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5G-based Smart Healthcare Network:
Architecture, Taxonomy, Challenges and
Future Research Directions.
ABDUL AHAD1, MOHAMMAD TAHIR 2,(MEMBER, IEEE), AND KOK-LIM ALVIN YAU 3,
(Senior Member, IEEE)
1,2,3Department of Computing and Information Systems, Sunway University, Selangor 47500, Malaysia
Corresponding author: Mohammad Tahir (e-mail: tahir@sunway.edu.my).
ABSTRACT Healthcare is undergoing a rapid transformation from traditional hospital and specialist
focused approach to a distributed patient-centric approach. Advances in several technologies fuel this
rapid transformation of healthcare vertical. Among various technologies, communication technologies
have enabled to deliver personalized and remote healthcare services. At present, healthcare widely uses
the existing 4G network and other communication technologies for smart healthcare applications and are
continually evolving to accommodate the needs of future intelligent healthcare applications. As the smart
healthcare market expands the number of applications connecting to the network will generate data that
will vary in size and formats. This will place complex demands on the network in terms of bandwidth,
data rate and latency, among other factors. As this smart healthcare market matures, the connectivity needs
for a large number of devices and machines with sensor-based applications in hospitals will necessitate
the need to implement Massive-Machine Type Communication. Further use cases such as remote surgeries
and Tactile Internet will spur the need for Ultra Reliability and Low Latency Communications or Critical
Machine Type Communication. Existing communication technologies are unable to fulfill the complex and
dynamic need that is put on the communication networks by the diverse smart healthcare applications.
Therefore, the emerging 5G network is expected to support smart healthcare applications, which can fulfill
most of the requirements such as ultra-low latency, high bandwidth, ultra-high reliability, high density and
high energy efficiency. The future smart healthcare networks are expected to be a combination of 5G and IoT
devices which are expected to increase cellular coverage, network performance and address security related
concerns. This paper provides a state-of-the-art review of 5G and IoT enabled smart healthcare, Taxonomy,
research trends, challenges and future research directions.
INDEX TERMS 5G, Smart Healthcare, Software-defined network, network function virtualization, Internet
of Things (IoT), Device-to-Device (D2D), Ultra Reliability and Low Latency Communications.
I. INTRODUCTION
Smart healthcare has a significant role in the economy. In
Europe, the average spending on smart healthcare is approx-
imately 10%of gross domestic product (GDP), and up to
99 billion Euros of healthcare cost can be saved through
smart healthcare by 2020. In smart healthcare, internet of
things (IoT) plays a pivotal role to improve and deploy a
diverse range of applications, including smart medication,
telemedicine, assisted the living, as well as remote and onsite
monitoring of assets in hospitals, patients behavioral change,
treatment compliance [1], [2]. According to a survey, IoT in
healthcare will be about 117 billion US Dollars market by
2020 [3]. A diverse range of smart healthcare applications
that integrate wireless mobile networks has been proposed
in the literature. In [4], smartphone using the next-generation
wireless mobile network, namely 5G and IoT based approach
has been proposed for continuous monitoring of chronic
patients. In [5], a mobile health system using 5G and IoT
has been proposed for constant assessment and monitoring
of diabetes patients. In [6], wearable devices using IoT has
been submitted to support smart healthcare applications (e.g.
remote monitoring, remote medical assistance). Wearables
devices (e.g., sensors, smart watches, smart clothes) collect
information, such as heart rate, amount of sleep, and physical
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activities for continuous health monitoring (e.g., heart rate,
blood pressure, blood sugar level). In [7], mobile gateways
using IoT has been proposed for intelligent assistance in
mobile health environment such as continuous monitoring of
chronic patients (i.e., continuous remote health monitoring in
real time). In [8], IoT is considered for medical application to
support remote monitoring of patients with chronic diseases.
In [9], wearable devices have been proposed for supporting
communication between wearables and cloud server, that is
a virtual server (rather than a physical server) operating in a
cloud computing environment and can be accessed remotely
via the internet. wearable devices collect information, such
as heart rate, amount of sleep, physical activities and send to
the cloud server through the internet. 5G and IoT have the
potential to boost the use of smart healthcare applications.
A. WHAT IS SMART HEALTHCARE?
Smart healthcare provides healthcare services through smart
gadgets (e.g., smartphones, smartwatch, wireless smart glu-
cometer, wireless blood pressure monitor) and networks
(e.g., Body area network, wireless local area network, ex-
tensive area network). The intelligent gadgets process health
information gathered from numerous sources, including sen-
sors and biomedical systems (i.e., the application having
information about medical science such as diagnosis, treat-
ment, and prevention of disease). In short, smart healthcare
allows people from different background and walks of life
(e.g., doctors, nurses, patient caretakers, family members,
and patients [10]) to access the right information and obtain
the right solutions, which are mainly to minimize medical
errors and improve efficiency, as well as to reduce cost at the
right time in the medical field.
B. WHAT IS IOT?
There are different definitions of IoT, and based on the defi-
nition from IoT European Research Cluster (IECR) project
[2], Internet of Things is dynamic network infrastructure
which has the capability of self-configuration on the bases
of interoperable and standard communication protocols. In
other words, IoT is flexible, complex and dynamic network
infrastructure that connects anyone, anything, anytime, any-
where, for any services [11]. The internet of things has
numerous applications in healthcare, from remote monitoring
to smart sensors and medical device integration. There is
now a growing trend in the synthesis of sensors and sensor-
based systems with device-to-device (D2D) communications
[12]. 5G wireless systems (5G) are on the horizon, and IoT
is taking center stage as devices are expected to form a
significant portion of this 5G network paradigm. But the
technology is still evolving. While one of the challenges of
IoT in healthcare is to manage the data from various source,
the future of IoT in healthcare application will depend on
deriving meaningful insight from gathered data. [13].
C. WHAT IS 5G?
5G is the next generation of the current 4G communication
network that can provide more features such as high speed,
capacity and scalability of the network. Standards, capabili-
ties and technologies vision for 5G are still under considera-
tion and discussion. International Telecommunication Union
(ITU) in 2015, presented their roadmap for 5G in term of
’IMT-2020’. ITU has defined a few parameters which can be
considered key capabilities for 5G technology [14].
•Requirement of low latency must be supported (1ms or
less than 1ms).
•10Gbps to 20Gbps data rate must be achieved in differ-
ent scenarios and condition.
•High dense network must be supported and enable mas-
sive machine-type communication.
•High mobility (up to 500km/h) must be achieved in
network.
