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Nowadays billions of smart devices or things are present in Internet of Things (IoT) environments, such as homes, hospitals, factories, and vehicles, all around the world. As a result, the number of interconnected devices is continuously and rapidly growing. These devices communicate with each other and with other services using various communication protocols for the transportation of sensor or event data. These protocols enable applications to collect, store, process, describe, and analyze data to solve a variety of problems. IoT also aims to provide secure, bi-directional communication between interconnected devices, such as sensors, actuators, microcontrollers or smart appliances, and corresponding cloud services. In this paper we analyze the growth of M2M protocol research (MQTT, AMQP, and CoAP) over the past 20 years, and show how the growth in MQTT research stands out from the rest. We also gather relevant application areas of MQTT, as the most widespread M2M/IoT protocol, by performing a detailed literature search in major digital research archives. Our quantitative evaluation presents some of the important MQTT-related studies published in the past five years, which we compare to discuss the main features, advantages, and limitations of the MQTT protocol. We also propose a taxonomy to compare the properties and features of various MQTT implementations, i.e. brokers and libraries currently available in the public domain to help researchers and end-users to efficiently choose a broker or client library based on their requirements. Finally, we discuss the relevant findings of our comparison and highlight open issues that need further research and attention.
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Received October 21, 2020, accepted October 31, 2020, date of publication November 4, 2020, date of current version November 17, 2020.
Digital Object Identifier 10.1109/ACCESS.2020.3035849
The Use of MQTT in M2M and IoT
Systems: A Survey
BISWAJEEBAN MISHRA AND ATTILA KERTESZ
Department of Software Engineering, University of Szeged, 6720 Szeged, Hungary
Corresponding author: Biswajeeban Mishra (mishra@inf.u-szeged.hu)
ABSTRACT Nowadays billions of smart devices or things are present in Internet of Things (IoT)
environments, such as homes, hospitals, factories, and vehicles, all around the world. As a result, the number
of interconnected devices is continuously and rapidly growing. These devices communicate with each other
and with other services using various communication protocols for the transportation of sensor or event data.
These protocols enable applications to collect, store, process, describe, and analyze data to solve a variety of
problems. IoT also aims to provide secure, bi-directional communication between interconnected devices,
such as sensors, actuators, microcontrollers or smart appliances, and corresponding cloud services. In this
paper we analyze the growth of M2M protocol research (MQTT, AMQP, and CoAP) over the past 20 years,
and show how the growth in MQTT research stands out from the rest. We also gather relevant application
areas of MQTT, as the most widespread M2M/IoT protocol, by performing a detailed literature search in
major digital research archives. Our quantitative evaluation presents some of the important MQTT-related
studies published in the past five years, which we compare to discuss the main features, advantages, and
limitations of the MQTT protocol. We also propose a taxonomy to compare the properties and features of
various MQTT implementations, i.e. brokers and libraries currently available in the public domain to help
researchers and end-users to efficiently choose a broker or client library based on their requirements. Finally,
we discuss the relevant findings of our comparison and highlight open issues that need further research and
attention.
INDEX TERMS IoT, IoT protocols, MQTT, MQTT brokers, survey.
I. INTRODUCTION
The Internet of Things (IoT) connects everyday devices like a
fridge, oven, vehicle, washing machine, fitness band, watch,
and even shoes to the internet [1]. It enables us to collect
monitored data from these devices over a network without any
human-to-human or human-to-computer interaction that can
be used to improve our life, business or environments [3]. For
example, a simple IoT application like an activity or fitness
tracker can inform many things about us, such as distance
we walked, ran, cycled or swam, pace, pulse rate, (swim)
stroke count, calories we burned, sleep quality to help us
improve our regimen. Efficient IoT solutions can help us to
control these devices remotely from our phones or tablets.
