Safeguarding MTC at the Physical Layer: Potentials and Challenges

Abstract

5G networks must provide a highly resilient, secure, and privacy-protected platform to support the emergence of new business and technologies expected from the so-called vertical-industry paradigm. However, as the definition and implementation of 5G networks are in progress, many security challenges arise. Thus, special emphasis will be given in the coming years to provide security and privacy for 5G and beyond networks. In this regard, physical layer security has been recognized as a potential solution to safeguard the confidentiality and privacy of communications in such stringent scenarios. In light of this, herein we provide an overview on some promising physical-layer techniques, focusing on the requirements and design challenges for machine-type communication scenarios. Key issues are discussed along with potential solutions.

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Digital Object Identifier
Safeguarding MTC at the Physical Layer:
Potentials and Challenges
D. P. MOYA OSORIO1, (Member, IEEE), E. E. BENITEZ OLIVO2, (Member, IEEE), H. ALVES.1,
(Member, IEEE), AND M. LATVA-AHO.1, (Senior, IEEE)
1Centre for Wireless Communications, University of Oulu, 90014 Oulu, Finland
2São Paulo State University (UNESP), Campus of São João da Boa Vista, 13876-750 São João da Boa Vista, Brazil
Corresponding author: D. P. Moya Osorio (e-mail: diana.moyaosorio@oulu.fi).
This work was supported in part by the Academy of Finland 6Genesis Flagship under Grant 318927, in part by European Union’s Horizon
2020 research and innovation program under the INSPIRE-5Gplus project (Grant No. 871808). The paper reflects only the authors’ views.
The Commission is not responsible for any use that may be made of the information it contains. The work was also supported in part by the
EE-IoT project under Grant 319008, and in part by the Brazilian National Council for Scientific and Technological Development (CNPq)
under Grant 421850/2018-3.
ABSTRACT 5G networks must provide a highly resilient, secure, and privacy-protected platform to
support the emergence of new business and technologies expected from the so-called vertical-industry
paradigm. However, as the definition and implementation of 5G networks are in progress, many security
challenges arise. Thus, special emphasis will be given in the coming years to provide security and privacy
for 5G and beyond networks. In this regard, physical layer security has been recognized as a potential
solution to safeguard the confidentiality and privacy of communications in such stringent scenarios. In
light of this, herein we provide an overview on some promising physical-layer techniques, focusing on the
requirements and design challenges for machine-type communication scenarios. Key issues are discussed
along with potential solutions.
I. INTRODUCTION
5G and beyond networks are envisioned to support a wide
range of use cases for a myriad of industry sectors. For
that reason, the International Telecommunication Union
(ITU) has classified 5G network services into three cate-
gories: enhanced Mobile Broadband (eMBB), Ultra-Reliable
and Low-Latency Communication (URLLC), and massive
Machine-Type Communication (mMTC). These services are
suppose to coexist in the same network architecture by
allocating network resources in such a way that the isolation
among different inner logical networks (slices) is ensured
through network slicing [1]. Particularly, in MTC networks,
devices can be connected to the Internet or directly to each
other, so that communication processes occur with little
or no human intervention. This way, MTC plays a pivotal
role on the concretization of the Internet of Things (IoT)
and Internet of Everything (IoE) [2, 3]. Therefore, it can
be said that, according to the service, MTC applications
can be divided into two main groups: mMTC and critical
(cMTC) [3].
mMTC comprehends scenarios with a large number of
low-complexity and low-power devices, such as sensors and
actuators, which are connected to a base station, using short-
range radios and short-overhead protocols (as in capillary
networks [4]) in order to allow for battery life savings. Then,
mMTC focuses on high-density applications, such as smart
wearables, smart agriculture, sensor networks, and smart
meters. On the other hand, cMTC refers to applications
with stringent requirements on availability, low latency, and
reliability, such as traffic safety/control, remote surgery,
vehicle-to-everything (V2X) networks, tactile Internet, and
industrial control. For the case of cMTC, cost and energy
constraints are not as critical as for mMTC applications.
Considering such heterogeneous requirements, it can be
glimpsed that communication security is one of the main
concerns for the deployment of MTC networks, since highly
sensitive information will be transmitted over unprotected
environments, thus being highly vulnerable to a plethora
of security and privacy threats. This way, the design of
secure MTC networks is a very challenging task that will
demand lightweight and efficient solutions that attends the
restrictions and the different requirements of mMTC and
cMTC applications. Considering this, it has been recognized
that traditional cryptography-based techniques, which are
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D. P. Moya Osorio et al.: Safeguarding MTC at the Physical Layer: Potentials and Challenges
ALICE
BOB
PASSIVE EVE
ACTIVE EVE
Spoofing: impersonation,
substitution
Denial of servisse
Jamming attack
Eavesdropping Confidentiality
Secrecy
Privacy
Authenticity
Integrity
Availability
Privacy
Covert/Stealth
Low probability of
detection/intercept
Detection
Interception
WILLIE
ALICE BOB
Shadow
Network
Security Threat
Security Requirement
Physical-layer
covert communication
Physical-layer
secure communication
FIGURE 1. Security threats and requirements for secure and covert wireless communications.
carried out at upper layers of data communication models,
are not suitable to comply with the requirements of many
MTC scenarios due to the following drawbacks: (i) the man-
agement of public-key cryptographic methods is extremely
challenging in large-scale and decentralized networks as
devices may randomly connect to or leave the network
(or alternate between active and inactive states) at any
moment [5]; (ii) secure links required for the exchange of
private keys cannot be guaranteed in some MTC scenarios;
(iii) eavesdropping and active attacks are facilitated by the
rapid evolution of computing and processing capabilities,
specially considering quantum computers; and (iv) demands
for extra delay and complexity to provide strong security are
undesirable for the MTC requirements.
