Device-to-Device Communications for 5G Internet of
Things
L. Militano1, G. Araniti1,*, M. Condoluci1, I. Farris1 and A. Iera1
1
Mediterranea University of Reggio Calabria, Italy,
[leonardo.militano|araniti|massimo.condoluci|ivan.farris|antonio.iera]@unirc.it
Abstract
The proliferation of heterogeneous devices connected through large-scale networks is a clear sign that the vision of the
Internet of Things (IoT) is getting closer to becoming a reality. Many researchers and experts in the field share the opinion
that the next-to-come fifth generation (5G) cellular systems will be a strong boost for the IoT deployment. Device-toDevice (D2D) appears as a key communication paradigm to support heterogeneous objects interconnection and to
guarantee important benefits. In this paper, we thoroughly discuss the added-value features introduced by cellular/noncellular D2D communications and its potential in efficiently fulfilling IoT requirements in 5G networks. State-of-the-art
solutions, enabling radio technologies, and current standardization activities for D2D communications are surveyed and
their pros and cons with reference to manifold IoT use cases pointed out. Future research directions are then presented
towards a fully converged 5G IoT ecosystem.
Keywords: Device-to-Device; Internet of Things; Proximity services; 5G systems.
Received on 06 October 2015, accepted on 19 October 2015, published on 26 October 2015
Copyright © 2015 Araniti et al., licensed to ICST. This is an open access article distributed under the terms of the Creative
Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and
reproduction in any medium so long as the original work is properly cited.
doi: 10.4108/eai.26-10-2015.150598
*
Corresponding author. Giuseppe Araniti, Email:
[email protected]
unlimited capabilities to effectively exploit the potential
of massive tiny sensors and actuators towards a so-called
Cloud of Things [4].
Despite all the conditions seem to be very favorable,
still much remains to do before reaching well-working,
reliable and efficient IoT ecosystem. In [5] the current
situation of the IoT arena is compared to the "Wild West"
of a couple of centuries ago with its vast, mostly
unexplored territories, without clear borders, where all
current technologies can play a role, and where ad hoc
solutions are often the norm. For instance, the high
heterogeneity of devices, technologies, and interaction
modalities (machine-to-machine, machine-to-human, and
machine-to-cloud) involved poses severe challenges
concerning the communication process. In this view, a
wide variety of low-power short-range wireless
technologies, such as IEEE 802.15.4, Bluetooth Low
Energy, IEEE 802.11ah, have been designed to provide
efficient connectivity among IoT devices and to the
Internet.
Recently, also long-range cellular networks are being
considered as promising candidates to guarantee the
desired internetworking of IoT devices, thanks to the
1. Introduction to the Internet of Things
The Internet of Things (IoT) holds the promise to improve
our lives by introducing innovative services conceived for
a wide range of application domains: from industrial
automation to home appliances, from healthcare to
consumer electronics, and many others facing several
societal challenges in various everyday-life human
contexts [1]. Currently we have 10 billion IoT devices
connected and 24 billion to 50 billion total connections
expected within the next five years [2]. The vision of a
"smart world" where our everyday furniture, food
containers, and paper documents accessing the Internet is
not a mirage anymore [3]! The IoT growth is sustained by
the constant increase in the number of devices able to
monitor and process information from the physical world
and by their decreasing costs. Most of them operate
through their virtual representations within a digital
overlay information system that is built over the physical
world. The majority of current IoT solutions, indeed,
requires Cloud services, leveraging on their virtually
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EAI Endorsed Transactions on
Internet of Things
10 - 2015 | Volume 1 | Issue 1| e4
L. Militano, G. Araniti, M. Condoluci, I. Farris and A. Iera
offered benefits in terms of enhanced coverage, high data
rate, low latency, low cost per bit, high spectrum
efficiency, etc. [3]. In this context, the Third Generation
Partnership Project (3GPP) has introduced novel features
to support machine-type communications† (MTC) [6] by
accounting for the intrinsic battery-constrained
capabilities of IoT devices and the related traffic patterns
(e.g., small data packets). At the same time, the efforts of
academic, industrial and standardization bodies are
pushing towards the fulfillment of IoT requirements
through the next-to-come fifth generation (5G) wireless
systems [7]. 5G will not only be a sheer evolution of the
current network generations but, more significantly, a
revolution in the information and communication
technology field [8] with innovative network features [9].
Among these we can mention: (i) native support of MTC,
according to which ad-hoc transmission procedures are
defined to efficiently handle the cellular transmission of
small packets by reducing latency and energy
consumption; (ii) small-cell deployments, envisaging
femto, pico and relay cells massively deployed to extend
coverage and capacity and to reduce energy consumption;
(iii) interoperability, i.e., seamless integration between
3GPP and non-3GPP access technologies to enhance
reliability and coverage; (iv) optimized access/core
segments, achieved through novel paradigms such as
softwarisation and virtualization of network entities and
functionalities, respectively. In this direction go the
initiatives of GSM Association towards embedded-sim (esim) solutions [10], to overcome the classic concept of
physical cellular sim, which could be a serious limitation
for large-scale tiny IoT device (e.g., sensors). The e-sims
will allow “over the air” provisioning of network
connectivity and possibility to subscribe to multiple
operators.
In the evolutionary scenario depicted so far, a new
device-to-device (D2D) will play an undoubted key role in
the IoT/5G integration [11]. D2D communications refers
to the paradigm where devices communicate directly with
each other without routing the data paths through a
network infrastructure. In wireless scenarios this means
bypassing the base station (BS) or access point (AP) and
relaying on direct inter-device connections established
over either cellular resources or alternative over WiFi/Bluetooth technologies. This approach has recently
gained momentum as a means to extend the coverage and
overcome the limitations of conventional cellular systems.
The main benefits it can introduce are [12]: (i) high data
rate transmissions supported also by devices remotely
located from the BS/AP; (ii) reliable communications also
in case of network failure, as may be the case of disaster
scenarios; (iii) energy saving since devices in close
proximity can interact at a lower transmission power
level; (iv) traffic offloading that reduces the overall
number of cellular connections; (v) heterogeneous
connectivity accounting that direct communications
among devices does not only rely on cellular radio
interface, but can be established through alternative radio
technologies; (vi) instantaneous communications between
a set of devices in the same way that walkie-talkies are
used for emergency services.
Needless to say, these same features make D2D a very
appealing solution to satisfy also the exacting
requirements imposed by IoT in emerging 5G network
scenarios (a possible IoT internetworking scenario is
depicted in Figure 1).
Figure 1. D2D communications in 5G IoT networks.
The numerous initiatives conducted by mobile and
wireless communication leading enablers of the twentytwenty information society (such as METIS European
project [13], 5G-PPP association [14], Networld2020
platform [15], etc.), confirm the role of D2D in various
scenarios such as vehicle-to-vehicle communications,
national security and public safety, cellular network
offloading, or service advertisement. Nonetheless, when
considering the possibility of D2D-based interconnection
of IoT devices in cellular environment, severe challenges
still need to be faced, such as efficient device discovery in
heterogeneous environment, optimized link selection for
highly dynamic multi-tenant networks, security issues,
and so on [16].
The aim of this paper is precisely to discuss the
benefits introduced by D2D technologies that may be
suitably exploited within IoT ecosystems operating within
future 5G systems. In detail, the expected contributions
are:
(i) highlighting the main features of D2D
communications that may come in handy to fulfil the
requirements of IoT;
(ii) discussing the state of the art on D2D-enabled
solutions and analysing possible enhancements to
further
boost
the
performance
of
D2D
communications in IoT environments;
(iii) introducing promising future trends and identifying
relevant IoT research areas by assessing the role of
†
MTC is the name used by 3GPP for identifying the machineto-machine (M2M) communications within the LTE-Advanced
(LTE-A) cellular environment [33].
2
Device-to-Device Communications for 5G Internet of Things
D2D communications to accomplish the view of a
fully integrated 5G IoT ecosystem.
The remainder of this paper is organized as follows. In
Section 2 the D2D paradigm is briefly addresses by
stressing the different implementation solutions and the
current standardization activities. Section 3 illustrates the
main requirements to comprehensively support IoT in
future 5G scenarios. In Section 4 an extended analysis of
how D2D may address the listed IoT requirements is
provided, whereas Section 5 discusses open research areas
for the evolution of D2D in the next-to-come 5G
networks. Section 6 concludes the paper.
(a) D2D with operator
controlled link establishment.
(b) Relaying with autonomous
link establishment.
Figure 2. Possible approaches for D2D link
establishment.