No matter it is agriculture, transport, sports, health, military,
energy, or entertainment; today the application space of IoT
is virtually endless. It is rare to find any industrial area
that does not get benefits from this IoT revolution. Social
The associate editor coordinating the review of this manuscript and
approving it for publication was Xingwang Li .
networking and smart city applications can also benefit from
this trend [4]. Instead of waiting for monthly or yearly reports,
business and industry can get accurate consumer-data in real-
time. They can analyze the data to make more informed
decisions, which can add value to their business [5].
Cloud Computing [6] provides on-demand network access
to a shared pool of configurable computing resources (such
as networks, servers, storage, applications and services). IoT
systems generate massive amounts of data, and Cloud Com-
puting paves the way for that data to reach its destination.
Clouds and IoT have a complementary relationship, and they
can increase efficiency of our everyday tasks. There are dif-
ferent communication protocols for the transmission of data
in IoT and M2M systems. MQTT was introduced by OASIS
in 2013 for the abbreviation of Message Queuing Teleme-
try Transport protocol [7]. It became standardized with the
release of version 3.1.1 in 2014, when the standardization
group omitted the abbreviation for MQTT, representing an
OASIS M2M communications protocol. Since then it is being
referred simply as MQTT, and itis not considered an acronym
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B. Mishra, A. Kertesz: Use of MQTT in M2M and IoT Systems: A Survey
any more. This open standard is now a widely used commu-
nication protocol across a variety of industries [8].
The aim of this article is provide answers for the following
problem statements:
1) Does the use of the MQTT protocol stand out in M2M
and IoT systems?,
2) Is there a growth in MQTT-related research works since
its introduction in 1999?, and
3) What kind of MQTT broker implementations are cur-
rently available for public use, and how do they differ
from each other?
The novelty of this study is that it performs a quanti-
tative evaluation of MQTT-related published works found
in Google Scholar, Dimensions, and Scopus; highlights the
potential and limitations of use of the MQTT protocol, iden-
tifies the annual exponential growth rate in MQTT research
works since its appearance in 1999, and gathers the appli-
cation areas of the MQTT protocol across various domains
within IoT.
The evolution of the MQTT protocol, and specially the
MQTT brokers using it, reflects the new trends of M2M and
IoT systems, hence they also need to react to these changes.
To reveal this evolution, this work conducts an in-depth com-
parative study of features of several MQTT broker implemen-
tations available in the public domain in various taxonomy
categories to empower researchers or end-users to select an
MQTT broker implementation based on their needs.
The remainder of this paper is structured and orga-
nized as follows: Section II introduces IoT reference model,
Section III highlights messaging protocols for IoT commu-
nication, basics of publish/subscribe type messaging service
and fundamentals of MQTT, Section IV discusses some
important MQTT works published over past five years and
presents a literature review. Section Vdefines the scope
and method of the literature search conducted in this study
and highlights its findings. Section VI introduces various
MQTT implementations and compares their features through
various taxonomy categories, and Section VII summarizes
the revealed open issues and challenges around MQTT and,
finally Section VIII concludes this work.
II. IoT REFERENCE MODEL
In an IoT system, things may refer to any sensor or devise,
starting from very ordinary tiny things to large living things.
Sensors in things work as input sources to produce or col-
lect data, microcontrollers process those data, transmit over
internet and actuators work as an output device to con-
trol or move something as per the instructions received from
the Internet. The connections between people and things are
increasing day by day, and the amount of data produced
every second is stupendous. The number of smart devices has
already outnumbered the number of humans on the planet.
Numerous research works suggest that the world is embrac-
ing digital infrastructure five times faster than it embraced
electricity and telephony. Industry estimates this pace in the
number of smart devices to reach 74.44 billion by 2025 [9].
FIGURE 1. The seven layers of the IoT Reference Model [10].
Data generated by different kinds of devices can be processed
in different ways and transmitted to geographically dispersed
locations to be acted upon by applications. In 2014, keeping
the view of creating a standardized global frame of reference,
Cisco proposed a seven-layer IoT model [10] – see Figure 1.
Next, we introduce this architecture.