Under these considerations, physical-layer (PHY) secu-
rity has shown to be an attractive solution to avoid the
heavy dependence on complicated traditional cryptography,
thus being a promising technique to complement existing
security techniques [6]. There are many reasons why PHY
security has raised such an attention. For instance, it can
be implemented in convenient ways without overburdening
the resources or infrastructure of the network [7, 8]. Impor-
tantly, PHY security has the potential to be performed faster
than other upper layer techniques.
PHY security relies on exploiting the physical properties
and randomness of wireless channels, thus being particularly
appealing in resource-limited application scenarios. Over the
last years, a copious number of schemes and techniques have
been proposed and analyzed, enriching the understanding on
the potentials of this technology. Among the most important
enabling approaches on PHY security we can mention
the following: secrecy-achieving channel coding, diversity-
aided secrecy techniques (such as multiple-input multiple-
output (MIMO) and relaying systems), injection of artifi-
cial noise (friendly jamming), physical layer authentication
(PLA), and physical layer key generation. We encourage
the readers to find interesting detailed information on those
and other techniques in [7, 8]. On the other hand, very
recently, techniques related to physical-layer covert com-
munications have gained attention in the context of security
for 5G and beyond networks, since those techniques can
prevent a legitimate transmission from being detected by an
adversary, thus diminishing the possibility of eavesdropping
attacks even if the adversary possesses unlimited processing
resources (as in the case of quantum attacks). Thus, those
techniques can guarantee a high level of security for MTC
in very stringent scenarios [9, 10].
Despite the significant advances achieved so far regarding
PHY security, as one of the most promising techniques to
help attain the security level required for the scenarios of 5G
and beyond networks, many challenging issues remain open
for further research. In Fig. 1, we illustratively summarize
some threats and requirements to be satisfied in the context
of secure and covert communications.
A. CHARACTERISTICS OF MACHINE-TYPE
COMMUNICATIONS
Herein, we introduce the main characteristics of both cat-
egories of MTC applications, namely, mMTC and cMTC,
which will guide the discussions in the following sections.
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D. P. Moya Osorio et al.: Safeguarding MTC at the Physical Layer: Potentials and Challenges
For the mMTC category, the main objective is to connect
a high density of low-rate, low-power, and low-complexity
devices—the so-called Machine-Type Devices (MTDs)—to
the cellular network (around 105to 106devices/km2) [11].
These connections are uplink dominant, asynchronous, and
sporadic, in which small data payloads are transmitted.
In this sense, a random subset of devices are active at a
given transmission instant, and the activation pattern can
be periodic or event based. Additionally, MTDs are often
battery powered, such that there exist a requirement of
more than 10 years of autonomy [11]. Therefore, access and
communication schemes should be highly energy efficient.
For the cMTC category, the main requirements are ultra-
low latency and extremely high reliability performance [12].
The target packet loss probability is on the scale of 10−5to
10−9. For cMTC, it is also expected to have ultra availability
up to 99.9999% with low to medium data rates (50 kbps
to 10 Mbps) as in most cases the messages are small.
Moreover, in terms of latency, the target is 1 ms Round-
Trip Time (RTT) over-the-air communication for a single
transmission, including the transmission of the payload until
the corresponding acknowledgment is received [12].
Considering the above description of MTC use cases,
in the following sections we highlight some of the most
prominent aspects to be addressed for safeguarding future
MTC networks at the PHY layer, for which an overview
and some challenging points are provided.
II. EFFECTIVE DESIGN OF FINITE BLOCKLENGTH
WIRETAP CODES
Traditionally, wiretap codes, mostly based on polar, lattice,
or low-density-parity-check (LDPC) codes, are evaluated
and analyzed as the blocklength approaches infinity, using
information-theoretic security measures, where error proba-
bility and information leakage can asymptotically be extin-
guished, such that the maximum achievable secrecy rate is
given by the secrecy capacity defined in [13]. Nonetheless,
for some MTC scenarios, short blocklengths are more appro-
priated. Particularly, mMTC deployments of IoT will mostly
consider energy-constrained devices that transmit only short
packets. Also, the use of short packets is mandatory for
minimizing the communication latency, which is imperative
for delay-constrained scenarios in cMTC networks. In this
context, while most popular coding schemes such as LDPC,
Turbo codes and Polar codes have similar performance with
large blocklengths, this is not the rule with short block-
lengths, where significant differences can be noticed [14].
For finite blocklength wiretap codes, a tradeoff among error
probability, information leakage, and transmission rate is
established [15]. Thus, the knowledge of the complexity
and performance of different wiretap code designs and their
optimization at the finite blocklength is imperative for an
effective application on practical MTC scenarios. Recently,
best binary wiretap codes properties at the finite blocklength
were analyzed in [16, 17], where it was shown that the
equivocation ensured by coset coding over a binary erasure
wiretap channel can be calculated with the knowledge of the
full-rank submatrices of the generator matrix, which results
in computational savings when optimizing wiretap codes at
finite blocklength.
Also very recently, a flexible design for the wiretap code
encoder and decoder in Gaussian wiretap channels under
finite blocklength, based on a feed-forward neural network
was proposed in [18], wherein a higher flexibility in terms
of the error rate and leakage tradeoff was attained when
compared to traditional error correcting codes.