2. Device-to-Device Communication:
Approaches, Enabling Technologies,
and Standards
2.1. Radio Technologies for D2D
Communications
From what said so far, any technology supporting the
direct communication between devices can enable D2D
communications. In the following, some details are given
on those mainly considered to this purpose. In particular,
notions about Wi-Fi Direct, Bluetooth, Radio Frequency
Identification (RFID) and IEEE 802.15.4 are given,
before going into the details of the cellular D2D
technology (named LTE-Direct) and the 3GPP
standardization achievement concerning D2D services.
Table 1 summarizes the main features of the available
technologies for D2D communications.
D2D communications aim to boost the performance of
conventional cellular networks (in terms of metrics, such
as power consumption, spectrum efficiency, throughput,
etc.) by exploiting direct interaction between devices in
proximity. Several solutions have been investigated in the
literature, and different classifications have been
provided.
A good taxonomy of D2D communications is given in
[17], where a first distinction is made based on the
spectrum adopted for D2D communications. This can be
either cellular licensed spectrum, like for cellular
communications (i.e., inband communication), or
unlicensed bands such as Wi-Fi (i.e., outband
communication). The inband solution, can be further
classified in (i) underlay inband D2D mode [18] and (ii)
overlay inband D2D mode. In the former, D2D and
cellular communications share the same licensed cellular
spectrum; in such a case, the main issue is the mitigation
of the interference between D2D and cellular
communications. In the latter, a portion of the cellular
resources is dedicated to D2D communications for
avoiding interference problems; in this case, the resource
allocation becomes the key issue to address to avoid
wasting precious spectrum resources. The outband
solution aims to eliminate the interference between D2D
and cellular link, but needs extra interfaces such as Wi-Fi
Direct or Bluetooth. Therefore, it needs to coordinate the
communication over two different radio spectrum ranges
(e.g., when cellular and Wi-Fi Direct radio interfaces are
involved). The coordination between radio interfaces is
either controlled by the BS/AP, i.e., controlled mode, as
illustrated in Figure 2 (a), or by the users, i.e.,
autonomous mode, as illustrated in Figure 2(b). Hence,
the studies on outband D2D involve both aspects of
power consumption and inter-technology architectural
design [19] [20].
Wi-Fi Direct
Wi-Fi Direct [21] allows mobile devices (e.g.,
smartphones, tablets) to directly connect over unlicensed
bands and transfer content or share applications anytime
and anywhere. Although the idea of supporting direct
links was already found in the original IEEE 802.11
standard through the ad-hoc mode, the lack of efficient
power saving and enhanced QoS support has limited the
market penetration of this functional mode [22]. Wi-Fi
Alliance has recently certified Wi-Fi Direct to support
peer-to-peer (P2P) communications between 802.11
devices by jointly exploiting the potentialities of ad-hoc
and infrastructure modes. Wi-Fi Direct allows devices to
implement the role of either a client or an access point
(AP), and hence to take advantage of all the enhanced
QoS, power saving, and security mechanisms typical of
the infrastructure mode. Wi-Fi Direct devices can connect
for a single exchange, or they can retain the memory of
the connection and link together each time they are in
proximity. Data communication is accomplished by
creating a P2P group, where a device with a role of P2P
group owner (P2P GO) can allow a cross-connection of
devices belonging to its P2P group to an external network
(e.g., a 3GPP network).
Table 1. Wireless D2D technologies comparison
3
L. Militano, G. Araniti, M. Condoluci, I. Farris and A. Iera
Wireless technologies comparison
LTE
Direct
3GPP
LTE-A
Licensed band
for LTE-A
IEEE 802.11
1000 m
200 m
Maximum
Data rate
1 Gbps
250 Mbps
Applications
Offload traffic,
Relaying,
Content
Sharing, Public
Safety, Local
Advertising
Context
sharing,
Group
gaming,
Device
connection,
voice data
Infrastructure
Devices transfer
data in licensed
spectrum
Standard
Frequency
band
Maximum
Transmission
distance
Wi-Fi
Direct
2.4, 5 GHz
NFC UHF RFID
ISO 18092 –
ISO 18000-6
13.56 MHz –
868/915 MHz
IEEE
802.15.4
868/915 MHz,
2.4 GHz
0.01 m -10m
10-100 m
10-100 m
250 kbps
24 Mbps
(version 3.0 >)
Environmental
sensing and
actuation
Home entertainment,
local advertising,
wearable devices
400 kbps
(NFC P2P
mode)
Identification,
Data sharing,
e-health and
environmental
monitoring by
sensorequipped tags
Zigbee
Bluetooth
IEEE 802.15.1
2.4 GHz
Devices transfer data in un-licensed spectrum
directly exchange any kind of data. On the other hand,
UHF RFID systems are the most promising solution for
long-range object identification and worldwide supply
chain management. Evolution of smart UHF RFID tags
with embedded sensors and miniaturization of readers
promotes this technology for high pervasive IoT
ecosystem [26].
Finally, to address the requirements of M2M
communication, standardization activities have recently
proposed IEEE 802.11ah [23], which aims to increase the
number of possible devices in the network and to lower
energy consumption.
Bluetooth
Bluetooth, together with WiFi, is the most widely known
D2D technology working at the 2.4GHz unlicensed band.
Bluetooth intends to provide wireless connectivity in
personal area networks. In order to enable short-range
communications, one device becomes the master of the
connection(s) serving up to seven slaves (clients) to form
a piconet. Bluetooth Low Energy (BLE) has recently been
standardized to meet constraints of IoT devices and opens
up the doors for novel application scenarios, such as
remote monitoring of BLE-enabled wearable sensors by
exploiting smartphone connectivity [24].
Zigbee
Zigbee is a protocol stack tailored for resource
constrained wireless sensor networks. It is built upon
IEEE 802.15.4, which defines physical and MAC layers
in a balanced trade-off between data rate, communication
range, and energy efficiency. Several enhancements have
been proposed, among which IEEE 802.15.4e and IEEE
802.15.4g are particularly noteworthy. The former has
redesigned the MAC layer to specifically support high
reliable industrial applications, by introducing time
synchronization and channel hopping. The latter addresses
extremely large-scale sensor networks, such as smart
utility networks.
NFC – RFID
The term RFID refers to a family of radio technologies
whose main objective is to provide fast identification of
objects through the interaction between transponders, also
known as tags, and readers. The former answer with their
identification codes when interrogated by the reader,
which manages the overall data exchange process.
Different classifications of RFID systems could be
provided according to operating frequency, radio
interface, communication range, tag autonomy
(completely passive, semi-passive, active), and different
standards have been ratified. For short-range
communications, NFC technology [25] plays a prominent
role for its wide adoption, as it is natively included in
2.2. 3GPP standardization for cellular
D2D communications
The cellular D2D communications technology has been
addressed in the Release 12 of 3GPP [27], and it is
expected to have a complete standardization of proximity
services in next 3GPP releases 13 and 14 [28]. 3GPP is
focusing its efforts on D2D communications for public
safety Proximity Services (ProSe). This strategy has been
initially targeted to allow LTE becoming a competitive
broadband communication technology for public safety
networks used by first responders. In detail, the 3GPP
Radio Access Network (RAN) working group has
proposed in TR 36.843 Rel. 12 [29] two basic functions
modern smartphones. In addition to interact with tags, it
foresees a peer-to-peer mode by which devices can
4
Device-to-Device Communications for 5G Internet of Things
for
supporting
ProSe
discovery
and
ProSe
communications over the LTE radio interface. ProSe
discovery allows a device using the LTE air interface
(User Equipment – UE) to identify other UEs in
proximity. Two kinds of ProSe discovery exist, namely
restricted and open; the difference consists in whether the
permission is necessary or not for the discovery for a UE.
Instead, ProSe communication is the data communication
between two UEs in proximity using the LTE air
interface. Any UE supports ProSe Discovery and/or ProSe
Communication is called as ProSe-enabled UE. 3GPP
Services working group (SA1) has defined in
specification TR 22.803 [30] the use cases and scenarios
for ProSe. In the document, conditions for service flows
and potential requirements for different use cases are
analyzed to support D2D systems design. Examples of use
cases for ProSe Discovery and ProSe Communication
scenarios are defined by 3GPP SA1 in specification TR
22.803 [30].