A. LEVEL 1: PHYSICAL DEVICES AND CONTROLLERS
This layer comprises of physical devices and device con-
trollers, which we call as things in the IoT. Being independent
of shape, size, location, or origin, these things are diverse.
A thing can be as small as the size of a silicon chip or as
large as the size of a vehicle. These things or devices are
endpoint devices that are capable of generating data, sending
and receiving information, analog to digital conversion: as per
the control commands they receive over the internet sent by
the physical devices and controllers [2].
B. LEVEL 2: CONNECTIVITY
Traditional data communication networks incorporate several
functions based on (ISO) 7-layer reference model, but even
so, an IoT system contains many levels in addition to the
data communications network. As the sole objective of the
IoT reference model is to carry out M2M communications
through the existing networks; it eliminates the need for the
creation of a different network. It’s designing principles make
it work seamlessly on existing networks. Connectivity and
reliable timely information transmission are two important
roles of level 2. Connectivity includes communicating with
and between the level 1 devices, reliable delivery across
the networks, implementation of various protocols, switching
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and routing, translation between protocols, security at the
network level, and (self-earning) networking analytics [2].
C. LEVEL 3: EDGE (FOG) COMPUTING
Level 3 revolves around converting network data flows into
suitable information for storage and further processing at
level 4. It focuses on high volume data analysis and trans-
formation. The fundamental principle of the IoT reference
model is that intelligent systems initiate data processing as
early and as close to the edge of the network as possible,
instead of relying on the cloud to do all the work. It is
often termed as Edge or Fog Computing. As data gener-
ated by things are usually sent to the connectivity level
(level 2) networking devices, level 3 involves limited, session-
less-transaction-less processing on a packet-by-packet basis.
Level 3 data element functions include: data filtering, clean
up, aggregation, packet content inspection, a combination of
network and data level analytics, thresholding. Event gener-
ation and processing functions include: evaluating data for
its norm be processed at a higher level, reformatting data
for consistent higher level processing, expanding or decoding
meaning adding additional information such as origin of data,
distillation/reduction that refers to minimizing the impact of
data and traffic on the network and higher-level process-
ing systems, and assessment that involves evaluating thresh-
old or alert, which process may redirect data to additional
destinations [2].
D. LEVEL 4: DATA ACCUMULATION OR STORAGE
This level implements mechanisms to make network data
usable by applications. It involves converting data in motion
to data-at-rest, formatting network packets to relational
database tables, enabling transition of event based computing
to query based computing, and reducing data through filtering
and selective storing. In short, level 4 captures and stores data
to be used by non-real-time-applications when necessary.
It bridges the gap between real-time networking world and
the non-real-time application world by working as a boundary
between event-based data generation and upper level query
based data use [2].
E. LEVEL 5: DATA ABSTRACTION
IoT systems require to scale to the need, may it be to a
corporate level or global level. To enable scaling on the
large amount of data received from IoT and non-IoT sys-
tems several storage systems are required. This in turn gives
birth to the necessity of information integration from various
data sources. Hence data abstraction level moves around
many activities. It reconciles the conflict between multiple
data formats from different sources, and it assures consistent
semantics of data across all sources. It also ensures that data
is complete to be used by any higher-level application, and it
integrates data into one place or giving access to multiple data
stores using data virtualization. Furthermore, it facilitates
data protection by suitable authentication and authorization
and normalizing or denormalizing, and use indexing to pro-
vide quick application access [2].
F. LEVEL 6: APPLICATION
Level 6 applications interact with Level 5 and data at rest.
So this level does not need to operate at network speeds. Some
of the important functionalities of this level are monitoring
device data. controlling devices, combining device and non-
device data, interpreting information, and reporting [2].
G. LEVEL 7: COLLABORATION AND PROCESSES
This level works beyond the technical model. It includes
humans and business processes. All the information created
by several IoT systems is of very little value unless it pro-
duces insightful analytics. Making an insightful decision and
taking appropriate action often involve people and processes.