Even though these promising solutions open the door
for the future employment of PHY security techniques into
practical MTC applications, there is still some challenges
ahead in order to identify the best suited channel coding
schemes. This way, for critical scenarios, bit-level granu-
larity of the codeword size and flexibility to enable hybrid
automatic repeat request (HARQ) are highly desired charac-
teristics [14]. Moreover, techniques to identify best coding
structures can be extremely helpful. For instance, in [19] was
proposed a technique for analysing and comparing wiretap
codes at the short blocklength regime over the binary erasure
wiretap channel. In that work, the authors proposed Monte
Carlo strategies for quantifying code’s equivocation by lim-
iting the analysis to coset-based wiretap codes. After several
comparisons of different code families, the authors shown
that using algebraic codes is advantageous for applications
that require small-to-medium blocklengths.
Therefore, the design of practical wiretap codes that
not only achieve the secrecy capacity limit at the finite
blocklength regime, but also satisfy the constrains of MTC
scenarios and heterogeneous environments (including highly
dynamic or poor scattering environments where a strong
correlation between legitimate and wiretap channels can
occur [8]) remains a challenging task.
III. IMPACT OF CHANNEL STATE INFORMATION
Several PHY security approaches, specially those related to
friendly jamming and diversity-aided security strongly rely
on idealized assumptions, such as the perfect knowledge of
the channel state information (CSI). This strong assumption
imposes big challenges for the use of most wireless PHY
security schemes in MTC applications, since an accurate
estimation of CSI is a difficult task in practice, for both
amplitude and phase information.
In most practical scenarios, the channel gains are mea-
sured by the receiver and then fed back to the transmitter.
Such process can introduce imprecision and delays into
the CSI, thus affecting the performance of PHY security
techniques. At least, a certain level of CSI knowledge is
required at the transmitter to attain a positive secrecy rate
for a secure transmission. That is, the statistics of the
channels should be known at the transmitter [20]. However,
in some cases, where an eavesdropper remains passive, the
statistics of the wiretap channel cannot be obtained at the
transmitter. It is of interest to dedicate effort on solutions
that do not rely on CSI knowledge—specially on that of the
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D. P. Moya Osorio et al.: Safeguarding MTC at the Physical Layer: Potentials and Challenges
wiretap channel— thus being suitable for delay-constrained
applications as those of cMTC networks.
Moreover, the acquisition of CSI for MTC-based ap-
plications will bring a huge feedback overhead, specifi-
cally in massive deployments of IoT [21]. However, the
data traffic pattern generated by MTC devices is typically
sporadic. Then, by exploiting the sparsity in the device
activity pattern, it can be possible to explore more efficient
schemes to support simultaneous device activity detection
and channel estimation [21]. This characteristic of some
MTC deployments can be exploited to introduce security
into communications at the physical layer. For instance, the
sparsity in the MTC devices activity pattern can optimally
be exploited by compressing sensing (CS) techniques in
order to detect the active devices and estimate the channel
conditions [21]. At the same time, some works have pointed
out the ability of CS to ensure security [22, 23]. Indeed,
by using CS, the channel asymmetry can be exploited in
order to allow a message, encoded as a sparse vector, to be
decodable with high probability at the legitimate receiver
while being unfeasible to be decoded with high probability
at the eavesdropper [22]. Particularly, in [23], the authors
proposed a CS security model for IoT applications by
exploiting the circulant matrix to improve the generating
efficiency of the measurement matrix, while using a binary
resilient function to guarantee security. Also, in [24], it
was shown that physical layer security of a multi-hop relay
network can be achieved by CS, without channel state
information of the eavesdropper. Besides, individual sparsity
patterns of devices can be predicted and used as fingerprints
to perform fast physical layer authentication [25].
In light of these advances, exploring less complex secure
CS techniques as well as improved techniques for device
activity detection and prediction constitute open research
areas.
Additionally, the accurate CSI estimation is more critical
when considering multiple antennas. Indeed, in some MTC
scenarios, massive MIMO techniques might be applied in
order to serve several users simultaneously, so that great
benefits in terms of security against passive and active
attacks can be attained. However, the pilot training period
for CSI estimation is vulnerable to attackers that can con-
taminate the uplink pilot sequences by generating identical
pilots in order to modify the estimation. This is referred
to as a pilot contamination attack (PCA), which is critical
in MIMO systems as the eavesdropper can obtain a better
signal-to-noise ratio (SNR) after beamforming. For instance,
in [26] the achievable secrecy rate for a massive MIMO
system under active and passive eavesdroppers was derived,
and a power-ratio-based active pilot attack detection scheme
was investigated. Therein, it was shown that the information
leakage rate vanishes asymptotically as the number of base
station antennas grows. However, active pilot attacks cannot
be asymptotically mitigated, thus the achievable secrecy rate
vanishes by increasing the eavesdropper pilot power.
The standard method to reduce pilot contamination is
known as regular pilot (RP), which consists on adjusting the
length of pilot sequences while the transmission of pilot and
data symbols is done separately within the coherence block
in order to reduce interference in the channel estimation pro-
cess. Other alternative is known as superimposed pilot (SP),
which sends a superposition of pilot and data symbols, and
allows the amount of samples that can be used for channel
estimation and data transmission to be increased. In [27], RP
and SP methods are compared in terms of the achievable
rate for a multicell massive MIMO network. Therein, it
was observed that SP is able to reduce pilot contamination
at the expense of incorporating further coherent and non-
coherent interference, thus limiting the system performance.
