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Information 2019, 10, 104; doi:10.3390/info10030104 www.mdpi.com/journal/information
Article
Discrete Wavelet Packet Transform-Based Industrial
Digital Wireless Communication Systems
Safa Saadaoui 1,2,*, Mohamed Tabaa 1,*, Fabrice Monteiro 2, Mouhamad Chehaitly 3
and Abbas Dandache 2
1 LPRI, EMSI, Casablanca 20250, Morocco
2 LGIPM, Lorraine University, 57073 Metz, France; fabrice.monteiro@univ-lorraine.fr (F.M.);
abbas.dandache@univ-lorraine.fr (A.D.)
3 TIMA, Grenoble Alpes University, 38000 Grenoble, France; che.liban.tly@hotmail.com
* Correspondence: ssaadaoui@gmail.com (S.S.); med.tabaa@gmail.com (M.T.); Tel.: 00212-5-2299-2323
Received: 5 February 2019; Accepted: 4 March 2019; Published: 7 March 2019
Abstract: The industrial internet of things (IIoT) known as industry 4.0, is the use of internet of
things technologies, via the Wireless Sensor Network (WSN), to enhance manufacturing and
industrial processes. It incorporates machine learning and big data technologies, to allow machine-
to-machine communication that have existed for years in the industrial world. Therefore, it is
necessary to propose a robust and functional communication architecture that is based on WSNs,
inside factories, in order to show the great interest in the connectivity of things in the industrial
environment. In such environment, propagation differs from other conventional indoor mediums,
in its large dimensions, and the nature of objects and obstacles inside. Thus, the industrial medium
is modeled as a fading channel affected by an impulsive and Gaussian noise. The objective of this
paper is to improve robustness and performances of multi-user WSN architecture, based on Discrete
Wavelet Transform, under an industrial environment using conventional channel coding and an
optimal thresholding receiver.
Keywords: industrial internet of things (IIoT); industrial wireless sensor network (IWSN); discrete
wavelet packet transform (DWPT); industrial channel model; channel coding; thresholding receiver
1. Introduction
Technological developments in wireless communication systems in recent decades have led to
the emergence of growing user needs in terms of accessibility, data volume and energy consumption.
These technologies are constantly evolving owing in particular to the integration of new techniques
to improve user connectivity and connect billions of objects together. These connected objects are
autonomous physical elements that are able to communicate with each other, thus, creating a
technological revolution that brings more ambitious innovations in different fields of application. The
intelligence embedded in these objects ensures their connectivity, and meets a need for control or
monitoring in different application areas, such as medicine, industry, environment, or security.
In the industrial world in which we are particularly interested, a trend towards connected,
robotic and intelligent factories are growing rapidly, to face competition from countries with low
production costs. The revolution in the digital world is considerably reducing the boundaries
between the physical and digital worlds. As a result, it interconnects factories in which employees,
machines and products interact with each other to form the new technological revolution known as
industry 4.0. This revolution allows interactions aimed to a seamless production with real-time
traceability of products, at different stages of production [1]. Indeed, this new generation of plants
will boost the dynamism of the industry by modernizing production and increasing competitiveness.
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Given the great interest in object connectivity in the industrial environment, it is necessary to
propose a communication architecture, based on robust and functional wireless sensor networks,
inside factories. These networks are characterized by their autonomy, low energy consumption, and
ability to exchange and process multiple data from different sources, in real time. The design of these
networks differs for each application, taking into account the constraints of the propagation
environment. As part of this work, we are interested in applications that take place in an industrial
environment. Such a propagation environment, unlike other traditional indoor environments as
residential buildings or offices, is distinguished by its large dimensions, and particularly the nature
of its objects and obstacles. During wireless data transmission, the interaction of signals with different
objects can lead to a partial or total loss of the data that must be compensated. The complexity of the
environment and the noise present in the industrial propagation environment makes it necessary to
offer a robust wireless communication system to deal with the various disturbances [2]. The
robustness of this architecture can be improved in various ways by inserting some optimal
techniques.
