A WIRELESS SENSOR NETWORK FOR MONITORING ATMOSPHERIC AGGRESSIVENESS IN METALS

International Journal of Ad hoc, Sensor & Ubiquitous Computing (IJASUC) Vol.2, No.4, December 2011
DOI : 10.5121/ijasuc.2011.2403 33
A WIRELESS SENSOR NETWORK FOR MONITORING
ATMOSPHERIC AGGRESSIVENESS IN METALS
Pablo Pancardo1
, Ebelia del Angel2
and Miguel A. Wister1
Juarez Autonomous University of Tabasco, Tabasco, Mexico
1
Informatics and Systems Division
2
Engineering and Technology Division
{pablo.pancardo, ebelia.delangel, miguel.wister}@ujat.mx
ABSTRACT
Humid tropical climate favours the existence of a corrosive atmosphere that causes deterioration of metals.
This article describes an automated system for monitoring environmental values (temperature and relative
humidity) in order to know the time of wetness (TOW), which is key factor in determining the atmospheric
aggressiveness which are exposed the metals used, for example, in industrial facilities. System is
implemented on a wireless sensor network and the main function of the software developed is to count the
time of wetness which is considered the effective time in which metals corrode. System was designed
considering the user requirements as the selection of the frequency of measurements, the calculation of
TOW and verification of the residual energy of sensor nodes. The results show the effectiveness of the
technology used, so that, we can conclude that this type of networks represent a feasible alternative for
automated monitoring of corrosion in metals.
KEYWORDS
Wireless Sensor Network, atmospheric aggressiveness, time of wetness, corrosion, humid tropical climate
1. INTRODUCTION
The process of atmospheric corrosion of metals and alloys is affected by several climatic
parameters such as relative humidity, temperature, wind speed, rainfall, solar radiation, and
etcetera. However, one of the most important factors involved is the temperature - relative
humidity complex (T-RH-C) in the atmosphere and the wetting time (time of wetness, TOW) on
metal surface, during which develops corrosion process. Relative humidity (RH) is the ratio of the
amount of water vapor in the air and its saturated value, expressed in percentage, and the
temperature is an inverse function of relative humidity. The TOW is the time during which
condensation occurs on a surface exposed to the environment, forming a thin film of moisture or
water droplets, which acts as an ionic conductor (electrolyte) for the initiation of the corrosion
process [1 - 2]. The TOW is determined based on certain critical values of RH and T, when the
condensation initiates. According to ISO 9223:92 [3], the TOW can be calculated from the T-RH-
C, considering those hours of moisture when the RH ≥ 80% and 0 ≤ T ≤ 25 ° C in clean air, free
of contaminants. Our scenario for system deployment in Tabasco, Mexico is part of the humid
tropical climate with average annual temperature of 26 ° C and annual mean RH 77%, likewise,
with heavy rainfall in certain periods, usually from July to December.
In order to determine atmospheric aggressiveness on air pipes in rural industrial facilities we
propose the use of a Wireless Sensor Network (WSN), which is composed of nodes with
processing capabilities, communication and measurement of environmental conditions. The nodes

34
are physically small and inexpensive; therefore, they have limited resources in terms of
bandwidth, power, wireless communication, memory and storage. It's envisioned such networks
as an opportunity to make an agile and flexible deployment phase, given the nature of wireless
nodes, which allows you to monitor environmental conditions that cause atmospheric
aggressiveness of metals [1].
The proposed system is intended to be an early warning of atmospheric aggressiveness which is
exposed metals that comprise the air pipes in rural areas free of contaminants. The hypothesis is
that the measurements made with the proposed system are as effective as those performed with
conventional equipment and more efficient in terms of automation and convenience. Under these
facts, the system is suitable to monitor physical parameters that need to be known.
2. RELATED WORK
Metal corrosion can be defined as the destructive attack of a metal through interaction with its
environment. Corrosion of metals takes place through the action of electrochemical cells. As in
all chemical reactions, corrosion reactions occur through an exchange of electrons. In
electrochemical reactions, the electrons are produced by a chemical reaction, the oxidation.