5G and IoT are expected to become important drivers of next-
generation smart healthcare. Some of the key technologies in
5G are device-to-device (D2D) communication mmWaves,
the macro cell and small cells (e.g., femto, pico and micro)
[15]. These technologies address two main challenges in the
next-generation wireless mobile network scenarios. Firstly,
ultra-densification in networks as a results of a large number
of devices (or nodes henceforth) within an area (i.e., approx-
imately 106connections per km2by 2020 [16]). Secondly,
high energy consumption as a result of IoT applications that
are based on wireless sensor networks. These sensors enable
every device in the network to exchange data. These devices
require energy to perform processing, sensing, communica-
tion and monitoring tasks. However, data transmission be-
tween devices consumes more energy. Therefore, a minimum
of 10 years of battery life is required for certain applications
[17]. Various network layer solutions, including scheduling,
routing, and congestion control, along with resource op-
timization, QoS enhancement, interference mitigation, and
energy efficient mechanisms, have been proposed in 5G and
IoT to address these two main challenges to support and
deploy smart healthcare solution. The proposed solutions
have shown to increase throughput (e.g., via high data rate
and bandwidth), reliability, energy efficiency, transmission
coverage, as well as to reduce delay.
D. OUR CONTRIBUTIONS
There have been efforts for reviewing smart healthcare with
different aspects in [18]- [22]. Table 1summarizes the con-
tributions of the research works related to smart healthcare
found in the literature. To the best of our knowledge, this
paper is first of its kind to present a review on this topic.
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TABLE 1: Existing survey on smart healthcare.
References Authors contributions
Qi et al. [18] In this review, the author explored various applications of IoT in smart healthcare from different
perspectives (i.e., Blood pressure monitoring, monitoring of oxygen saturation, heartbeat monitor-
ing etc.). Secondly, the author reviewed the existing work of IoT that enable technologies for smart
healthcare applications. From various perception, such as infrastructure and current technologies
(i.e., Networking, Sensing and Data processing technologies).
Islam et al. [19] In this review, the author focused on IoT-based healthcare technologies and present architecture for
healthcare network and platforms which support access to the IoT backbone and enable medical
data reception and data transmission. Secondly, the paper delivers detailed research events and how
the IoT can address chronic disease supervision, pediatric, care of elderly and fitness management.
Baker et al. [20] In this review, the author presented a new model for future smart healthcare systems, which
can be used for both special (i.e., special condition monitoring) and general systems. Secondly,
the author presented the overview of the state-of-the-art works related to the component (i.e.,
wearables and non-intrusive sensors monitoring blood pressure, blood oxygen level and vital signs)
of the presented model. Secondly, the author review on short-range and long-range communication
standards for smart healthcare.
Mahmoud et al. [21] In this review, the author surveyed on Cloud of Things (CoT), platforms and how to implement
it in smart healthcare applications. Secondly, the author review in detail Cloud of Things (CoT)
issues related to energy efficiency in smart healthcare applications.
Dhanvijay et al. [22] In this review, the author focused on different IoT-based healthcare systems for Wireless Body
Area Network (WBAN) that can enable smart healthcare data reception and data transmission.
Secondly, the author presented a detailed review of resource management, power, energy, security
and privacy related to IoT-based smart healthcare.
In particular, our contribution is to deliver a review of 5G
smart healthcare with different aspects as follows:
•5G smart healthcare architecture, considering specific
key enable technologies (i.e., Small cells, D2D commu-
nication, mmWaves, Software-defined network (SDN),
Network function virtualization (NFV) for 5G smart
healthcare.
•A taxonomy for 5G smart healthcare, covering com-
munications technologies, requirements, objectives, and
performance measures, are presented.
•Review of research work at network layer, including
scheduling, routing, and congestion control, applied to
IoT based 5G smart healthcare and future research op-
portunities.
•Challenges and future research direction in 5G and IoT
based smart health care.
E. ORGANIZATION OF THIS PAPER
The paper is organized as follows. Section II presents 5G
network architecture for smart healthcare solution. Section
III presents a taxonomy of 5G smart healthcare, covering the
communication technologies, requirements, objectives, and
performance measures. Section IV presents network layer
solutions for 5G smart healthcare. Section Vpresents open
issues. Finally, Section VI presents conclusion.
II. 5G NETWORK ARCHITECTURE FOR SMART
HEALTHCARE SOLUTIONS
5G is the next-generation wireless mobile networks that su-
persede the existing 4G networks. The rest of this subsection
presents the architecture, features, and performance enhance-
ment of 5G. Figure 1illustrate the 5G smart healthcare
architecture.
1) 5G Architecture
Small cells are low-powered radio access nodes having a
range of few meters to a mile in diameter. The numerous
types of small cells can play an essential role in many
applications of 5G smart healthcare. As smart healthcare
applications demand high data rates (e.g., remote surgery
required data rate between 137 Mbps to 1.6 Gbps [23]), one
of the solution is small cells [24]. Small cells are three types
and ranging from shorter to larger they are called femto,
pico and microcells. These are considered as small cells
as compared to the macro cell, which has about 20 miles
of range. Femtocells are used to increase the coverage and
capacity within a small vicinity, such as hospital, home etc. It
is supporting up to 30 users over a range of 0.1 km. Picocells
provide more coverage and capacity, supports up to 100 users
over a scale of 1 km. Picocells are typically deployed to boost
the cellular and wireless coverage within a small vicinity.
Microcells are challenging to differentiate from picocells,
but the coverage area and support more user is the main
difference. Microcells can support up to 2000 users within 2
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FIGURE 1: 5G smart healthcare architecture.
km range. Marco cell is used in the cellular network to offer
radio coverage to a wide area of mobile network access. It
provides extensive coverage and high-efficiency output [25].
A macrocell is installed on station having high output power,
typically in a range of tens of watts. It supports more than
2000 users in the range of 30 km. By using small cells, the
network can increase area spectrum efficiency by reusing of
higher frequency. Furthermore, in small-cells control plane
and user plane works separately, connectivity and mobility
provided by control plane while data transportation provided
by user-plane [26]. So the user equipment’s (UEs) must be
connected with both macro-cell and small-cell base stations
simultaneously. Macro-cell base station uses lower frequency
bands to provide connectivity and mobility (control plane)
and a small-cells base station using a higher frequency to pro-
vide high throughput data transport [27]. A cellular network
comprising of macro, micro, pico and femto base station is
typically referred to as heterogeneous networks (HetNets).
These are used to achieve flexible coverage and spectral
efficiency. Table 2. shows the summary of small cells.
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TABLE 2: Summary of small cells.