Studies have shown the value of wavelet theory in designing pulse modulation systems that can
be embedded in sensor networks [3,4]. Through wavelet transforming and filter banks, it is possible
to generate orthogonal pulses in time and frequency, to design flexible communication systems,
based on a multicarrier modulation. The time–frequency multi-resolution property of these systems,
allow for reaching the optimum level by choosing the appropriate waveform. On the other hand, the
sensitivity to interference generated by the propagation channel, can be significantly reduced by
using the discrete wavelet transform, through the orthogonality characteristics of the wavelet shapes,
at the input of the filter banks.
In this work, a multi-user wireless communication system, based on industrial sensor networks,
in two distinct operating modes, has been proposed. The first mode provides Many-To-One (MtO)
communication between several transmitters and a single receiver. The second mode connects a
transmitter sensor to several receivers in the One-To-Many (OtM) mode. These modes of
communication illustrate the different links between levels 0, 1, and 2 of the Computer-Integrated
Manufacturing (CIM) pyramid, deployed in industrial environments. The communication
architecture is based on the wavelet packet transform, which the analysis scale controls through the
number of inputs activated and, therefore, also the number of users or sensors. An optimal choice of
wavelet is made, in terms of the binary error rate, to perform the simulations in an industrial channel.
A model of this channel has also been proposed to simulate the operation of our communication
architecture, in an environment that is as close as possible to a real industrial environment.
This paper is structured as follows. In the next section, an overview of the evolution of the
communication systems in industrial environments is given. Then, the theory of wavelets, as well as
the multi-resolution analysis based on filter banks, is presented. This was done in order to introduce
our multi-user communication architecture based on the wavelet transform. In this section, the
architecture is presented, with its two operating modes; MtO and OtM. Before performing the
architectural simulations, the industrial channel model used is established. A discussion about the
different results of architecture simulations on the industrial channel is given. Finally, a general
conclusion, as well as perspectives for future works, is presented.
2. Industrial Communication System
Over the past twenty years, and thanks to the deployment of communication networks, the
communicating industrial systems have made remarkable progress. These networks, which have
evolved from wired to wireless communication, have facilitated access to data, at any time and place.
Basically, communication in an industrial environment was achieved by connecting automatisms
between them, by different modes and local networks [5]. Automation architectures have made great
progress, with the arrival of new information and communication technologies. To reduce wiring
costs, it was necessary to take into account the topology of the automation systems. To meet this need,
manufacturers of automation products have proposed networks and fieldbuses. These made it
possible to manage the decentralized I/O, first, followed by the automation periphery [5].
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Due to the emergence of industrial communication technologies, the concept of the classic CIM
model 9shown in Figure 1) gave rise to an organization that functions, around networks. In fact, this
model (or pyramid) makes it possible to describe the organization of the various systems (Company,
factory, machine, etc.), according to a vertical segmentation of four hierarchical communication
levels. Therefore, it does not solve the problem of managing the increase in traffic on media.
Communication providers adapt the performance of their networks, according to the CIM levels on
which they will be positioned. Then, several communication protocols are used to connect the
different levels of the CIM pyramid, by including standard protocols, such as Ethernet and TCP/IP.
In the instrumentation level (level 0), including sensors, wireless technologies are used to connect the
different sensors to each other, for more flexibility. Wireless communication standards that are
applied in industrial environments, depend on the range and equipment used. For WPAN wireless
personal networks at a low range, technologies such as Bluetooth, WirelessHART, and ZigBee are
deployed [6]. WLAN wireless local area networks, use the IEEE 802.11, commonly referred to as Wi-
Fi. The WWAN long-range network deploys the LPWAN cellular and Low-Power Wide Area
networks.
Figure 1. Automation Pyramid, Standard Computer-Integrated Manufacturing (CIM).
A recent emergence of industrial communication consists of introducing the concept of the
Internet of Things IoT and Cyber-Physical Systems (CPS) in the world of automation and
industrialization. This concept, known as industry 4.0 or Connected Factory, is based on the
convergence between the industry and digital applications to create intelligence in a manufacturing
system. This provides for a great adaptability in production and a more efficient allocation of
resources [1]. Data consist of the most important part of the IoT. They come from various terminals
and sensors, and allow users to be informed, in real time, about the evolution of their environment.