The native support of D2D communications becomes
crucial in 5G systems where the exponentially increasing
data traffic exchanged over radio mobile systems requires
novel communications paradigms. Research activities in
this field are, therefore, numerous. A first example of
D2D communications into the LTE-Advanced (LTE-A)
network is provided by Qualcomm Company, which
developed a mobile communication system called
FlashLinq [31]. In particular, FlashLinq allows cellular
devices automatically and continuously discovering
thousands of other FlashLinq enabled devices within 1
kilometer and communicating peer-to-peer, at broadband
speeds and without the need of intermediary
infrastructures. Similarly, in [32] a first implementation
for 3GPP LTE-Assisted Wi-Fi-Direct communication has
been presented showing promising results.
connectivity among the IoT devices, while wireless
gateways typically provide remote connectivity to the
Internet. Recently, also wide area wireless technology
with enhanced coverage capability, such as the modern
LTE-A cellular networks, are being considered as
enablers of the IoT. In this regards, energy-efficient
networking solutions are being introduced, to account for
the stringent battery constraints of sensors and actuators.
These tend to exploit local communication to reduce
transmission power consumption and/or data aggregation
to lower the amount of data exchanged.
Scalability
The huge number of smart devices, willing to connect to
the forthcoming IoT world, draws the researchers’
attention on issues that may result challenging for current
network infrastructures. Existing wireless networks could
especially suffer from dynamic crowded IoT scenarios,
where massive machine-type communications (MTC)
need to be handled while also guaranteeing the requested
quality of service. This aspect is particularly evident in
cellular networks, where human-oriented and MTC shall
be accommodated in the same infrastructure. Despite the
recent efforts by 3GPP to efficiently support MTC in
LTE-A, several challenges remain to be faced in the view
of full 5G based IoT systems [33]. These include, among
others, avoiding congestion in connection access,
providing a high system capacity, guaranteeing efficient
radio resource allocations and efficiently handling small
size data communications.
Resiliency
The intrinsic dynamic nature of wireless IoT ecosystems
requires guarantees of system continuity also in harsh
conditions, including lack of the network infrastructure
connectivity. Apart from the efforts made to provide a
capillary network coverage (thanks to multi-tier cellular
architectures), an unexpected lack of infrastructure
support is highly likely in case of congestion due to
crowded events, failures of network node, bad wireless
link conditions, and disastrous events. These situations
should not prevent the correct functional behavior of IoT
solutions, typically relying on interoperation and
cooperation among devices and often deployed in critical
scenarios (eHealth, e-energy management, transportation
systems, smart farms, etc.). In fact, a connection failure
could cause tremendous consequences for critical usecases, such as safety road data dissemination, health alarm
systems, and automated industrial processes. Also, realtime interactive application, e.g., multimedia IoT, could
undergo a significant reduction of user quality of
experience. Therefore, advanced and reliable IoT systems
shall foresee a high-level network recovery capacity,
quickly identify connectivity failures, and automatically
establish alternative communication paths.
3. IoT Requirements to be Supported by
Forthcoming 5G Systems
In this Section we spot the main requirements and
challenges to be met to exhaustively support IoT use cases
in the next-to-come 5G cellular systems.
Energy efficiency
Energy handling during its harvesting, conservation, and
consumption phases is one of the major issues
characterizing IoT ecosystems and that claims for the
design of novel energy efficient solutions [5]. Achieving
high energy efficiency in communications is crucial to
IoT devices, typically relying on either small batteries or
on harvesting technologies. This is even more important
to application scenarios involving remote areas, which are
difficult to reach and make it hard or almost impossible to
recharge or replace the objects power suppl. A noticeable
contribution to energy consumption reduction may derive
from the adoption of direct communication between IoT
devices. A plethora of short-range standardized wireless
technologies are already adopted to guarantee local
Interoperability
The IoT is populated by highly heterogeneous objects,
each one providing specific functions accessible through
5
L. Militano, G. Araniti, M. Condoluci, I. Farris and A. Iera
its own dialect and network. Thus, one of the key
requirements is to manage this intrinsic heterogeneity, i.e.,
to provide efficient solutions for the seamless integration
of different types of devices, technologies, and services.
On the communication side, IoT heterogeneity should
account for the plethora of radio technologies involved in
the support of low-power devices. An emerging trend is
promoting cellular communication for IoT devices in the
view of an all-inclusive 5G framework. However, to
support the extremely differentiated IoT application
scenarios, next generation cellular networks need of
effective mechanism to handle heterogeneous data
handling capabilities, flexibility in managing different
radio technologies, integrated mobility management, etc.
Also from the application point of view, common
interfaces to access services offered by IoT mobile
devices are required. This requires appropriate
virtualization techniques to abstract from the underlying
networking protocol and to provide syntactic and
semantic interoperability [34]. This attracts the attention
from research and industrial communities on the “virtual”
counterparts of physical objects. As a consequence,
manifold IoT Cloud platforms have been designed to
support large-scale applications which rely on
heterogeneous sensor infrastructures. Still much efforts
are required to reduce latency in the interaction between
physical devices and their digital counterparts, to provide
distributed virtualization functions, and to reduce network
traffic generated by IoT devices by means of an efficient
composition of their services.
Cloud-based IoT service environment
A further key challenge is the support of a dynamic
execution environment for complex IoT applications. Ondemand processing and storage resources, provided by
Cloud data centers, represent a fertile underground to
develop and deploy scalable IoT platforms for: (i)
virtualization of IoT devices; (ii) offloading of
computationally intensive applications, such as complex
sensor event processing, face recognition, video
transcoding, etc.; (iii) addressing the so-called Big Data
challenge, i.e., storage and analysis of the huge amount of
data generated by IoT devices. However, Cloud-assisted
solutions could suffer from high delays in interacting with
remote data centers, and cause a remarkable increase of
data traffic. To overcome these issues, the concept of
Cloudlet [37] for vehicular networking and Fog
computing [38] for the IoT have been introduced to define
a distributed infrastructure of edge micro data centers,
which offer Cloud services closer to the end-users. Thus,
the role of network providers is evolving from
straightforward flow traffic manager to a ubiquitous
service enabler, which exploits its pervasive infrastructure
to offer integrated service-network solutions. To this aim,
the network provider becomes highly interested to exploit
novel form of communications, which accommodate IoT
devices’ requirements in terms of delay and energy
saving. In addition, to assure network interoperability,
appropriate solutions shall be designed to allow also noncellular IoT devices, such as sensors and RFID tags, to
interact with the distributed Fog architecture. For
instance, this can be done by relying on multi-interface
devices, such as smartphones, which act as access points
to the envisaged platform. Relay-based approaches should
also preferably provide in-network processing for data
transformation and aggregation, to enable more efficient
resource allocation.
Group communications
In IoT pervasive environments, data provided by a single
object may not be reliable or useful enough to support
specific applications and the desired Quality-ofInformation. At the same time, automated IoT systems
may have advantages in triggering simultaneous actions
on multiple devices (such as, for example, street light
lamps) in a smart city. The relevance of group
communication in IoT is also testified to by the interest in
this issue by the IETF Core working Group, involved in
the standardization of an IPv6-based application protocol
for resource-constrained devices [35].
Group communications can be provided by multicast
and unicast-oriented approaches. The former case is the
most challenging, as the network natively needs to
support simultaneous packet delivery to a group of
receivers. This allows to reduce network traffic and to
enhance the efficient resource usage. However, multicast
communication has some drawbacks: it does not provide
reliable service in IP network and the group formation my
result complex, especially in dynamic heterogeneous IoT
scenarios. Thus, ad-hoc proxy must be exploited to
forward data from/to a group of IoT devices by multiple
unicast communications and to provide dynamic group
management [36]. 5G systems shall provide efficient
support for group IoT communication, by optionally
leveraging on proximity communications to reduce
energy consumption and traffic congestion.
Support to Multimedia IoT
To deploy a comprehensive IoT framework, also smart
multimedia devices shall be properly included to sustain
multimedia services. Sample use-cases include ambient
assisted living and patient monitoring based on
telemedicine, integrated monitoring systems of smart
homes, advanced multimedia surveillance of smart cities
involving real-time sensor data acquisition. Besides, the
so-called “Internet of Multimedia Things” [39] introduces
features and network requirements that are different from
those of the typical resource-constrained IoT landscape.
Multimedia things foresee higher computation capabilities
to manage multimedia flows and, above all,
communications are more focused on bandwidth, jitter,
and loss rate to guarantee acceptable delivery of
multimedia contents. Low-power radio technologies are
not well suited to support these types of traffic, whereas
cellular networks provide better performance for
multimedia flows. However, accounting for the additional
traffic generated by multimedia things, 5G shall include
novel efficient techniques to meet both machine and
6
Device-to-Device Communications for 5G Internet of Things
human requirements, e.g., by leveraging on edge content
caching and proximity content distribution.
technology being adopted - see Table I in Section 2 - and
on the scenario considered). The cited features are
attractive for several application scenarios involving the
support of multimedia traffic over future IoT systems. In
particular, the authors of [40] consider base station
controlled D2D communications to transmit cached video
files in modern smartphones to other users through
multiple D2D links over the same time/frequency
resources within one cell. This leads to a huge increase in
the spectral efficiency. Similarly, the higher data rate over
D2D links is used for multimedia content dissemination in
[41] [42], and for social-aware video multicasting in [43].