People use different applications and methods to derive useful
information out of IoT data. Typically it is not a one-man
job. People need to communicate and collaborate to make the
most of IoT data to make the right business decisions at the
right time. Hence, Level 7 represents a higher level involving
humans than a single application [2], see Figure 2.
FIGURE 2. A general IoT architecture.
III. MESSAGING PROTOCOLS FOR IoT
The previous section introduced the reference model of IoT
systems, and reflected the need for a reliable communica-
tion protocol, for the implementation of it. In this section,
we discuss IoT protocols in general, define the basics of a
publish/subscribe (pub/sub) service, and introduce the MQTT
protocol in detail.
IoT networks deploy several radio technologies like
RFID, WLAN (IEEE 802.11), WPAN (IEEE 802.15) and
WMAN (IEEE 802.16), etc. for communications at the
lower level. Lower level communication protocols may
include LoRaWAN, SigFox in long-range (km)-low data rate
(bps-kbps), Cellular/4G/5G in long-range(km)-high data rate
(Mbps), Zigbee, Zwave in medium range (m)-medium-data
rate (kbps), WiFi in medium range(m)-high-data rate (Mbps),
and NFC short-range (cm)-medium data rate (kbps) cate-
gory. No matter which radio technology is used to deploy
an IoT network, all independent data generating end devices
must make their data available to the internet for further
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FIGURE 3. TCP/IP Model-Main IoT Protocols.
processing and send control information back [11], [12]. The
performance of M2M communication heavily relies on the
special messaging protocols designed for M2M communi-
cation within IoT applications [10]. The web uses a single
standard messaging protocol HTTP; on the other hand, being
too diverse in its characteristics IoT cannot rely on a ‘‘one-
protocol-fits-all’’ philosophy. Therefore nowadays there are
many messaging protocols available to select from for various
needs of IoT systems. MQTT, CoAP, AMQP, and HTTP are
the four widely accepted and emerging messaging protocols
for IoT systems [12]. Figure 3shows the TCP/IP protocol
stack for IoT systems.
A. BASICS OF PUBLISH/SUBSCRIBE SERVICE
In a pub/sub type messaging service, ‘‘message’’ refers to the
data that moves through the service, ‘‘topic’’ is a named entity
that denotes a feed of messages [13], and ‘‘subscription’
refers to an interest in receiving messages on a particular
topic. The ‘‘publisher or producer’’ refers to a device or a
program that creates messages and publishes them to the mes-
saging service on a set topic, and ‘‘subscriber or consumer’
refers to a device or program that receives messages on a
specified subscription [12], [14].
B. FUNDAMENTALS OF MQTT
MQTT is an open OASIS and ISO standard (ISO/IEC PRF
20922) for client-server, publish/subscribe type messaging
transport protocol [8]. It was invented by Dr. Andy Stanford-
Clark of IBM, and Arlen Nipper of Arcom, in 1999. The
design principles of this protocol focus on minimizing net-
work bandwidth and device resource requirements ensuring
reliable delivery. It is capable of transmitting data over low-
bandwidth or unreliable networks with very low consumption
of power [8], [12]. Characteristics like lightweight, open,
simple, and easy deployment make MQTT an ideal com-
munication protocol for constraint environments. The proto-
col runs over TCP/IP, or over other network protocols that
provide ordered, lossless, bi-directional connections. It uses
IANA registered port number 8883 for SSL/TSL connections,
and 1883 port number for Non-TLS connections [8].
MQTT offers three qualities of service for message deliv-
ery. The first quality of service is QoS 0. It is otherwise known
as ‘‘at most once’’ message delivery service. In this quality of
service, messages are delivered at most once according to the
operating environment. The chance of message loss remains
intact. An example of the use case of QoS 0 is sending real-
time pressure, humidity, and temperature sensor data to a
remote reading application where it does not matter if the
connection to the application reading sensor data is lost for a
while. The second quality of service is QoS 1. It is otherwise
known as ‘‘at least once’’ message delivery service. In this
service, messages are assured to be delivered at least once,
duplicity may occur. The third quality of service is QoS 2.
