necessityof
access immediately of the signals derived from a naturaldisaster in
order to determine emergency early warnings. Ouraim was to
determine the number of sensors will be deployedin a Volcano
Monitoring System based on simulation results andcorroborated with
an in-situ testbed. We used ns-2 as simulationtool, where two
scenarios were evaluated. This study determinedthe optimal scenario
in volcano monitoring is Randomly withmaximum eighteen nodes to
satisfy metrics as throughput, timedelay and packet loss. We
deployed sixteen sensors in a strategyarea at Cotopaxi Volcano, the
information was obtained duringthree days of continuous monitoring.
This information was sentto a surveillance laboratory located 45 km
away from the stationplaced at the volcano, a WiFi-based long
distance technology wasused to this purpose. Volcanic information
was processed in time,frequency and scale domain, a spectral
pattern of seismic eventsdetermined four kinds of events,
corresponding to a long periodevents, volcano tectonic earthquakes,
volcanic tremor, and hybridevents.
Index TermsWSN, 802.15.4, throughput, delay, packet
loss,monitoring system, volcano applications.
I. INTRODUCTION TO WSN
Wireless Sensor Network (WSN) have become critical inthe
evolution of Telecommunications, its constant evolutionpermits the
possibility to implement devices with low cost andenergy autonomy,
without periodic maintenance, to be capableof obtaining
environmental information, and for these reasonsCyber-Physical
Systems (CPS), Internet of Things (IoT), andSmart Cities are new
research topics based on technologieslike WSN, all of them are
based in a similar infrastructure ofevery heterogeneous networks
where data must be transmitted,processed, and finally enable people
through any applicationto monitor or to control objects [1][6].
Ecuador has a special interest to use WSN in volcanomonitoring
applications, as it is located in the Pacific Ringof Fire a place
with high seismic activity. WSN systems are
Manuscript received July 5, 2014R.A. Lara is with Wireless
Communication Research Group (WiCOM)
and Ad Hoc Networks Research Center (CIRAD) of the Depart-ment
of Electrical and Electronic Engineering, Universidad de lasFuerzas
Armadas ESPE, Sangolqu-Ecuador, 171-5-231B e-mail:
(seehttp://wicom.espe.edu.ec/contactos.html).
A. Caamano and J.L. Rojo are with Information and
CommunicationTechnology Department, Rey Juan Carlos University,
Camino del Molino s/n28943, Fuenlabrada-Madrid, Espana
M. Zennaro are with ICT for Development Laboratory , The Abdus
SalamInternational Centre for Theorical Physics, Trieste-Italy
much cheaper and reliable than bulky and energy-hungry
tradi-tional systems. Currently, South America lacks of
permanentmonitoring systems deployed in active volcanoes, but
someWSN-based systems have been installed just in a couple ofweeks.
Volcano monitoring using WSN still requires furtherresearch in
order to present information in real-time and tolaunch an early
emergency warning. The main constraint tobe considered as real-time
systems is the time delay causedby processing data, there are some
solutions at Network andMAC layer referred to data acquisition
in-situ, data gatheringand data dissemination which significantly
reduces time delay,but it is impossible to give an early warning
with this kind ofsystems, because data are processed off-line in a
far distancedsurveillance laboratory. The time delay related to
digital signalprocessing and the signal propagation must be solved
withinappropriate processing and telecommunication techniques
[7][9].
Our aim was to determine a number of sensors that max-imize the
network capacity, we considered as main metricsthroughput, time
delay and packet loss in the WSN, andthen corroborated by an
in-situ testbed deployed at Cotopaxivolcano [10].
The rest of the paper is organized as follows. Section2
summarizes previous research on the subject. Section 3describes the
performance evaluation of the WSN in detail.Sections 4 and 5
describe the simulation results obtainedand the experimental study
performed, respectively. Finally,Section 6 presents our conclusions
and future work [11].