Although this single mechanism is responsible, the corrosion can take many forms. Some of them
are: Microbiological corrosion, which is caused or promoted by microorganisms, usually
chemoautotroph. Another type is erosion-corrosion that refers to a corrosion process enhanced by
the action of flowing fluids. Fretting corrosion represents other type, which is an attack that is
accelerated by the relative motion of contacting surfaces. Despite these types of corrosion we
focus on the atmospheric corrosion that varies drastically depending on the geographical location.
There are some related works, although no one of them refers to atmospheric corrosion. In [4],
Sridhar and Tormoen use a mobile sensor network to monitor corrosion in pipelines, then, they
refer to erosion corrosion. Hedley et.al. [5] developed a sensor network for structural health
monitoring. In this case the project focus on detecting and characterizing damage from high-
velocity impacts in the skin of aerospace structures that lately causes corrosion, so, they are
considering a kind of fretting corrosion. Other research can be found in [6], [7]. Moreover, our
proposal is a preventive tool because it allows to anticipate and predict the corrosion levels given
environmental conditions, other proposals relate to tools to measure corrosion when it already
exists.
3. SCENARIO CHARACTERISTICS AND CONDITIONS
Due to the very high annual time of wetness (TOW) values, which provide conditions for higher
corrosion rates, even in the presence of low levels of contaminants, compared to different
moderate climates. The tropical humid climate of southeastern Mexico and the Gulf of Mexico
creates large annual TOW. In rural sites (more than 30 km from the sea shore), corrosion usually
doesn't occur during the day because the air relative humidity is below the critical value (<80%)
for moisture formation on the metal surface, but many times it is above 80% during the night,
therefore, corrosion occurs. Moreover, it is very important to notice that in tropical humid
climates, the TOW coincides with high temperature range. For example, at marine coastal sites,
64–73% of TOW period occurs when the temperature range is between 25 and 30°C; in rural
sites, 54–64% of TOW occurs in a lower temperature range 20–25°C (each 10°C difference
corresponds to a tenfold, e.g. by one order of magnitude acceleration of the corrosion process). In
our scenario are considered steel pipes coated with zinc, which is a metal used as a corrosion

International Journal of Ad hoc, Sensor & Ubiquitous Computing (IJASUC) Vol.2, No.4,
protector. However, given the climatic conditions of the stage, these pipes protected with zinc are
not exempt of corrosion.
The ability of zinc to galvanically protect steel has made this metal important from an industrial
point of view [1–2]. Zinc is relatively resistant to corrosion in neutral environments [
increasing its localized corrosion degradation in marine
of airborne particles [9]; it is very sensitive to any change of atmosphere acidity, e.g., SO2
presence.
Although zinc is available in many forms, such as sheet, plate, strip, and pressure die castings, its
main use is for corrosion control as a coating
steel (air pipelines, oil rigs, offshore structures, and bridges).
Particularly, zinc in galvanized hot dip steel is a common metal used for effective corrosion
protection of steels (laminated roofs, f
very often exposed to outdoor atmospheres with different corrosive aggressiveness.
The period during which a metallic surface is covered by adsorptive and/or liquid film of
electrolyte, necessary for initiation and progress of the atmospheric corrosion, is defined as time
of wetness (TOW) of the metal surf
or calculated. Therefore, TOW as a fraction of the total exposure time, determines the
the electrochemical corrosion process and denotes explicitly when the mass transport and charge
transfer occur during the electrochemical corrosion, and usually TOW does not coincide with all
time exposure of metal to atmospheric conditions. D
metal surfaces in order to understand the process controlling atmospheric corrosi
gaining attention [11]. The time of wetness of zinc surface, under wet
different drying periods, is a controlling parameter for the corrosion process kinetic and specifics:
the shorter drying period in each cycle leads to higher amount of corrosion products on the metal,
because the surface has been under wet conditions for longer periods, neces
development of the corrosion process [
distribution of TOW in different temperature ranges, in order to explain metal corrosion process
and its rate [11].