Also, by optimizing the pilot length with RP, the average
spectral efficiency and energy efficiency are comparable
to SP when estimated pilot subtraction is used. Besides,
when the pilot symbols are subtracted perfectly with SP, the
spectral efficiency and energy efficiency are improved. Then,
the authors suggest to use iterative decoding algorithms for
further improvements of SP method.
Recently, PCA detection was investigated in the context
of non-orthogonal multiple access (NOMA) in millimeter
wave and massive MIMO 5G communication networks.
NOMA systems are promissory for MTC networks once
they can improve spectral efficiency and achieve massive
connectivity with low transmission latency and signaling
cost; however, having superposed signals imposes new
challenges for securing those systems. In [28] a binary
hypothesis test and a machine learning based detection
framework were proposed to perform PCA detection for
static and dynamic environments. The results of that work
showed that the detection rate can approach 100% with 10−3
of false alarm rate in the static environment and above 95%
in the dynamic environment.
In [29], a secure communication for time-division du-
plex multi-cell multi-user massive MIMO systems was
investigated when an active eavesdropping performs PCAs.
Therein, it was shown that decreasing the desired user’s sig-
nal power can be beneficial to combat a strong active attack
from an eavesdropper. Therefore, a data-aided secure down-
link transmission scheme was proposed, which achieves
significant secrecy rate gains compared with alternative
approaches based on matched filter precoding with artificial
noise generation and null space transmission. Moreover,
the authors in [30] investigated the vulnerabilities of pilot
sequence design methods in realistic massive MIMO net-
works, where the assumption of strict orthogonality between
the pilot sequence set is relaxed. Thus, the pilot sequences
set is non-orthogonal and every pilot sequence has a non-
zero cross-correlation with other pilot sequences. However,
the use of correlated pilots could make the massive MIMO
network more susceptible to PCAs. To proof this point,
the authors in [30] proposed an effective active attack
strategy with correlated pilot sequences revealing that the
user capacity region of the network is significantly reduced
in the presence of the PCA, and the SINR requirements for
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D. P. Moya Osorio et al.: Safeguarding MTC at the Physical Layer: Potentials and Challenges
ALICE BOB
EVE
JAMMER
ALICE BOB
UNTRUSTED
RELAY
Main channel
Wiretap channel
Jamming signal
1
1
1
1
11
2
1
2
First interval
Second interval
a
c
ALICE BOB
RELAY
EVE
JAMMER
1
2
2
b
FIGURE 2. Different jamming strategies in physical layer security: a) cooperative jamming scenario with an external jammer, b) cooperative jamming scenario with
a trusted relay and external jammer, and c) untrusted relay scenario with destination-based jamming.
the worst-affected users may not be satisfied even with an
infinite number of antennas at the base station.
IV. ROBUST JAMMING APPROACHES FOR PHYSICAL
LAYER SECURITY
Friendly jamming strategies have proved to be promising as
a way to degrade the channel quality of eavesdroppers for
ensuring secret transmission by generating an artificial noise
to confuse potential eavesdroppers. However, appropriate
jamming strategies must be ensured in order to prevent
the generation of undesired interference that can lead to a
degradation on the performance of the legitimate channel
or the information leakage to the eavesdroppers. Several
strategies have already been proposed in the literature,
including self and non-self cooperative jamming, jamming
with perfect or imperfect eavesdropper’s CSI, and uniform
or directional jamming (some of them are illustrated in
Fig. 2) [31].
In this sense, robust jamming strategies need to be de-
signed in order to attain the benefits promised by those
techniques. Particularly, the constraints on delay, energy, and
massive deployments of MTC scenarios will give rise to
special challenges for the utilization of jamming techniques
in practical applications.
So far, game-theoretic approaches have emerged as attrac-
tive solutions to deal with the interactions among legitimate
users, eavesdroppers and friendly jammers, which can be
employed to make optimal decisions [32]. In game theory,
agents are rational entities whose target is to maximize
their individual gains or payoff functions. For instance,
Stackelberg games have been used to model the interactions
and power allocations between the legitimate pair and the
friendly jammer as a seller-buyer interaction game, where
the jammer is the seller and the legitimate pair are the
buyers [33].
However, there is still a vast area to be explored in order
to create more effective and suitable jamming strategies
for the different constraints of different MTC applications.
Therefore, the development of robust game-based jamming
strategies that consider channel uncertainties or the lack of
knowledge of eavesdroppers’ CSI, in addition to energy
and/or latency constraints, is an open challenge. For in-
stance, Bayesian games have been suggested for scenarios
with incomplete information where imperfect or no eaves-
dropper’s CSI is available [32].
Additionally, the benefits of wireless energy transfer and
energy harvesting technologies can be exploited to provide
jamming strategies with an alternative source of energy
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D. P. Moya Osorio et al.: Safeguarding MTC at the Physical Layer: Potentials and Challenges
EVE
ALICE BOB
Received RSS
at Alice
Received RSS
at Bob
Received RSS
at Eve from
Alice
Received RSS
at Eve from
Bob
Channel
reciprocity
Spatial
decorrelation
EVE
ALICE BOB
Path delay
CIR-based PLA RSS-based key generation
(a) (b)
AUTHENTICATED
CIR
Spatially
distributed
NON AUTHENTICATED
CIR
FIGURE 3. Schematics on (a) channel impulse response-based PHY authentication and (b) receive signal strength-based secret-key generation.
for jammers, thus being more attractive for real-world IoT
applications [34, 35].