II. RELATED WORK
Our interest consist in determining the network behavior,for
that reason we must study the performance network, thiscan be
evaluated by some Quality of Service (QoS) metricssuch as:
availability, reliability, response time, time delay,throughput,
bandwidth capacity, and packet loss ratio. In orderto offer
real-time in WSN with guaranteed QoS metrics, thenetwork must be
analyzed in a different way than traditionalreal-time systems, as
far as WSN requires to need severechallenges due to its wireless
nature, distributed architectureand dynamic network topology. The
state of the art of real-time solutions currently developed have
been presented withemphasis at level of MAC, routing, data
processing, and crosslayer, this denote a direct relationship
between real-time andQoS metrics, as well as new general concepts
related to real-time WSN systems [12] [13]. Real-Time (RT) WSN
could bedefined as a WSN capable of ensuring a Maximum
Sustained
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Traffic Rate (Throughput), a Minimum Latency, and PacketLoss as
main QoS metrics. An ideal development processshould start from the
theoretical analysis of the protocol toprovide bounds and
information about its performance [14],[15], it must be verified
and refined by simulations [16][21], and finally confirmed in a
testbed [22], [23]. We foundseveral works which presented a mixed
analysis, since in realscenarios it is possible to obtain measures
of main metrics as:RSSI, PER, and EED through tools developed by
manufactures[24][26].
III. PERFORMANCE EVALUATION OF THE MAC PROTOCOL
A. Real-Time WSN for a Volcano Monitoring System
Require-ments
For our application we have to consider the environmentpresented
by a Volcano a wild terrain and a lack of energyto implement a WSN.
A mesh topology would present the bestway to communicate sensors in
this kind of scenario, speciallybecause we have to define the
position of the nodes accordingto the requirements that an in situ
visit could give us accordingto the variables to monitor.
It is developed a research about topologies for ad hocnetworks
and it is presented the concept of tessellation, inwhere, this
model could be applied in WSN to obtain regularnetwork formed by
geometrical figures [27], that can bespecified using the notation
of Schlafli [28].
B. Simulation Environment
There is a wide range of simulators that can be used to testWSN
in order to obtain several results to be analyzed basedon [29] we
have chosen ns-2 as simulation tool.
In order to obtain the performance of the network
throughsimulation we have chosen two scenarios: tessellation
andrandomly, for the first one we have chosen a
triangulartessellation pattern network {3, 6}, in the second one
wedefined a random position of the sensor nodes placed on theplane
at a distance of 30 meters each (typical mean value forconnection
in practice).
The number of nodes (n) in the triangular tessellation couldbe
obtained as function of the number of layers C, this is,
n = 1 + 3C(C + 1). (1)
In both scenarios we started with 6 nodes growing until60 nodes
in 6 nodes step each, we defined one coordinator,all transmissions
nodes were directed to the coordinator. Weassumed an event occurred
with a duration equal to 220seconds in an approximated area of 300
300 m.
In our simulation process it was necessary to determinea number
of replications to reduce the mean square error,consequently, we
defined for tessellation scenario to run 6replications, and for
randomly scenario the number of repli-cations depends of the number
of the nodes in the scenario,in other words, we run n replications
in each scenario.
The main simulation parameters have been defined amongothers:
time simulation, topology, routing protocol, transmis-sion rate,
etc., we could cluster all of them in three main
0
20
40
60
80
0
0.02
0.04
0.06
0.08
0.1
0.12
0
50
100
150
200
250
300
Number of nodes (n)Delay (s)
2^(
n+
1)
(%)
Fig. 1. XXXXXX
groups: general parameters, power parameters, and node
pa-rameters, the rest of the parameters to simulate the
networkmodel are detailed in the Tables I, II and III
respectively.
TABLE IGENERAL PARAMETERS
Parameter ValueRadio Propagation Model Two-Ray GroundRouting
Protocol AODVRaw Bit Rate (kbps) 250Antenna Type
DirectionalSimulation Time (s) 260Simulation Time (s) 220
TABLE IIPOWER PARAMETERS
Parameter ValueTransmission Power (dBm) 0 (1mW)Sensitivity (dBm)
-94Transmission antenna gain Gt (dB) 1.0Reception antenna gain Gr
(dB) 1.0Trajectory loss (dB) 1.0
TABLE IIINODES PARAMETERS
Parameter ValueTraffic type FTPTraffic direction all to
CoordinatorPackage size 55 bytesNumber of Coordinators 1
coordinatorDistance between nodes 30 mNumber of nodes 1 to 45
nodes
EnabledBeacon mode Beacon Order:3
Superframe Order:3
The scenarios of both topologies of the networks defined forour
simulations are shown in Fig. 2, to obtain the results ofnetwork
performance and verify the operation with an optimalnumber of
sensors.