4. SYSTEM OVERVIEW
Our system proposal follows a simple single hop system layout. It’s a single level hierarchical
model, where sensor nodes are at the bottom of the hierarchy. A
readings to the base station, and the base station serves those data to users via
1 shows a sketch of the network architecture.
cally protect steel has made this metal important from an industrial
2]. Zinc is relatively resistant to corrosion in neutral environments [
increasing its localized corrosion degradation in marine-coastal atmospheres due to the presenc
]; it is very sensitive to any change of atmosphere acidity, e.g., SO2
main use is for corrosion control as a coating on steel and as Zn anodes for cathodic protection of
steel (air pipelines, oil rigs, offshore structures, and bridges).
protection of steels (laminated roofs, fences, containers and tubes for water transport, etcetera),
very often exposed to outdoor atmospheres with different corrosive aggressiveness.
for initiation and progress of the atmospheric corrosion, is defined as time
of wetness (TOW) of the metal surface, according to ISO 9223:92 [10], which can be measured
or calculated. Therefore, TOW as a fraction of the total exposure time, determines the
time exposure of metal to atmospheric conditions. Due to this fact, the study of the wetting of
metal surfaces in order to understand the process controlling atmospheric corrosi
]. The time of wetness of zinc surface, under wet–dry cyclic conditions with
iods, is a controlling parameter for the corrosion process kinetic and specifics:
because the surface has been under wet conditions for longer periods, neces
development of the corrosion process [12]. It is highly recommendable to calculate the
follows a simple single hop system layout. It’s a single level hierarchical
model, where sensor nodes are at the bottom of the hierarchy. All sensor nodes send their sensor
readings to the base station, and the base station serves those data to users via the network. Figure
sketch of the network architecture.
Figure 1. System overview.
December 2011
35
cally protect steel has made this metal important from an industrial
2]. Zinc is relatively resistant to corrosion in neutral environments [8],
coastal atmospheres due to the presence
]; it is very sensitive to any change of atmosphere acidity, e.g., SO2
on steel and as Zn anodes for cathodic protection of
ences, containers and tubes for water transport, etcetera),
for initiation and progress of the atmospheric corrosion, is defined as time
], which can be measured
or calculated. Therefore, TOW as a fraction of the total exposure time, determines the duration of
ue to this fact, the study of the wetting of
metal surfaces in order to understand the process controlling atmospheric corrosion has been
dry cyclic conditions with
iods, is a controlling parameter for the corrosion process kinetic and specifics:
because the surface has been under wet conditions for longer periods, necessary for the
]. It is highly recommendable to calculate the
follows a simple single hop system layout. It’s a single level hierarchical
ll sensor nodes send their sensor
the network. Figure

36
5. MATERIALS AND METHODS
For the implementation of wireless sensor network, Tmote Sky modules [13] with temperature,
humidity and light measurement capabilities have been selected. Tmote Sky platform consists of
a wireless board with embedded sensors. It is a board based on the MSP430 microcontroller with
a compatible radio chip 802.15.4 standard with a megabyte of external flash memory. Humidity
and temperature is measured using the sensor of Sensirion SHT11 [14]. The nodes use two
standard AA batteries as a power source. The sensor nodes use TinyOS [15] operating system,
which is a reduced version of operating system for sensor nodes. This operating system is built
using nesC, a small open language with minimal instruction which has its foundations in the C
language.
Open source software components programmed in nesC [16] were used for acquisition of the
data. Data flows of the nodes reach the base station (Personal Computer), by means of a sensor
node (base node) inserted into the USB port of the base station. For communication between the
base station (PC) and the sensor node placed in its USB port is used
net.tinyos.sf.SerialForwarder[15] application, a java-based applet that creates a TCP socket to
allow data to be shared with other PC applications. It takes incoming messages from the UART
(Universal Asynchronous Receiver-Transmitter) and forwards on an Internet connection so that
the PC-based applications can use the data. SerialForwarder also works in another direction:
forward requirements packets to the UART and sensor node attached to the computer forwards
the message via radio transmission.
In the base node is installed a program called BaseStation (available from the TinyOS
components) to process the data that come from nodes and convert them to a text file, which users
can access or download this file from the base station and store in their respective computers,
where they can analyze.