It is otherwise referred to as ‘‘exactly once’’ message delivery
service. In this service, messages are ensured to be delivered
exactly once. In QoS 2, a small transport overhead is observed
and protocol exchanges are minimized to reduce network
traffic. This service is useful to notify interested parties when
an unusual disconnection occurs. This level could be used
with billing systems where redundancy of loss of messages
may lead to incorrect charges being imposed [8].
MQTT has three constituent components:
1) Publisher or Producer (An MQTT Client)
2) A broker (An MQTT Server)
3) Consumer/ Subscriber (An MQTT Client)
An MQTT client is a program or device that uses the
MQTT protocol. A client is responsible for opening network
connection to the server, creating messages to be published,
publishing application messages to the server, subscribing to
request application messages that it is interested in receiving,
unsubscribing to remove a request for application messages
and closing network connection to the server. Application
message refers to the data carried by the MQTT protocol
across the network for the application. All application mes-
sage transported by MQTT contains payload data, a QoS,
a collection of properties, and a topic name [13], [15].
An MQTT server is a program or device based on MQTT
that acts as a post office between publishers and subscribers.
An MQTT broker is responsible for accepting network con-
nections from clients, accepting application messages pub-
lished by clients, processing subscribing and unsubscribing
requests from clients, sending application messages to clients
as per its subscriptions, and closing the network connection
from the client. MQTT is a bi-directional communication
protocol. This helps in both sharing data, managing, and
controlling devices. It normally requires a fixed header of
2-bytes with small message payloads up to the maximum size
of 256 MB [8].
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TABLE 1. Some important works in MQTT.
FIGURE 4. Timeline of MQTT development.
C. VERSION HISTORY OF MQTT
Since its release in 1999, MQTT has been riding high on its
developmental path: see Figure 4. Its first royalty-free release
occurred in 2010. In 2013, it was submitted as a standard to
the OASIS standard group. The first published release was
version 3.1.1. It became an OASIS standard in 2014, and
in 2016 it became an ISO standard. The most recent MQTT
version is MQTT v5 that was published in 2018, and got
standardized in 2019. Version 5 incorporates significant fea-
ture updates to meet the requirements of modern IoT-Cloud
systems. It offers better error handling for mission-critical
implementation, better scalability for native cloud comput-
ing, greater flexibility, and easier integration into an existing
computing infrastructure [8].
IV. LITERATURE STUDY
In this section we summarize some of the important MQTT-
related studies published in the literature in the last five years.
Table 1represents an overview of important related works
in MQTT. We have categorized these studies into four main
topics, which we discuss next:
1) MQTT advantage,
2) Comparison of MQTT with other IoT protocols,
3) Limitations of MQTT,
4) Approaches to secure MQTT communications.
A. MQTT ADVANTAGE
Prada et al. [16] discussed the lightweightness of MQTT pro-
tocol for communication with resource-constrained devices.
To evaluate the ability of MQTT to work with low-end
devices, authors have developed a module for an EjsS (Easy
Java/Javascript Simulations) based educational tool to com-
municate with an Arduino based device. The module devel-
oped by the authors empowers the educational tool to use
MQTT protocol to communicate with the physical device
powered by an Arduino microcontroller. Their experimental
results show that the MQTT protocol can be successfully
used to communicate between an interactive educational tool
running on a web browser and a resource-limited hardware
platform without adding any time or complexity burden to
the educators who apply it.
Wagle [17] presented the viability of MQTT protocol for
IoT centric wireless sensor networks interfacing with the
Internet and implementing machine learning algorithms over
the cloud. The paper implements an IoT Application involv-
ing ubiquitous sensing, M2M communication, cloud comput-
ing, and semantic data extraction and outlines the advantages,
disadvantages, and suitability of MQTT towards IoT appli-
cations. The findings of the authors reinstate the retention
of messages and other features of MQTT to make it fit for
semantic data extraction and easy integration of new devices.