The possibility to cluster devices into groups connected
through D2D links has also been widely investigated.
Examples of applications exploiting D2D-based grouping
are content sharing & dissemination (e.g., multicasting)
[44] [45] [46] [47].
All the cited examples confirm that D2D can help, not
only to meet the group communication requirements of
multimedia IoT devices. It also allows to overcome
typical scalability and heterogeneity issues of IoT. In fact,
clustering the devices in a network may ease the handling
of the expected large number of IoT devices with different
capabilities and available communication technologies.
4. D2D Features as Enabling Factors for
the future 5G Internet of Things
This section will browse through the main features of
D2D communications with the potential to meet the IoT
requirements discussed in the previous Sections. In
particular, we will discuss key research contributions and
highlight what has been done so far and what still remain
to do for allowing IoT to take advantage of 5G system
features. Indeed, proximity communications enabled by
D2D communications represent a fertile ground for use
cases where devices detect their vicinity and subsequently
trigger different services, such as social interactions and
gaming, advertisements, local information exchange, etc.
(Figure 3).
Low energy consumption communication
D2D communications guarantees a lower energy
consumption [48] w.r.t. to classic transmission modalities,
where devices communicate to the BS/AP. This feature
makes D2D communications very attractive in the view of
meeting the energy efficiency requirements of the IoT
[49]. The lower energy consumption is a direct
consequence of the lower transmission power necessary
over short-range connections with neighboring devices.
Furthermore, the channel quality achievable on shortrange links is better than that on long-range links [50].
This implies that the active time for the device in data
transmission and reception can be severely reduced, with
a consequent energy consumption reduction, highly
valuable to typical IoT things.
The idea of adopting short-range links for energy
consumption reduction is not novel per-se, as several
contributions in the literature investigate on this aspect. A
very recent survey of cooperative content delivery
techniques based on multiple wireless interfaces available
on mobile devices has been presented in [51]. In
particular, wireless cooperative networking, guaranteeing
performance enhancements to handheld devices, is a well
investigated research field. More specifically, cooperative
content sharing have been in focus thanks to its easy
implementation by modern multi-interface mobile devices
and the many applications that can derive from it.
According to this paradigm, users share portions of data
of common interest downloaded over costly long-range
cellular links while exchanging the downloaded portions
over short-range radio links. Significant research activity
has been conducted to design strategies that
simultaneously exploit the multiple radio interfaces of
modern wireless devices and maximize the gains. As an
Figure 3. Application scenarios for D2D-enhanced
IoT environments.
By means of D2D discovery and communication
functions, for instance, a user can find other near users to
share data (multimedia content, environmental sensing,
traffic condition, etc.), play interactive games, and so on.
In applications for public safety support and emergency
handling, devices can provide at least local connectivity in
case of damage to the network infrastructure. Similarly,
D2D communications may contribute to solve problems
in emerging wireless communication scenarios, such as
vehicle-to-vehicle (V2V) communication in Intelligent
Traffic Systems (ITS) for traffic control/safety
applications, or indirect indoor localization.
High data rate/Low delay
Short-range communications are typically characterized
by higher throughput, lower delay and energy
consumption
when
compared
to
long-range
communications (clearly, this also depends on the D2D
7
L. Militano, G. Araniti, M. Condoluci, I. Farris and A. Iera
example, the beneficial effects of integrating cellular and
Wi-Fi networks are shown in [52] and [53]. The rewards
of cooperation in terms of energy consumption and
transfer delay are demonstrated also for cellular-Bluetooth
scenarios [54]. Several other contributions investigated on
the energy savings introduced by the synergistic use of
multiple wireless network interfaces either located within
the same device, or associated to several devices. At the
same time, the short-range communication capability of
modern wireless devices over unlicensed frequencies
fostered the proliferation of a significant number of
decentralized, spontaneous, and ubiquitous user
interactions for content exchange.
When specifically considering the IoT scenarios,
further constraints influencing the energy efficiency
requirements shall be considered because exchanged data
may vary greatly in size down to very small amounts in
several scenarios. However, experiences made over the
past years may be used to exploit at the best the assessed
energy savings potentialities in the field of D2D
communications.
(a)
Aggregation
In IoT environments, most of the interactions are expected
to take place locally, i.e., between physically co-located
devices [5]. Where needed, end-to-end interactions can be
addressed by smart ways of aggregation, where small
data from several objects (close to each other, either with
similar traffic patterns or belonging to the same IoT
application) are collected by a terminal, namely the
aggregator, which then forwards the aggregated data to
the final destination. In these cases, the D2D paradigm is
natively appropriated to support aggregation of data from
neighboring nodes. An example of D2D aggregation in
5G environment is depicted in Figure 4, which shows the
differences and the introduced benefits compared to
legacy uplink data transmission.
Aggregation of industrial IoT (M2M) traffic is
considered in [55], where D2D links are exploited to
mitigate the capacity limitations of traditional large-scale
transmissions (i.e., limited radio resources shared among
a large set of users). Aiming at properly managing the
D2D transmissions by a potentially large group of
devices, the work defines a D2D-based access procedure:
devices contend for access through an access reservation
mechanism that allocates the slots of time for data
transmission toward the aggregator. Following the packet
aggregation over D2D links, the aggregator adds its own
data and performs a transmission to the BS by adapting to
the channel conditions the power, the transmission rate,
and the actual amount of data to send.
(b)
Figure 4. Differences between: (a) legacy uplink,
and (b) D2D-enhanced aggregation transmissions.
Moreover, novel solutions are needed to enable the
efficient use of radio resources to convey small data
packets in cellular environment (i.e., LTE/LTE-A), which
are designed for supporting high data rates and big data
sizes. As shown in [56], it is possible to improve the
communication and the energy efficiency for small data
transmission by using more robust Modulation and
Coding Schemes (MCS) in the uplink, thus reducing data
rate and lowering the transmission power. This simple
approach guarantees better energy efficiency w.r.t. classic
cellular-mode uplink transmissions. Building on this
concept and on the possibility to aggregate data, D2D
communication techniques may introduce further power
savings. As proposed in [57], by smartly adapting the
MCS of the aggregator node, radio resource utilization
could be maximized depending on the total amount of
data to send upon aggregation. If properly designed, this
approach will allow low power transmissions both in
intra-cluster communications over IoT D2D links, and in
the uplink transmissions from the aggregator; thus
reducing the overall energy consumption of the IoT
devices.
The benefits introduced by D2D-based aggregation
solutions motivate further work on this field. Possible
trends are the definition of multi-criteria algorithms
tailored to properly select the most suitable IoT device to
act as aggregator. Further benefits are also expected by
the design of enhanced D2D procedures aiming at
boosting the performance (e.g., reducing the latency and
the energy consumption) during the phase of data
collection.
8
Device-to-Device Communications for 5G Internet of Things
Coverage extension
The possibility to exploit local D2D communications
among devices supports coverage extension that may
allow to reach nodes otherwise out of coverage of a
cellular communication [58]. The idea of enabling D2D
communications as a means for performing relaying in
cellular networks was already addressed in ad hoc
networks, e.g. in [59]. Nevertheless, the concept of
allowing local D2D communications to (re)use cellular
spectrum resources simultaneously with ongoing cellular
traffic is relatively new [60] and coverage extension may
be enhanced by relay-assisted multi-hop communications
[61] [62]. In particular, network assisted two-hop D2D
communications enhances the coverage and the energy
efficiency of cellular networks and can be useful in
providing national security and public safety services [63]
[64] [65]. In a recent paper also multi-cell cellular
systems have been modeled where UEs assist cell-edge
users for relaying, and different approaches (amplify-andforward and decode and-forward with either digital or
analogue network coding) are compared to optimize the
system performance [66]. Although the focus so far has
been mainly on downlink services; uplink direction
scenarios are of undoubted interest as witnessed by recent
publications, such as [67], where relaying by smartphones
is proposed to send out emergency messages from
disconnected areas.
The mentioned researches are an undoubted good
starting point to conceive and design mechanisms able to
meet the scalability and resiliency requirements typical of
IoT in future 5G scenarios. The simultaneous presence of
highly mobile and stationary devices in the IoT may be
particularly challenging. Mobile devices may get
disconnected from the network as they move, which may
lead to intermittent connectivity, thus causing
unpredictable network topology changes that may benefit
from D2D assistance from devices, as proposed for
instance in [68].
additional challenges due to limited capabilities of the
UE. This issue is partially alleviated in cellular
environments, where the UEs are assisted by the BS.