V. IMPROVED PHYSICAL LAYER AUTHENTICATION
AND SECRET-KEY GENERATION SCHEMES
Authentication methods target to verify the identity of the
legitimate parts, thus preventing two types of spoofing
attacks, namely, impersonation and substitution. In the for-
mer, the attacker sends messages to a legitimate receiver
in order to confuse it with other legitimate users, while
in the latter, the attacker intercepts legitimate messages,
modifies them and then retransmits the altered messages
to legitimate users. These methods, traditionally conducted
at upper layers, may result in exorbitant latencies in large-
scale networks, whereas the limited resources of a massive
number of heterogeneous devices from MTC applications
will demand robust and lightweight authentication alter-
natives [36]. Moreover, because digital keys are generally
used to identify and provide rights to users, attackers
using unauthorized security keys cannot be efficiently de-
tected in those scenarios, when physical-layer properties are
overlooked. Therefore, physical-layer attributes of devices
and environments, i.e., the so-called physical-layer device
fingerprints, can be used to perform authentication with
low computational power, energy and overhead require-
ments, while being robust as those attributes are hard to
be mimicked or predicted. This technique is referred to
as physical-layer authentication (PLA) [36]. Fingerprints
can be of two types, channel-based fingerprints or analog
front-end (AFE) imperfection-based fingerprints. Channel-
based PLA exploits wireless channel parameters such as
channel state information (CSI), received signal strength
(RSS), channel frequency response (CFR), and channel
impulse response (CIR), in order to design the authentication
of devices, as depicted in Fig. 3(a). As a downside, this
approach requires significant channel monitoring, which is
subject to imperfect estimates, thus being critical in highly
dynamic environments as those of V2X communications.
On the other hand, the AFE imperfection-based PLA relies
on specific characteristics introduced during the fabrication
of devices, including in-phase and quadrature imbalance
(IQI), digital-to-analog converter, carrier frequency offset,
power amplifier, among others. In practice, the reliability of
estimating differences among the aforementioned attributes
can be deteriorated due to noise and dynamic interference
conditions.
The authentication process must be carried out period-
ically during the secret message transmission, within the
coherence time of the channel, in order to guarantee a suf-
ficient agreement of the channel signatures. Therefore, due
to the time-varying nature of these attributes and imperfect
estimation, PLA techniques may be difficult to design and
standardize, thus presenting low reliability and accuracy.
However, multi-attribute authentication techniques can be
used to improve the robustness and accuracy of PLA, by
combining a number of selected attributes according to the
specific application scenario, thus attaining an increased
level of security in the presence of attackers [36]. On
the other hand, the use of multiple attributes may lead to
extremely complex searches in highly dynamic scenarios,
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D. P. Moya Osorio et al.: Safeguarding MTC at the Physical Layer: Potentials and Challenges
Channel
Probing Alice and Bob collect channel measurements. If channel reciprocity
exists, these measurements are highly correlated.
Randomness
Extraction Alice and Bob extract randomness caused by channel fading to
generate shared keys by removing the large-scale component.
Quantization
Information
Reconciliation
Privacy
Amplification
Quantize the random channel measurements into bits.
Alice and Bob exchange side information to correct errors, and a
certain amount of bit information could be revealed to the
eavesdropper
Eliminates Eve’s partial information about the key and the
correlation among the bits.
FIGURE 4. Typical physical layer key generation process.
whereas the adaptation of the PLA process requires to be
performed almost instantaneously.
A. INCREASING THE ACCURACY AND ROBUSTNESS
OF PLA
Machine learning (ML) as well as other artificial intelligence
(AI) techniques can be used to improve the robustness of
PLA, thus opening new opportunities for the application of
PLA techniques in practical MTC scenarios. For instance,
in [37], a multi-attribute PLA was proposed, based on the
kernel of a ML technique to avoid the requirement of
knowing the statistical distribution of the attributes, such that
a multi-dimensional space is reduced to a single dimension
to decrease the authentication process complexity. Also, an
adaptive algorithm was adopted for tracking the attribute
variations in order to achieve a more reliable authentication
performance.
B. IMPROVING EFFICIENCY DURING HANDOVERS IN
HETEROGENEOUS NETWORKS
In future heterogeneous MTC networks, frequent handover
and authentication processes, due to transfer of users be-
tween small cells, will inflict challenges in terms of latency,
which is a crucial concern for cMTC. Therefore, improving
the efficiency of the authentication process during a han-
dover process is imperative for the deployment of future
MTC networks. In this sense, the handover authentication
process may not occur in a totally new context, so that
many stable attributes can be predicted from their previous
observations. For instance, physical-layer key generation,
described next, has a huge potential to be used as a network-
wide unique and unforgeable key, thus reducing some repet-
itive steps in cryptographic authentication schemes [36].
C. PHYSICAL-LAYER KEY GENERATION CHALLENGES
In physical layer key generation, wireless devices measure
highly correlated wireless channel characteristics, such as
CIR or RSS, and use those measurements as shared random
sources to generate a shared key. The typical process
followed by Alice and Bob to generate this secret key
follows 5 steps as illustrated in Fig. 4 [38]. Regarding
that process, physical-layer key generation is based on
three principles, namely, temporal channel variation, channel
reciprocity, and spatial decorrelation [38, 39] (as illustrated
in Fig. 3(b)). Temporal channel variation is introduced by
the movement of the transmitter, receiver, or surrounding
objects in the environment. Channel reciprocity implies that
bidirectional wireless channel states are identical between
two transceivers during a given time interval, so that it
is feasible to generate the same key. This is only valid
for time division duplex (TDD) based systems. Spatial
decorrelation indicates that the wireless channel proper-
ties are unique to the locations of the transceivers at the
legitimate link, such that an eavesdropper at a position
farther than one-half wavelength away from the legitimate
transceivers experiences a different and uncorrelated channel
state. However, these assumptions may not be satisfied in all
the environments. Therefore, physical-layer key generation
faces some challenges to be overcome before their efficient
use in MTC networks.
