# Reconfigurable Intelligent Surfaces
in Challenging Environments:
Underwater, Underground, Industrial and Disaster

Steven Kisseleff, Symeon Chatzinotas, Björn Ottersten  This work was supported
by Luxembourg National Research Fund (FNR) under the CORE project RISOTTI
C20/IS/14773976. The authors are with the Interdisciplinary Centre for
Security, Reliability and Trust (SnT), University of Luxembourg, Luxembourg.
E-mail: {steven.kisseleff, symeon.chatzinotas<EMAIL_ADDRESS>
###### Abstract

Reconfigurable intelligent surfaces (RISs) have been introduced to improve the
signal propagation characteristics by focusing the signal power in the
preferred direction, thus making the communication environment ’smart’. The
typical use cases and applications for the ’smart’ environment include beyond
5G communication networks, smart cities, etc. The main advantage of employing
RISs in such networks is a more efficient exploitation of spatial degrees of
freedom. This advantage manifests in better interference mitigation as well as
increased spectral and energy efficiency due to passive beam steering.

Challenging environments comprise a range of scenarios, which share the fact
that it is extremely difficult to establish a communication link using
conventional technology due to many impairments typically associated with the
propagation medium and increased signal scattering. Although the challenges
for the design of communication networks, and specifically the Internet of
Things (IoT), in such environments are known, there is no common enabler or
solution for all these applications. Interestingly, the use of RISs in such
scenarios can become such an enabler and a game changer technology.
Surprisingly, the benefits of RIS for wireless networking in underwater and
underground medium as well as in industrial and disaster environments have not
been addressed yet. In this paper, we aim at filling this gap by discussing
potential use cases, deployment strategies and design aspects for RIS devices
in underwater IoT, underground IoT as well as Industry 4.0 and emergency
networks. In addition, novel research challenges to be addressed in this
context are described.

###### Index Terms:

Reconfigurable intelligent surfaces, Internet of Things, challenging
environments, underground, underwater, industry 4.0, emergency networks

##  I Introduction

Reconfigurable intelligent surfaces (RISs) is a promising technology, which
has been motivated by the increasing demands of wireless networks beyond 5G
[1]. RISs are intelligently designed artificial planar structures with
reconfigurable properties enabled via integrated electronic circuits. These
electronic circuits can be programmed to reflect an impinging electromagnetic
(EM) wave in a controlled manner, thus making the communication environment
controllable and smart. RISs are manufactured with low-profile and light
weight using inexpensive materials and can be deployed on a variety of
surfaces, such as facades of buildings, walls, ceilings, etc. [2]. The signal
propagation from transmitters to receivers can therefore be assisted by
steering the RIS-reflected signals in directions that enhance the signal
quality, suppress co-channel interference and consequently improve the
spectral efficiency. The corresponding performance gains of RIS-assisted
networks compared to current wireless systems are well-known from the previous
works in this domain [3]. Furthermore, co-channel interference can be reduced
or avoided by choosing different propagation paths for the interfering
signals. This is important in urban environments with dense deployment of
terminals, cf. [4].

Challenging environment (CE) refers to a communication environment, in which
the signal propagation and connectivity using traditional signaling
technologies are heavily impaired, so that a stable, reliable and secure
communication link is very difficult to establish. Unlike in the so-called
extreme environments, the challenges in CEs are mostly related to wireless
communication rather than to the operability of sensor nodes under extreme
conditions, cf. [5]. Depending on the actual environment, the reasons for the
impaired connectivity can be very different. One of the typical problems is
the increased signal scattering and absorption. Through this, the path loss
increases with transmission distance much faster compared to traditional
communication environments, i.e. path loss exponent higher than 4. Another
problem is related to the accessibility of the network nodes and security of
the links. In this context, it is important to find either the shortest or the
least impaired path to the intended receiver. With the introduction of the
Internet of Things (IoT) in CEs, the problem of establishing a reliable
communication link between the IoT nodes has become a real challenge, which
has no unified solution yet.

Among various challenging and extreme communication environments, in this
work, we focus on four use cases: underwater, underground, industrial, and
disaster environments. These environments have very different characteristics
and challenges. These problems of the CEs typically motivated the researchers
to look for alternative communication-enabling technologies which are very
different as well. As an example, magnetic induction has been proposed for the
signaling in the underground medium, since it is less prone to scattering and
absorption than the traditional EM waves. For the same reason, acoustic
signaling has been selected for the underwater communication. Correspondingly,
each use case has evolved into an individual research field with specific
system design methods and limited generalization potential. Moreover, the
interconnection of various IoT networks, such as disaster and industrial
environments or underground and underwater, is hardly possible due to the
divergent design guidelines and techniques, which prohibit joint optimization.
Similarly, it is very challenging to incorporate these networks into
terrestrial networks and to achieve homogeneity in their design.

To solve this issue, RIS can be proposed in nearly all possible CEs in order
to improve the connectivity. Using the additional degrees of freedom
associated with RISs, it is therefore possible to determine the optimal signal
path and reduce the impact of the environment on signal propagation. Hence,
RIS has the potential of becoming the key enabler for the future wireless
systems in CEs. It might be even possible to unify the design methods for the
wireless systems in various CEs based on this common enabler. This might be
possible via conversion of the individual challenges into common challenges
for all environments. In this context, future research on RIS-assisted CEs
becomes very promising and interesting.

In this work, we aim at providing our vision on the application of RIS in CEs
in order to open the discussion on the evolution of RIS-assisted CEs and pave
a way to their systematic design.

###  I-A Contributions

Throughout the open literature, the research challenges of integrating RISs
into wireless networks are solely discussed within the existing network
architectures and the traditional urban ecology. These challenges differ from
the ones discussed in the context of CEs, which motivates us to fill this gap
by providing a vision of RIS-assisted CE networks along with the proposal of
some relevant research challenges. Specifically, we discuss relevant
application scenarios, deployment strategies and aspects of the system design.
The contributions of this work provide a basis for the future investigations
in this field as it points in the direction of the most relevant research
problems to be addressed in the near future. The main contributions are as
follows:

  * •

A brief review of the RIS technology is provided including the application of
RISs with unconventional signaling techniques;

  * •

An overview of the peculiarities and features of each considered environment
is provided in order to identify the main challenges for the wireless
networking without RIS;

  * •

Benefits from the deployment of RIS in the respective environment are
explained, which motivates future research in the respective area;

  * •

Deployment strategies for RIS as part of each respective IoT network are
discussed;

  * •

Specific hardware design is proposed to account for the autonomous operation
of RIS in the inaccessible environments;

  * •

Expected improvements of the signal quality are derived;

  * •

Future research challenges for the unified RIS-based technology in CEs are
revealed.

This paper is organized as follows. Prior works and recent advances of the RIS
technology are discussed in Section II. Sections III-VI provide overviews and
describe the peculiarities for the application of RIS in the underwater,
underground, industrial and disaster environments, respectively. Specifically,
the most beneficial strategies for the RIS deployment in these scenarios and
the respective challenges for the signal propagation and system design are
explained. In Section VII, a promising hardware concept for the RIS
application in CEs and a generic system model is proposed. Future challenges
and research opportunities are outlined in Section VIII. Finally, the paper is
concluded in Section IX.

##  II Advances of RIS technology

Communication channels have been traditionally considered as an uncontrallable
entity. Hence, most of the conventional wireless communication techniques
attempt to adapt the system parameters to the peculiarities of the
uncontrolled channel. However, substantial performance gains can be achieved,
if the communication channel becomes reconfigurable and correlates with the
signaling. This idea has been realized using RIS, which can be used to enhance
the performance of communication systems by controlling the reflections of the
impinging signals.

