Implementation of RSSI Module in Omnet++ for Investigation of WSN
Simulations based on Real Environmental Conditions
Mohamed Khalil Baazaoui
1,2
, Ilef Ketata
1,2
, Ghofrane Fersi
3
, Ahmed Fakhfakh
2
and Faouzi Derbel
1
1
Department of Electrical Engineering and Information Technology,
University of Applied Sciences Leipzig, Germany
2
Laboratory of Science and Technologies of Information and Communication,
National School of Electronic and Telecommunication of Sfax, Tunisia
3
Research Laboratory of Development and Control of Distributed Applications (ReDCAD),
Department of Computer Science and Applied Mathematics,
National School of Engineers of Sfax, University of Sfax, Tunisia
Keywords:
WSN Simulations, Temperature, Humidity, RSSI, LQI, Omnet++.
Abstract:
The simulation of different scenarios and protocols under environmental conditions is a success key for a
reliable Wireless Sensor Network (WSN). For some applications, the variation of the weather conditions as
the temperature and humidity could affect the Received Signal Strength Indication (RSSI). Running multi-
simulation scenarios is required to evaluate the proposed protocols and architectures before their deployment
in a real-world network. The more the simulator mimics the real world, the closer the evaluation results to
the real network. In this paper, we have integrated temperature and humidity in the Omnet simulator and have
taken into account the impact of these environmental factors on the RSSI based on real experiments that were
carried out using the CC1101 radio ship of Panstamp Avr2 at 868 MHz frequency.
1 INTRODUCTION
Nowadays, Wireless Sensor Networks (WSNs) ap-
pear as an active research field in which challeng-
ing topics include energy consumption, routing algo-
rithms, selection of sensors location according to a
given premise, robustness, efficiency, and so forth.
WSN is used in several applications such as disas-
ter management, entertainment, education, environ-
ment monitoring (Debashis De, 2020). Although the
applications of WSN increase rapidly in the modern
era, it has several limitations such as limited energy
capacity of the nodes, shortage memory capacity of
the nodes as well as limited computational capacity.
Many standards are approved in wireless commu-
nication and different metrics have been used to en-
hance the reliability of the network. Those metrics de-
pend on several factors, such as the transceiver type,
external environment, modulation scheme, data rate,
etc. Thus, a good link quality between sensors is an
essential condition for successful communication.
Many studies were approved out based on the per-
formance analysis of link quality to estimate the ef-
ficiency of the communication link between nodes.
Received Signal Strength Indicator (RSSI), which in-
cludes also information about the link quality, is an
essential parameter in the receiver packet. RSSI
degradation, mentions degradation of the quality of
the signal (David Rojas, 2018), especially when it is
exposed to many environmental conditions, or when
the network is deployed in a harsh area. As a conse-
quence the RSSI provides a precise estimation of the
robustness and reliability of transmitted data packets.
The use of simulators nowadays is required,
when developing or researching in the field of WSN
(Milo
ˇ
s Jevti
´
c, 2009). Reasons for this are numer-
ous. Manufacturers have not achieved expected low
costs for sensor nodes yet, so experiments on a real-
world WSN (which could consist of hundreds or thou-
sands of nodes) are expensive. Moreover, simulations
give the users a general idea of multiple scenarios that
could happen in real-world territories and allow them
to evaluate their proposed protocols and/or architec-
tures before the real deployment (Ilef Ketata, 2019).
Many researches have already proved the impact
of weather conditions on the RSSI in indoor and out-
door systems (Boano, 2010) (Amir Guidaraa, 2018)
(Luomala, 2015) (C. Boano, 2009) (K. Bannister,
Baazaoui, M., Ketata, I., Fersi, G., Fakhfakh, A. and Derbel, F.
Implementation of RSSI Module in Omnet++ for Investigation of WSN Simulations based on Real Environmental Conditions.
DOI: 10.5220/0011012600003118
In Proceedings of the 11th International Conference on Sensor Networks (SENSORNETS 2022), pages 281-287
ISBN: 978-989-758-551-7; ISSN: 2184-4380
Copyright
c
2022 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
281
2008). However, for the best of our knowledge, none
of the wireless simulators has taken into account those
impacts. Our proposed work developed the idea of in-
tegrating those factors on the advanced simulator Om-
net++ with several built-in functions and a powerful
representative Graphical User Interface. The outline
of our paper is as follows: The second Section dis-
cusses the related work and sets out the novelty of our
paper. The third section will show real measurements
for the RSSI variation which will be adopted in the
RSSI bloc integration in Omnet++. We will focus in
section four on the implementation of the RSSI mod-
ule and the presentation of some simulating scenar-
ios. Finally, a conclusion and future work will be pre-
sented for opening new aspects in the research field.