C. Performance Metrics
We selected three main metrics required for a
real-timemonitoring: normalized throughput (), end-to-end delay
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150 100 50 0 50 100 150150
100
50
0
50
100
150Tessellation topology
Position x (m)
Po
sitio
n y
(m
)
Coordinator
FFD Nodes
150 100 50 0 50 100 150150
100
50
0
50
100
150Randomly topology
Position x (m)
Po
sitio
n y
(m
)
Coordinator
FFD Nodes
Fig. 2. Tessellation and Randomly Scenarios evaluated.
(EED), and packet loss (PL). As mentioned in the previousworks
there are other metrics that can be considered but wecould
determine that all of them have a direct relation to ourmain
metrics, for example: duty cycle, energy consumption,average
jitter, load factor, and traffic type.
In several meetings maintained with experts in volcanologyfrom
Instituto Geofsico de la Escuela Politecnica Nacional(IGEPN), we
determined that the system must be able to workin a permanent way;
to monitor several variables in a volcano itis not effective to
have a WSN in a saving power mode, for thatreason we did not
consider power consumption metrics. Forthose reasons, with respect
to our metrics we need a maximum, PL must be less than 20%, a
minimum EED, and at least5 stations must be necessaries. For we
used the informationthat permitted us to define the Eq.(2), and we
calculated defined in Eq.(3), meanwhile for EED and PL we used
theinformation obtained in the trace file. We use a tool
developedby Jaroslaw Malek named tracegraph to analyze the trace
fileyielding [30],
Throughput = 8B
ttx
[bits
s
](2)
where, B is the number of transmitted bytes, and ttx is thetime
of transmission in seconds. Also, parameter representsthe
normalized throughput and is obtained as
=Throughput
RBR(3)
where, RBR is the theoretical bite rate = 250 kbps.
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
6 12 18 24 30 36 42 48 54 60 66nodes (n)
Normalized Throughput vs n
a) Tessellation Scenario
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
6 12 18 24 30 36 42 48 54 60 66nodes (n)
Normalized Throughput vs n
b) Randomly Scenario
Fig. 3. Normalized throughput as function of n in both:
Tessellation andRandom scenarios, this metric had an indirect
relationship related to thenumber of nodes.
IV. SIMULATION RESULTS
Figures 3, 4, and 5 show our main metrics related to thenumber
of nodes obtained in Tessellation and Randomly sce-narios. Figure
3a shows an irregular decay of in Tessellationscenario, its maximum
and minimum values were 0.58 and0.28 respectively; its boxplots
corresponded a line becauseits standard deviation was
insignificant. Meanwhile, Fig. 3bshows a directly relationship
between and n in Randomlyscenario, its maximum and minimum values
were 0.57 and0.41 respectively; its boxplot showed a significant
standarddeviation due to its own random nature and the number
ofreplications we have done.
The data suggest PL in both scenarios presented an incre-ment as
an exponential function of n, in Fig. 4a we determinedfor
Tessellation scenario in the range of nodes from 36 to 48presented
an irregularity, meanwhile in Fig. 4b we observedRandomly scenario
increase its PL directly to n, the maindifference between both
scenarios was the Randomly scenariohad less PL than Tessellation
scenario.
Finally, Fig. 5 shows the EED, we observed that in Fig.5a
Tessellation scenario presented more irregularity than Ran-domly
scenario related to this metric, both scenarios presenteda mean
value around to 3 ms.