For instance, in cases where the eavesdropper is co-
located with Alice or Bob, it will observe channel measure-
ments that are highly correlated, such that the communi-
cation is vulnerable to attacks. To prevent this issue, the
authors in [40] proposed a solution for millimeter wave
(mmWave) massive MIMO communication systems that
relies on the high directionality of beams that can be attained
by using massive-MIMO-based beamforming techniques.
Transceiver hardware, time delay in TDD systems as well
as fine grain quantization may introduce disagreements in
initial keys. Therefore, in the reconciliation step (forth step
in Fig. 4), an error correction process with information
exchange between Alice and Bob is carried out to improve
the initial keys. Existing approaches include the cascade
algorithm and LDPC codes. However, a significant recon-
ciliation overhead can be introduced if the bit disagreement
before the reconciliation is high [38], thus decreasing the
key generation rate. To overcome this issue, the authors
in [41] proposed two new channel characteristics, virtual
AoA (Angle of Arrival) and AoD (Angle of Departure),
in order to generate a shared secret key with low bit dis-
agreement between two devices in mmWave Massive MIMO
channel. This is attained by exploiting the sparsity and
robusteness against noise of these channel characteristics.
Also, in wireless networks with multiple nodes—or with
a massive number of nodes, as those of mMTC scenarios—
group key generation schemes would be more appealing
compared to one-by-one generation methods, especially for
broadcast/multicast communications [42]. However, the key
generation process may suffer from a high complexity,
as more channels and more parameters for optimization
need to be considered, thus requiring a longer channel
coherence time, in addition to the risk of leaking information
VOLUME 4, 2016

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10.1109/ACCESS.2020.2996383, IEEE Access
D. P. Moya Osorio et al.: Safeguarding MTC at the Physical Layer: Potentials and Challenges
to eavesdroppers when sharing channel information to the
various nodes.
Therefore, pursuing novel, robust and low-complex solu-
tions for secret group-key generation schemes from physical
layer characteristics is an appealing research area for pro-
viding security in MTC networks.
VI. INTEGRATION SECURITY IN THE RANDOM ACCESS
From the perspective of the uplink communication, pro-
viding scalable connectivity for a large number of devices
with a sporadic and low data rate traffic pattern (such
as in smart metering applications) is one of the primary
challenges of mMTC [11]. This contrasts with downlink-
focused communications for human-centric services with
high data rates, on which the design criteria for LTE and
earlier cellular generations technologies relied on. In this
context, non-orthogonal grant-free (rather than orthogonal
grant-based) medium access control turns out to be more
suitable for mMTC, as a certain degree of radio-resource
overloading is allowed, at the cost of an augmented number
of collisions and increased receiver complexity at the base
station. Moreover, medium access protocols employed in
current cellular networks demands a considerable signaling
overhead, a large part of which is required to provide access,
as well as authentication and security. Nonetheless, this is
compensated since large-payload packets are employed, so
that the protocol efficiency (defined as the ratio between
the data and signaling overhead) is high. On the other
hand, the protocol efficiency of traditional medium access
protocols is compromised when considered the short-packet
traffic pattern inherent to mMTC. Thus, new PHY and MAC
layer technology solutions are needed to deal with a huge
amount of asynchronous, low data-rate, small-packet, spo-
radic connections, so that the same functionality of existing
access protocols in terms of radio resource reservation and
security is achieved, but with significantly less signaling
overhead. In this sense, mMTC traffic is shown to benefit
significantly from signature-based random access schemes,
which embed both authentication and security information,
thus ensuring a high access reliability for increasing access
loads, while reducing access latency and signaling overhead
when compared to traditional access protocols [43]. Fur-
ther solutions in this direction can be specially attractive
for cMTC scenarios as well, which are characterized by
stringent restrictions on high reliability and extremely low
latency.
VII. PHYSICAL-LAYER COVERT COMMUNICATIONS
With the introduction of MTC, mMTC, and cMTC in 5G
networks, we expect an exponentially increasing number of
devices communicating with the most diverse requirements,
not only in terms of data rate, reliability, and latency, but
also in terms of security. Traditional cryptography tech-
niques cannot cope with all security problems in MTC
scenarios. For instance, if a MTC node intends to com-
municate covertly with another without being detected by
an adversary, cryptography is not enough. An eavesdrop-
per could infer information relative to a node, or even
of the entire network, from the conveyed metadata (e.g.,
the network traffic pattern), which can reveal sensitive
information about the users. Even though the eavesdropper
is not able to decode the message, it could estimate the
user’s location or transmission behavior. Unlike traditional
cryptography, covert wireless communications aims to hide
the transmission behavior by providing covertness, stealth
or low probability of detection for communications. Then,
if a malicious entity cannot detect transmissions, there is no
chance to perform an eavesdropping and decoding attack,
even if unlimited resources or quantum potentialities are
used.
In this context, PHY covert wireless communica-
tions have recently received increased attention from
academia [9, 10]. In a PHY covert wireless communication
system, privacy is preserved once the adversary is prevented
from knowing the existence of transmissions by using
PHY techniques or wireless channel properties, such as
spread spectrum, friendly jammers, or background noise.
