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

, Ahmed Fakhfakh

and Faouzi Derbel

Department of Electrical Engineering and Information Technology,

University of Applied Sciences Leipzig, Germany

Laboratory of Science and Technologies of Information and Communication,

National School of Electronic and Telecommunication of Sfax, Tunisia

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

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

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 4≈10 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

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 =

0.642

+ 0.672

+ 0.62

= 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

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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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