RIS has been named a new paradigm in wireless communications in [1]. The
authors proposed RIS as one of the key enablers for the 5G and beyond systems.
In particular, research challenges and directions for the design and
deployment of RIS based on the prior work [6] have been addressed.
Furthermore, the idea of controllable communication environment has been
proposed in various works under different names, such as Intelligent
Reflective Surface (IRS), Large Intelligent Surface (LIS), Intelligent
Mirrors, Intelligent Metasurfaces [7, 8, 9].

###  II-A Promising research directions

Since RIS represents a fundamentally new concept of signal propagation,
channel modeling and acquisition are of utmost importance for the system
design. Hence, multiple works have been dedicated to these topics, e.g. [10,
11, 12, 13, 14, 15, 16]. Since RIS can actively modify the communication
environment, the proposed channel estimation methods depend on the individual
scenarios, number of RISs, users, etc. [14, 12].

Furthermore, advanced techniques of multi-antenna transmissions need to be
developed. Specifically, novel precoding and beamforming methods have been
proposed for RIS-assisted multiuser networks [17, 18, 19, 20, 21, 22, 23]. In
this context, joint passive and active beamforming have been investigated for
various system configurations. It has been shown that optimized reflective
coefficients provide substantial performance gains in terms of spatial
diversity and quality of service. Statistical improvements of the spatial
diversity and especially the matrix rank have been addressed in [24] and
validated in practical experiments.

Most of the relevant works in the research field of RIS focus on indoor
scenarios, where the reflections from the walls can be potentially very
harmful without RIS. Using RIS, the number of connected devices in such
scenarios can be increased without causing a performance degradation for the
network due to harmful co-channel interference signals. Note that the indoor
deployment of RIS provides the biggest advantage and diversity gain due to
relatively short transmission distances, such that the attenuation of the
reflection-based signal paths is not significantly higher than the direct
path.

Outdoors, the main target application will be Smart Cities, cf. [4]. So far,
however, not many works have investigated RIS-assisted outdoor scenarios. The
main innovation addressed in these works is the use of unmanned aerial
vehicles (UAVs) [25, 26, 27, 28, 29]. In such scenarios, UAVs can act not only
as receivers, but also as mobile relays to serve as access points for multiple
network nodes. As an example, the beamforming towards UAVs and the tracking of
the UAV position by the BS has been investigated in [26].

An additional motivation for the theoretical investigations in this area has
been provided in [30, 31, 32], where the first experimental results for RIS-
assisted signal propagation have been presented. Recently, more thorough
practical design aspects and experimental studies have been published, cf.
[33, 34, 35, 36, 37]. Specifically, in [33], detailed guidelines for the
hardware design of RIS as well as the complete signal flow model has been
presented. In [36], a hardware design for RIS configuration as
transmitter/modulator has been proposed and implemented. However, the main
drawback of these existing designs lies in the assumed accessibility of the
RIS devices, which is not true in CEs. Hence, more advanced functionalities,
such as autonomous operation and energy independence need to be incorporated
into the hardware design. In Section VII-A, we propose such an architecture
for RIS in CEs.

Promising new research directions, which have been addressed recently
comprise:

  * •

path loss modeling [35, 38, 39, 40]

  * •

physical layer security [41, 42, 43, 44, 45],

  * •

non-orthogonal multiple access (NOMA) [46, 47, 48, 49, 50],

  * •

wireless power transfer and simultaneous wireless information and power
transmission (SWIPT)[51, 52, 53, 54],

  * •

vehicular communication [4, 45, 55, 56, 57],

  * •

cell-free networking [19, 58, 59],

  * •

localization [60, 61, 62]

  * •

RF sensing [34, 63].

The research in these directions is very active, such that multiple high-
quality publications appear on a daily basis. For more insight in the advances
in RIS technology and the creation of a smart environment, we refer the reader
to [3, 9, 64, 65], where all relevant studies have been described and future
challenges in general context have been addressed.

###  II-B Alternative signaling technologies

In this work, we consider some of the most CEs for the wireless communication,
where the traditional EM waves in the moderate frequency range may not provide
a sufficient signal quality. In such environments, alternative signaling
technologies have been proposed to overcome the drawbacks of the EM waves,
such as absorption and scattering. Hence, it is worth looking into the
advances of RIS specifically designed with respect to these technologies in
order to understand their benefits and practical limitations.

(a) Transceivers for optical signaling.

(b) Transceivers for magnetic signaling.

(c) Transceivers for acoustic signaling.

Figure 1: Transceivers for alternative signaling technologies.

####  II-B1 Millimeter-Wave, Terahertz and optical communications

High signal frequencies are very attractive for the wireless communication
systems due to a large frequency band and thus high symbol rate. For
Millimeter-Wave (mmWave), Terahertz (THz) and optical signaling, the frequency
ranges are up to 300 GHz, 3 THz and several hundreds of THz, respectively.
Correspondingly, the signal carriers are characterized by a very small
wavelength, which leads to rather small transmit antennas and increased
reflection of the impinging waves from the surface compared to the moderate
carrier frequencies, e.g. traditional WLAN frequencies of 2.4 GHz. In
particular, for optical signaling, lasers and photodiodes are typically
employed, see Fig. 1(a). Hence, the application of RIS is very promising with
this type of signaling. Most of the existing works focus on THz and mmWave
based technologies, cf. [66, 67, 68]. However, recent studies have been
conducted to introduce RIS in optical communication networks, cf. [69, 70].
Here, the weak absorption of light can be exploited in order to guide the
signal to the destination with a very little path loss.

####  II-B2 Magnetic induction/resonance

Magnetic induction (MI) or magnetic resonance refers to non-propagating quasi-
static magnetic fields using induction coils as part of respective resonance
circuit instead of traditional EM antennas, see Fig. 1(b). Typically, MI is
used with very low signal frequencies, i.e. between few kHz (in conductive
media) and 13.56 MHz (according to Near-Field Communications standard), which
results in very narrow frequency band. Due to the low frequency, the signal
wavelength is very large and does not suffer from reflections or scattering.
Unfortunately, this fact prevents from directly utilizing the RIS technology
with MI. Nevertheless, a similar effect can be achieved via passive relaying
of magnetic fields using the so-called MI waveguides, cf. [71]. Specifically,
passive MI coils aligned in a waveguide structure generate secondary magnetic
fields without explicit power consumption, which can be viewed as pseudo-
reflections. These pseudo-reflections can be made reconfigurable by modifying
the resonance circuit impedance of each MI relay [72]. Hence, these passive MI
relays can be viewed as a special case of magnetic RISs with a single
reflective element per device. Note that even with a single element RIS can
contribute to the signal propagation in a network by manipulating the angle of
reflection leading to changes of the phase and magnitude of the reflected
signal. In case of MI relay, the pseudo-reflection can be similarly adjusted
to change the phase and the magnitude of the reflected signal.

####  II-B3 Acoustic communications

Depending on the application, acoustic signals are typically generated in the
frequency range between few Hz (infrasound) and few MHz (ultrasound). Due to
the different type of signal carrier (pressure rather than EM fields), the
signals are transmitted using speakers and received using a microphone (or
hydrophone in the water medium), see Fig. 1(c). Although the conventional RIS
technology has been proposed for EM waves, the possibility of redirecting
acoustic waves has been introduced in [73]. Recently acoustic RISs have been
proposed, cf. [74, 75, 76]. The primary scope of these works was the cloaking
of uneven surfaces to avoid the scattering and improve the ambient sound.
However, the use of such acoustic RIS devices for communications has been
recognized in [77, 78]. Specifically, in [77], space-time coding using
acoustic RIS has been proposed and in [78] the possibility of acoustic
beamforming using specifically designed metasurfaces has been explored.
Furthermore, the application of RISs for the underwater communication has been
demonstrated in [79] using experimental setup. Unlike EM and MI based wireless
systems, the passive signal steering or beamforming are not achieved via
circuit impedance reconfiguration, but via actual mechanical manipulation of
the surface elements [75].