Solutions
for
network-assisted
multicast
D2D
communications have been proposed in research papers
like [70] [71] and patenting activities [72]. These have
paved the way to the future required activities specifically
targeted to design similar methods performing well in IoT
environments.
D2D for Multi-RAT Heterogeneous Networks
Future IoT environments will foresee the presence of
wireless networked devices employing multiple radio
access technologies (RAT) to perform device-to
infrastructure and device-to-device communications; this
will lead to heterogeneous multi-radio architectures. In
this regard, a key aspect to investigate is how to deliver
uniform connectivity and service experience in future 5G
technologies. As an example, [73] investigate on the way
a distributed unlicensed-band network (e.g., WiFi) takes
advantage of the centralized control function residing in
the cellular network (e.g., 3GPP LTE). In such an
heterogeneous scenario, D2D communications may
contribute to the proper management of devices. For
instance, in [74] D2D communications allow to improve
the performance of a converged network. In particular, a
resource allocation scheme is proposed to perform mode
selection and allocate resources in the involved networks,
i.e., LTE-A cellular network and IEEE 802.11n WLANs.
A further example, more closely related to the IoT
environment, is presented in [75], where the authors
explore the opportunity of supporting low-rate low-power
IoT traffic through D2D links with human-related devices
(i.e., smartphones). In the proposed scheme, a multiple
access channel for IoT devices is created by relying on
underlying D2D transmissions from IoT terminals to a
smartphone, which acts as a gateway for the IoT nodes.
The key observation is that the low rate and the low
power of the IoT traffic may allow the gateway to
successfully decode the downlink transmissions from the
BS to other devices, cancel them, and then attempt to
decode the signal sent by IoT terminals via D2D links. In
this scenario, heterogeneity is granted by the BS, which
being aware about the presence of D2D links, can
therefore adjust the power/rate of its transmissions to
improve the IoT traffic reliability and to guarantee the
simultaneous transmission of heterogeneous IoT/non-IoT
traffic.
The IoT ecosystem can benefit from the use of D2D
also in scenarios with multi-interface devices. In this case,
the availability of different access technologies introduces
the opportunity of properly selecting the best connection
link. An example in [57] considers the pros and cons of
D2D via LTE-Direct and WiFi-Direct by assuming
different application requirements and network load
conditions. This study outlines that LTE-Direct D2D
technology is able to provide the most energy-efficient
communication scheme when the number of user is
relatively high (i.e., better scalability). However, WiFi-
Multicast/Group communication
Researchers are currently active in the definition of
multicast communications over D2D links in a similar
way as it is known for classic cellular downlink
transmissions.
In
particular,
for
D2D-based
communications, direct multicast transmissions where the
same packets from a UE are sent to multiple receivers are
important in scenarios such as Local file transfer/video
streaming (e.g., advertising messages), Device discovery,
Cluster head selection/coordination (e.g., reaching out of
coverage devices), Group/broadcast communications
(e.g., for safety networks) [69]. Multicast transmission
will support the deployment of IoT ecosystems and help
in overcoming issues of scalability, energy efficiency, and
efficient support of IoT group communications.
To efficiently support user diversity and serving more
(or all) receivers in each multicast cluster, either
retransmissions are required or more robust modulation
and coding schemes should be used. Moreover, having a
UE instead of the BS performing multicast, introduces
9
L. Militano, G. Araniti, M. Condoluci, I. Farris and A. Iera
Direct outperforms LTE-Direct in terms of energy
efficiency in case of small amount of data. The results
shown in [57] motivate the definition of algorithms that,
according to IoT traffic patterns (e.g., packet size),
network conditions (e.g., device load) and device
capabilities (e.g., level of residual battery charge),
properly select the most suitable D2D technology to
guarantee traffic/network optimization in heterogeneous
IoT scenarios.
Higher cellular system capacity
The use of D2D communications has an overall positive
impact also on the system capacity in cellular
environments. The motivations behind this are mainly
related to two factors: data offloading and reuse gain.
Several studies in the literature investigated the positive
impact of mobile data offloading [76] [77], that reduces
the amount of data being carried over the cellular bands
and, consequently, frees bandwidth for other users.
However, the possibility to adopt underlay frequencies
allows for data offloading solutions also on cellular radio
resources [78] [60]. As for the reuse gain, the capacity of
cellular networks is known to be strongly limited by
interference at the receiver from communications ongoing
on the same frequencies. Advanced methods for
management of the interference between local D2D
communications and with the BS (e.g., [58]), resource
allocation in the cell (e.g., [79]) and mode selection
techniques (e.g., [50]), have fostered frequency reuse
techniques that tremendously increase the spectral
efficiency and consequently the network capacity.
Considering the future IoT applications, higher capacity
systems will play in favor of scalable environments able
to support also high capacity demanding multimedia
services in densely deployed IoT scenarios.
Figure 5. Target benefits for 5G IoT vs. D2Denhanced 5G IoT.
5. Rethinking D2D for IoT in 5G Systems:
Towards a Device-oriented Anything-asa-Service Ecosystem
In the last years, the capabilities of mobile devices are
constantly improving in terms of computation, storage,
and networking capabilities. This has recently pushed
research towards innovative networking paradigms that
exploit the potentialities of the single devices. Among
these, local edge-clouds [80] are proposed as a means to
cooperatively share computing, storage and network
capabilities among devices in close proximity. In this
view, D2D communications may play a fundamental role
to enable efficient exchange of data and services among
mobile devices without necessarily relying on a cellular
network. Besides, smart devices can provide virtualization
of IoT objects. This would allow to include also resource
constrained wearable sensors and their relevant
functionalities in the local mobile clouds [81].
Nonetheless, infrastructure-less mobile cloud computing
solutions present various obstacles towards an effective
deployment, such as complex distributed management,
weak authentication, and others. The network provider is
expected to still play a key role by offering appropriate
orchestration functionalities in a new networking
landscape where the border between infrastructure and
devices becomes even more blurred. Related to this, the
METIS project has proposed a new concept of radio
access network, the so-called RAN 2.0, where end-user
devices can be in charge of network infrastructure nodes
to provide seamless connectivity [82]. However,
supporting ubiquitous networking only represents the first
step towards a complete integration of the IoT into nextgeneration cellular systems.
By leveraging on virtualization, telco providers can
indeed integrate heterogeneous systems into a unified
service environment, which facilitates the development
and execution of highly integrated and distributed IoT
Concluding remarks
To summarize the analysis reported in this Section, in
Figure 5 the mapping between IoT requirements and
features of D2D communication for 5G is reported. A
visual idea is reported on the contribution that D2D
communications can give to meet the expected
requirements of IoT in 5G systems.
10
Device-to-Device Communications for 5G Internet of Things
applications. According to this vision, 5G should not be
considered as a straightforward evolution of the current
4G network, but as a novel framework to enable the socalled “Anything-as-a-Service” paradigm [83], where
also end-user devices can be directly exploited to provide
“any type of service”. This solution allows to go beyond
the concepts of Cloudlet and Fog architectures. Telco
providers (i), on the one hand, are relieved of the financial
costs related to the deployment of a large number of
micro data centers (e.g., femtoclouds [84]) located very
close to the customers, and, (ii) on the other hand, are
evolving into ubiquitous service providers, by maintaining
control, authentication and coordination functions,
whereas delegating task execution to end-user devices.
According to their capabilities, IoT objects will offer
manifold services, ranging from computation to storage,
from sensing and actuation to networking where D2D
communication will be the core technology to provide the
requested flexible interactions among end-user devices.
To enable the envisaged framework, great efforts are
required in the next future to enhance the current networkoriented 3GPP ProSe by integrating functionalities for
application service delivery. In this direction,
softwarization and virtualization may come in handy to
realize the view where devices, by acting as small-cells,
become “active” units of 3GPP networks. In particular,
ProSe discovery could be enriched to provide registration
of both services offered by devices, and application
requests of end-users, which will operate as prosumers of
data and services. Furthermore, this novel paradigm opens
up several research areas which will be detailed below
and that should be in focus for future activities.
When accounting for their sensing, actuation, and data
processing capabilities, appropriate abstraction layers
shall be implemented to provide common understanding
between interacting devices. The establishment of D2D
communications should also guarantee the most suitable
matching between user requests and available device
capabilities, while considering wireless channel
conditions and network load. Besides, to improve
resource reusability in IoT scenarios [88], traffic routes
could be properly selected for sharing common service
links among multi-hop D2D paths. Another approach to
enhance the IoT navigability is proposed in [89] where,
based on social networking concepts integrated into the
IoT, links are selected to exploit overall network
navigability.