The Industrial Internet of Things (IIoT) is the deployment of IoT in an industrial environment. Thanks
to the embedded technology (sensors, actuators, RFID chips, etc.), IIoT consists of identifying and
establishing the communication between all elements (machines, products in process, employees,
suppliers, customers, infrastructure, etc.), which can be referred to as objects [7]. These objects
exchange considerable amounts of data that are then conveyed through a local network or Internet.
Thanks to IIoT, the user can act in real time on its environment, in a manual or automated way,
to facilitate several tasks, such as production optimization, machine control, or the optimization of
supply chains, in real time. There are many wireless connectivity technologies for objects. The choice
of connectivity strategy is made according to several criteria, and is based on the choice of the sensor.
This choice can depend mainly on the location (indoor, outdoor, etc.), mobility, power consumption,
remote control, data quantity, sending frequency, and security. Among the networks dedicated to
IIoT are Sigfox, LoRaWAN, NB-IoT, and LTE-M. Faced with this range of networks dedicated to IoT,
the choice will, therefore, necessarily depend on the connected object. It is necessary to consider the
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simplified use of transmissions related to connected objects and the security of users and transmitted
data. This will be possible when the quality of the radio link used to transmit the data is reliable.
3. Wavelet Transform
The main challenge associated with sensor networks deployed in industrial environments is the
harshness of this environment, which requires the adaptation of their physical layer. Given the
limited resources of these networks, whether in terms of computing power, energy consumption,
size, or connectivity to the environment, appropriate digital modulation and information coding
techniques must be used, to improve communications via industrial wireless sensor networks [8]. A
large number of physical layers for wireless sensor networks have been proposed to meet their
different constraints. The first modulation techniques to be used are narrow-band modulations,
which are derived from analogue modulations. Then, other modulations based on spread spectrum,
or multi-carrier or pulse modulations, were proposed. Pulse techniques allow the increase in the
transmitted bit rate, at the expense of the complexity of the transmitter and the receiver, depending
on the number of pulses used. Another alternative to all these techniques is the modulation of pulses
by the orthogonal wavelet transform, to increase the throughput, but above all to benefit from
simplicity in the design of the receiver that is capable of detecting the different waveforms received.
In the wavelet transform (WT) theory, the wavelet basis functions are obtained from a single
prototype function called “wavelet”, by translation and dilation or contraction:
Ψ,()=
√∗Ψ
, (1)
where ∈ ℝ∗and ∈ℝ . For large , the basis function becomes a stretched version of the prototype
wavelet, that is a low frequency function, while for small , the basis function becomes a contracted
wavelet, that is a high frequency function. The discrete wavelets transform (DWT) are discretely
scalable and translatable. This was achieved by modifying the wavelet representation to create
Daubechies (1992) [9]:
Ψ,()=
∗Ψ
, (2)
We usually choose =2 so that the sampling of the frequency axis corresponds to dyadic
sampling. In addition, = 1 gave a dyadic sampling in time. Discretizing the translation and
dilation contraction parameters of the wavelet in Equation (1), the dyadic discrete WT of () is:
(,)=2
∫()
Ψ∗(2 − ), (3)
where , ∈ ℤ.
It should be mentioned that WT can be implemented as non-uniform filter banks, formed by
both smooth and wavelet coefficients. The smooth coefficients are separated into low-pass digital
filter and a high pass-filter . By using the scaling function and there corresponding mother
wavelet, we obtained both the digital filter and . We suppose and , like non-recursive FIR
filters with length, the transfer functions of and can be represented as follows:
()=ℎ+ℎ +ℎ +⋯+ℎ() (4)
()=+ + +⋯+() (5)
Mallats tree algorithm or pyramid algorithm [10] can be used to find the multi-resolution
decomposition of DWPT, the two scale relations, Equations (4 and 5) leads to scaling and wavelet
functions similar to that in scalar wavelets. However, the equations are two scale matrix equations
and can be given by:
Φ()=ℎ()
Φ(2−) (6)
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Ψ()=ℎ()
Ψ(2−) (7)
where Φ()=[Φ() Φ()⋯Φ()] and Ψ()=[Ψ() Ψ()⋯Ψ()]form the set of scaling
functions and their corresponding wavelets. The suffix denotes the number of wavelets and is
dubbed as multiplicity.