For instance, the authors in [9] determined how much covert
information can be transmitted reliably over additive white
Gaussian noise (AWGN) channels. Therein, it was found
a square root law according to which a legitimate source
can transmit O(√n)bits reliably and covertly to a legitimate
destination over nchannel uses, with the transmission power
at the legitimate source being a decreasing function of the
blocklength n.
More recently, by virtue of distinctive features of future
MTC networks which are expected to be highly heteroge-
neous and dense, in [10] was proposed to leverage another
kind of noise source (in addition to the background noise)
in order to achieve covert communications, namely, the
aggregated interference from other transmitters. In fact, the
adversary’s uncertainty on the aggregated interference is
beneficial for the legitimate transmitter to remain covert,
as the randomness of aggregated interference, which comes
from random locations of potential transmitters and fading
channels, is greater than the background noise. Furthermore,
it was concluded in [10] that, from the network perspective,
this approach of hiding communications in interference
shows considerably higher spatial throughput, although the
covert throughput for any pair of users may become lower.
In this instance, some challenges can be pointed out, as
follows. It was demonstrated in [10] that hiding informa-
tion in interference is effective when a static and passive
eavesdropper is considered. This is appealing for mMTC
scenarios where the eavesdropper will be overwhelmed by
a dense wireless network with a large number of nodes,
such that discriminating the actual transmitter from others
in a network is a difficult task. However, if an active adver-
sary is considered (i.e., an eavesdropper that dynamically
adjust its distance to the legitimate transmitter, based on
its observations), so that it can move to the vicinity of the
legitimate transmitter, the strategy of hiding transmissions
VOLUME 4, 2016

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D. P. Moya Osorio et al.: Safeguarding MTC at the Physical Layer: Potentials and Challenges
mMTC
Large number of devices
Low complexity
Low power
mcMTC
Ultra reliability
Low latency
Availability
Edge Cloud
Robust solutions for
PHY authentication
PHY key generation
Robust jamming strategies
Security-context
sharing
SDN/NFV
Smart
Weareables
Smart
meters
Capillary
networks
Smart
agriculture
V2X networks
Traffic safety/control
Industrial control
Remote surgery
Tactile Internet
CSI-irrelevant solutions
More accurate
channel models
Energy-constrained
solutions
Delay-constrained
solutions
QoS-based
solutions
Lightweight
security solutions
Random Acces
with integrated security
Practical design for
wiretap codes
AI-aided security solutions
Solutions against pilot
contamination Cross-layer
security solutions
FIGURE 5. General view of physical layer security and key performance indicators for MTC networks.
in interference could be compromised. This situation could
be aggravated when considered multiple active adversaries,
which can cooperate to improve their detection ability, or an
adversary equipped with more antennas than the legitimate
transmitter and receiver. In this sense, randomized transmis-
sion scheduling [44], cooperative jamming or coordinated
interference mechanisms [45] can be explored as potential
solutions to provide covert regions, where the private-
message exchange can occur regardless of the computational
power of the adversary, while providing location privacy
preservation to the legitimate transceivers.
On the other hand, although the characteristics of MTC
vary for different applications, it is a common assumption
in the literature and for standardization bodies that MTC
traffic will be dominated by small packages from periodic
or asynchronous alarm-based transmissions. The square
root law is attained with asymptotic assumptions on the
number of channel uses; therefore, a better understanding
of the fundamental limits of covert communications under
practical limitations, where the codeword length is finite (in
the order of hundreds of channel uses), is still an open issue.
VIII. CONCLUDING REMARKS
So far, we have provided a detailed overview on decisive
aspects that need to be further investigated for securing
MTC networks from the physical layer perspective, as well
as some interesting solutions which are summarized in
Fig. 5. In the following, we will discuss some key challenges
relevant to those aspects.
A. PRACTICAL CHANNEL MODELS
Accurate channel models are crucial for the appropriate
design of system parameters and system performance eval-
uation. In this sense, 5G networks bring huge challenges
regarding the search for accurate channel models that effi-
ciently fit 5G environments, as a wide diversity of scenarios
need to be considered, such as indoor, urban, suburban,
and rural areas, as well as rapid train and unmanned-
aerial-vehicles (UAV) networks, among others. In addition,
future approaches on PHY security techniques and metrics
should be designed according to the challenges imposed
by more practical and accurate channel models for MTC
networks, which also consider extremely wide frequency
bands (in the mm-Wave, THz, and visible light spectrum)
and new scenarios (e.g., tunnel, underground, underwater,
and even human body). A wide variety of scenario-specific
5G channel models have been already proposed in the
literature (an excellent survey can be found in [46]). Then,
it is essential to revise PHY security techniques and met-
rics regarding these new channel models [47–49]. Indeed,
various PHY security techniques are invalidated in poor
scattering environments where a strong correlation between
legitimate and wiretap channels exists. Additionally, quasi-
static and poor scattering channels can be challenging for
secret key generation, whereas highly-dynamic channels
are challenging for performing PLA. Moreover, physical-
layer covert communications techniques are still limited to
basic channel models, thus exploring its benefits in realistic
scenarios is a big challenge ahead.
VOLUME 4, 2016

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D. P. Moya Osorio et al.: Safeguarding MTC at the Physical Layer: Potentials and Challenges
B. CROSS-LAYER TECHNIQUES
Performing different authentication processes at different
protocol layers can increase the security level of MTC
networks at the expense of increased complexity and latency,
which should be avoided for the practical scenarios of MTC.