2 RELATED WORKS FOR
SIMULATION OF WSN UNDER
REAL ENVIRONMENTAL
CONDITIONS
2.1 Importance of Simulations for WSN
WSN simulators are widely used in different wire-
less applications, they are the most known approach
for protocol testing. Most researchers in the field of
WSN use simulators to predict the node’s behavior
since simulators provide several advantages like flex-
ibility and test period. Every WSN application has
to involve a conception and designing phase, after
which, the test phase will take place. The simula-
tion phase is relevant to help both designers and re-
searchers to get a general idea of the network. The
ability to integrate real testing conditions into the sim-
ulator interfaces enhance the challenge of network ac-
curacy and reliability. Also, the prediction of param-
eters that could affect communication could be taken
into account, e.g, the variation of RSSI and link qual-
ity degradation. This classifies simulation as the most
used evaluation technique in the wireless communica-
tion field due to its low-cost implementation and eases
to use (Ilef Ketata, 2020).
The Fig. 1 shows the most cited WSN simu-
lators in the state of the art (Michel Bakni, 2019).
OMNET++ is among the top list of simulating tools
that have drawn significant interest in the research
area. Although OMNET++ provides powerful and
clear simulation frameworks, such as Simu5G, Veins,
RinaSim, INET framework, etc (Org, 2021). the
most utilized framework is the INET framework that
provides protocols, agents, and other models for re-
searchers and students working with communication
Figure 1: Number of citations that present each simulator
(Michel Bakni, 2019).
networks.INET is especially useful when designing
and validating new protocols, or exploring new or ex-
otic scenarios. The Omnet++ hierarchical structure
and powerful Graphical User Interface(GUI) made
him the best choice for developing and integrating
new strategies and applications but also it has some
limitations, it lacks observing of the RSSI variation
as a consequence of the climate changes. So the idea
was to integrate the RSSI module into OMNET++,
which contains a description of how signals act when
temperature or humidity change, after a detailed dis-
cussion on real measurements.
2.2 State of the Art of RSSI Variation in
Wireless Communication
WSN is a gaggle of specialized autonomous sensors
and actuators with a wireless communications infras-
tructure, intended to watch and control physical or en-
vironmental conditions at diverse locations and to co-
operatively pass their data to the main location and/or
pass their control command to the desired actuator
through the network. The wireless network is com-
posed of a finite set of sensor devices geographically
distributed in a given indoor or outdoor environment
(usually predefined). A WSN aims to gather environ-
mental data when the node devices placement may be
known or unknown (Farahani, 2008).
In the ideal non-obstructive environment, the na-
ture of electromagnetic waves propagation attenuates
the signal power abruptly near the transmitter and
yields much less attenuation at longer distances. This
is described by the Friis Equation (Nouha Baccour,
2013), directly derived from fundamental theory. In
the real environment, a lot of external factors that
could affect the electromagnetic waves and attenu-
ate the signals near the receiver, take the reflection,
diffraction, or absorption caused by a different type of
obstacle as an example, nature effects such as wind,
rain, temperature, thunder and so on.
WSN applications have been integrated into sev-
eral areas either in indoor or outdoor scenarios. Dif-
EWSN-IoT 2022 - Special Session on Energy-Aware Wireless Sensor Networks for IoT
282
ferent works studied the impact of various environ-
ments on LQI and RSSI. David Rojas and John Bar-
rett studied the link quality and the Received signal
strength using TelosB nodes of WSN in metal marine
environment (David Rojas, 2018), where they manage
to distribute 18 nodes in a complex metallic environ-
ment composed of fright containers, engines, and dif-
ferent materials that cause the attenuation of signals.
Boano et al. studied the impact of temperature on
the RSSI and LQI in an oil refinery using CC2420
radio ships (Boano, 2010), the experimental results
show that temperature has a major effect on the signal
strength and link quality. In (Amir Guidaraa, 2018)
the impact of humidity and temperature on the RSSI
in indoor WSN have been studied, they deployed
868MHz Panstamp NRG 2.0 wireless modules, where
different distances show different values of correla-
tion between humidity and the RSSI, and the temper-
ature and the RSSI.