Now that the theory of wavelets is presented, the wavelet packet transform will serve as a
modulation basis, for our impulse architecture. This architecture is illustrated in Figure 2 with a depth
of 3, allowing 2=8 different data entries [,…,] to be modulated by the IDWPT. This data
will be retrieved at the receiver, by a DWPT transformation, in order to reconstruct the data
[,…,].
Figure 2. IDWPT in transmitter and discrete wavelet packet (DWPT) in receiver.
4. Digital Wireless Communication Based on DWPT
4.1. DWPT System
The proposed communication architecture is based on wavelet packet transformation for
industrial wireless sensor networks. The constraints related to the propagation environment are
numerous and diverse. Hence, the need to validate the robustness of the various architectural aspects,
which depend on the intended applications. Regardless of the mode of operation, the scale of analysis
provides information on the number of possible users. The activation of one or more inputs generates
a waveform, orthogonal to all the others, from the different inputs.
For a multi-user mode, it should be noted that all emitters are based on the IDPWT, implemented
as a synthesis filter banks, and the receiver is based on the DWPT, implemented as an analysis filter
bank, as illustrated in Figure 2. The input of each filter to the transmission side, contains either a bit
or a binary frame, so that the inputs can be activated or not. These binary data might differ from one
input to another and is modulated by pulse modulations. A study on the different types of binary or
pulse modulations is presented in [4], which makes it possible to make an appropriate choice of the
type of pulse to be used, for a multi-resolution architecture.
4.2. Operating Modes:
For this work, two multi-user (or multi-sensor) operating modes will be presented and tested;
Many-To-One (MtO) and One-To-Many (OtM) mode.
4.2.1. Many-to-One Mode
The architecture in the MtO mode corresponds to a multi-sensors communication from several
transmitters (or users), to a single receiver, as presented in Figure 3. Each transmitting user is
equipped with an IDWPT block that ensures the activation of a single input for this user and, thus,
identifies the equivalent sensor. In other words, each input of the IDWPT block to the transmission,
corresponds to only one output of the DWPT block to the reception (Figure 4).
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Figure 3. Many-to-One (MtO) Mode.
Figure 4. Transmitter in the MtO mode.
This mode of communication corresponds to a communication from level 0 and 1 to level 2 of
the CIM pyramid (Figure 5). Information from several sensors, at a low rate, is transmitted at the
same time to the same receiver. In this transmission mode, the activation of one of the inputs, results
in the activation of a user. Figure 4 illustrates a 16-input architecture, corresponding to 16 potential
sensors (scale 4). Each uses a single input that is different from the other inputs. For this example,
input number 7 (sensor 7) is activated and all others are deactivated. The waveform on each activated
input is different from the waveforms of the other remaining inputs. Inputs that are not activated will
be set to zero.
The DWPT receiver receives the data flow from all sensors in the network—each sensor is
identified by a single-filter output at reception. The received data must be detected and assigned to
the corresponding transmitter sensor. This mode has a higher bandwidth occupancy than the single
user mode because each user (input enabled) will occupy a separate sub-band. This will lead to
frequency selectivity of the channel, due to interference between users, for whom it will be necessary
to protect the transmitted data, as much as possible. Nevertheless, it will allow synchronous
communication from several sensors to the same receiver.
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Figure 5. CIM with operating modes OtM and MtO.
4.2.2. One-to-Many Mode
For the One-To-Many OTM mode, an IDWPT transmitter with n inputs can transmit the
information to m DWPT receivers, each with n outputs.
The information sent from the input (i) is retrieved at the output (i). This is the reverse mode of
the MTO mode, where the equipment at level 1 and 2 of the CIM pyramid (Figure 5), sends the same
information to the level sensors. This is equivalent to the Master-Slave mode in a conventional
industrial network architecture. While the transmitted data rate might be low, receiving information
from multiple sensors creates spatial diversity that allows the data sent, by at least one of the sensors,
to be retrieved. Figure 6 shows a transmission to a single transmitter and five receivers. The sent data
is detected and restored at the 7th output of each DWPT receiver, as shown in Figure 7.
Before elaborating on the performance of our architecture with its two modes—MtO and OtM—
a simulated industrial channel model has been presented below.
Figure 6. One-to-Many (OtM) mode.
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Figure 7. Receiver in the One-to-Many mode.