###  II-C Challenging environments

In the following sections, we discuss four most relevant scenarios associated
with CEs. The importance of some of these scenarios and their characteristics
have been discussed in [5, 80]. We start with the underwater and underground
communications, where the propagation medium is the main challenge for the
system design. Then, we move on to the industrial and disaster environments,
where the large number of obstacles impacts the signal propagation. Note that
we do not address higher-layer challenges for the network design such as
routing and topology control, which depend on the actual applications and
system configuration. Instead, we focus on the challenges associated with the
connectivity and digital signal transmission.

##  III Internet of Underwater Things (IoUwT)

###  III-A Overview

Underwater communication networks have been analyzed in a plethora of works
for various applications. The research in this area can be traced back to the
early days of wireless communications, cf. [81].

The main difference between underwater networks and the terrestrial networks
lies in the propagation medium, which dramatically affects the path loss.
Among the main relevant properties of the water medium, its conductivity is by
far the most important one. The conductivity of water depends on its salinity,
temperature and pressure, such that it has values of around 0.01 S/m in sweet
water and up to 4-5 S/m in sea water, cf. [82, 83]. These high conductivity
levels lead to absorption of the EM fields and very high path loss. Thus, data
rates above 8 kbit/s are prohibited over distances beyond 10 m in the sea
water [84].

For this reason, underwater communications have been traditionally designed
using acoustic waves, which do not suffer from absorption as much as the other
signaling techniques. Acoustic signaling relies on the propagation of pressure
variations, which can reach up to tens of kilometers in the open water.
However, the issues with the acoustic signaling are related to the typical
maritime objects, such as ground/shore or fish, to the interference from
dolphins/whales, and to the signal scattering/reflections in the acoustically
heterogeneous medium, i.e. water streams with different pressure and
temperature levels. The latter is due to the varying speed of sound, which
depends on the local properties of the medium according to [83]:
$c=1412+3.21T+1.19S+0.0167z$, where $S$ is the salinity, $T$ is the
temperature and $z$ is depth. In addition, the absorption loss is substantial.
All these issues impact the communication channels by means of multipath
propagation, frequency-selective and time-variant impulse responses as well as
interference. Alternative solutions based on extremely low signal frequencies
using MI or based on optical communication have been introduced as well [85,
86]. However, these technologies are much less common due to the path loss,
which is not necessarily lower than using traditional EM waves, cf. [84].

Recently, the idea of establishing a large wireless network similar to the
upcoming IoT has been introduced. It has been named the Internet of Underwater
Things111The usual abbreviation of the Internet of Underwater Things is IoUT,
which is commonly used in the literature. However, in this work, we consider
also the Internet of Underground Things, which would have the same
abbreviation. Hence, in order to distinguish between the two types of network,
we utilize IoUwT for the underwater networks and IoUgT for the underground
networks. (IoUwT) and should connect various devices deployed under the water
surface, e.g. submarines, autonomous underwater vehicles (AUVs), sensors and
ships, see Fig. 2.

Figure 2: Example of the IoUwT. Connectivity between networks nodes, i.e.
submarine, buoy, ship and sensors, is impaired by reflections from the typical
maritime obstacles like fish and rocks.

Here, the goal is to provide sufficient connectivity for the wireless network
in the underwater medium. Usual scenarios for the deployment of the IoUwT
involve the observation of maritime animals and fish, monitoring of seismic
activities (e.g. tsunami) and wireless communication with underwater vehicles.
Sometimes, the network includes floating buoys to collect the data from the
sensors deployed below the surface. In all these scenarios, the mentioned
issues of time-variant multipath channels (when using acoustic waves) seem to
be the main challenge for the communication.

IoUwT is a promising research area, which has seen a lot of advances in the
recent years. For more information on underwater communications and IoUwT we
refer to [87, 83].

###  III-B RIS in Underwater medium

While EM, MI and even optical signal propagation in the water medium
(especially in clean sweet water) are possible over short distances,
transmissions over larger distances using acoustic signaling are preferred in
the context of the IoUwT. Hence, we focus on acoustic signaling in this
application222It is worth mentioning that the main challenge for the EM
signaling is the signal absorption, as mentioned earlier, which can hardly be
combated using RIS technology..

As mentioned earlier, acoustic signal propagation in the underwater medium
suffers most from scattering at uneven surfaces, water streams and fish. This
leads to a high path loss and extreme frequency selectivity of the
communication channel due to the multipath, such that only very narrow signal
bandwidths are tolerated. Correspondingly, the effective data rate is very
low. Hence, these weaknesses need to be mitigated using RIS. The deployment of
RIS in this scenario is illustrated in Fig. 3.

Figure 3: IoUwT assisted by RIS.

####  III-B1 General approach

The steering of the signals in the preferred direction would help
constructively align the interference and reduce the multipath. Of course, it
may not be possible to completely remove the multipath due to a large number
of possible reflections in the water. However, the orientation of RIS elements
can be optimized to reduce the frequency selectivity and thus increase the
effective signal bandwidth. In this context, the design problem can be
formulated as a maximization of the signal quality measured after the applied
practical equalization filter in the receiver. Since this signal quality is
typically related to the discrete length of the equivalent impulse response
(which is associated with the maximum delay spread according to the
multipath), an alternative objective can be formulated as a minimization of
the delay spread333The potential improvement of signal quality with increasing
number of RIS elements and properly selected phase shifts is discussed in
Section VII-B.. The solution to this optimization problem needs to be updated
very quickly due to the typically short coherence time in the water medium.
Hence, the corresponding computational complexity needs to be very low in this
scenario. Thus, learning-based solutions, e.g. deep learning, seem especially
promising, since the most computationally expensive part of the optimization
is performed offline. Alternatively, robust optimization can be applied in
order to avoid the necessity of rapid updating the phase shifts.

The deployment of RIS in the underwater medium can have various configurations
depending on its location:

  * •

attached to the ground or to the shore;

  * •

attached to autonomous underwater vehicles;

  * •

floating beneath the surface.

Next, we would like to discuss some of the challenges and potential solutions
related to the operation of the RISs in these three underwater scenarios.

####  III-B2 Stationary deployment

The first option, i.e. stationary deployment of RIS, is the simplest and the
most beneficial option, since it does not pose too many new challenges for the
system design. Hence, RIS can be readily integrated into the IoUwT.

The main benefit of the stationary deployment is that the geometrical
configuration does not change over time. In principle, this should lead to
more or less stationary communication channels, which is however only valid
for relatively short distances. For large distances, e.g. above 100 m, due to
the fast time-varying channels in the underwater medium, it might be difficult
to reach a sufficiently high level of synchronization and channel estimation
accuracy in order to fully benefit from the capabilities of RIS. Hence, robust
optimization of the orientation of RIS elements seems to be the key solution
that helps to mitigate the impact of the time-varying multipath channels (to
some degree) at least on average.

####  III-B3 Autonomous underwater vehicles (AUVs)

AUVs are expected to serve the deployed nodes of the IoUwT by moving from one
node to another, which resembles the traditional mobile relaying. While the
main task is to ensure a better connectivity and to recharge the batteries of
the distant nodes, AUVs are well suited for carrying RIS. Through this, they
can contribute to the smart underwater environment. This type of mobility is
therefore steerable and can be adjusted to improve the signal propagation
using phase shifting capabilities of RIS in addition to the mobility of the
vehicles. In this context, the trajectory of the AUVs is of special interest.