Incentives for user participation
Classic cellular-based transmissions require a user
subscription to the wireless network provider, whereas
D2D communications are typically based on spontaneous
cooperation between end-users where either reciprocal
benefits are obtained or support is offered as a form of
altruism. In this latter case, when users are actually
rational in the sense that they pursue their own payoff,
novel incentive mechanisms are a basic requirement for
realistic implementations of any D2D-based solution. As
an example, rational users may be willing to provide their
personal device resources only if sufficiently rewarded for
the additional power consumption this may require. These
incentives may come in different forms according to the
considered scenarios and the devices/users being
involved. For instance, besides the intrinsic networking
benefits introduced by D2D (e.g., energy savings, lower
delay, higher capacity), also economic incentives and
social-based incentives may be considered [90]. In the
futuristic vision where users’ devices expose further
capabilities, such as computation, storage, and sensing, in
the Anything-as-a-Service paradigm, the above discussed
challenge becomes even more arduous and critical for a
successful implementation. Thus, network and service
providers, as well as application developers, should
design well-defined incentive schemes to stimulate user
cooperation [91].
Joint service-network optimization
Also for delay-constrained IoT applications (e.g.,
industry-chain management), one of the key challenges is
to guarantee the desired Quality of Service (QoS).
Dynamic resource allocation schemes shall be designed to
jointly consider service deployment and network status to
the purpose of achieving adequate levels of user
experience. Emerging paradigms, such as SDN (Software
Defined Networking) and NFV (Network Function
Virtualization), are considered as key enablers of 5G
system to introduce flexibility in network and service
functionalities. These support D2D communications as
recently shown in [85], [86], [87]. However, the process
of integration is still in its infancy. The evolution of D2D
communications in 5G systems moves away from the
current view of providing just bit pipes. In the
forthcoming 5G systems, ProSe are expected to offer ondemand advanced services, such as protocol conversion,
in-network processing, semantic data transformation, thus
guaranteeing high degree of network and application
interoperability.
Service provisioning with multiple operators and
networks
The support of D2D and proximity services may require
new complex modalities of interaction between different
network and service providers. Users with subscriptions
to different cellular operators should be allowed to
reciprocally authenticate and cooperate. Furthermore, in
absence of services provided by other intra-operator
subscribers, a user can receive the requested services from
subscribers of different operators, similarly to the case of
roaming for network connectivity. Further challenges are
linked to the extremely heterogeneous IoT ecosystem,
composed of a large set of different scenarios, such as
those where D2D and non-D2D devices coexist in the
same coverage area. A further issue, pushed by the
Efficient IoT service proximity discovery
Efficient procedures to minimize the cost of peer
discovery in terms of energy and traffic exchange are
highly recommended for battery-enabled IoT devices.
11
L. Militano, G. Araniti, M. Condoluci, I. Farris and A. Iera
heterogeneity in the requirements of IoT over 5G systems,
is the dependence on cellular networks. This introduces
additional challenges to integrate devices such as RFID
tags and sensors that are part of the IoT. This is of high
relevance in the IoT vision, where devices differ as a
result of their diverse functionality and offered service
and have the ability to interconnect and communicate
anytime in a collaborative manner with any other device.
Moreover, D2D communications within the IoT
ecosystem involves devices that belong to network
domains with different characteristics.
To give an answer to the mentioned challenges, some
architectural solutions that can be envisaged are: (i)
relying on a centralized third-entity node, e.g., a broker,
which mediates cooperation among multiple operators
and networks; (ii) promoting direct interactions between
the interested operators in a distributed way; and (iii)
defining inter-operator control information exchange via
the device, which shall be temporary registered to the
foreign operator as long as the user needs the service.
applications, e.g., in scenarios where wearable devices
interact with external entities to transfer personal health
information. Similarly, in industrial automation systems,
which rely on remote actuation control to trigger real-time
operations, this is of high interest. As also discussed in
[93], multi-hop D2D communication introduce potential
security risks when not trusted relays are used to
forward/aggregate data from multiple devices. Thus,
novel reputation-based mechanism shall be included to
identify and avoid malicious users. A viable solution may
be to exploit social network relationships among users
and device themselves [94] to provide a trustworthy D2D
system [95].
6. Conclusions
In this paper, the potentialities of D2D communications
for the Internet of Things are investigated. A broad
overview of ongoing research and standardization
activities for D2D communications technology in future
generation systems is given. Particular attention has been
devoted to possible use cases and benefits this technology
may introduce to meet the manifold key requirements and
open issues in the IoT. Finally, a look into the novel and
futuristic visions of the IoT is reported. This highlighted
the manifold challenges ahead of us and research
directions that need further investigation to realize the full
convergence of IoT in next-to-come 5G systems, where a
device-oriented Anything-as-a-Service ecosystem is
expected to be the reality.
Mobility support
D2D-based interaction is unpredictable by nature because
the chances that the users meet each other and, as a
consequence, establish a D2D connection are strongly
influenced by their mobility patterns. This results in
highly opportunistic contacts due to potential mobility of
all involved user devices. Therefore, on the way to
integrate the native support of D2D communication into
the 5G system architecture, the effects of user mobility
have to be thoroughly characterized as they may have a
profound impact on the resulting system performance.
Mobility-related parameters determine the individual D2D
link performance (length, duration, throughput, etc.) and
the overall D2D system performance. The resulting
performance depend also on other factors, including the
type of application running on top of the D2D links.
Although supporting communication in dynamic
scenarios is essential for seamless service provisioning,
still a few works in the literature address the issues of
mobile D2D communications. As an example, the impact
of mobility and network assistance (i.e. allowing the
network to relay the multicast signals) has been studied in
[69] where solutions on how to optimize multicasting by
choosing the optimal multicast rate and the optimal
number of retransmission times are proposed.
Noteworthy, in heterogeneous IoT scenarios, a service
orchestrator can more efficiently distribute tasks among
devices accounting for both the required time of task
processing and estimated contact time interval between
users, based on their mobility prediction [92] as the
effects of mobility may be very different for alternative
user movement patterns.
References
[1] L. Atzori, A. Iera and G. Morabito, “The internet of things:
[2]
[3]
[4]
[5]
[6]
Privacy and security issues
Another key issue, which could lag the large-scale
adoption of D2D communications for proximity-based
services, is the risk for privacy and security attacks. These
aspects are also of utmost importance for IoT
[7]
[8]
12
A survey,” Computer networks, pp. vol. 54, no. 15, pp.
2787–2805, 2010.
Ericsson, “More than 50 Billion Connected Devices,”
2011.
S. Andreev, O. Galinina, A. Pyattaev, M. Gerasimenko, T.
Tirronen, J. Torsner, J. Sachs, M. Dohler and Y.
Koucheryavy, “Understanding the IoT connectivity
landscape: a contemporary M2M radio technology
roadmap,” IEEE Communications Magazine, 2015.
N. Mitton, S. Papavassiliou, A. Puliafito and K. S. Trivedi,
“Combining Cloud and sensors in a smart city
environment,” EURASIP journal on Wireless
Communications and Networking, 2012.
M. Zorzi and e. al., “From today's intranet of things to a
future internet of things: a wireless-and mobility-related
view,” IEEE Wireless Communications, 2010.
“3GPP TS 22.368, "Service Requirements for MachineType Communications (MTC)," V13.1.0,,” Dec. 2014.
A. Gupta and R. K. Jha, “A Survey of 5G Network:
Architecture and Emerging Technologies,” IEEE Access,
2015.
D. Soldani and A. Manzalini, “Horizon 2020 and Beyond:
On the 5G Operating System for a True Digital Society,”
Device-to-Device Communications for 5G Internet of Things
[9]
[10]
[11]
[12]
[13]
[14]
[15]
[16]
[17]
[18]
[19]
[20]
[21]
[22]
[23]
[24]
[25]
[26]
[27]
IEEE Vehicular Technology Magazine, 2015.
F. Boccardi, R. W. Heath, A. Lozano, T. L. Marzetta and
P. Popovski, “Five disruptive technology directions for
5G,” IEEE Communication Magazine, 2014.
GSMA, “Remote SIM Provisioning for Machine to
Machine,” [Online]. Available:
http://www.gsma.com/connectedliving/embedded-sim/.
O. Bello and S. Zeadally, “Intelligent Device-to-Device
Communication in the Internet of Things,” IEEE Systems
Journal, 2014.