5. Industrial Channel Characteristics
In industrial medium, signals are subject to several perturbations, due to the propagation
phenomenon that might significantly degrade the performances of the system. Such environment is
affected by high level noise and interferences caused by the operating temperature, vibrations,
metallic structures, and heavy machinery [11]. In addition, the signal suffer from attenuation and
shadowing effects, caused by abstractions in the propagation channel. Random movement of objects
and people in the wireless medium might also cause time-varying effects. Those propagation effects
can significantly destroy the exchanged information and, hence, degrade the performance of IWSN
[2]. A good estimation of the propagation channel is, thus, required to design and evaluate WSNs for
industrial applications.
5.1. Fadings
Due to the wireless propagation in the industrial medium, received signals are subject to
attenuation and fading effects. The expression of the received signal is:
()=ℎ()∗ ()+(), (8)
where, ℎ() is the channel impulse response, () is the transmitted signal, and () is the additive
noise. Inside a factory, generally, sensors are arranged in a line-of-sight configuration. Some
narrowband and wideband indoor channel measurements, in various industrial settings, have been
conducted over the past few years [12,13]. These measurements showed that the temporal impulse
response ℎ(), at a fixed location in an industrial environment, follows a decreased exponential
distribution [2]. This distribution depends on delays and power of each path, as an established Saleh
Valenzuela model [14]. Channel delay spread can be concluded from impulse response, according to
transmission frequency and LOS (Line of Sight) or NLOS (Non Line of Sight) configurations. The
main objective of this paper is to validate our impulsive architecture, under a simulated industrial
channel, we thus, generated channel impulse responses, based on measurements, as presented in
[2,15], for both configurations LOS and NLOS, at 2.4 GHz. The simulated impulse response included
ten significant paths, as shown in Figure 8.
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Figure 8. Simulated channel impulse response.
According to several previous studies [2], all paths follow the same statistical distribution to
represent the fading channel phenomenon. The temporal received signal envelope, follows the Rician
statistical distribution in the LOS scenario and the Rayleigh distribution in the NLOS case.
()=
−
, (9)
where () is the modified Bessel function at order zero. is the shaped-parameter called Rician
factor. For =0, () converges with the Rayleigh distribution.
5.2. Noise
Usually, for wireless communication, the additional noise to the received signal is White
Gaussian Noise (Additive WGN). For an industrial environment, the signals will be affected by
additional noise, which is impulsive noise coming from motors, regulators, electrical equipment, and
others. Thus, the industrial noise () in Equation (8), will be modelled as a superposition of white
Gaussian noise AWGN () and impulsive noise (), having a very high variance (Equation 9).
() is modelled as a first-order, two-state Markov process, which describes a typical impulse noise
[16].
()=()+(), (10)
where () and () are Gaussian processes of zero mean, whose probability density functions are,
respectively:
[()]=
−()
, (11)
[()] =
−()
, (12)
where ≥ 1 is a scaling constant of impulse noise amplitude. The higher this amplitude is, the
higher the noise is. For our simulations, we use =50, which corresponds to a significant impulsive
noise.
6. Discussions
This section presents simulation results of our architecture for a noisy industrial channel. All
simulations were done under MATLAB. The different parameters to define the study context are
presented in Table 1.
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Table 1. System parameters.
Parameters Description
Communication Multi user: MtO and OtM
Applications Wide Band
Frequency 2.4 GHz
Sensors number 16 (MtO) & 4 (OtM)
Modulation Impulsive
Transmission IDWPT
Reception DWPT
Wavelet Symlet
Transmission configurations LOS & NLOS
Paths number 10 paths
6.1. Simulations
The system model is based on IDWPT/DWPT multi-user architecture for 4 and 16 sensors, over
an industrial environment. All emitters are based on IDPWT implemented as a synthesis of filter
banks, and receivers are based on DWPT, implemented as analysis filter banks [4,17]. Industrial
channel is described as a Rician fading channel, in the case of LOS configuration and Rayleigh fading
channel for NLOS, at a 2.4 GHz frequency, affected by an impulsive noise. According to our previous
study on the optimal choice of wavelet, published in [17], we chose the "Symlet" wavelet which has
demonstrated the lowest binary error rate for the IDWPT/DWPT architecture, over an AWGN
channel.