One of the challenges related to the mobility scenarios is the channel
prediction. Unlike with stationary deployment, the geometry of the system
configuration is not fixed, but changing. Correspondingly, the communication
channels may become time-varying. However, the motion of the AUVs may be
relatively slow, which leads to a rather long motion-related coherence time.
Hence, the motion is not crucial for the system assisted by RIS-equipped AUVs.

On the other hand, the surface of the AUVs will be probably designed to reduce
the friction in water, which may otherwise become the main problem for the
steering of the vehicle. Hence, only relatively small areas should be covered
by RISs in order to reduce this harmful effect while preserving the control
over the smart environment.

####  III-B4 Floating RIS

One of the most promising deployment strategies involves a floating RIS. In
this scenario, RIS can be attached to the ground by a cable, see Fig. 3. On
the one hand, this allows RIS to be placed at the desired depth, e.g. between
the sensors deployed on the ocean ground and the surface, and on the other
hand restricts the maximum distance between the anchor position and RIS. Also,
the cable connection can be used for the control signaling, which would
substantially simplify the design of RIS, since the control signal does not
need to be transmitted wirelessly.

Unlike traditional RIS deployment scenarios, i.e. smart buildings, smart
homes, etc., where the impinging signals arrive always from the same half-
space, see Fig. 4(a), the floating RIS should be able to reflect signals
coming from all directions.

(a) Planar RIS.

(b) Spherical RIS.

Figure 4: Possible designs of RIS for underwater scenario.

Hence, for this specific type of RIS, a novel design is required. In
particular, the deployment of reflective RIS elements on the surface of a
sphere seems promising, see Fig. 4(b). But other geometrical bodies can be
considered as well depending on the specific application. Similarly to the
planar RIS, each element can reflect the impinging signals in a controlled
manner. Nevertheless, the number of reflective elements, which can be used to
steer the signals in the same direction is reduced compared to planar RIS,
such that the benefit from the spherical RIS is in general lower than with
planar RIS. In order to avoid the performance degradation, the size of RIS
needs to be substantially increased compared to the planar RIS. Through this,
a similar number of elements pointing in similar direction can be
accommodated, which implies a similar performance. However, this drawback is
compensated by the very advantageous property of the spherical RIS, i.e. the
operation of RIS independently of its orientation. This property is very
important for the application in underwater scenarios due to potential
rotational motion of RIS in water streams or after collisions with other
objects.

##  IV Internet of Underground Things (IoUgT)

###  IV-A Overview

The research on communications in the underground medium is nearly as old as
that of the underwater communications. Similarly, the main challenge of this
environment is related to conductive propagation medium, although the
conductivity is typically much lower compared to the water medium, below 0.01
S/m. The soil conductivity and correspondingly the absorption rate for the EM
radiation depend on the so-called volumetric water content (VWC), i.e. the
relative amount of water in soil, as well as on the soil content, such as
sand, clay, etc. [88]. Furthermore, variations of the soil content can lead to
signal reflections and scattering. These effects lead to a severe multipath
and very high path loss. Besides, signal reflection at the ground surface due
to the abrupt change of the EM properties contributes to the multipath
propagation as explained in [88].

Due to much lower average soil conductivity, most of the works on underground
communications are based on traditional EM waves despite the high path loss.
However, it is still extremely difficult to reach transmission distances above
50 m. Moreover, low frequency MI solutions have been proposed to overcome the
issues related to the low coverage, cf. [89]. The drawback of the latter is
however due to a very narrow bandwidth (centered around the resonance
frequency) and relatively large transceivers.

In the context of both EM waves and MI, there has been much progress in the
design of wireless networks lately. In particular, wireless underground sensor
networks (WUSNs) have been proposed [90], which evolved into a concept of the
Internet of Underground Things (IoUgT) in the recent years [91]. Here, the
goal is to establish wireless connectivity between distributed sensor nodes in
order to monitor seismic activity, structural health of buildings as well as
soil quality for the smart agriculture and clever irrigation, see Fig. 5.

Figure 5: Example of the IoUgT for smart agriculture applications. The sensors
are deployed below the ground surface and monitor the water content in the
soil. The signals are scattered by the heterogeneous soil medium.

The main challenge for this type of wireless networks remains the medium-
dependent path loss, which leads to a low coverage area and low signal
quality.

Furthermore, such wireless networks can be used to provide a communication
infrastructure to hardly accessible areas like mines and tunnels. In such
scenarios, signal reflections contribute on the one hand to the signal
propagation, since each tunnel can be viewed as a waveguide, and on the other
hand to the amount of intersymbol interference, which drastically increases
with each reflection. This can lead to a poor signal quality at the receiver.

Similarly to the IoUwT, the research on IoUgT is very promising and has been
popularized in the recent years. For more information on IoUgT, we refer to
[91, 92].

###  IV-B RIS in Underground medium

For the underground communications, we focus on signaling using traditional EM
waves in the moderate frequency range. While MI based underground
communication represents a promising alternative, the potential of magnetic
RIS has been thoroughly studied and did not show sufficient performance
improvement to justify the deployment of additional resonant circuits under
realistic assumptions444The reason for this seems to be the limitation of the
number of reflective elements which can be used in parallel by one magnetic
RIS., cf. [93]. Also, acoustic signaling is not possible due to severe path
loss and scattering effects inside the acoustically heterogeneous soil medium.

The signal propagation in the underground is very vulnerable to the
heterogeneous conductive medium of soil and in particular to the VWC as
pointed out earlier. Additional effects like scattering and reflections are
related to the soil composition. Similarly to the underwater communication,
these effects lead to a very high EM path loss even at short transmission
distances, cf. [94]. Furthermore, multipath propagation results directly from
the signal reflections and can be very harmful for the receive signal quality.
Hence, the main challenge that motivates the application of RIS in this
context is to reduce the multipath and the path loss by avoiding the signal
scattering and by steering the signal through the soil layers with low VWC.

####  IV-B1 General approach

An example for the deployment of RIS in the underground environment is
illustrated in Fig. 6.

Figure 6: IoUgT assisted by RIS.

Similarly to the application of RIS in the underwater medium, the phase
shifters of RIS can be optimized to reduce the frequency selectivity and
increase the effective signal bandwidth. Correspondingly, a practical design
problem would aim at minimizing the delay spread and signal quality after the
equalization filter. Furthermore, the general challenges associated with RIS
remain mostly the same as with underwater communications. Correspondingly, the
solution based on machine learning seems promising for this use case, too.
However, the main difference lies in the substantially longer coherence time
in the underground medium. While the time-varying channels in the underwater
medium are much more dynamic due to the water streams, fish, etc., the
underground medium can remain constant for a very long period of time, e.g.
between two rain periods. Even during the rainy weather, the VWC in soil does
not change abruptly, but requires several minutes to provide a measurable
change in the signal propagation characteristics. Hence, the complexity of the
optimization problems does not need to be as low as in the underwater medium.
Specifically, the parameter tuning may need to be executed only a few times in
the entire lifetime of the designed IoUgT, since the transmission channels
remain mostly unchanged, such that the solution remains unchanged as well.
Through this, a more thorough channel estimation and more exact solution can
be obtained using traditional convex optimization techniques.

####  IV-B2 Deployment in tunnels and mines

One of the main application scenarios for the IoUgT is the deployment of
sensor nodes in mines and tunnels. The main challenge for the EM signal
propagation is related to the uncoordinated reflections from the walls of the
tunnel or mine as well as scattering at the edges of the walls. This leads to
the performance degradation for the signal quality and connectivity of the
network. To address this issue, RISs can be installed in the walls and the
ceiling of the tunnel in order to improve the directivity of signal
propagation by steering the signal according to the direction of the passage,
see Fig. 7.