Thus, cross-layer security approaches can be attractive to
further improve the level of security and privacy in those
scenarios, while reducing cost and overhead, by enabling
the information exchange across different protocol layers or
mixing the information from two or more layers in order to
design security strategies that can comply with QoS, latency
or energy constraints [3]. Then, these approaches deserve
further attention, as exchanging information across layers
can demand meticulous tasks to design efficient solutions.
C. MACHINE-LEARNING TECHNIQUES FOR PHY
SECURITY
Very recently, a number of multi-attribute PLA approaches
have been investigated by relying on the potentialities of ML
techniques. These solutions have shown to be effective to
design robust and accurate PLA techniques, which will open
great opportunities for the integration of PHY security in
the design of 5G and beyond networks. However, regarding
MTC networks, some important issues should be considered,
as follows [50]: (i) the time consumed for the convergence
of a given ML technique may reduce the time for data
transmission, then this trade-off should be considered for the
design, (ii) distributed implementation of the learning algo-
rithm across multiple learning devices, (iii) parameters such
as learning rate, discount rate, and exploration/exploitation
trade-off should be dynamically adapted to enhance the
performance of a Q-learning algorithm in highly dynamic
environments, and (iv) the heterogeneity of MTC devices
must be taken into account in terms of learning capability,
cache size, delay tolerance, and data rate.
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DIANA PAMELA MOYA OSORIO (M’16) was
born in Quito, Ecuador. She received the B.Sc.
degree in electronics and telecommunications
engineering from the Armed Forces University
(ESPE), Sangolquí, Ecuador, in 2008, and the
M.Sc. and D.Sc. degrees in electrical engineering
with emphasis on telecommunications and telem-
atics from the University of Campinas (UNI-
CAMP), Campinas, Brazil, in 2011 and 2015, re-
spectively. In 2018, she was a Visiting Researcher
at the Centre for Wireless Communications (CWC) at University of Oulu,
Finland, for one year. Since 2015, she is acting as an Adjunct Professor
with the Department of Electrical Engineering, Federal University of São
Carlos (UFSCar), São Carlos, Brazil. In 2020, she joined CWC as Senior
Research Fellow under the 6GFlagship program at University of Oulu.
She has served as TPC and reviewer for several journals and conferences.
Her research interests include wireless communications in general, physical
layer security, security for 5G and beyond networks, and UAV-based
communications.
EDGAR EDUARDO BENITEZ OLIVO (M’16)
received the B.Sc. degree in Electronics
and Telecommunications Engineering from the
Armed Forces University-ESPE, Ecuador, in
2008, and the M.Sc. and Ph.D. degrees in Electri-
cal Engineering from the University of Campinas,
Brazil, in 2011 and 2015, respectively. In 2014,
he held a visiting researcher position with the
Centre for Wireless Communications, University
of Oulu, Finland. Since 2016, he has been with
São Paulo State University (UNESP), Campus of São João da Boa Vista,
Brazil, as an Assistant Professor. He has served as a reviewer for many
IEEE and non-IEEE journals and has been involved as a TPC member
in several conferences. His research interests lie in the area of wireless
communications, with a current focus on emerging technologies towards
5G wireless networks.
HIRLEY ALVES (S’11-M’15) received the B.Sc.
and M.Sc. degrees from the Federal University of
Technology-Paraná (UTFPR), Brazil, in 2010 and
2011, respectively, both in electrical engineering,
and the dual D.Sc. degree from the University of
Oulu and UTFPR, in 2015. In 2017, he was an
Adjunct Professor in machine-type wireless com-
munications with the Centre for Wireless Com-
munications (CWC), University of Oulu, Oulu,
Finland. In 2019, he joined CWC as an Assistant
Professor and is currently the Head of the Machine-type Wireless Commu-
nications Group. He is actively working on massive connectivity and ultra-
reliable low latency communications for future wireless networks, 5GB
and 6G, full-duplex communications, and physical-layer security. He leads
the URLLC activities for the 6G Flagship Program. He is a co-recipient of
the 2017 IEEE International Symposium on Wireless Communications and
Systems (ISWCS) Best Student Paper Award, and 2019 IEEE European
Conference on Networks and Communications (EuCNC) Best Student
Paper Award and a co-recipient of the 2016 Research Award from the
Cuban Academy of Sciences. He has been the organizer, chair, and TPC
and tutorial lecturer for several renowned international conferences. He is
the General Chair of the ISWCS’2019 and the General Co-Chair of the 1st
6G Summit, Levi 2019, and ISWCS 2020.
VOLUME 4, 2016

This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI
10.1109/ACCESS.2020.2996383, IEEE Access
D. P. Moya Osorio et al.: Safeguarding MTC at the Physical Layer: Potentials and Challenges
MATTI LATVA-AHO received the M.Sc.,
Lic.Tech. and Dr. Tech (Hons.) degrees in Electri-
cal Engineering from the University of Oulu, Fin-
land in 1992, 1996 and 1998, respectively. From
1992 to 1993, he was a Research Engineer at
Nokia Mobile Phones, Oulu, Finland after which
he joined Centre for Wireless Communications
(CWC) at the University of Oulu. Prof. Latva-aho
was Director of CWC during the years 1998-2006
and Head of Department for Communication
Engineering until August 2014. Currently he is Professor of Digital
Transmission Techniques at the University of Oulu. He serves as Academy
of Finland Professor in 2017-2022. His research interests are related to
mobile communication systems and currently his group focuses on 5G and
beyond systems research.
VOLUME 4, 2016