The data that are coming to the base station can be viewed on the screen of the base station
because it uses a modified Java program based on TinyOS Oscilloscope tool. Oscilloscope
modifications have been made necessary to meet user requirements and a tool called MIG
(Message Interface Generator) has been used to create a Java object-message and thus to get
criteria and events of interest user into the system. The modified Java program contains both
elements for network management and dynamic configuration options for data acquisition
models: event-driven, periodic and on-demand.
Deployed sensor network provides three measurement readings: humidity, temperature and
internal voltage. The internal voltage reading is used to determine the amount of battery power
currently residing in the sensor node. And therefore, determine the advisability of replacing the
batteries when the voltage is less than 2.6, the limit under which the module manufacturer does
not guarantee the effectiveness of the readings.
Frequency radio system used by the modules is in the 2.4 GHz band. This band belongs to the
radio bands originally reserved internationally for industrial, scientific and medical projects. In
general, communications equipment must accept any interference generated by a computer ISM
(Industrial, Scientific and Medical). In recent years, these bands have been shared with
communications applications fault-tolerant and unlicensed, such as wireless local area networks
within the bands of 915 MHz, 2450 MHz and 5800 MHz.
The selected relative humidity sensor has an accuracy of ± 2% and the temperature sensor has an
accuracy of ± 0.5 º C. Thus, the expected values from the system have ensured a level of
confidence.

For the management of heterogeneous hardware components, has been use
formats independent of the platform for TinyOS using nesC language. This decision allows data
to be easily accessed and used independently of the underlying hardware chip.
In our study case is considered an industrial setting where
tropical climate (hot and humid) in Tabasco. Our system consists of nodes that are deployed on
the air pipe and capable of measuring ambient temperature and humidity.
Our method was to make measurements of temperature and
period from 10 pm to 6 am of each day that includes the month of September 2008.
Measurements were made at night because it is in those moments when the temperature is more
likely to be less than 25°C and relative humid
temperature of 26°C and an average relative humidity of 80%.
Once we have the number of hours per day the temperature was less or equal to 25°C and relative
humidity greater than or equal to 80%, and then we
from TOW month. With previous data we can estimate mass loss of the metal and the corrosion
rate according to the formula created for it [1
Conventional equipments considered for comparison are stationary
thermometer. These kinds of devices can’t send information wirelessly and a user has to take
measurements reading them directly.
6. RESULTS
The developed system to obtain data and manage wireless sensor network has a GUI as shown in
Figure 2. The first bottom called “Monitoring” is used to select data acquisition mode, it is a user
can choose a periodic sampling (between 3000 and 10,000 ms), on
moment) or event-driven sampling (when a condition is met). These op
Figure 3 and they are used to obtain values for temperature and relative humidity. As shown in
Figure 3, there are two options for WSN management, the first related to node ID change and the
second to activate sleep mode in nodes.
example in sunny days or simply during days because in humid tropical climates temperatures
minor 25°C and relative humidity higher to 80% usually occurs during nights.
Figure 2. Monitoring atmospheric
For the management of heterogeneous hardware components, has been used to specify data
In our study case is considered an industrial setting where there is air pipes embedded in the
the air pipe and capable of measuring ambient temperature and humidity.
Our method was to make measurements of temperature and relative humidity every hour, in the
likely to be less than 25°C and relative humidity increases. Tabasco State has a mean annual
temperature of 26°C and an average relative humidity of 80%.
humidity greater than or equal to 80%, and then we have the time of wetness each day, and in turn
rate according to the formula created for it [17].
Conventional equipments considered for comparison are stationary humidity
measurements reading them directly.
. The first bottom called “Monitoring” is used to select data acquisition mode, it is a user
can choose a periodic sampling (between 3000 and 10,000 ms), on-demand sampli
driven sampling (when a condition is met). These options are illustr
and they are used to obtain values for temperature and relative humidity. As shown in
, there are two options for WSN management, the first related to node ID change and the
second to activate sleep mode in nodes. This last option is employed for energy saving. For
minor 25°C and relative humidity higher to 80% usually occurs during nights.
. Monitoring atmospheric aggressiveness main panel.