For our multi-sensor system in the MtO mode, the data frames for each user were binary, with
a length of 16 bits, generated randomly. This was due to the fact that sensors in the industrial medium
transmit short packets of information data. These data frames were modulated according to the same
pulse modulation and each transmitter (sensor) was identified by a unique signal. Figure 9 illustrates
the signals from four different sensors (1, 5, 12, and 16) of an architecture, with 16 transmitter sensors.
These sensors wore chosen in order to give a simple example to illustrate the signals. All 16 signals
were different from each other, because the binary data at the input of each filter was different.
Figure 9. Transmitted signals, after IDWPT, for the four sensors.
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Considering the effect of the fading channel, due to the delay of spread, in addition to the AWGN
noise for LOS and NLOS configurations, it was clear that the effect of the multi-paths disturbs the
signals of different users and, thus, causes interference between them. However, our architecture
allows signal detection at reception, for all users, as shown in Figure 10, for an SNR (Signal to Noise
Ratio) greater than 20 dB.
Figure 10. Linear BER (Bit Error Rate) over a fading channel with AWGN noise for the MtO mode.
Beside fading effects, and by adding industrial noise composed of Gaussian noise and impulse
noise, the binary error rate was determined (presented in Figure 11). Our communication architecture
converged more slowly and the performance decreased, but it allowed us to fully detect information
from an SNR, up to 35 dB. In the presence of industrial noise, the information might be completely
lost if the effects of the channel were not properly taken into account.
For the OtM mode, only one transmitter based on IDWPT with n inputs sent the data to m
receivers, based on DWPT with n outputs, each. The concept of this transmission is to activate a single
input i of the transmitter and deactivate the others (set them to zero) (Figure 6). On reception, the
data was detected at the output i, for each receiver (Figure 7). All binary data were modulated by
pulse modulation using the ‘Symlet’ wavelet. The communication system studied here was based on
one transmitter sensor and four sensors at the reception. Input number 7 of the transmitter was
activated and all others were forced to zero. The number of receivers did not matter, because at the
reception it was a broadcasting technique that was used. Figure 12 shows the received signals on the
four receivers. The data signal was restored at the 7th output, corresponding to the activated input.
Considering the effect of the fading channel, in addition to the AWGN noise for the LOS and the
NLOS configurations, our impulse architecture allowed signal detection at reception. According to
the simulation results presented in Figure 13, the transmitted signal was detected at all receiver
sensors for the LOS and NLOS channels, at 2.4 GHz. Detection was done almost with no errors above
20 dB. Some differences between the LOS and the NLOS configurations were detected from an SNR
of 14 dB. This was mainly due to the effects of fading and channel dispersion, which could be
corrected by using channel encoding during transmission.
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Figure 11. BER over the fading channel with industrial noise for the MtO mode.
Figure 12. Detected signal at the 7th output (in red) for the OtM mode with four receivers.
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Figure 13. BER over a fading channel at 2.4 GHz with AWGN noise for the OtM mode.
By now, considering the effect of industrial noise, our communication architecture made it
possible to fully detect information from an SNR of 30 dB, as shown in Figure 14. The difference in
error rates was very large and depended on the propagation channel.
Figure 14. BER over a fading channel with industrial noise for the OtM mode.
6.2. Performances
To improve the robustness of our architecture, error-correcting coding was used, before the
IDWPT block. The encoder used a convolutional code, because it was most suitable for wireless
sensor networks [18,19]. To convolutionally encode data, memory registers were used with generator
polynomials gi. To carry out the simulations, we opted for the convolutional codes presented in Table
2, using generator polynomials g0, g1, g2, and g3, all having a constraint length of 7. These were the
optimal encoders most commonly used by digital communication standards and have shown a better
compromise between performance and complexity. A Viterbi decoder was used at the reception, after
DWPT.
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Table 2. The used conventional codes.