The availability of topics for subscription and publishing ren-
ders data routing procedures largely redundant and eliminates
the requirement for heavy mechanisms for directing data to
specific buffers at the program level. QoS and Last Will and
Testament enhances the reliability of MQTT and makes it
suitable in constraint-bound situations.
The work by Yiming et al. [18] proposed an SDN-based
fog computing architecture and developed its working pro-
totype. They have implemented broker functionalities inte-
grated at the edge-switches, that acts as a reliable message
delivery platform and performs message-based analytics at
the switches. They found that the fog node delivers at signif-
icantly higher throughput, as compared with the respective
traditional client and end-host setup.
B. COMPARISON OF MQTT WITH OTHER IoT PROTOCOLS
Naik [11] provided an in-detail evaluation of the four widely
used messaging protocols MQTT, CoAP, AMQP, and HTTP
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for IoT systems. This work presents an overall comparison
of the characteristics of these protocols. Furthermore, this
work gives insight into the strengths and limitations of these
IoT protocols by conducting an in-depth and relative analysis
based on some interrelated criteria. Based on the static com-
ponents and some empirical evidence from the literature the
author is able to demonstrate a bigger and comparative picture
of messaging protocols that give the end-user a clear under-
standing to select the suitable protocol for their requirements.
Yokotani and Sasaki [19] compared the performance of
MQTT in the category of protocols based on ICN architecture
and HTTP in the category of legacy protocols also proposes
enhancements to MQTT for better performance. Their exper-
iment confirms MQTT performs better than HTTP and con-
cludes protocols based on ICN architecture are better suited
for IoT systems.
Luzuriaga et al. [20] presented an experimental evaluation
of AMQP and MQTT protocols over unstable and mobile net-
works in terms of message loss, latency, jitter, and saturation
boundary values. The findings of this work are that during
message bursts, the delivery follows a LIFO (last-input first-
output) order, in the case of AMQP, but in MQTT packet
delivery retains its order. AMQP is more security-oriented
than MQTT and MQTT is more energy-efficient than AMQP.
The authors suggest the use of AMQP protocol to build
reliable, scalable, and clustering messaging platforms over an
ideal WLAN, and the use of MQTT protocol to connect edge
Nodes over-constrained environments.
C. LIMITATIONS OF MQTT
Dizdarevic and Cheng [21] showed a lightweight designing
principle of MQTT to eliminate the encryption of data being
transmitted. The encryption implemented as a separate fea-
ture via TLS by several MQTT brokers, in turn, increases
overhead. The default plain-text data exchange by MQTT
poses a big threat from a security point of view.
Dan and Cheng [22] analyzed the rising security attacks
on smart devices that are capable of communicating over a
network using several IoT protocols. The authors shed light
on security issues surrounding MQTT, and other protocols
and show how every MQTT based broker implementation has
no equal abilities for entity authentication or encryption. They
review some of the existing security methods used to secure
a communication channel and finally demonstrate a Novel
approach named ‘Value-to-Keyed-Hash Message Authenti-
cation Code (Value-to-HMAC) that achieves better perfor-
mance than traditional symmetric-key encryption algorithm
without compromising the integrity of the information being
transmitted.
D. VARIOUS APPROACHES TO SECURE MQTT
COMMUNICATIONS
Niruntasukrat [23] introduced an authorization mechanism
for MQTT-based IoT systems. The authorization mechanism
presented in this work is based on OAuth 1.0a, an open autho-
rization standard for web applications. Considering many
aspects such as limited node resources, lack of user interface,
and key/secret distribution and management, the authors have
done careful modification in the OAuth 1.0a mechanism to
make it suitable for the MQTT deployment. The proposed
authorization mechanism requires two sets of credentials for
a device to connect to the MQTT broker. Another set of
credentials including Device ID and Device Secret is sent
to the user through his phone or computer which can afford
HTTPS. Later, it gets embedded offline into the device’s
memory. Authors have tested the design on a real MQTT
based IoT service platform and demonstrate that it works with
minimal authorization delay and message overhead without
affecting user experience.