December 2011
37
d to specify data
is air pipes embedded in the
relative humidity every hour, in the
ity increases. Tabasco State has a mean annual
have the time of wetness each day, and in turn
humidity-temperature
. The first bottom called “Monitoring” is used to select data acquisition mode, it is a user
demand sampling (at any
tions are illustrated in
and they are used to obtain values for temperature and relative humidity. As shown in
, there are two options for WSN management, the first related to node ID change and the
This last option is employed for energy saving. For

Figure 3. Options for monitoring and management.
In our study case the most convenient data acquisition mode is periodic sampling due it’s an
automatic unattended monitoring. In this panel users can change sampling rate too.
considers an interval between 3000 and 10,000 ms. This panel shows residual energy in volts
each node. Figure 4 shows these
Figure 4. Periodic monitoring and sampli
. Options for monitoring and management.
automatic unattended monitoring. In this panel users can change sampling rate too.
considers an interval between 3000 and 10,000 ms. This panel shows residual energy in volts
options.
. Periodic monitoring and sampling rate selection, including residual energy.
December 2011
38
automatic unattended monitoring. In this panel users can change sampling rate too. Our system
considers an interval between 3000 and 10,000 ms. This panel shows residual energy in volts for
, including residual energy.

39
The system has been deployed in Tabasco, Mexico. This place has warm humid tropical climate
and there are many oil installations that use air pipes for the transport of their products (gas, oil
and gasoline), therefore the environment is representative of the target system scenarios.
We have monitored the temperature and relative humidity during the month of September 2008.
As seen in Table 1 and Table 2, the data obtained by the sensor network are very similar to those
obtained using conventional equipment for measuring physical parameters, which do not have
wireless communication capabilities, portability problems and cannot be accessed wirelessly to
modify the requirements as to the means of acquisition of the data or the frequency in obtaining
them, to cite two examples.
For statistical analysis of the data test was used Student's t-distribution, which is appropriate for
populations with normal or Gaussian distribution. After that we can infer that the difference
between data obtained with conventional equipment and the data obtained with the WSN is
negligible.
As mentioned in [18], the rate of zinc corrosion is generally greater than that for copper and less
than that for many steels, at least under outdoor atmospheric exposure. It is suggested the
following ranges in different environments: rural from 0 to 2-3 µm/year, and urban from 2 to 16
µm/year.
Another study of relationship between zinc corrosion and atmospheric parameters is that of
Benarie and Lipfert [19]. They stated the corrosion rate as
( )b
w t
t
A
M =
(1)
where tw is the “time of wetness”, i.e., the time during which the zinc surface is sufficiently
wetted to be covered by an electrolytic solution, and t is the total exposure time. Rather
arbitrarily, they define constants in (1) in terms of three chemical parameters at the zinc surface.
{ } { }
( )
−
−
+
+
= Cl
SO
A 2
53
.
0
8
.
4 (2)
( )
pH
b 142
0
55
.
1 −
−
= (3)
in which the terms {SO2} and {Cl--
} refer to surface deposition rates and (pH) is the average
acidity precipitation.
The mass loss data obtained during the test described in [20] show that can be described with a
linear equation:
( )
w
t
A
C =
(4)
where C is the loss of mass (g/m2
); tw - the time of exposure of the samples (TOW), A - the
corrosion rate in different environments, depending on climatic parameters and features. In
accordance to experiment data obtained in [20], the values of A are approximately 0.0036 g/m2
.h
for the urban environment and 0.0031 g/m2
.h for the rural environment.

40
Table 1. Media temperature during september 2008
Table 2. Media relative humidity during september 2008
Hour WSN sensing
Measurements with
conventional equipment
22:00:00 72% 74%
23:00:00 77% 76%
00:00:00 79% 80%
01:00:00 83% 82%
02:00:00 83% 83%
03:00:00 84% 83%
04:00:00 81% 82%
05:00:00 81% 82%
06:00:00 78% 79%
Using our system and knowing TOW, corrosion rate and mass loss formulas we can estimated
impact of atmospheric aggressiveness in metals, specifically in our scenario, corrosion in zinc
galvanized pipes, of course, it can be used to be applied to other metals.