In (Luomala, 2015), the author explored the ef-
fects of ambient temperature and humidity on ra-
dio signal strength of Atmel ZigBit 2.4GHz wireless
modules in outdoor WSNs.The experimental results
demonstrate that changes in weather conditions affect
received signal strength. Temperature seems to have
a significant negative impact on the signal strength in
general, while high relative humidity may have some
effect on it, particularly below 0ºC.
Boano et al.(C. Boano, 2009) exposed that the in-
crease in temperature decreases both RSSI and LQI,
they have used Tmote Sky nodes (CC2420 radio)
and MSB430 nodes (CC1020 radio) in indoor exper-
iments. Bannister et al. (K. Bannister, 2008) exper-
iment demonstrate a linear decrease of 8dB in signal
strength when the temperature rose from 25ºC to 65ºC
using TI CC2420 radio chip on a Tmote Sky node.
They also showed the implications of the experiment
on different communication range.
All the proposed works present physical imple-
mentation of sensor nodes with limited results that
could be non-sufficient for analyzing and models in-
terpretation in the relation of the proposed problem.
As described before, dealing with an infinite num-
ber of simulations could determine better results for
node’s behavior, which will be implemented in our
work based on real measurements.
3 STUDY OF THE IMPACT OF
TEMPERATURE AND
HUMIDITY ON THE RSSI
3.1 Received Signal Strength Indicator
(RSSI) in CC1101
The received signal strength is the power level of
the signal received at the antenna of the device. In
the ideal transmission, the RSSI can be determined
with the transmission power and the distance between
the nodes. The RSS-based location techniques have
shown the relation between the RSSI and the distance
in wireless sensor networks, and this relation is com-
puted according to the following equation:
RSSI = RSSI0 10 n log(d/d0) + X σ (1)
RSSI0 indicates the RSSI when the reference dis-
tance is d0
n indicates the path loss index in a specific envi-
ronment.
Xσ is in dB; it is a cover factor when the range of
standard deviation σ is 410 and the mean value
is 0; the larger the σ, the greater the uncertainty
of the model.Indicates the speed of attenuation of
the signal.
The RF transceiver CC1101 estimates the RSSI val-
ues based on the current antenna gain in the Rx chan-
nel and the measured signal level in the channel it-
self. In reception mode, the RSSI values could be read
continuously from the RSSI status register. The RSSI
register is a two-complement number. To convert the
RSSI reading to an absolute power level a conversion
algorithm is executed:
Beg in
RSS I r eg re a d ( RS SI st atu s r e gis t e r )
RSS I d ec c o n vertTo D e c i m a l ( R SSI r e g )
If RSS I d ec > 1 28 the n
RSS I d Bm ( RSS Idec - 256) /2 - RS S Ioff s e t
Els e
RSS I d Bm RSS I dec /2 - RS S Ioff s e t
En d if
En d
3.2 Experiment Setup for RSSI
Variation Measurements
The experiment is carried using two Panstamp AVR2
wireless sensor nodes, a sender and receiver with
built-in C1101 868 MHz radio ship and a DHT11
temperature and humidity sensor. The sender node
Implementation of RSSI Module in Omnet++ for Investigation of WSN Simulations based on Real Environmental Conditions
283
sends a beacon message every 0.4 seconds with par-
ticular transmission power. The receiver node mea-
sures the RSSI for each beacon message the receiver
also gets the values of humidity and temperature val-
ues through the DHT11 sensor, recording RSSI, hu-
midity, and temperature and sending them through a
UART communication to the computer where the data
is saved in a CSV file to be later analyzed. During the
experience, two different scenarios were launched,
the first was wired communication, where the sender
and the receiver are connected through an SMA cable
using a step attenuator in between to variate the atten-
uation of the signal from 0 to 80dB where the receiver
was placed in the climatic test chamber. The second
experience was wireless, using the radio communica-
tion between the two nodes at different input power
levels.
To test the temperature impacts, the same setup
is followed in the climatic test chamber CTC256 for
both wired and wireless communication, the humidity
is off and the variation of temperature was in [-10..40]
°C range. The variation of temperature was by 5 °C
scales, and for each scale of temperature 100 samples
are taken to be analyzed in the next step. For the hu-
midity impacts, the temperature was fixed at 30 °C.
The humidity varies in the [40..90]RH % range. The
variation of humidity was by 10RH % and in every
scale of humidity, up to 100 samples are recorded to
be analyzed later.