Figure 7: Mines and tunnels scenario. RISs are deployed in the walls to
improve the directivity of signal propagation.

Furthermore, as mentioned earlier, soil may partially absorb the signal, such
that each reflection potentially contributes to the overall path loss. On the
other hand, RISs are typically designed with the maximized reflection
coefficient in order to avoid additional losses, which would otherwise render
the use of RIS inefficient. Hence, the main challenge for the design of RIS-
assisted communication in tunnels and mines is to determine the optimal
strategic placement of the RISs and their phase shifts, which would guide the
signal in a ping-pong manner through the passage. Due to the stationary
deployment of the IoUgT, the optimal position and coarse values of the phase
shifts can be determined very accurately offline using solely the map of the
passage.

####  IV-B3 Other scenarios

In other scenarios of the underground communication networks, such as soil
monitoring, the nodes are typically embedded in the soil medium, such that the
communication between them is established directly through the ground. In this
context, the use of large RISs may not always be feasible due to a high
deployment effort. On the other hand, the effectiveness of RIS deployment
increases with increasing RIS size, since it is proportional to the number of
RIS elements555Here, we can assume that larger number of RIS elements enables
a higher spatial diversity [24].. Hence, it is important to investigate a
potential tradeoff between the performance gain and the deployment costs needs
with respect to the number of RIS elements and deployment strategies. In some
cases, a large set of small RISs may be preferable compared to a single large
RIS, leading to a distributed RIS operation666This problem occurs in disaster
environments as well and will be addressed in Section VI..

In practice, the soil medium is typically heterogeneous due to the variations
of its content, e.g. amount of sand and water. Hence, the signal propagation
may vary as well. In many cases, the ground medium is layered, i.e.
neighboring areas along a certain direction or a plane show similar content
and therefore similar signal propagation characteristics [95]. This property
can be exploited for the design of RIS-assisted underground networks, if the
signal is focused in the most suitable layer for the communication. However,
since the distribution of the soil layers and their individual properties are
unknown in general, this task is very challenging and should be tackled via
adaptive beamforming.

##  V Industry 4.0

###  V-A Overview

Industrial applications of wireless communications have gained an increased
attention by the research community in the context of the initiative named
Industry 4.0. Similarly to the IoT, the concept of Industry 4.0 relies on
massive connectivity provided by densely deployed small sensor devices, which
are installed in factories and production lines, see Fig. 8.

Figure 8: Production line in automobile industry. Sensors monitor the
automated process. The data is collected by a UAV.

These sensors are supposed to monitor the accuracy of the manufacturing
process and possibly influence this process by providing their feedback to the
central processing unit. Through this, the operation of large factories can
become fully automated, which may reduce the manufacture and maintenance
costs, cf. [96].

Unlike the previously discussed CEs, the propagation medium in the industrial
environment has typically almost no influence on the system performance.
Instead, the main challenge for the signal propagation in industrial wireless
networks is related to a strong wideband interference generated by the
production processes. In addition, a large number of metallic objects as parts
of the factory equipment may disturb the transmission. For the massive
connectivity of the Industry 4.0, the resulting signal scattering poses a
burden, since signal transmissions are impaired by time-variant channels as
well as fading effects and multipath. This effect is especially crucial due to
the low signal quality from the aforementioned interference.

Further challenge for the design of the industrial wireless networks is
related to very strict requirements regarding point-to-point latency and
reliability, since potential malfunction of an industrial process needs to be
detected and corrected as fast as possible. Otherwise, the efficiency of
production may substantially decrease, cf. [97].

Industry 4.0 and related advances for the wireless communications is an active
research area. Unlike the previous use cases, i.e. IoUwT and IoUgT, there is a
clear demand and interest of the industrial stakeholders and their involvement
in the continuous evolution of this research field. More information about it
can be found in [97, 98, 96].

###  V-B RIS in Industrial Environments

Industrial environment is best characterized by a large number of obstacles as
parts of the infrastructure. These obstacles are responsible for a high degree
of signal scattering, which leads to a high path loss as well as severe
multipath propagation. Furthermore, some of the target receivers may be
blocked from the signal reception and have only a very little field of view
for the communication. In addition, the interfering signals from the
production lines need to be suppressed. These challenges need to be tackled
using RISs.

We focus on traditional EM signaling in the moderate and high frequency ranges
as well as on optical communications. Alternative signaling techniques, i.e.
MI or acoustic seem to be less efficient due to shorter transmission distances
and interference, respectively.

####  V-B1 General approach

The application of RIS in industrial environment is illustrated in Fig. 9.

Figure 9: Production line control assisted by RIS.

Apparently, this scenario has some similarities with the underground
communications and specifically with the communication in tunnels and mines,
where the scattering is as severe as in the industrial environment. Hence, a
similar approach for the signal steering can be selected in this case. The
reflective elements of the RISs need to be optimized in order to steer the
signal on the shortest path around the obstacles towards the target node.
However, in case of industrial environment, the nodes may be deployed in
inaccessible areas, such that it might be extremely difficult to find the
right phase shift in order to properly navigate the signal. This problem is
even more challenging, if discrete phase shifts are used, which substantially
reduces the degree of freedom for the optimization.

Additionally, strong interference from some of the production processes should
be reduced by destructively aligning the interference signal paths at the
receiver. This is especially challenging, since no channel estimation can be
performed for the interfering signals, such that only iterative adaptation
methods can be designed.

Furthermore, some parts of the infrastructure can be mobile according to the
respective automated process. The implications of such moving objects are
discussed in the following.

####  V-B2 Moving industrial infrastructure

Industrial processes usually have a property of repetitive execution of the
same actions in order to produce multiple copies of the same product. This can
be exploited for the design of RIS-assisted communication networks even in
presence of mobile infrastructure. In particular, the repetition of the
infrastructural motion manifests as correlated (in time) and deterministic
communication channels due to the repetitive positions of the obstacles. The
same holds for the positions of mobile RISs, if they are attached to the
mobile infrastructure.

This property is very relevant for the design of RIS-assisted networks in this
environment, since it substantially eases the synchronization and control of
RIS. Specifically, the channels can be described via Hidden Markov Models
(HMMs), such that the well-known methods of signal detection and
synchronization for HMMs can be directly employed here. Alternatively,
communication channels can be learned using the well-known methods of
artificial intelligence and machine learning. Furthermore, the scheduling of
transmissions can be adapted to the repetitions of the channel, which pertain
to a high signal quality. Through this, the performance compared to other
scheduling methods would significantly improve.

##  VI Emergency networks in disaster areas

###  VI-A Overview

Disasters related to earth quake, tsunami, landslide, etc. bring about the
destruction of the human habitat. Specifically, the infrastructure of whole
cities can be destroyed by such natural causes. On the other hand, human
activities like gas explosions can lead to similar results leaving behind
collapsed buildings and human bodies trapped inside, see Fig. 10.

Figure 10: Collapsed buildings and humans trapped under rocks. UAV assists
rescue operations. Signals mostly scattered at the broken infrastructure
reducing the chances of localizing the humans.

The search and rescue (SAR) of humans is the first and most important task to
be performed after the disaster. In this context, the destroyed infrastructure
is one of the main challenges for the SAR, since it is difficult to search for
people underneath. Furthermore, the coordination of the rescue teams and
potential application of steerable rescue tools like robots, UAVs, etc. depend
on connectivity between the network participants and on reliable signal
transmission, cf. [99, 100, 101]. However, the communication infrastructure is
likely to be destroyed in the disaster event as well, which substantially
impacts the connectivity and operability of the dedicated wireless network.