Cell
Types
Cells
Radius (Km) Users Locations
Femto Cell 0.010 to 0.1 1 to 30 Indoor
Pico Cell 0.25 to 1.0 30 to 100 Indoor/Outdoor
Micro Cell 0.2 to 2.0 100 to 2000 Indoor/Outdoor
Macro Cell 8 to 30 More than 2000 Outdoor
2) 5G Features and Enabling Technologies
G.1 Device to Device (D2D) Communication: D2D is a
direct communication between two devices in the net-
work without involving the base station (BS) or the
core network. Highly dense network problems can be
solved through D2D communications [28]. In D2D
communication, each terminal can communicate with
each other directly to exchange information or to share
their radio access connection. Interference can reduce
by D2D communication, specifically in non-licensed
frequency bands [29]. In the 4G network, there is no
concept of D2D communication. All communications
are routed through gateway and base station. This rout-
ing is inefficient, especially when devices are near each
other. In the machine-to-machine scenario, where a high
number of devices are involved, direct communication
between these devices is more sensible. Devices may
communicate with each other in unlicensed spectrum
outside cellular network by using different technologies
such as Bluetooth or WLAN in ad-hoc mode. However,
these connections are vulnerable to interference. On the
other hand, licensed spectrum guarantees the quality of
services if the connection is managed properly. To fa-
cilitate connections, these D2D communications require
base stations to ignore intra-cell interference [30].
G.2 Millimeter Waves (mmWaves) communication: mmWave
is the band of spectrum between 20 GHz and 300 GHz.
Due to lack of spectrum below 3GHz, the 5G must
extend its frequency to the mmWaves band, mostly
between 20 GHz to 90 GHz, because there is a huge
amount of unused bandwidth. By using mmWaves with
small cells may reduce the high path loss problem [31],
and will be beneficial for several applications including
smart healthcare. mmWaves are now realistic with low
cost, and they are finding all variety of advanced uses.
Best of all, mmWave take the burden of the lower
frequencies and extend wireless communication in the
outer limitation of radio technology.
G.3 Software-defined network (SDN): SDN is architecture
which is active, manageable, flexible and cost-effective,
to deliver high bandwidth, required for several applica-
tions. SDN incorporates several types of network tech-
nologies to create the network more agile and flexible,
to maintain the modern data centre, virtualized servers
and storage infrastructure. SDN networking defines an
approach to build, designing and handling networks
by separating network control planes and forwarding
planes [32]. SDN can support various requirements of
smart healthcare in 5G. Some of the use cases handled
on cloud depending on operator policies, while immedi-
ate response that needs virtual functions are handled on
edge cloud.
G.4 Network function virtualization (NFV): NFV is a devel-
oping network approach which enables the replacement
of expensive devoted hardware devices, i.e., firewalls,
routers with software-based network tools which run as
virtual machines on standard servers. 5G must enable
d2d communication in smart healthcare, due to which a
massive amount of data is predictable to be generated.
It is not possible to send all of the generated data to the
centralized data center for processing. Therefore, some
intelligent decisions are required to manage data at edge
cloud and cloud servers. By using NFV, data can be
placed in the network based on QoS requirement. This
must ensure network scalability and flexibility.
G.5 Edge computing: Edge computing is a distributed tech-
nology design in which data is processed at the edge
of the network, close to the originating source. In fu-
ture smart healthcare, machines are expected to take
decisions and response according to the task. For such
responses and decisions, processed data is needed by
machines. In many cases, real-time processed data is
essential. Edge computing plays an important role in
such cases, where decision time is more important [33],
especially in 5G based network.
3) Performance Enhancement of 5G
Table 3shows a comparison of the characteristics and
performance enhancement of 4G and 5G [34].
•Peak data rates can reach up to 10Gbps, and 20Gbps
are expected under different conditions and scenarios.
•Ultra-low latency requirement services can be sup-
ported (1ms or less than 1ms).
•High mobility can be achieved in the network (up to
500km/h).
•Enable massive machine-type communication and
support high dense network.
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TABLE 3: Comparison between 4G and 5G.
Characteristics Performance enhancement
4G 5G
Data Rate 0.01 - 1 Gbps 0.1 - 20 Gbps
Latency (Control plane) 100 ms 50 ms
Latency (User plane) 10 ms 1 ms
Mobility Upto 350 km/h Upto 500 km/h
Spectral efficiency 1.5 4.5
Energy efficiency 0.1 mJ per 100 bits 0.1 µJ per 100 bits
Device density 100k/km21000K/km2
•Enable 3×more spectrum efficiency and 10×energy
efficiency.
III. TAXONOMY
Figure 2Illustrates the taxonomy of the 5G smart healthcare.
The presented taxonomy is based on the following parame-
ters: communication technologies, requirements, objectives,
performance measures and approaches.
A. COMMUNICATION TECHNOLOGIES FOR SMART
HEALTHCARE
Smart healthcare depends on various short range and long
range communication technologies to transport data between
devices and servers [35]. Most of the short-range wireless
technologies are Wi-Fi, Zig-Bee, Bluetooth and Wireless
Metropolitan Area Network (WiMAX) which are primarily
used for short communication in smart healthcare such as
BAN (Body Area Network). Wide range technologies like as
GPRS and Mobile Communication (GSM), LTE Advanced,
Long-term Evolution (LTE) are used to transport data from
local server to base-station in smart healthcare. Moreover,
LTE-M is proposed to accommodate the need of IoT devices
in cellular networks. In release 13, 3GPP wants addition-
ally enhance battery lifetime, coverage and device complex-
ity [36]. Other than existing protocols, LoRa partnership
institutionalizes the LoRaWAN protocol to support smart
healthcare applications to guarantee interoperability between
many operators. Besides, SIGFOX is ultra-tight band radio
technology having a full star-based framework which offers
a highly versatile worldwide network for smart healthcare ap-
plications with very low power utilization [37]. A comparison
of significant communication technologies is shown in Table
4.
B. REQUIREMENTS FOR SMART HEALTHCARE
R.1 Ultra-low latency: Ultra-low latency defines network,
which is optimized to process huge amount of data
packet with a very low tolerance for delay (latency)
[38]. Some of the smart healthcare applications required
very low latency. For example, in telesurgery, during
communication latency impact the operation of robotic
instruments. Less than 200 ms end-to-end latency is
acceptable for future telesurgery [39]. However, the
inherent latency of robotic systems is almost less than
100 ms. The 5G network can minimize latency up to 1
ms, which can lead to new telesurgery applications with
strict latency requirements. In future, modern solutions
might be possible in the healthcare environment. For
example, surgeons can perform operations with robots
virtually from anywhere in the world [40].
R.2 High bandwidth: Bandwidth is the capability of wire-
less or wired network communication link to send a
high amount of data from one point to another in a
given amount of time over a network [41]. Biomedical
sensors can send a limited amount of information due
to restricted bandwidth in current 3G and 4G network,
especially in real time monitoring applications [42]. A
key feature of the 5G network is to support higher
frequencies (including above than 10 GHz frequencies).
More spectrum is available by using these frequencies,
which leads to very high transmission rates (on the order
of Gbps). Physicians can see high-resolution pictures
remotely and deployed healthcare solution with ultra-
high-definition (UHD) content through the high-speed
5G network. Furthermore, the 5G network can allocate
bandwidth in a scalable and flexible way during com-
munication, which can enable D2D solutions in medical
field [43]. Smart gadgets can be used by these solutions,
such as wearables sensors, medical devices and medical
monitoring equipment’s.