The wired connection didn’t show a variation of
RSSI that could be adopted later in OMNET++. The
reason for this link quality stability is that the signal
is well protected with the cable and the factor of at-
tenuation is very low. To ensure that both temperature
and humidity had no effect on the RSSI in wired com-
munication the correlation has been calculated and
shown in the results below:
Corr(RSSI,humidity) = -0.028
Corr(RSSI,temperatur)= -0.017
The correlation is considered too low (close to the
zero value) as a consequence of the stable value of the
RSSI even the changes applied on temperature and
humidity.
The second setup is to implement a wireless con-
nection between the sender and the receiver, which
were placed at the edges of the climatic test cham-
ber. After getting the dimension of the climatic cham-
ber(width, height, and depth) CTC256, the distance
between the two nodes could be calculated using the
Euclidian distance:
Distance =
p
0.642
2
+ 0.672
2
+ 0.62
2
= 1.1m (2)
In this case, the attenuator has no place so the idea
is to control the output transmission power from the
CC1101 ship through sending different values to the
PATABLE register. the output transmission power
was -10dBm, -20dBm and -30dBm. In the beginning,
the humidity was off, the temperature was at 25 °C,
the output power was programmed at -10dBm, the
spectrum analyzer shows that there were 8dBm losses
due to the transmitter, the received signal strength was
at -71dBm.
Losses = 71 + 10 + 8 = 53dB. (3)
The conclusion of the 50dB losses that are coming
from reflections of the metallic climatic chamber. The
same distance will be kept for the whole measure-
ments setup inside the testing chamber.
3.3 RSSI Data Measurement’s
Discussion
After saving data, the idea was to format it using mul-
tiple methods and search for a suitable correlation. In
each case, the simple moving average had the highest
value of correlation. So the simple moving average is
manipulated in each level of transmission power ap-
plying the linear regression and calculating the slope
and the intercept. the window of the simple moving
average was equal to 20. The reason behind choosing
a window of 20 is that we took 100 samples for each
scale of humidity and temperature, in some scales the
RSSI varies up to 6dBm, so centralizing the data was
a better choice to align it to a definite behavior, if we
close the window to 100 so we are closer to the mean
of the data which is not a base form in data analysis
that depends on a high number of sets if less than 20
so we are going in a path of decreasing the correlation
and decentralizing the set of data. This step made the
implementation easier since the simulator is depend-
ing on the close behavior of the CC1101 ship but not
the exact way of working.
Fig. 2 shows the impact of Humidity on the RSSI
using -10dBm transmission power. The correlation
was -0.92. the slope and the intercept were calculated
after applying the linear regression, those variable are
showed in the equation below:
RSSI = RSSI0 0.2234 H (4)
were:
RSSI0 = -53.8565 in dBm.
H is the humidity in RH%
Fig. 3 shows the impact of Temperature on the
RSSI using -10dBm transmission power. But as
shown in the graph there’s some fluctuation around
-5°C. That was the effect of the relative humidity in
the climatic chamber and that’s when showed the role
EWSN-IoT 2022 - Special Session on Energy-Aware Wireless Sensor Networks for IoT
284
Figure 2: Linear graph of the variation of the RSSI accord-
ing to the humidity, Tx power =-10dBm.
Figure 3: graph of the variation of the RSSI according to
the temperature, Tx power =-10dBm.
Figure 4: Linear graph of the variation of the RSSI accord-
ing to the temperature, Tx power =-10dBm.
Figure 5: Graph of the variation of humidity according to
the temperature.
of the DHT11 sensor, to record the data of the relative
humidity inside the climatic chamber, the recorded
data is shown in Fig. 5. The two red lines present
the working domain when the humidity is considered
stable. And as the calculated data is up to 100 sam-
ples for each scale the output info is still fair enough
to apply a data model on it. The Correlation increase
from -0.65 to -0.8 which is considered as a high neg-
ative correlation and the new data is printed in Fig. 4.
The slope and the intercept have been calculated af-
ter applying the linear regression, those variables are
shown in the equation below:
RSSI = RSSI0 0.1841 T (5)
were:
RSSI0 = -65.9759 in dBm.