7. DISCUSSION AND CONCLUSIONS
Importantly, the proposed technology is very suitable for the scenario of study and the
measurement of atmospheric parameters performed with the proposed devices (Tmote) is as
effective as those made with conventional equipment (stationary humidity-temperature
thermometers), which do not provide the ease of wireless communication devices. Thus it is
found that the results are good, although low-cost technology (Tmote) is commonly used for
academic purposes. Specialized conditions of moist warm climate of Tabasco, make the
equipment used are sufficient to determine with much certainty the level of aggressiveness
Hour WSN sensing
Measurements with
conventional equipment
22:00:00 28.3°C 28.5°C
23:00:00 26.4°C 26.9°C
00:00:00 25.0°C 25.3°C
01:00:00 23.7°C 23.6°C
02:00:00 23.2°C 23.3°C
03:00:00 22.5°C 22.5°C
04:00:00 22.6°C 22.5°C
05:00:00 23.4°C 23.5°C
06:00:00 24.2°C 24.7°C

41
atmosphere to which metals are exposed. The devices used and software developed provide
information to take control of wetting times which mean the effective time when metals corrode.
The proposed system represents an opportunity to automate monitoring of atmospheric
aggressiveness, such as corrosion of metals in the warm humid tropical climates. Sensor nodes
are capable of efficiently measuring temperature and relative humidity environment, so it is
enough to know the time of wetness that a metal is exposed and therefore estimate the rate of
corrosion and mass loss.
The wireless sensor network used allows measurement data are transmitted wirelessly to the base
stations, where data are accumulated and made the process of calculating the time of wetness
when temperatures are below 25°C and relative humidity is above 80%. A future work considered
is the construction of a tool to schedule the preventive corrosive maintenance of metals, based on
the corrosion rates and mass loss estimated from measurements made by the wireless sensor
network.
ACKNOWLEDGEMENTS
This work has been funded by UJAT-POA 20080104 project.
REFERENCES
[1] C.J. Slunder, W.K. Boyd, Zinc: Its Corrosion Resistance, second ed., Int. Lead Zinc Research Org.
Int., New York, 1983.
[2] X.G. Zhang, Corrosion and Electrochemistry of Zinc, Plenum, 1996.
[3] I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, E. Cayirci. “A Survey on Sensor Networks”, IEEE
Communications Magazine, Vol. 40-8, pp. 102-116, 2002.
[4] N. Sridhar, G. Tormoen. “Development and Application of Mobile Sensor Network to Monitor
Corrosion in Pipelines”, CORROSION 2006, March 12 - 16, 2006 , San Diego, Ca.
[5] M. Hedley, N. Hoschke, M. Johnson, C. Lewis, A. Murdoch, D. Price, M. Prokopenko, A. Scott, P.
Wang, and A. Farmer. “Sensor Network for Structural Health Monitoring”, International Conference
on Intelligent Sensors, Sensor Networks and Information Processing, Melbourne, Australia,
December, 2004.
[6] F. Ayello, D. Hill, S. Marion, N. Sridhar. “Integrated Sensor Networks for Corrosion under
Insulation: Monitoring, Cost Reduction, and Life Extension Strategies”, NACE Conference 2011.
[7] J. Demo, A. Steiner, F. Friedersdorf, M. Putic, “Development of a Wireless Miniaturized Smart
Sensor Network for Aircraft Corrosion Monitoring”, Aerospace Conference, 2010 IEEE.
[8] M. Pourbaix, Atlas of Electrochemical Equilibria in Aqueous Solutions, NACECebelcor, Houston,
1974.
[9] A.K. Neufeld, I.S. Cole, A.M. Bond, S.A. Furman, Corros. Sci. 44 (2002) 555–572.
[10] ISO 9223:92 Corrosion of metals and alloys, Corrosivity of atmospheres, Classification, ISO Int.,
Geneva, 1992.
[11] L. Veleva, R. Kane, Atmospheric corrosion, in: S.D. Cramer et al. (Eds.), Corrosion, Fundamentals,
Testing and Applications, ASM Handbook Series, vol. 13A, ASM Int., OH, 2003, pp. 196–209.