R.3 Ultra-high reliability: Reliability is related to the capa-
bility of a network to carry out preferred operation with
very low error rates. Deployment of numerous biomed-
ical sensors with IoT capabilities generates more data
that could exceed network capacity [44]. Therefore, the
large number of connection and massive data capacity
must be supported by the new communication infras-
tructure. Signaling traffic and transmission from mas-
sive number of biomedical devices with different traffic
patterns must be handled by new network infrastructure.
Scalability is the main requirement in smart healthcare
because the network must allow to increase or decrease
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FIGURE 2: Taxonomy of 5G smart healthcare.
TABLE 4: Comparison of available wireless communication technologies for smart healthcare.
Technology Types Frequency Data
Rate Range Power
Usage
C.1 Short Range Communication
NFC PAN 13.56MHz 100-400
kbps 10cm Very
Low
Bluetooth 4 PAN 2.4GHz 1Mbps 0.1Km Low
Bluetooth 5 PAN 2.4GHz 2Mbps 0.25Km Very
Low
Z-Wave Alliance LAN 900MHz 9.6/40/100kbps 30m Very
Low
Wi-Fi LAN 2. 4GHz
and 5GHz
802.11(b)11M;
(g) 54M;
(n) 0.6,
(Gac) 1Gbps
50m Low-High
ZigBee LAN 2.4GHz 250kbps 10-100m Very
Low
WiMAX WAN 10 - 66GHz 11-100Mbs 50km High
C.2 Long Range Communication
LoRa WAN 868/915 MHz 50 kbps 25Km Low
LoRaWAN WAN Various 0.3-50 kbps
2-5 km (Urban)
15 km (Sub urban)
45 km (rural)
Low
Sigfox WAN 868/915MHz 300bps 50Km Low
4G WAN 800, 1800,
2600MHz 12Mbps 10Km High
5G WAN Lower Bands 3.6Gbps 10Km High
5G WAN Higher Bands 10Gbps <1Km High
NB-IoT (NB1) WAN 900MHz 250kbps 35km High
EC-GSM WAN 900MHz 140kbps 100Km High
LTE-M (M1) WAN 700, 1450 - 2200, 5400MHz 0.144Mbps 35km High
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of nodes without affecting network performance [45].
Therefore, massive MTCs (Machine Type Communica-
tions) with wide range coverage needs a scalable and
flexible network.
R.4 High battery life time: Battery lifetime is a measure
of nodes battery performance and longevity, Which im-
prove the network lifetime. To connect large numbers of
sensors and biomedical equipment’s, low-cost devices
with high battery life is important [46]. For continuous
remote monitoring, the aim is to connect self-sustainable
devices in the network for the full duration of medical
operation [48]. In 5G, low-power sensors are intended
to work on the same battery for 10 years [47]. Therefore
the network lifetime must be improved.
C. OBJECTIVES FOR SMART HEALTHCARE
O.1 Optimization of resources: Resource optimization tech-
niques are used to minimize energy consummation
while maximizing network lifetime [49]. Resource op-
timization techniques play an important role in 5G
based smart healthcare network. A huge number of IoT
devices enable smart healthcare, which can produce a
massive amount of data and consume more bandwidth
of the network. Improper resource optimization can lead
to several issues in network [50]. Network resource
optimization must be able to guarantee optimal usage of
system resources, to increase network efficiency without
taking extra software or hardware. Resource optimiza-
tion in one of the key objectives for smart healthcare.
O.2 Enhancing QoS: Quality of service (QoS) refers to the
ability of the network to achieve high bandwidth and
handle other network performance such as error rate,
latency and uptime. QoS also includes managing and
controlling network resources on priority basis for a
different type of data (audio, video, files) in the network.
The main goal of QoS is to provide priority to networks,
including low latency, dedicated bandwidth, controlled
jitter and enhanced loss characteristics [51]. Enhancing
QoS is one of the critical objectives of smart healthcare
because smart healthcare has different types of data with
different priorities.
O.3 Reducing interference: The concept of frequency reuse
can be used in the smart healthcare system to achieve
optimized resource utilization. Along with this, user
throughput densification and traffic capacity can be im-
proved in the network. Therefore, with the densification
and frequency reuse, there can be more enhancement
in terms of efficient load sharing between macro cells
and local access networks. These advantages come up
with different problems such as load and density of net-
work increased from co-channel interference [52]. So,
co-channel interference postures a threat, which effect
smart healthcare system. Hence efficient interference
schemes are required.
O.4 Enhancing energy efficiency: Energy efficiency has be-
come the main criterion for designing smart healthcare
network, not just due to the environmental concerns, but
also because of the nature of IoT devices participating
in the network. Due to the density of access point in
the network increases the energy consumption of the
network [53]. Therefore, to reduce operational cost, it is
important for network operators to minimize the energy
consumption of a network. However, batteries capacity
keeps increasing, which is not enough to satisfy user
expectations. Therefore, energy efficient schemes are
required to increase the lifetime of the devices deployed
in the network [54].
D. PERFORMANCE MEASURES
P.1 Data rate is the speed of transferred data form source to
destination in network.
P.2 Throughput refers to the packets transferred per unit
time. It is the performance of a task completed by the
network in a specific time.
P.3 Packet loss ratio is the average loss of packets during
transmission between source to destination in the net-
work.
P.4 End-to-End delay is the time taken for transmission of
packets across the network between source to destina-
tion.
P.5 Energy required by the source/destination to trans-
mit/receive data. Therefore, energy efficient routing pro-
tocols is required.
IV. RECENT STUDIES ON 5G AND SMART
HEALTHCARE
Many of studies explore the potential of 5G to improve smart
healthcare applications. Some of them are given below.
A. CONGESTION CONTROL SCHEMES
In [55], a priority rate-based routing protocol (PRRP) is
proposed for congestion control in low-resource bandwidth
networks such as wireless multimedia sensor networks. Due
to the high bandwidth demand for multimedia traffic, conges-
tion may easily occur in low-resource bandwidth networks.
Congestion can waste scarce resources such as the energy
of nodes, and affect application specific QoS requirements,
which can easily result in the poor visual and sound quality
of the transmitted images, audio and video for the healthcare
application (e.g., remote surgery, remote health monitoring).
The main objective of the scheme is to enhance the QoS
(O.2) by reducing congestion in the network. PRRP uses
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a hop-by-hop technique for congestion control. PRRP en-
ables congestion detection and notification, as well as rate
adaptation. PRRP uses buffer occupancy. The maximum and
minimum value of the threshold is used to calculate the
value of congestion at each node. Hence, there are three
possible cases. Firstly, the congestion value is less than the
minimum threshold, which indicates no congestion, so the
source node congestion protocol considers the energy levels
of the upstream nodes and less congested node. Therefore, it
sends traffic to them, which maintains the data rate. Secondly,
the congestion value is between the minimum and maximum
threshold, which indicates a moderate congestion level. So,
the data send to the node according to buffer size, energy
level, or to be sent to another node to avoid congestion
by adjusting the data rate. Thirdly, the congestion value is
more than the maximum threshold, which indicates a high
congestion level, and so the child node reduces the data
rate. The protocol is suitable for applications in 5G smart
healthcare. PRRP has shown to increase throughput (P.2), and
reduce packet loss rate (P.3) and end-to-end delay (P.4).