Another challenge for the operation of the wireless network (even if the
infrastructure is operational) is the large amount of obstacles, i.e. pieces
of the destroyed homes, trees, etc. These obstacles provide a high degree of
scattering, such that the resulting interference among the adjacent network
links can render the communication unfeasible and unreliable.

Various types of emergency networks to be used in case of disaster have been
proposed in the recent years. However, many of the proposed solutions assume
that parts of the infrastructure remain whole and capable of assisting the
rescue operations. Even though such assumptions are not always valid, the
challenge related to the signal scattering is very difficult to assess without
these assumptions.

With increasing popularity of mobile robots, the rescue missions become more
and more advanced. For more information on the design of emergency networks in
disaster areas, we refer to [102, 103].

###  VI-B RIS in Disaster Environments

As mentioned earlier, wireless communication in disaster environments suffers
from the scattering and reflections from broken infrastructure, i.e. pieces of
buildings, trees, etc. Note that the disaster can occur in any medium
including underwater and underground. However, the impact of the disaster on
the existing infrastructure is higher on the earth surface, since dense medium
can partially absorb the energy from the disaster and reduce the force applied
to the infrastructure. Correspondingly, the infrastructure deployed in dense
media is typically less deformed by possible disasters or may even partially
remain unchanged.

Hence, we focus on disasters on the ground surface, where the traditional EM
signaling in the moderate frequency range is the most promising technique,
since it can provide connectivity over large distances (unlike acoustic and MI
signaling) and penetrate many types of obstacles (unlike optical signaling).
The challenges here are:

  * •

rich scattering of signals,

  * •

use of partially broken infrastructure.

Both challenges should be resolved using the specifically designed RIS
devices.

####  VI-B1 General approach

In order to improve the performance of the mentioned wireless networks
supporting the rescue operations in disaster environments, RIS can be deployed
as part of the original infrastructure, where the future rescue operations
would potentially take place. An example of this infrastructure is the
mentioned concept of Smart Cities, where most of the buildings are covered by
the reflective surfaces in order to facilitate the massive wireless
connectivity. This approach is very beneficial also with respect to the
potential danger of a disaster, since parts of the surfaces may still be
operational and thus help improving the signal quality of the wireless network
established specifically for the SAR operations.

During the disaster, the infrastructure, e.g. smart buildings, may break in
pieces. This includes also the RIS as part of this infrastructure. After the
disaster, the separated parts of RISs are no longer functional with existing
RIS technology. Nevertheless, it is possible to specifically design RIS in
order to account for such scenarios, where parts of RIS need to be easily
detached from the main surface.

(a) Co-located operation.

(b) Separated operation.

Figure 11: Separable RIS: parts of the RIS can easily detach in order to avoid
damage and preserve the functionality of the individual parts.

Such separated RIS parts are expected to have sufficient intelligence to
provide a basic RIS functionality for a small set of corresponding RIS
elements. During the main RIS operation, the parts of RIS are combined to one
surface. On the other hand, the parts should be easily detachable in a
controlled manner in order to not damage the main processors or the steering
mechanisms. One possibility to achieve this is using magnetic connectors. The
main benefit of these connectors is the ease of deployment and their
robustness.

Specifically, there are two modes of RIS operation, which are separated in
time by the disaster event: co-located operation and separated operation.
These modes are illustrated in Fig. 11. The application of this strategy for
the rescue operations is illustrated in Fig. 12.

Figure 12: Detached RIS patches support the data collection during rescue
operations. Dark colored patches indicate malfunction. Cooperative operation
of multiple RIS patches improves the connectivity and the localization.

In the following, we discuss the potential research challenges for each mode.

####  VI-B2 Co-located operation

We consider a large RIS, which consists of multiple smaller RIS patches
connected to each other. The large RIS should be able to exploit the
processing capabilities, memory and stored energy of all its parts to provide
the best possible performance.

The joint processing can be performed in a distributed manner, i.e. similar to
edge computing. In order to maintain the connectivity between individual RIS
patches, one of the patches may take the role of the master node of the local
patch cluster. This master node would manage the operation of all connected
slave patches. The management of the respective master-slave connections can
be achieved using one of the existing protocols, e.g. on the basis of
Bluetooth or ZigBee technology.

For the RIS optimization, the objective in this mode is mostly related to the
beam steering in order to maximize the receive signal power or enhance the
secrecy.

####  VI-B3 Separated operation

After the separation, the distances between the patches of RIS may be
substantially larger, such that it probably does not make sense to consider
them as parts of the same entity. Instead, each RIS patch can be assumed to
have only very limited number of reflective elements and shall be responsible
only for its own operation. On the other hand, some RIS patches may find
themselves in close proximity of other parts, such that distributed
computation may still be possible. Correspondingly, these patches would
preferably reconfigure and build a new edge computing cluster777This
methodology is also applicable in underground environments, as mentioned
earlier, where the cost of deploying large RISs may be very high. Instead,
clusters of RIS in close proximity can be employed.. In order to determine,
which RISs should for the cluster, each patch should possess the sensing
capabilities or be able to localize itself.

In this mode, i.e. after the disaster event, the objective for the phase shift
optimization of each individual RIS patch is related to the reduction of the
scattering effect and the harmful multipath propagation. Hence, the individual
RIS patches should act as ad hoc nodes and collaborate with each other in
order to solve this task cooperatively. A similar challenge for the ad hoc
networking of RIS relays has already been identified in [4]. In case of
disaster environment, the difficulty is even higher due to the reduced memory,
energy and processing capabilities of the small RIS patches.

##  VII System design aspects

In this section, we propose a basic hardware architecture applicable in the
target scenarios and a generic channel model for RIS-assisted signal
transmission for wireless networks in CEs.

###  VII-A Hardware architecture

As recognized in various works, e.g. [4], future RIS-assisted wireless
networks will heavily depend on the autonomous operation of RIS. While this
will remain an optional feature for the traditional wireless networks, it will
be certainly a necessary condition for the operation of RIS in CEs, since most
of the RIS devices will be hardly accessible after the initial deployment.
Hence, each RIS may not only have a passive functionality of signal
reflection, but also an advanced functionality of a wireless sensor/actuator.
Hence, future hardware design will substantially differ from the ones
discussed in the references of Section II. In the following, we propose a
novel architecture, which should enable this advanced functionality.

Based on the traditional architecture of wireless sensor nodes, cf. [90], it
is possible to obtain a generic architecture of a RIS node for the application
in CEs, see Fig. 13. In addition to RIS employed as a front-end, each node may
be also equipped with traditional antennas depending on the system
requirements and scenario. In particular, such additional antennas can be
employed in order to enable more advanced functionalities, such as active
relaying, signal buffering or frequency conversion, without changing the
behavior of the reflective elements of RIS. In this case, RIS and the antennas
are operated in parallel and have identical connections to other hardware
components.

Figure 13: Proposed generic architecture for RIS devices in CEs.

The power unit contains a battery which is used to power all other units and
to manipulate/activate the reflective elements of RIS. Since energy resources
are very scarce in CEs, energy harvesting modules can be utilized to harvest
the signals, which are not reflected (i.e. absorbed) by RIS. Typically, only
parts of the signals are utilized for energy harvesting in order to maintain
the connectivity of the network. Hence, SWIPT technology seems promising,
which may utilize power splitters prior to energy harvesting. Alternatively,
one cluster of reflective elements can be assigned to signal reflection (or
reception of the control signaling) and another cluster to energy harvesting.