We found based on the requirements gave from IGEPNrespect to PL
must be less than 20%, Tessellation reached this
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4
0
50
100
150
200
250
300
350
400
450
500
6 12 18 24 30 36 42 48 54 60 66nodes (n)
Pa
ke
t L
oss
PL vs n
a) Tessellation Scenario
0
50
100
150
200
250
300
350
400
450
500
6 12 18 24 30 36 42 48 54 60 66nodes (n)
Pa
ke
t L
oss
PL vs n
b) Randomly Scenario
Fig. 4. Packet Loss as function of n in both: Tessellation and
Randomscenarios, this metric had a direct relationship related to
the number of nodes.
value when n = 12 and Randomly reached this value when n =18,
Randomly scenario permit us to use more nodes with sameor better
characteristics of and EED presented in Tessellationscenario.
V. RESULTS FROM COTOPAXI VOLCANO DEPLOYING
The Wireless Communications Research Group (WiCOM)from
Universidad de las Fuerzas Armadas ESPE, developed afirst attempt
for replicating the predecessors works by usingMicaz and Iris
platform. Thus, Cotopaxi, which is currentlythe highest snowcapped
volcano on Earth, was selected fordeploying a WSN. This work was
deployed at an altitude of4870 meters, where 16 sensor nodes were
implemented, anddata were collected continuously for three days,
Table. IVsummarize the comparison among previous works and
ourdeployment.
0
0.5
1
1.5
2
2.5
3
3.5
4
x 103
6 12 18 24 30 36 42 48 54 60 66nodes (n)
En
d
to
End
EED vs n
a) Tessellation Scenario
0
0.5
1
1.5
2
2.5
3
3.5
4
x 103
6 12 18 24 30 36 42 48 54 60 66nodes (n)
En
d
to
En
d
EED vs n
b) Randomly Scenario
Fig. 5. End-to-end delay as function of n in both: Tessellation
and Randomscenarios, this metric had a quasi-constant relationship
related to the numberof nodes.
TABLE IVCOMPARISON DEPLOYMENTS
WSN Variables/ Number Operation Frequency DurationDeploy.
Platform sensors frequency Sampling (Days)
(MHz) (Hz)[31] Aa/Micaz 3 2400 100 2[32] SAb/Tmote 16 2400 100
19[33] SA/Imote2 5 900 1000 AbidingProposed Sc/MicazWork e Iris 16
2400 100 3
aAcusticbSeismo-AcusticcSeismo
a) MICAz motes deployed on Volcano b) IRIS motes deployed on
Volcano
Fig. 6. Wireless Sensor Networks deployed on Cotopaxi
Volcano
One of the problems of this network is their dependence on
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an aggregator, added to the information that must be
storedbefore being transmitted to the surveillance laboratory
througha wireless link of 40 km of distance where information
isprocessed in order to determine whether or not the events
arefalse alarms, this is the main problem to be solved, the
signalprocessing is off-line, because of that, it is not possible
toguarantee a real-time monitoring data [34].
Our first visit to the Cotopaxi Volcano was for locatingthe
geographic coordinates for placing the wireless commu-nications
system (0o 39 49 S, 78o 26 17 W), and todetermine the necessary
requirements for WSN deployment onCotopaxi Volcano. In our second
visit WSN were deployed,at an altitude of 4870 meters, two
differents WSNs, oneof them consisted of 10 motes MICAz, and
another with 6motes IRIS with MTS400 and MTS310 sensor cards
usingtwo gateways MIB520 each. Mote Config 2.0 was the softwareused
to configure nodes. FTSP was implemented for timesynchronization.
Fig. 6 shows the location of the two networksdeployed. Data were
collected continuously for three days. Theenergy problem was solved
with a generator placed in situ. Theinformation was stored in a
central station placed in situ, thenit was transmitted to the
surveillance laboratory, located at adistance of 40 km from volcano
to ESPE, through a wirelesslink.
A. WiFi-Based Long Distance Link
A wireless link was used for transporting data sensed byWSN, it
has proved to be cost effective for long distanceapplications. The
two major limitations for using WiFi overlong distances (WiLD) are
the requirement for line of sightbetween the endpoints and the
vulnerability to interference inthe unlicensed band. Two further
hurdles have to be overcomewhen applying WiLD Technology, power
budget and timinglimitations. The first was easily solved by using
high gaindirectional antennas, while the timing issue was addressed
bymodifying the media access mechanism, as done by the TIERgroup at
the University of Berkeley [35].