In [56], a congestion control based on reliable transmission
(CCRT) scheme for real-time streaming media services (e.g.,
remote surgery) is proposed to avoid congestion. CCRT
use priority-based congestion control mechanism for reliable
transmission for serious information. The main objective of
the scheme is to enhance QoS (O.2) by congestion detection
mechanism based on the queue variation rate and the queue
length in the network. Each packet has either low, medium
or high priority in a queue, which helps to prioritize high-
priority packets for reliable transmission of these packets.
Receiver node] detects congestion based on two criteria.
Firstly, the queue length is adjusted as High, Medium and
Low. Arrived packet is added to the tail of the queue accord-
ing to the packet type. High priority packet gets service first
make sure of emergent information’s low latency and high
reliability. Medium and low priority packets wait until the
high priority queue empty. Secondly, the queue variation rate
represents positive and negative. Positive queue variation rate
represents the larger amplification of queue length, which
indicates that congestion level increases in the next time
instant; while negative queue variation rate represents that the
queue length becomes smaller, which indicates that conges-
tion level reduces in the next time instant. In addition, a queue
variation rate of more than a maximum threshold can cause
congestion. So, the receiver node adjusts the data rate based
on the congestion detection method of queue variation rate.
CCRT has shown to increase throughput (P.2), and reduces
packet loss (P.3) and end-to-end delay (P.4).
In [57], a congestion control and energy balance based on
hierarchy (CcEbH) scheme is proposed to avoid congestion
in highly congested network with limited resources (e.g.,
continuous health monitoring). The main objective of the
scheme is to enhance the QoS (O.2) by reducing congestion
in the network. Firstly, CcEbH arrange the network into hier-
archical, from a single node in a network (e.g., a sink node in
a wireless sensor network), so each node can be an upstream
(or a node at the upper hierarchical level), a downstream
(or a node at the lower hierarchical level), and a similar
hierarchical level node. Congestion at a node can be detected
based on its queue size, whereby the incoming data rate is
greater than the outgoing data rate. The upstream nodes can
probe the buffer occupancy (or queue size) of the downstream
nodes. The upstream node can select a downstream node
with lower congestion level. So, when a downstream node
becomes congested (e.g., buffer occupancy is greater than the
queue length of the upstream node by 20%of total buffer
size) another downstream node is selected to receive data
collect and forward packets. CcEbH has shown to reduce
energy consumption (P.5).
In [58], a healthcare aware optimized congestion avoidance
(HOCA) scheme is proposed to avoid congestion for health-
care applications, such as medical emergencies or monitoring
vital signs of patients, because of the importance and critical-
ity of transmitted data. The main objective of the scheme is
to enhance energy efficiency (O.4) by reducing congestion in
a network. Each packet has either low (i.e., requires low data
rate (P.1)) or high (i.e., requires high data rate (P.1)) priority.
There are four main steps for a sink node (e.g., a medical
center) to gather data or events from nodes embedded in
patients. Firstly, the sink node broadcast a request for data
to nodes. Secondly, nodes embedded in patients reply to the
sink node with data and events by specifying the level of their
importance. Thirdly, the sink node selects final node based on
the specific level of importance and establishes multiple-path
(i.e., spreading of traffic over multiple paths from source to
destination node in the network) routes towards the selected
node to reduce congestion. Fourthly, the selected node senses
the event and generate the packet. High priority packet selects
next hop from high priority table (i.e., pre-defined) to send
data while low priority packet selects from low priority
table (i.e., pre-defined). HOCA has shown to reduce energy
consumption (P.5) to provide a longer network lifetime P.5),
and reduce end-to-end delay (P.4).
In [59], a window-based rate control algorithm (w-RCA)
is proposed to adjust a source node sending rate (i.e., the
window size of unacknowledged packets) and a destination
node’s buffer size for achieving a balanced trade-off between
peak-to-mean ratio and standard deviation in order to opti-
mize the QoS (O.2) of video transmissions for telesurgery.
Edge computing (G.5) is used as it enables cloud computing
capabilities and IT services at the edge server, in a wireless
mobile network. Machine-based algorithms (i.e., is based on
computer programs which can access data and use it learn
for themselves) [60] are used to configure buffer (i.e., tempo-
rary storage) capacity for upcoming frames. Which leads to
smooth video transmission over a single-hop from a remote
location (e.g., for anywhere in the internet coverage) in 5G
networks. The proposed scheme optimizes network parame-
ters, including the peak-to-mean ratio, the standard deviation
of delay, and jitter. Different protocols (i.e., Transmission
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control protocol, User datagram protocol, Session description
protocol etc.) are used on both client and server side in the
scheme for exchanging video frames. The proposed scheme
has shown to reduce end-to-end delay (P.4) and jitter (P.3) in
video transmission.
B. SCHEDULING SCHEMES
In [61], network slice based 5G wearable networks is pre-
sented, to improve the network resource sharing and energy-
efficient utilization. The main objective of the scheme is to
optimize the resources (O.1) and energy efficiency (O.4) of
the network by deploying Software-defined network (SDN)
(G.3) and Network function virtualization (NFV) (G.4). SDN
handle both control and data plane, to provide flexible control
of network flow consequently. NFV distributes the function
of the network into various functional areas with the help
of virtualization technology, and work over software, not
the physical hardware. Therefore, distributed coordination
of storage, communication resources and computing can be
realised through network slicing, which can provide low
latency (R.1) requirement and reduce end-to-end delay (P.4).
Further, a data-driven resource management framework is
presented, based on a cognitive service engine, the cogni-
tive resource engine, and the global cognitive engine. The
cognitive resource engine deployed at the infrastructure layer
to allocate resources through a machine learning algorithm
in an efficient way. By using a machine learning algorithm,
cognitive service engine implemented at wearable service
layer to cognition user services by acquiring service data. The
global cognitive engine is deployed to realise tight coupling
between resources and services to improve utilization QoE of
users and resources (O.1).
In [62], a network service chaining (NSC) model is pre-
sented to flexibly integrate SDN and NFV to automate virtual
network devices, rather than using manual connections, for
healthcare services in 5G. The main objective of the scheme
is to enhance QoS (O.2). The model works in collaboration
with the software-defined network (SDN) (G.3) and network
function visualization (NFV) (G.4) technologies. The model
integrates with both advanced technologies, to provide fast
services (i.e., lower delay) in 5G environment with the help
of different types of communication protocols, including
routing scheme for low-power loss-networks, Open Flow
constrained application protocol (CoAP) for messaging, and
transport layer security (TLS) server for security enhance-
ment, to enable operation of smart devices. Moreover, Wi-
Fi, cellular technologies, (See TABLE 4) small base-stations
(i.e., macro, pico, femtocells) (See TABLE 2) are used to
provide better Quality of Service (O.2) to smart devices.