Coding rate g0 g1 g2 g3
½ 171 133
1/3 133 165 171
¼ 121 133 165 171
For the MtO mode, as shown in Figures 15a–c, by choosing four different sensors (1, 5, 12, and
16), the gain reported by the channel coding varied, according to the different coding rates. For a
fading channel, detection errors were eliminated from SNR = 8 dB, by using an error correcting code,
with a rate of 1/4. Errors were eliminated from 10 dB, for a rate of 1/3 and from 14 dB, for a rate of
1/2. For a BER set at 0.1, the coding gain was around 6 dB, at a rate of 1/4, for a fading channel. The
gain reported by the error correcting coding was very interesting, because all signals were fully
detected on reception from an SNR of 8 dB, for a rate of 1/4.
In the OtM mode, using an error correcting code at a rate of 1/2, the detection of the signals was
done without any error, at an SNR of 8 dB, for a fading channel (Figure 16a). For a rate coding of ¼,
errors were eliminated from 6 dB (Figure 16b). A significant gain that exceeded 10 dB was obtained
using a rate of ¼, over a fading channel.
(a) (b)
(c)
Figure 15. BER over a fading channel with the AWGN noise for the MtO mode, using the error
correcting code. (a) BER for a 1/2 coding rate; (b) BER for a 1/3 rate; and (c) BER for a 1/ 4 rate.
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(a)
(b)
Figure 16. BER over a fading channel with the AWGN noise for the OtM mode, using the error
correcting code. (a) BER for a 1/2 coding rate; and (b) BER for 1/4 rate.
The majority of optimal receivers implemented to eliminate the effects of impulsive noise were
based on thresholding the amplitudes at the receiver input [20–22]. This technique was used to
improve the robustness of our communication system, to face the industrial noise. We chose an
adaptive thresholding at the input for which the detection thresholds were adapted to the different
SNR values.
At the receiver side, and after detection of all signals, the thresholding of industrial noise was
done, based on the received signals amplitude. In Figure 17, the curves representing the BER as a
function of the SNR, for both modes (MtO and OtM), are presented. Figure 17a shows BER in the
MtO mode for the four sensors (1, 5, 12, and 16), with and without thresholding. Optimal receiver
using an adaptive thresholding allowed a gain greater than 10 dB, compared to the initial
architecture, in a very noisy channel. Errors were completely eliminated from an SNR of 24 dB. In the
case of the OtM mode, Figure 17b shows that the errors were eliminated over 25 dB, using adaptive
thresholding.
The major advantage of this optimal receiver based on thresholding, was that, it was very well
suited to wireless sensor networks, and could be easily implemented in software, as well as in
hardware.
(a)
(b)
Figure 17. BER over a fading channel with industrial noise, using the thresholding receiver. (a) MtO
mode for sensors (1, 5, 12, and 16); and (b) the OtM mode for receiver 2.
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7. Conclusions and Perspectives
A robust IWSN multi-user architecture, based on the IDWPT in transmitter and DWPT in
receiver, under an industrial channel, was presented in this paper. The industrial channel was
modeled as a fading channel affected by the impulsive noise, combined with the AWGN. The
presented wireless sensor network architecture, with its two communication modes, MtO and OtM,
offered better results in terms of data reception, for a noisy industrial environment. The robustness
of the architecture could be improved by using channel coding or thresholding of industrial noise, at
the reception. By using conventional error correcting codes with a 1/ 4 rate, robustness of the MtO
mode was highly improved and all signals were fully decoded from an SNR of 8 dB, over a fading
channel. For the OtM mode, signals were decoded from 6 dB, over the same channel. Using optimal
thresholding receiver, errors were eliminated, about 25 db, for both the MtO and the OtM modes,
over an industrial noisy channel. An in-depth study on the optimal error correcting code for IWSNs
could be considered as a perspective for this work. The choice of the type of encoder, as well as the
length of the code adapted for sensor networks, while respecting the energy consumption constraint
could be considered. This involves a thorough study of the energy consumption for our multi-sensor
architecture, which would be the subject of future research. To improve the robustness of data
exchange in industrial medium, an extension to Many-to-Many mode of operation might be possible.
Especially because, this mode will promote spatial diversity and will allow permanent
communication between the different entities.
Author Contributions: Validation, M.T. and S.S.; Writing—original draft preparation, S.S.; visualization, F.M.
and M.C.; supervision, A.D.
Funding: This work was supported by LPRI EMSI Casablanca Morocco.
Conflicts of Interest: The authors declare no conflicts of interest.
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