For our purposes IEEE 802.11b was selected, basically,because
the 2,4 GHz ISM band presents less losses that5,8 GHz ISM band, we
used antennas gain of 24 dBi andthe transmission power of 1 watt.
Meanwhile in the MACsublayer, three types of limitations can be
extracted: the timerwaiting of ACKs, RTS/CTS and the definition of
time relatedto the slottime. We used Alix boards, in which we
embeddeda middleware that permit us to modified these parameters
inorder to link endpoints according. The performance of the linkwas
determined using DITG traffic injector, the injection timewas 1
minute, Fig. 7 is a portion of total file analyzed showingthat the
mean throughput obtained is less than 2 Mbps anda packet loss is
less than 5%, this data rate is enough fortransmitting information
sensed by our WSN, but the packetloss must still be improved.
B. Seismic Signal Analysis
For a better representation of the signal is necessary tohave a
representation in the time and frequency domain. UsingFourier
Transform, it is possible to detect the presence of a
a) Throughput b) Number of packet loss
Fig. 7. Parameters of Cotopaxi-ESPE link
certain frequency but it does not provide information aboutthe
evolution over time of the spectral characteristics, forthis reason
it is important to note that a spectrogram wasconstructed using the
Short Time Fourier Transform (STFT)with a sampling frequency of 100
Hz.
The information in time and frequency domain of seismicsignals
on a determined time often cannot be known, therefore,we cannot
define if the spectral component exists at any instantof time, the
only thing that can be distinguished are the timeintervals at
certain frequency bands where we can find spectralcomponents.
Since seismic signals are non-stationary, it is necessaryto vary
the size of the window, for this reason, we usedscale domain
through wavelet analysis. The seismic signalsrecorded was processed
using discrete and continuous wavelettransform, in order to
discriminate the occurrence of a par-ticular seismic event. The
mother wavelet employed is DB6(Daubechies 6), in the project were
used decomposition levels2 and 4 for the records of MICAz motes and
IRIS motesdeployed respectively due to the amount of data they
hold.We must define if the event belongs to a volcanic
tremor,hybrid, long period or volcano tectonic events that permit
usto determine an abnormal behavior.
In Fig. 8, it was determined that the spectral componentspresent
in the frequency range [1,25 - 2,5 Hz] are stableand present a low
energy, this signal corresponds to a noiseseismic event. Meanwhile,
in Fig. 9, the signal spectral contentbetween [2,5 - 5 Hz] shows a
seismic noise in a short period,this is due to sudden temperature
changes. In Fig. 10, thesignal between [5 - 10 Hz] is equally
distributed in energy,its acceleration remains constant during all
time, this eventcorrespond to a volcanic tremor, this event is
associated togas outlet due to high pressures. Finally Fig. 11
shows anothercase, a high frequency volcanic tremor, signal between
[10 -20 Hz] it is related to strong gas outlet inside of the
crater.
VI. CONCLUSION AND FUTURE WORKS
We determined the most optimal scenario in volcano mon-itoring
is Randomly, it presents the best performance relatedto all our
metrics, to reach the PL 20% and the mean valuereached to EED = 3
ms, with its consequent of maximizationthroughput. In this range we
determine that the maximumthroughput is approximately equal to 145
kbps. This wascorroborated with an in-situ deployment, we determine
a PLequal to 25%, the mean value of EED = 1,1 s and a
maximumthroughput equal to 130 kbps, the main difference
between
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Fig. 8. Noise seismic event determined after digital processing
signal
Fig. 9. Short period noise seismic determined after digital
processing signal
simulation and testbed was EED, data suggest this differencedue
to the processing data must take to much time and thisvalues is
unconsidered in simulation tools.
As future work we will modeling this system consideringthe value
of EED related to processing data, moreover we areinterested in
feature extraction of volcano signals in order toclassify
automatically these kind of events.
VII. ACKNOWLEDGMENTS
The authors gratefully acknowledge the contribution
ofUniversidad de las Fuerzas Armadas ESPE for the economicalsupport
in the development of this project through the
WirelessCommunications Research Group (WiCOM).
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