Further, a secure model with Kerberos (i.e., secure commu-
nication protocol allow node to communicate another non-
secure network securely.) authentication server is presented,
to secure the cloudlet mesh from the DDoS attack. TLS
(Transport layer security) protocol is deployed into the server
to provide secure communication between communication
parties. The model has shown as increasing QoS (O.2) by
decreasing end-to-end delay (P.4) with high security.
In [63], 5G cognitive system (5G-Csys) is proposed. The
main objective of the scheme is to enhance the QoS by
achieving ultra-low latency (R.1) and ultra-high reliability
(R.3) in the heterogeneous network for cognitive applications
(i.e., Remote surgery). The system consists of a resource
cognitive and data cognitive engine. The resource cognitive
intelligence, based on the learning of network contexts (i.e.,
software-defined network) by cognise the resources in the
network, to achieve required ultra-low latency and ultra-
high reliability of the system. The data cognitive engine
leverage machine and deep learning algorithms (i.e., is based
on computer programs which can access data and use it
learn for themselves [60].) to analyse healthcare data e.g.,
speech emotion recognition. The proposed system has shown
to achieve QoS (O.2) by reducing end-to-end delay (P.4) in
the network.
In [5], 5G-based Smart Diabetes scheme is presented. The
main objective of the scheme is resource optimization (O.1)
to provide better QoS (O.2) for real-time monitoring of pa-
tients remotely. The scheme consists of three layers; Sensing
layer collects data from different resources (i.e., sensors)
in real time. The personalized diagnosis layer process the
collected data with modern machine learning algorithms (i.e.,
is based on computer programs which can access data and
use it learn for themselves [60].) to analyses the disease.
The data sharing layer shares the data on social space and
data space through the social network using the 5G network,
to the doctors and relatives of the patient. Emerging 5G
network with the smartphone and wearable medical devices
(i.e., smart clothes) are used in the scheme. The scheme is
shown to be highly accurate by reducing packet loss (P.3)
and end-to-end delay (P.4).
In [64], 5G small cells (i.e., femto, pico, microcells) network
approach is presented to enhance the QoS (O.2) by achieving
a high data rate (P.1). Transmission of the medical ultrasound
video stream from moving ambulance to hospital uplink is
considered in the case. A heterogeneous network, containing
macrocell with eNodeB coexisting with a small mobile cell
is considered. A small cell network is deployed in the ambu-
lance name as mobile small cells which allow users to move
around and connect to the operator’s network in the vicinity.
It utilities a standard radio interface technique (e.g., FDMA
(frequency division multiple access), TDMA, CDMA, and
OFDMA (orthogonal frequency division multiple access)) to
connect with the serving macrocell base station (eNodeB).
LTE-Sim, system level simulator, is used to obtain the results.
From the result it shown that the network improves the QoS
in term of throughput (P.2), Packet Loss Rate (PLR) (P.3) and
end-to-end delay(P.4).
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TABLE 5: Summary of an approaches, objectives, performance measures, requirements and communication technologies of smart healthcare schemes proposed in
literature for 5G.
Reference Year Approaches Objectives Performance Requirements Communication
measures technologies
A.1 Scheduling
A.2 Routing
A.3 Congestion control
O.1 Optimization of resources
O.2 Enhancing QoS
O.3 Reducing interference
O.4 Enhancing energy efficiency
P.1 Data rate
P.2 Throughput
P.3 Packet loss
P.4 End-to-End delay
P.5 Energy efficiency
R.1 Ultra-low latency
R.2 High bandwidth
R.3 Ultra-high reliability
R.4 High battery lifetime
C.1 Short range
C.2 Long range
Sodhro et al. [59] 2018 × × × × × ×
Hao et al. [61] 2018 × × × × × ×
Chaudary et al. [62] 2017 × × × × × × ×
Chenanan et al. [63] 2017 × × × × × × ×
Lal et al. [70] 2017 × × × × ×
Chen et al. [5] 2018 × × × × × × × × × × ×
Rehman et al. [64] 2018 × × × × × × × × ×
De et al. [69] 2015 × × × × × × × ×
Orsino et al. [65] 2015 × × × × × × × × ×
Yaun et al. [67] 2017 × × × × × × × ×
Mishra et al. [68] 2016 × × × × × ×
Xing et al. [66] 2017 × × × × × × × × ×
Lloret et al. [4] 2017 × × × × × × × × × × × ×
Tshiningayamwe et al. [55] 2016 × × × × × × × ×
Hua et al. [56] 2014 × × × × × × ×
Chen et al. [57] 2016 × × × ×
Razaee et al. [58] 2014 × × × × × × × ×
VOLUME 4, 2016 11
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C. ROUTING SCHEMES
In [65], handover scheme in 5G, to assist the cellular user
in D2D communication (G.1) at the cell edge is presented.
The main objective of the scheme is reducing interference
(O.3) by managing mobility to minimize end-to-end delay
(P.4) during motion of cells. During a handover, nodes (or
UEs) can move from one cell to another and form D2D links
with neighboring nodes to provide seamless connectivity
with better channel quality. eNB is responsible for resource
management, power control and D2D session establishment.
The eNB generates handover decision based on channel
Quality Indicator (CQI). The handover process divided into
three steps, in the preparation step, UEs send information to
its serving eNB related to channel, which chooses whether to
recruit the handover process on specific conditions or not. In
the execution step, it is decided to transfer the behavior and
information of UEs to other cell or not. In the completion
step, acknowledgment is shared between cells and status is
updated in new cells about UEs. The scheme is shown energy
efficient (P.5).
In [66], device-aware routing and scheduling algorithm
(DARA) for device-to-device communication (G.1) in multi-
hop network is presented. The main objective of the scheme
is the resource optimisation of network to increase network
lifetime (energy efficiency) (P.5). In DARA Each node de-
cides the amount of data it can handle based on its residual
energy and computing power. A network utility maximiza-
tion (NUM) formula is deployed for the incorporation of
devices capabilities. A node decides whether to forward data
or not (routing decision) and also switch on the link for the
next hop node (scheduling decision). Therefore, algorithms
have both routing and scheduling decisions. Test-bed is used
for implementation, and the result shows significant improve-
ment in throughput (P.2) as compared to the traditional back-
pressure algorithm.