B. Zhou, H. Hu, S. Huang and H. Chen, "Intra-cluster
device-to-device relay algorithm with optimal resource
utilization," IEEE Transactions on Vehicular Technology,
2013.
"EU Project METIS," [Online]. Available:
https://www.metis2020.com.
"The 5G Infrastructure Public Private Partnership
(5GPPP)," [Online]. Available: https://5g-ppp.eu.
"NetWorld2020," [Online]. Available:
http://networld2020.eu..
M. Tehrani, M. Uysal and H. Yanikomeroglu, "Device-todevice communication in 5G cellular networks: challenges,
solutions, and future directions," IEEE Communications
Magazine, 2014.
A. Asadi, W. Qing and V. Mancuso, "A Survey on Deviceto-Device Communication in Cellular Networks," IEEE
Communications Surveys & Tutorials, 2014.
C.-H. Yu, K. Doppler, C. Ribeiro and O. Tirkkonen,
"Performance impact of fading interference to device-todevice communication underlaying cellular networks," in
IEEE PIMRC, 2009.
A. Asadi and V. Mancuso, “Energy efficient opportunistic
uplink packet forwarding in hybrid wireless networks,” in
Proceedings of the fourth international conference on
Future energy systems, 2013.
A. Asadi and V. Mancuso, “On the compound impact of
opportunistic scheduling and D2D communications in
cellular networks,” in Proceedings of the 16th ACM
international conference on Modeling, analysis &
simulation of wireless and mobile systems (MSWiM ’13),
2013.
WiFi-Alliance, "P2P Technical Group, Wi-Fi Peer-to-Peer
(P2P) Technical Specification v1.0," 2009.
D. Camps-Mur, A. Garcia-Saavedra and P. Serrano,
"Device-to-device communications with Wi-Fi Direct:
overview and experimentation," IEEE Wireless
Communications, 2013.
T. Adame, A. Bel, B. Bellalta, J. Barcelo and M. Oliver,
"IEEE 802.11 ah: the WiFi approach for M2M
communications," IEEE Wireless Communications, 2014.
J. Nieminen and e. al., "Networking solutions for
connecting bluetooth low energy enabled machines to the
internet of things," IEEE Networks, 2014.
V. Coskun, B. Ozdenizci and K. Ok, "A survey on near
field communication (NFC) technology," Wireless
personal communications, 2013.
S. Roy and e. al., “RFID: From Supply Chains to Sensor
Nets,” Proceedings of the IEEE, 2010.
D. Astely, E. Dahlman, G. Fodor, S. Parkvall and J. Sachs,
"LTE release 12 and beyond," IEEE Communications
[28]
[29]
[30]
[31]
[32]
[33]
[34]
[35]
[36]
[37]
[38]
[39]
[40]
[41]
[42]
[43]
13
Magazine, 2013.
Ericsson, "Whitepaper: LTE Release 13," [Online].
Available:
http://www.ericsson.com/res/docs/whitepapers/150417wp-lte-release-13.pdf.
"3GPP TR 36.843, ”Study on LTE device to device
proximity services; Radio aspects”, v12.0.1," March 2014.
"3GPP TR 22.803, ”Feasibility study for Proximity
Services (ProSe)”, v12.2.0," June 2013.
X. Wu and e. al., "FlashLinQ: A Synchronous Distributed
Scheduler for Peer-to-Peer Ad Hoc Networks," IEEE/ACM
Transactions on Networking, 2013.
A. Pyattaev, J. Hosek, K. Johnsson, R. Krkos, M.
Gerasimenko, P. Masek and Y. ... Koucheryavy, "3GPP
LTE-Assisted Wi-Fi-Direct: Trial Implementation of Live
D2D Technology," ETRI Journal, 2015.
H. Shariatmadari and e. al., “Machine-type
communications: current status and future perspectives
toward 5G systems,” IEEE Communications Magazine,
2015.
I. Khan, F. Belqasmi, R. Glitho, N. Crespi, M. Morrow and
P. Polakos, “Wireless Sensor Network Virtualization: A
Survey,” IEEE Communications Surveys & Tutorials,
2015.
A. Rahman and E. Dijk, “RFC 7390, Group
Communication for the Constrained Application Protocol,”
2014.
I. Ishaq, J. Hoebeke, F. Van den Abeele, J. Rossey, I.
Moerman and P. Demeester, “Flexible Unicast-Based
Group Communication for CoAP-Enabled Devices,”
Sensors, 2014.
M. Satyanarayanan, P. Bahl, R. Caceres and N. Davies,
“The case for VM-based cloudlets in mobile computing,”
IEEE Pervasive Computing, 2009.
F. Bonomi, R. Milito, J. Zhu and S. Addepalli, "Fog
computing and its role in the internet of things," in
Proceedings of the first edition of the MCC workshop on
Mobile cloud computing. ACM, 2012.
S. A. Alvi, B. Afzal, G. A. Shah, L. Atzori and W.
Mahmood, "Internet of multimedia things: Vision and
challenges," Ad Hoc Networks, 2015.
N. Golrezaei, P. Mansourifard, A. F. Molisch and A. G.
Dimakis, "Base-station assisted device-to-device
communications for high-throughput wireless video
networks," IEEE Transactions on Wireless
Communications, 2014.
A. Zhang, J. Chen, L. Zhou and S. Yu, "Graph Theory
based QoE-Driven Cooperation Stimulation for Content
Dissemination in Device-to-Device Communication,"
IEEE Transactions on Emerging Topics in Computing,
2015.
L. Militano, M. Condoluci, G. Araniti, A. Molinaro, A.
Iera and G. M. Muntean, “Single frequency-based deviceto-device enhanced video delivery for evolved multimedia
broadcast and multicast services,” IEEE Transactions on
Broadcasting, 2015.
Y. Cao, T. Jiang, X. Chen and J. Zhang, “Social-aware
video multicast based on device-to-device
communications,” IEEE Transactions on Mobile
Computing, 2015.
L. Militano, G. Araniti, M. Condoluci, I. Farris and A. Iera
[44] S. Mumtaz, S. Huq, K. Mohammed and J. Rodriguez,
[45]
[46]
[47]
[48]
[49]
[50]
[51]
[52]
[53]
[54]
[55]
[56]
[57]
[58]
[59]
[60]
[61] B. Zafar, S. Gherekhloo and M. Haardt, "Zafar, B.,
“Direct mobile-to-mobile communication: Paradigm for
5G,” IEEE Wireless Communications, 2014.
J.-B. Seo, T. Kwon and V. Leung, “Social groupcasting
algorithm for wireless cellular multicast services,” IEEE
Communications Letters, 2013.
A. Antonopoulos, E. Kartsakli and C. Verikoukis, “Game
theoretic D2D content dissemination in 4G cellular
networks,” IEEE Communications Magazine, 2014.
A. Pyattaev, O. Galinina, S. Andreev, M. Katz and Y.
Koucheryavy, “Understanding practical limitations of
network coding for assisted proximate communication,”
IEEE Journal on Selected Areas in Communications, 2015.
L. Lei, Z. Zhong, C. Lin and X. Shen, "Operator controlled
device-to-device communications in LTE-advanced
networks," IEEE Wireless Communications, 2012.
A. Laya, L. Alonso, J. Alonso-Zarate and M. Dohler,
"Green MTC, M2M, Internet of Things," Green
Communications: Principles, Concepts and Practice, 2015.
N. Reider and G. Fodor, "A distributed power control and
mode selection algorithm for D2D communications,"
EURASIP Journal on Wireless Communications and
Networking, 2012.
Z. Khan, A. V. Vasilakos, B. Barua, S. Shahabuddin and
H. Ahmadi, "Cooperative content delivery exploiting
multiple wireless interfaces: methods, new technological
developments, open research issues and a case study,"
Wireless Networks, 2015.
R. Bhatia, L. E. Li, H. Luo and R. Ramjee, "ICAM:
Integrated Cellular and Ad Hoc Multicast," IEEE
Transactions on Mobile Computing, 2006.
D. Cavalcanti, D. Agrawal, C. Cordeiro, B. Xie and A.
Kumar, “Issues in Integrating Cellular Networks, WLANs,
and MANETs: A Futuristic Heterogeneous Wireless
Network,” IEEE Wireless Communications Magazine,
2005.
A. Iera, L. Militano, L. P. Romeo and F. Scarcello, "Fair
cost allocation in cellular-bluetooth cooperation scenarios,"
IEEE Transactions on Wireless Communications, 2011.
G. Rigazzi, N. Pratas, P. Popovski and R. Fantacci,
"Aggregation and trunking of M2M traffic via D2D
connections," in IEEE ICC, 2015.