[12] A.P. Yadav, A. Nishikata, T. Tsuru, Corros. Sci. 46 (2004) 169–181.
[13] Tmote Sky. Available in Web: www.snm.ethz.ch/pub/uploads/Projects/tmote_sky_datasheet.pdf
[14] Sensirion SHT11 datasheet. Available in Web. www.sensirion.com
[15] TinyOS operating system. Available in Web: http://www.tinyos.net/

[16] nesC: A Programming Language for Deeply Networked Systems. Available in Web:
http://nescc.sourceforge.net/
[17] L. Veleva, M. Acosta, E. Meraz, Corros
[18] E. Mattsson. Chemtec 15, 234 (1985).
[19] M. Benarie, F.L. Lipfert, Atmos.
[20] E. Del Angel, L. Veleva, M. Acosta. (2006): Evaluación de la corrosión atmosférica del acero
galvanizado en el clima tropical húmedo del Golfo de México. Congreso Latinoamericano de
Corrosión, Fortaleza, Brasil, 21
Authors
Pablo Pancardo got a Master Science degree in Distributed Systems from ITESM
(Mexico); he is a PhD Candidate in Telematic Systems Engi
University of Madrid (Spain). Currently he works as a teacher in Autonomous Juarez
University of Tabasco (Mexico). His areas of interest are wireless ad hoc sensor
networks, mobile and ubiquitous computing, and middleware for heterog
devices.
Ebelia del Angel is a Researcher at Autonomous Juarez University of Tabasco.
got a Phd in Corrosion from Campeche University (Mexico)
atmospheric Corrosion, sp ecifically about Runoff
of Zinc.
Miguel A. Wister is currently a professor in the Information Technology and Systems
Division at the Autonomous Juarez University of Tabasco (UJAT). His research
interests are in wireless communications and networking, mobile Ad hoc networks
(MANET), and service discovery and routing protocols in MANET. Professor Wister
received his Ph.D. in Information Technology and Communications Engineering from
the University of Murcia, Spain, in 2008; he also received his M.Sc. degree in
Informatics Technology from the Monterrey Institute of Technology and Higher
Education (ITESM), Mexico, in June 1997.
nesC: A Programming Language for Deeply Networked Systems. Available in Web:
L. Veleva, M. Acosta, E. Meraz, Corrosion Science 51 (2009) 2055-2062.
E. Mattsson. Chemtec 15, 234 (1985).
M. Benarie, F.L. Lipfert, Atmos. Environ. 20 (1986) 1947–1958.
E. Del Angel, L. Veleva, M. Acosta. (2006): Evaluación de la corrosión atmosférica del acero
tropical húmedo del Golfo de México. Congreso Latinoamericano de
Corrosión, Fortaleza, Brasil, 21-26 de mayo.
Pablo Pancardo got a Master Science degree in Distributed Systems from ITESM
(Mexico); he is a PhD Candidate in Telematic Systems Engineering from Technical
University of Madrid (Spain). Currently he works as a teacher in Autonomous Juarez
University of Tabasco (Mexico). His areas of interest are wireless ad hoc sensor
networks, mobile and ubiquitous computing, and middleware for heterogeneous
Researcher at Autonomous Juarez University of Tabasco. She
from Campeche University (Mexico). Her interest area is
ecifically about Runoff-Induced Atmospheric Corrosion
Miguel A. Wister is currently a professor in the Information Technology and Systems
Division at the Autonomous Juarez University of Tabasco (UJAT). His research
interests are in wireless communications and networking, mobile Ad hoc networks
MANET), and service discovery and routing protocols in MANET. Professor Wister
received his Ph.D. in Information Technology and Communications Engineering from
the University of Murcia, Spain, in 2008; he also received his M.Sc. degree in
ology from the Monterrey Institute of Technology and Higher
Education (ITESM), Mexico, in June 1997.
December 2011
42
E. Del Angel, L. Veleva, M. Acosta. (2006): Evaluación de la corrosión atmosférica del acero
tropical húmedo del Golfo de México. Congreso Latinoamericano de

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