In [67], interference-aware routing (IAR) in 5G for D2D
communication (G.1) is presented. The main objective of the
scheme is to reduce interference (O.3). The scheme enables
the route between source and destination by using nodes on
the cell edges. Firstly, the router sends data to present nodes
in the edge, then along with cell edge data travels towards
the destination. Then lastly data is sent to the destination
from cell edge. At each stage, IAR consumes Shortest Path
Routing (SPR). Consequently, routes in IAR are longer as
compared to the direct shortest path, but IAR has lesser inter-
ference overall. The scheme is shown to be energy efficient
(P.5) with a high data rate (P.1).
In [68], a secure trust-base relay node selection scheme in
5G for D2D communication (G.1) is presented. The main
objective of the scheme is to avoid interference (O.3) by
selecting the trusted node for data forwarding. At each node,
the trust value is calculated based on experience, particu-
larly the packet successful delivery rate and the decoding
error. Each node maintains a trust table to keep track of
its neighbours nodes and select a next-hop node based on
their updated table. The trust values are considered based
on four parameters, namely, buffer capacity, SNR, energy
and reliability of devices. The scheme is shown to be energy
efficient (P.5).
In [4], 5G based scheme and routing protocol for continuous
monitoring of the chronic patient is presented. The main
objectives of this scheme to optimize network resources
(O.1) to reduce end-to-end delay (P.4) and provide high band-
width (R.2) to the user with a guarantee to avoid congestion.
The author used a smart phone, wearables devices and 5G
network to share data between sensors, smartphone and base
station. The author also used machine learning algorithm
(i.e., is based on computer programs which can access data
and use it learn for themselves [60]) having name decision
taking an algorithm to analyse the data to take necessary
action. The decision taking algorithm analyses the data and
activate the alarm in case of an abnormal situation. The
scheme has the ability of data collection in real time with
fast response. The author used 4G and 5G in the presented
scheme and analysed the performance. The result shows that
5G increase the throughput (P.2), end-to-end delay (P.4) and
network lifetime (Energy efficient)(P.5).
In [69], femtocell network and cloud computing-based
scheme is presented. The main objective of the scheme is
to optimize network resources (O.1) to achieve a high data
rate (P.1). Body sensor network collects data of the user
from the sensors and sends to the user’s mobile devices,
which are registered under a femtocell. Femtocell is used as
a low power home base-station with good coverage at the
indoor region. Femtocell maintains a database, a received
data is verified with the information in the database, and if an
abnormality is detected, the data is sent to the cloud through
femtocell for further analysing and access by the doctors.
Markov Chain model (i.e., an order of possible events, where
every event probably depends only on the state attained in
the previous event.) is used in the cloud, to provide the
best solution. This femtocell network approach minimizes
the consumption of the network resources to achieve high
energy efficiency (O.4) and with high security as compared
to a macrocell network.
V. OPEN ISSUES AND CHALLENGES
Besides the above-mentioned advances, there are numerous
challenges and open research issues in adopting 5G for smart
healthcare. The aim of discussing these issues is to provide
research direction in this domain for new researchers. Table 6
presents the future research directions and their importance.
A. CONNECTIVITY IN IOT
A smart healthcare network consists of billions of devices.
Smart healthcare concept can succeed only if it can provide
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connectivity to every device present in the network with the
capabilities of sensing to produce important information. In
smart healthcare, any available communication network can
be used by IoT devices, such as Bluetooth, Wi-Fi, cellular
network (LTE, emerging 5G). However, guaranteeing con-
nectivity in smart healthcare postures many challenges, such
as:
•Guaranteeing connectivity to huge devices deployed in
the network in wide range.
•Providing connectivity to high mobility (i.e., high-speed
ambulance, carrying patients) devices in the network.
B. INTEROPERABILITY
Interoperability is an ability of two or more different net-
works and devices to interconnect with each other for ex-
changing data. Smart healthcare includes different IoT de-
vices from various range of domains (i.e., remote surgery,
ECG monitoring and remote health monitoring). Interoper-
ability plays an important role in smart healthcare, providing
connectivity between different devices using different com-
munication technologies. Interoperability between different
devices in different domains is a key limitation for IoT suc-
cess due to lack of universal standards [71]. To overcome the
challenge of interoperability must be identified at various lev-
els (i.e., application, devices, communication and network).
An artificial intelligent and integrated approach is needed
to allow billions of IoT devices to communicate with each
other. For instance, FIRARE and oneM2M standardization is
working on the interoperability issues in collaboration with
different standardization bodies such as OMA, 3GPP and
ETSI.
C. LOW POWER AND LOW-COST COMMUNICATION
Generally, IoT devices used for smart healthcare are limited
in size and connected with a collection of sensors. A con-
tinuous source of energy is required to drive these devices,
which presents a severe challenge in term of cost and battery
life. To address these issues in smart healthcare, devices
must have the features of low power consumption with low
cost. Therefore, intelligent algorithms are required, that allow
devices to communicate with each other with less energy
consumption. Another method is an advancement in wireless
communication and micro-electronics domain.
D. BIG DATA ANALYTICS
Big data analytics is a key research direction in smart health-
care. In smart healthcare, billions of devices are connected,
which can produce a huge amount of data and information
for analysis. This data can consist of information about user
private data (i.e., Patient Data) and from the surrounding
environment (i.e., ECG, Heart Rate monitoring). Intelligent
algorithms and techniques are required to analyse this data.
For example, data produced by locally connected devices can
be analysed efficiently by adopting deep learning algorithms.
The key issues that must be addressed are:
•During data analysis user privacy must be protected.
•Data secrecy must be provided for sensitive data.
•Infrastructure must be provided to collect, analyse and
store a massive amount of data.
•Computation power must be provided to extract infor-
mation from the data.
E. SECURITY, TRUST AND PRIVACY
Security is required for smart healthcare. Since smart health-
care is based on the internet connectivity of different devices;
security becomes an important challenge. Due to constrained
nature of IoT device (limited processing and battery life)
it is difficult to implement complex security protocols and
algorithms. 70%of IoT devices (including smart healthcare
devices) are at risk of attack in future. This leads to numerous
attacks and threats in term of security and privacy. To design
successful smart healthcare in the future 5G network, the
following issues must be taken into account.
•For user data, privacy-aware communication must be
provided.
•For data integrity and authenticity, a simple and secure
communication must be provided between smart health-
care devices and a cloud-based application centre.
•User approval and trust must be ensured by delivering
strong privacy.
•Risk assessment must be made in detail to detect present
and new attacks.
VI. CONCLUSION
In this paper we have presented a review of recent works
along with research opportunities on the networking aspect
of 5G and IoT for smart healthcare. We firstly presented
an architecture for 5G smart healthcare and the essential
techniques (i.e., D2D communication, Small cells, Software-
defined network (SDN), Network function virtualization
(NFV), mmWaves and Edge computing) to enabled 5G smart
healthcare. Secondly, we introduce the taxonomy of 5G smart
healthcare, and analysed the new requirements (i.e., ultra-
low latency, high bandwidth, ultra-high reliability and high
battery lifetime) and objectives (optimizations of resources,
enhancing QoS, reducing interference and improving en-
ergy efficiency) for 5G smart healthcare. Third, we pre-
sented a detailed review of network layer solutions, including
scheduling, routing, and congestion control, applied to IoT
based 5G smart healthcare covering both recent work and
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TABLE 6: Future research challenges.