K. Wang, J. Alonso-Zarate and M. Dohler, “Energyefficiency of LTE for small data machine-to-machine
communications,” in IEEE ICC, 2013.
M. Condoluci, L. Militano, A. Orsino, J. Alonso-Zarate
and G. Araniti, “LTE-Direct vs. WiFi-Direct for MachineType Communications over LTE-A Systems,” in
Workshop on M2M Communications: Challenges,
Solutions and Applications, IEEE PIMRC, 2015.
G. Fodor, E. Dahlman, G. Mildh, S. Parkvall, N. Reider, G.
Mikl s and Z. Tur nyi, "Design aspects of network
assisted device-to-device communications," IEEE
Communications Magazine, 2012.
H. Wu, C. Qiao, S. De and O. Tonguz, "Integrated cellular
and ad hoc relaying systems: iCAR," IEEE Journal on
Selected Areas in Communications, 2001.
K. Doppler, M. Rinne, C. Wijting, C. B. Riberio and K.
Hugl, "D2D communications underlaying an LTE cellular
network," IEEE Communications Magazine, 2009.
[62]
[63]
[64]
[65]
[66]
[67]
[68]
[69]
[70]
[71]
[72]
[73]
[74]
[75]
[76]
14
Gherekhloo, S., & Haardt, M. (2012). Analysis of multihop
relaying networks: Communication between range-limited
and cooperative nodes," IEEE Vehicular Technology
Magazine, 2012.
L. Chen and e. al., "Mobile relay in LTE-advanced
systems," IEEE Communications Magazine, 2013.
J. M. B. da Silva, G. Fodor and T. F. Maciel, "Performance
analysis of network-assisted two-hop D2D
communications," in IEEE Globecom Workshops, 2014.
G. Fodor, S. Parkvall, S. Sorrentino, P. Wallentin, Q. Lu
and N. Brahmi, “Device-to-Device Communications for
National Security and Public Safety,” IEEE Access, 2014.
K. Vanganuru, S. Ferrante and G. Sternberg, "System
capacity and coverage of a cellular network with D2D
mobile relays," in IEEE MILCOM, 2012.
A. Abrardo, G. Fodor and B. Tola, “Network Coding
Schemes for Device-to-Device Communications Based
Relaying for Cellular Coverage Extension,” Transactions
on Emerging Telecommunications Technlogies, 2015.
H. Nishiyama, M. Ito and N. Kato, “Relay-by-smartphone:
realizing multihop device-to-device communications,”
IEEE Communications Magazine, 2014.
A. Orsino, M. Gapeyenko, L. Militano, D. Moltchanov, S.
Andreev, Y. Koucheryavy and G. Araniti, “Assisted
Handover Based on Device-to-Device Communications in
3GPP LTE Systems,” in IEEE Globecom Workshop on
Emerging Technologies for 5G Wireless Cellular
Networks, 2015.
L. Xingqin and e. al., “Modeling, Analysis, and
Optimization of Multicast Device-to-Device
Transmissions,” IEEE Transactions on Wireless
Communications, 2014.
W. Gong and X. Wang, "Particle Swarm Optimization
Based Power Allocation Schemes of Device-to-Device
Multicast Communication," Wireless Personal
Communications, 2015.
Y. Chen, X. Xu and Q. Lei, “Joint subcarriers and power
allocation with imperfect spectrum sensing for cognitive
D2D wireless multicast,” KSII Transactions on Internet
and Information Systems (TIIS), 2013.
K. Etemad, "User equipment, evolved node b, and method
for multicast device-to-device communications". Patent
U.S. Patent Application 13/716,919, 2012.
S. Andreev, M. Gerasimenko, O. Galinina, Y.
Koucheryavy, N. Himayat, S. P. Yeh and S. Talwar,
“Intelligent access network selection in converged multiradio heterogeneous networks,” IEEE Wireless
Communications, 2014.
A. T. Gamage, H. Liang, R. Zhang and X. Shen, “Deviceto-device communication underlaying converged
heterogeneous networks,” IEEE Wireless Communications,
2014.
N. K. Pratas and P. Popovski, "Underlay of low-rate
machine-type D2D links on downlink cellular links," in
IEEE ICC Workshops, 2014.
S. Andreev, O. Galinina, A. Pyattaev, K. Johnsson and Y.
Koucheryavy, “Analyzing assisted offloading of cellular
user sessions onto D2D links in unlicensed bands,” IEEE
Journal on Selected Areas in Communications, 2015.
Device-to-Device Communications for 5G Internet of Things
[77] S. Andreev, A. Pyattaev, K. Johnsson, O. Galinina and Y.
[78]
[79]
[80]
[81]
[82]
[83]
[84]
[85]
[86]
[87]
[88]
[89]
[90]
[91]
[92]
[93] J. Liu, N. Kato, J. Ma and N. Kadowaki, "Device-to-
Koucheryavy, "Cellular traffic offloading onto networkassisted device-to-device connections," IEEE
Communications Magazine, 2014.
F. Rebecchi, M. Dias de Amorim, V. Conan, A. Passarella,
R. Bruno and M. Conti, "Data Offloading Techniques in
Cellular Networks: A Survey," IEEE Communication
Survey & Tutorials, 2015.
P. Phunchongharn, E. Hossain and D. Kim, “Resource
allocation for device-to-device communications
underlaying LTE-advanced networks,” IEEE Wireless
Communications, 2013.
U. Drolia and e. al, "The Case for Mobile Edge-Clouds," in
IEEE Ubiquitous Intelligence and Computing, 2013 IEEE
10th International Conference on and 10th International
Conference on Autonomic and Trusted Computing
(UIC/ATC), 2013.
J. Ko, B. B. Lee, K. Lee, S. G. Hong, N. Kim and J. &
Paek, "Sensor Virtualization Module: Virtualizing IoT
Devices on Mobile Smartphones for Effective Sensor Data
Management," International Journal of Distributed Sensor
Networks, 2015.
J. F. Monserrat, G. Mange, V. Braun, H. Tullberg, G.
Zimmermann and Ö. Bulakci, “METIS research advances
towards the 5G mobile and wireless system definition,”
EURASIP Journal on Wireless Communications and
Networking, 2015.
D. Soldani and A. Manzalini, “A 5G Infrastructure for
Anything-as-a-Service,” Journal of Telecommunications
System & Management, 2014.
S. Barbarossa, S. Sardellitti and P. Di Lorenzo,
“Communicating While Computing: Distributed mobile
cloud computing over 5G heterogeneous networks,” IEEE
Signal Processing Magazine, 2014.
M. Jo, T. Maksymyuk, B. Strykhalyuk and C. H. Cho,
"Device-To-Device-Based Heterogeneous Radio Access
Network Architecture For Mobile Cloud Computing,"
IEEE Wireless Communications, 2015.
J. Liu, S. Zhang, N. Kato, H. Ujikawa and K. Suzuki,
"Device-to-device communications for enhancing quality
of experience in software defined multi-tier LTE-A
networks," IEEE Network, 2015.
M. Usman, A. Gebremariam, U. Raza and F. Granelli, “A
Software-Defined Device-to-Device Communication
Architecture for Public Safety Applications in 5G
Networks,” IEEE Access, 2015.
S. Oteafy and H. S. Hassanein, “Resource re-use in
wireless sensor networks: Realizing a synergetic internet of
things,” Journal of Communications, 2012.
M. Nitti, L. Atzori and I. P. Cvijikj, “Network navigability
in the social Internet of Things,” IEEE World Forum on
Internet of Things, 2014.
M. Nitti, L. Atzori and I. Cvijikj, "Friendship Selection in
the Social Internet of Things: Challenges and Possible
Strategies," IEEE Internet of Things Journal, 2015.
G. Colistra, V. Pilloni and L. Atzori, “The problem of task
allocation in the Internet of Things and the consensusbased approach,” Computer Networks, 2014.
J. Harri, F. Filali and C. Bonnet, "Mobility models for
vehicular ad hoc networks: a survey and taxonomy," IEEE
Communications Surveys & Tutorials, 2009.
Device Communication in LTE-Advanced Networks: A
Survey," IEEE Communications Surveys & Tutorials,
2014.
[94] L. Atzori, A. Iera, G. Morabito and M. Nitti, "The social
internet of things (siot)–when social networks meet the
internet of things: Concept, architecture and network
characterization," Computer Networks, 2012.
[95] Y. Li, T. Wu, P. Hui, D. Jin and S. Chen, "Social-aware
D2D communications: qualitative insights and quantitative
analysis," IEEE Communications Magazine, 2014.
15