T is the temperature in °C
4 IMPLEMENTATION OF RSSI
MODULE IN OMNET++
Every WSN application has to involve a conception
and designing phase, after which, the test phase will
take place. The simulation phase is relevant to help
both designers and researchers to get a general idea of
the network.The implementation of the experience re-
sults under an open acess simulator platform is a chal-
lenging task to be available for other OMNET users
in the future. The experiences show that the RSSI has
a considerable correlation with both temperature and
humidity. The RSSI module will be implemented un-
der the physical layer into the compound module of
the radio medium. The RSSI module uses the RSS-
based location technique equation 1 with the temper-
ature and the humidity attenuation. Therefore, the pa-
rameters of the RSSI module are:
Distance: is calculated using the position from
both receiver and sender. The location of the
sender is obtained from the ITransmission mod-
ule, where the receiver position is gotten from the
IArrival module. a special API built in the Rss-
Base file that calculates the distance between the
nodes using the Euclidean method.
Temperature: is set by the user using the INI file
of the simulation, registered in the IphysicalEnvi-
ronement module, and converted into the RssBase
file. The impact of the temperature was added us-
ing the equation 4 by defining a second attenua-
tion factor as the slope parameter and the RSSI0
get the intercept.
Humidity: is set by the user using the INI file
of the simulation, registered in the IphysicalEnvi-
ronement module, and converted into the RssBase
file. The impact of the humidity was added using
the equation 5 by defining a second attenuation
factor as the slope parameter and the RSSI0 get
the intercept.
Implementation of RSSI Module in Omnet++ for Investigation of WSN Simulations based on Real Environmental Conditions
285
Figure 6: UML class diagram.
The UML class diagram in Fig. 6 can resume and
clarify the implemented RSSI module.
After finishing with the implementation of the
RSSI module, a test phase will take place, the con-
figuration is involved in the INI file of the simu-
lation the RSS-based location default variables are
taken from the Location Estimation Algorithm Based
on RSSI Vector Similarity Degree (Fengjun Shang,
2014) study and the CC1101 characteristics.
Fig. 7 shows the simulation results after integrat-
ing the RSSI module. The humidity was 40RH% and
the temperature is 30°C.
In the first step, we compile the impact of tempera-
ture on the RSSI, the RSSI0 is taken from the equation
4 of the temperature and the values are compared to
the real experiment results in the table 1. The results
are almost the same.
Table 1: Variation of RSSI according to temperature in Om-
net++ Simulator.
Temperature(°C) 5 30
RSSI in Omnet++(dBm) -68.8723 -72.0323
RSSI in Experiment(dBm) -66 -74
In the next step the simulator tests the humidity
impact on the RSSI, the RSSI0 is taken from the equa-
tion 5 of the humidity and the values are compared to
the real experiment results in the table 2. The simula-
tion results are close to the experiment results.
Table 2: Variation of RSSI according humdity in Omnet++
Simulator.
Humidity(RH%) 40 70
RSSI in Omnet++(dBm) -67.92 -72.87
RSSI in Experiment(dBm) -64 -68
Figure 7: Humidity= 40RH%.
5 CONCLUSIONS
The main contribution of this paper is to implement
the impact of the humidity and temperature on the
RSSI in the Omnet++ simulator. The implementation
of the module was based on real experiments. In those
studies, the created model was for the CC1101 radio
ship transceiver study case.
EWSN-IoT 2022 - Special Session on Energy-Aware Wireless Sensor Networks for IoT
286
Experimental measurements were carried out in-
side a climate test chamber using the Panstamp AVR
wireless module and DHT11 temperature and humid-
ity sensors. To comply with this step, several test sce-
narios were designed to evaluate the measurable cri-
teria. Results have shown that, for wired communica-
tion, no correlation was found between temperature,
humidity, and RSSI nor LQI.
However, for wireless communication, a strong
negative correlation between RSSI and both humid-
ity and temperature was observed. Once the relation
between the RSSI, humidity, and temperature was ob-
tained, it was implemented into the Omnet++ simula-
tor to make it valuable for future applications.
The findings presented here can help when design-
ing adaptive RSSI-based applications such as RSSI-
based indoor localization. Furthermore, this study can
be used to address WSN evaluation, such as scalabil-
ity and the modeling of mobility, wireless medium,
and energy consumption.
This will never end the work in further experi-
ments we can apply the changes on different chips to
build other models and blocks in Omnet++, this will
large the options for users, and using different choices
in the simulator.
ACKNOWLEDGEMENTS
This research was performed at Leipzig University of
Applied Science (HTWK). The authors would like
to thank the German Academic Exchange Service
(DAAD) and the European Social Fund for the finan-
cial support and their encouragement
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