Features Importance Reseach Challenges Major Requirements
Connectivity Guaranteeing that IoT devices
from different domains can com-
municate.
•How to guarantee con-
nectivity of massive IoT
devices in a wide range
during high mobility?
•How to guarantee re-
source management in
highly dense network?
•How to utilize
power/energy of IoT
devices?
•Usage of spectrum efficiently
for communication of IoT de-
vices.
•Smart usage of communication
mediums (i.e., LTE, LTE ADV,
WiMAX, WLAN etc.).
•Development of intelligent al-
gorithms to provide connectiv-
ity to large number of devices
deployed in the network in the
absence of communication net-
works.
•Development of clustering
services to increase resource
availability and support mixed
workload.
Interoperability Provides communication plat-
form for IoT devices using dif-
ferent protocols.
•Incorporating devices
for retailer locked-in
services.
•Universal, integrated and flex-
ible models for IoT devices to
incorporate and communicate
(i.e., IP, CoAP).
Low-power
and low-cost
communication
Provides smart healthcare appli-
cation on large scale, if commu-
nication is low-cost.
•How to extend IoT de-
vices battery life?
•Advancement in wireless com-
munication domain and micro-
electronics to deliver low-cost
communication and extend bat-
tery life.
•Development of artificial in-
telligence based routing proto-
cols.
Big data Enhancing performance of IoT
network by processing effective
information recognized from
valid sources. (i.e., analysing
patient data can reduce network
congestion process).
•Lack of useful tools to
process huge amount of
generated information.
•Resourceful centralized
data acquisition and
information.
•Big data centralized processing
centre.
•Public appreciation how to
use IoT network resources se-
curely.
Security Secure environment (attack free)
to deploy services. •Secure integration and
deployment of services
(cloud-based) at both de-
vice and network levels.
•Early detection of both
outsider and insider
threats.
•Standardised security so-
lutions without delaying
data integrity.
•Identification of vulnerabilities
at a various level in the net-
work. which work as entry
points for numerous attacks.
14 VOLUME 4, 2016
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10.1109/ACCESS.2019.2930628, IEEE Access
Author et al.: Preparation of Papers for IEEE TRANSACTIONS and JOURNALS
TABLE 7: Definitions of all acronyms used in the paper.
Abbreviations
BAN Body Area Network
BS Base Station
CMTC Critical Machine Type Communication
CoT Cloud of Things
CDMA Code Division Multiple Access
CQI Channel Quality Indicator
D2D Device-to-Device
ETSI European Telecommunications Standards Insti-
tute
FDMA Frequency Division Multiple Access
GSM Global System for Mobile
GPRS General Packet Radio Service
HetNets Heterogeneous Networks
IoT Internet of Things
ITU International Telecommunication Union
IECR IoT European Research Cluster
LTE Long-Term Evaluation
LTE-M Long-Term Evaluation Advance
LoraWAN Long Range Wide Area Network
Lora Long Range
M2M Machine-to-Machine
mMTC Massive Machine-Type Communication
MTCs Machine-Type Communications
MBS Macro Base Station
NFV Network Function Virtualization
NB-IoT Narrowband Internet of Thing
NFC Near Field Communication
OFDMA Orthogonal Frequency Division Multiplexing
OMA Open Mobile Alliance
QoS Quality of Service
SNR Signal-to-Noise Ratio
SBS Small Base Station
SDN Software Defined Network
TDMA Time Division Multiple Access
TCP Transmission Control Protocol
UHD Ultra High Definition
UEs User Equipment’s
URLLC Ultra-Reliable and Low Latency Communica-
tion
WLAN Wireless Local Area Network
WBAN Wireless Body Area Network
WiMAX Worldwide Interoperability for Microwave Ac-
cess
Wi-Fi Wireless Fidelity
3GPP Third Generation Partnership Project
4G Fourth Generation
5G Fifth Generation Mobile Network
future research opportunities. Because of the adaptability
and growing nature of computer networks, it was difficult to
cover every single approach: however, an endeavour has been
made to cover all essential approaches. Finally, we briefly
presented the open issues and challenges for future 5G smart
healthcare.
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ABDUL AHAD received the M.Sc degree in com-
puter science from Swat University, Pakistan in
2014, the MS degree in computer science from
Virtual University, Pakistan in 2017. He is cur-
rently pursuing a PhD degree in computer sci-
ence with the Sunway University, Malaysia. His
research interests include 5G, Internet of Things,
wireless networks and artificial intelligence.
MOHAMMAD TAHIR received his PhD and MSc
in Electrical and Computer Engineering from the
Department of Electrical and Computer Engineer-
ing, International Islamic University Malaysia in
the year 2016 and 2011 respectively. Prior to join-
ing academics, he worked in the R&D division of
industry for seven years on several projects related
to the internet of things and cognitive radio. His
research interest includes 5G, Internet of things,
Game theory for wireless networks, wireless secu-
rity, Blockchain and autonomic computing.
KOK-LIM ALVIN YAU received the B.Eng. degree
(Hons.) in electrical and electronics engineering
from Universiti Teknologi PETRONAS, Malaysia,
in 2005, the M.Sc. degree in electrical engineer-
ing from the National University of Singapore, in
2007, and the PhD degree in network engineering
from the Victoria University of Wellington, New
Zealand, in 2010. He is currently a Professor with
the Department of Computing and Information
Systems, Sunway University. He is also a Re-
searcher, Lecturer, and Consultant in cognitive radio, wireless networks,
applied artificial intelligence, applied deep learning, and reinforcement
learning. He was a recipient of the 2007 Professional Engineer Board
of the Singapore Gold Medal for being the Best Graduate of the M.Sc.
degree, in 2006 and 2007, respectively. He serves as a TPC member and
a Reviewer for major international conferences, including the ICC, VTC,
LCN, GLOBECOM, and AINA. He also served as the Vice General Co-
Chair of the ICOIN’18, the Co-Chair of the IET ICFCNA’14, and the Co-
Chair (Organizing Committee) of the IET ICWCA’12. He serves as an
Editor for the KSII Transactions on Internet and Information Systems, an
Associate Editor for the IEEE ACCESS, a Guest Editor for the Special
Issues of the IEEE ACCESS, the IET Networks, the IEEE Computational
Intelligence Magazine, and the Journal of Ambient Intelligence and Human-
ized Computing (Springer), and a regular Reviewer for over 20 journals,
including the IEEE journals and magazines, the Ad Hoc Networks, and the
IET Communications.
VOLUME 4, 2016 17