The phase shifts are set by the processing unit, which consists of a processor
and a memory storage. In addition, RIS can be used as a transmitter according
to [36] via a specifically designed transceiver connected to the processor.
Similarly, the receiver functionality can be enabled in order to receive the
control signals and configuration parameters from other RISs.

###  VII-B Signal propagation

For the basic system model, we consider a single point-to-point (P2P)
connection using single antenna transceivers in a CE with multiple RISs. Note
that this is different from most of the works on RIS, where the spatial
diversity is enhanced by utilizing multiple antennas at the transmitter or at
the receiver. As mentioned earlier, the main challenge in CEs is not to
exploit the spatial diversity to boost the achievable data rate, but to
improve the connectivity of a wireless network despite very harsh propagation
conditions. Furthermore, the employed transceivers can belong to small
wireless sensor nodes, which can only be equipped with a single antenna due to
complexity and space limitations.

In addition, we focus on frequency-selective channels, which are typical for
CEs. In this case, the signal propagation from one transceiver to another can
be described using the following classical equation:

$y(t)=h_{\boldsymbol{\theta}}(t)*x(t)+w(t),$ (1)

where $x(t)$ and $y(t)$ represent the transmitted and received signals,
respectively. Here, ’*’ denotes the convolution operation. In addition, $w(t)$
represents a realization of a zero-mean additive white Gaussian noise (AWGN)
with the variance $\sigma_{w}^{2}$. Note that RISs only reflect the signals
without injecting any additional noise signals unlike traditional relays.
Furthermore, the channel impulse response $h_{\boldsymbol{\theta}}(t)$ is a
function of the parameters $\boldsymbol{\theta}_{n}\in\mathbb{C}^{Q_{n}\times
1}$, with $n\in\\{1,\dots,N\\}$, comprising the reflection coefficients
associated with the $Q_{n}$ reflective elements/planes of the $n$-th RIS.

It is assumed that the $q$-th complex reflection coefficient of the $n$-th RIS
can be written as

$\displaystyle\theta_{n,q}\\!=\eta_{n,q}\\!\cdot{\rm e}^{{\rm
j}\phi_{n,q}}\\!,\forall(n,q)\in\\{1,\dots,N\\}\times\\{1,\dots,Q_{n}\\},$ (2)

with $\phi_{n,q}\in[0,2\pi)$ denoting the corresponding phase shift, and
$\eta_{n,q}\in[0,1]$ denoting the corresponding reflection efficiency.

In case of multiple RISs deployed in the same environment, it is important to
consider multi-hop reflections as well. Specifically, some signals arriving at
the $n$-th RIS can be reflected towards $k$-th RIS, which either shortens the
overall signal path or prolongs it. In the latter case, the signals will be
substantially more delayed and more attenuated due to additional path losses.
Hence, such multipath components are likely to be negligible.

###  VII-C Expected signal quality improvements

For a deeper analysis, we focus on a single RIS in the following. A two-hop
RIS-assisted wireless link can be thus described via channel impulse response
given by888Due to much higher path loss of the direct links in CEs, we focus
on the signal components, which are reflected from RIS.

$\displaystyle
h_{\boldsymbol{\theta}}(t)=\sum_{q=1}^{Q_{1}}\left(h^{\mathrm{Tx->RIS}}_{1,q}(t)*h^{\mathrm{RIS->RX}}_{1,q}(t)\right)\theta_{1,q},$
(3)

where $h^{\mathrm{Tx->RIS}}_{1,q}(t)$ and $h^{\mathrm{RIS->RX}}_{1,q}(t)$
represent the sub-channels connecting transmitter with RIS and RIS with
receiver, respectively.

Each of the sub-channels may comprise a different set of multipath components
and can be associated with a different frequency selectivity. As an example,
some experimental measurements obtained for underwater acoustic channels
suggest that the multipath components follow a Rician, Rayleigh or
$K$-distribution, cf. [104]. For the target applications, we can assume Rician
fading with a large variance of the random components. Such fading models are
also partially applicable in other scenarios of CEs. Interestingly, if each
multipath component is treated as interference, it is possible to express the
channel impulse response as
$h_{\boldsymbol{\theta}}(t)=\bar{h}_{\boldsymbol{\theta}}(t)+\tilde{h}_{\boldsymbol{\theta}}(t)$,
where

$\bar{h}_{\boldsymbol{\theta}}(t)=\delta(t-\tau_{0})\sum_{q=1}^{Q_{1}}\left(h^{\mathrm{Tx->RIS},0}_{1,q}h^{\mathrm{RIS->RX},0}_{1,q}\right)\theta_{1,q}$
(4)

is the deterministic part associated with the main tap of the channel999Here,
$\delta(t-\tau_{0})$ corresponds to the delay of the main channel tap by
$\tau_{0}$ and $\delta(\cdot)$ denotes the Dirac pulse.
$h_{\boldsymbol{\theta}}(t)$, and

$\tilde{h}_{\boldsymbol{\theta}}(t)=\sum_{q=1}^{Q_{1}}\sum_{m=1}^{M_{1,q}}\tilde{h}_{1,q,m}(t),$
(5)

where $\tilde{h}_{1,q,m}(t)\in\mathbb{C}$ denotes the stochastic part
associated with the signal reflections from random scatterers101010Here,
$\tilde{h}_{1,q,m}$ denotes the $m$th multipath component of the signal
reflected from the $q$th element.. Note that
$\left|h^{\mathrm{Tx->RIS},0}_{1,q}h^{\mathrm{RIS->RX},0}_{1,q}\right|\approx
g,\forall q$ with a suitable $g$, since the path length is almost equal for
all signal paths independently of the reflective element. Let
$\sigma_{\tilde{h}}^{2}$ denote the variance of the stochastic channel
components $\tilde{h}_{1,q,m}(t),\forall q,m$. Due to the circular symmetry of
these channel taps and their similar distribution, we obtain the interference
power for a large number of RIS elements $Q_{1}$:

$\left|\tilde{h}_{\boldsymbol{\theta}}(t)\right|^{2}\approx\sigma_{\tilde{h}}^{2}Q_{1}M_{1,q}.$
(6)

In contrast, if the phase shifts $\theta_{1,q}$ are selected to align the
multipath components at the main tap, we obtain for the channel power gain of
the main tap

$\left|\bar{h}_{\boldsymbol{\theta}}(t)\right|^{2}\approx(gQ_{1})^{2}=g^{2}Q_{1}^{2}.$
(7)

Correspondingly, with increasing number of RIS elements, the signal-to-
interference-plus-noise ratio (SINR)

$\mathrm{SINR}=\frac{\left|\bar{h}_{\boldsymbol{\theta}}(t)\right|^{2}}{\left|\bar{h}_{\boldsymbol{\theta}}(t)\right|^{2}+\sigma_{w}^{2}}\approx\frac{g^{2}Q_{1}^{2}}{\sigma_{\tilde{h}}^{2}Q_{1}M_{1,q}+\sigma_{w}^{2}}$
(8)

increases. By doubling the number of RIS elements, the SINR can be improved by
at least 3 dB (interference-limited case) and at most 6 dB (noise-limited
case). This makes the use of RIS in such scenarios especially promising. On
the other hand, in some challenging environments, such as underwater
communications, the noise is not necessarily white, but colored, cf. [104].
Hence, to maximize the achievable rate in such scenarios, RIS may need to be
optimized by taking into account the use of a whitening filter at the
receiver.

##  VIII Future challenges

The research challenges can be split in two categories:

  * •

general RIS-related challenges,

  * •

specific RIS-related challenges associated with the deployment in the CEs.

At first, we briefly review the general challenges associated with RIS and
then we discuss the specific challenges for the respective use cases specified
in the previous section.

###  VIII-A Known challenges for RIS

Like in the terrestrial RIS-assisted communication networks, the main
challenges can be outlined as

  * •

channel estimation/pilot decontamination,

  * •

precoding/beamforming,

  * •

control signaling.

Channel estimation is one of the biggest challenges for RIS-assisted wireless
networks, since RISs are typically passive111111’Smart’ RIS can be produced by
connecting it to a wireless sensor node, which can perform some signal
processing and computation. However, this functionality is viewed as a long-
term evolution of RIS and discussed as one of the future challenges in Section
VIII-C. and cannot perform channel estimation by themselves. Hence, the
channel between the network nodes and RIS is difficult to obtain in practice.
This problem becomes even tougher with increasing number of RIS elements, for
which the estimation needs to be performed, and with increasing number of RIS
devices. In addition, similarly to the Massive Multiple-Input Multiple-Ouput
(MIMO) systems, the channel estimation is impacted by the correlation between
training sequences in terms of pilot contamination. However, the methods of
pilot decontamination may not be entirely applicable due to the problematic
channel estimation with passive RISs.

Precoding and beamforming are considered the key functionality of RIS. Despite
a large number of research works dedicated to this system aspect, the design
rules for the precoding/beamforming in wireless networks with multiple RISs is
not yet fully understood. In fact, passive beamforming using multiple RISs
with ping-pong multihop signaling seems very challenging. Furthermore, the use
of symbol-level precoding for large RIS-assisted networks has only been
briefly addressed in few works so far.

Control signaling is important for the operability of RIS. In order to update
the phase shifts of the RIS elements, the connectivity between RIS and its
parental node (e.g. base station) has to be very reliable and fast. This
becomes a burden in case of a large number of elements, since the
corresponding large amount of data may require a very high data rate to ensure
that the update frequency satisfies the system requirements.

Since these challenges are common for all target scenarios as well, similar
concepts as already proposed in the literature (and mentioned in Section II)
for the respective terrestrial networks can be applied in the CEs. The
traditional challenges for the design and use of RIS technology in future
wireless networks have been well described in the previous works and surveys.
Hence, we refer to [9, 64, 4] for more information. In addition to the
specific challenges identified for each use case in the previous section, we
consider the most promising future research challenges:

  * •

deployment planning and automation,

  * •

powering of RIS-equipped nodes and

  * •

distributed operation.

These open problems are discussed in the following.

###  VIII-B Deployment planning and automation

The strategic RIS deployment has been considered in some works so far in the
context of future wireless networks. Furthermore, the RIS-equipped UAVs have
been introduced in order to find the optimal position for the RIS. This
optimal position may depend on the scenario as well as on the time-varying
channel conditions and available power resources.

In the context of CEs, the strategic deployment of RISs is of paramount
importance for the effectiveness of RIS due to the high complexity and spatial
selectivity of the communication channels.

Specifically, the transmission distances in underwater environment are rather
large, which leads to a large path loss both for the main signal tap and for
the signal reflections. Correspondingly, by placing RISs in certain areas, the
signals reflected from the RIS may be impaired by a higher pathloss than the
signals reflected from other obstacles. In this case, RIS would not always be
able to compensate the multipath components.

For the deployment in the tunnels and mines, we expect a large number of ping-
pong reflections for the signal redirection. However, each reflection may
contribute implies additional path loss due to partial absorption of the
signal. Hence, the strategic deployment of RISs is needed in order to reduce
the number of reflections. This would lead to a better overall signal quality.

In disaster environments, it is difficult to predict the distribution of RISs
e.g. after the explosion. Hence, it is necessary to put RIS in as many
locations as possible in order to make sure that the expected density of the
RIS parts does not undergo the required minimum.

In general, the path finding optimization for the signal propagation is
required in order to identify the best possible locations for the RIS
deployment.

CEs are additionally characterized by the difficulties associated with the
deployment of the network nodes. The reason is that it might be difficult for
the humans to reach the deployment location and perform the deployment work.
Hence, this process needs to be automated.

Specifically, the deployment procedure in the underground and underwater media
as well as crowded factories will make use of robots to carry RISs and attach
them to the dedicated surfaces. In this context, the automation problems
related to the design of such robots are of interest. For the disaster
environments, the deployment automation is less important, since it is similar
to the traditional deployment strategies for the smart cities.

###  VIII-C Distributed operation

As mentioned in the previous works, distributed operation of RIS can be very
beneficial, especially with respect to the discussed application in disaster
environments. Distributed RIS operation requires that each RIS or its part
should be attached to a sensor node with respective capabilities, i.e. memory,
signal processor, battery, etc. Correspondingly, the entire set of wirelessly
connected RIS needs to be arranged in wireless sensor/actuator network.
Subsequently, the wireless networking aspects need to be investigated, in
particular the higher layers, such as network and transport layers.

In this context, routing and topology control for the RIS network (not to
confuse with the target network, for which the smart environment is created)
are of special interest. These aspects are difficult to optimize due to a very
complex dependency between the communication environment and the results of
the optimization. Hence, it seems that the most promising optimization methods
in this context will be based either on metaheuristics or on game theory. The
latter seems promising due to the ad hoc behavior of RIS-equipped nodes.

###  VIII-D Powering of RIS

One of the main open problems for the distributed operation of RIS is the
recharging of its battery. This problem has been identified in the previous
works, cf. [4], but in the context of CEs, it will become even more tough. The
reason is that the channels to be used for wireless powering may be time-
variant with a very short coherence time or extremely dispersive, such that
the wireless power signals would suffer a very high path loss, which can
reduce the transfer efficiency to a minimum. Furthermore, in some applications
like disaster environments, there is no common energy source for the wireless
powering of small sensor devices. Hence, the battery recharging can be done
only using one of the following alternative approaches:

  * •

each RIS may not only reflect the signals, but also partially absorb their
energy and use it for the own recharging. A distinct advantage of this
strategy is that RIS would drain the EM radiation from the medium and thus
make the environment more green;

  * •

RIS should be able to generate power from any type of received signaling, e.g.
EM and acoustic radiation, vibration, x-ray, etc.

The first strategy implies that the signal routing and the optimization of the
reflective elements need to be adapted in order to account for the charging of
the RIS batteries. The second strategy implies that various types of sensors
need to be attached to each RIS in order to be able to harvest energy from the
respective sources. While the first strategy is challenging with respect to
the online operation, the second strategy is challenging for the initial RIS
design. In future applications, energy scavenging might be considered, which
would rely on even more intelligent autonomous operation of RISs.

##  IX Conclusion

In this paper, we presented our vision and potential research challenges for
the deployment of RIS in CEs. Specifically, the benefits for the signal
propagation in large wireless networks, such as IoT, have been highlighted.
Regarding the target scenarios, four most challenging use cases have been
investigated, i.e. underwater, underground, industrial and disaster
environments. These use cases have been unified under the umbrella of a single
enabler, i.e. RIS-assisted networking. In this context, the enabling
properties of RIS have been discussed. Specifically, for the underwater and
underground media, RIS can be used to reduce the impact of the harmful
multipath propagation. In industrial environment, RIS can be used to redirect
the signals in order to avoid absorption and reflection by metallic objects.
In disaster environment, future RIS can be employed to preserve connectivity
and thus assist the SAR operations even after a possible infrastructure
damage.

Potential deployment strategies, hardware design and expected analytical
improvements for the signal propagation have been provided. Furthermore, open
research directions for the near future and for the long-term evolution have
been presented. Correspondingly, this paper is expected to contribute to the
rapid advancement of the research field both for the CEs and for the RIS
technology.

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