Singh et al. [24] experimented with the feasibility of
CP/KP-ABE to enable communication security for IoT
devices based on Pub-Sub architecture. They proposed a
secure version of MQTT and MQTT-SN protocols with
a new secure publish command called ‘‘SPublish’’, which
publishes encrypted data based on CP/KP-ABE scheme
using lightweight ECC techniques. Furthermore, they demon-
strated their feasibility for various IoT requirements through
simulations. The authors proved that the SMQTT-based
CP/KP-ABE scheme performs better than the mechanism
proposed by Wang et al. [25].
Lesjak [26] proposed a security design that uses the Trans-
port Layer Security (TLS), which adds a secured commu-
nication layer beneath the MQTT protocol. Their proposed
architecture incorporates a hardware security controller, that
performs the TLS client authentication. With due relevant
experiments, they show no significant performance overhead
is imposed by the hardware security element and confirms
the robustness of the proposed security mechanism. This
work widens the realization of secure and privacy orientated
futuristic smart service infrastructures.
V. ARTICLE SEARCH RESULTS AND
STATISTICAL INFORMATION
This section focuses on the method and results surrounding
our literature search. The primary objective of this study
was to find MQTT-related research works published since
its appearance in 1999, for identifying application areas of
MQTT, and observing how MQTT stands out in the M2M
protocol research race. To achieve this, relevant articles were
searched in digital databases like Google Scholar, Dimen-
sions, and Scopus. These digital databases provide efficient
ways to search for various linked scholarly articles, books,
abstracts, etc. Our literature search was focused on two cate-
gories:
1) Finding research works in various M2M Communica-
tion Protocols. Keywords used: ‘‘MQTT’’, ‘‘CoAP’’,
‘‘MQTT’’.
2) Finding MQTT Application Areas. Keywords used:
‘‘MQTT in Healthcare’’, ‘‘MQTT in Agriculture’’,
‘‘MQTT in Logistics’’, ‘‘MQTT in Disaster Manage-
ment’’, and ‘‘MQTT in Smart city Services’’.
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TABLE 2. Initial release years of some important IoT protocols.
In the M2M communication protocols search category,
we focused on finding relevant research articles surround-
ing ‘‘MQTT’’ published between the years 2000 and 2019.
We also searched to get the number of published MQTT
based research works every 5 years from 2000 up to 2019 ie:
2000-2004, 2005-2009, 2010-2014, and 2015-2019. The pur-
pose was to calculate the exponential growth rate of MQTT
research in a 5-year moving window period since its arrival
in 1999, up to 2019. To understand how MQTT related
research works stands out in comparison to other IoT pro-
tocols, we used keywords like ‘‘CoAP’’ [12], [27], [28] and
‘‘AMQP’’ [20], [29]. As the initial release of CoAP and
AMQP dates to years 2014 and 2003 respectively, we took the
last five years’ literature search data surrounding CoAP and
AMQP for a fair comparison with the last five years’ literature
search data surrounding MQTT.
FIGURE 5. Overview of the literature search method.
The second search category was MQTT application areas.
In this category following keywords, were used: ‘‘MQTT in
Healthcare’’, ‘‘MQTT in Agriculture’’, ‘‘MQTT in Logis-
tics’’, ‘‘MQTT in Disaster Management’’, and ‘‘MQTT in
Smart city Services’’. Period filter was applied to find pub-
lished scholarly articles between 2000 and 2019 (20 years’
data). This independent literature search was conducted on
7th November 2019. All the articles that explicitly matched
the search criteria were included in this study. Figure 5shows
an overview of the used method.
The total number of MQTT based research works
found between 2000 to 2019 on Google Scholar, Dimen-
sions database, and Scopus are 16,461, 7,078, and 1048
respectively. See Table 3and 4.
FIGURE 6. Number of research works in MQTT between 2000 to 2019.
FIGURE 7. Number of research works in MQTT, CoAP and MQTT between
2015 to 2019.
To analyze the progress in MQTT-based research works in
every five-year interval starting from 2000 to 2019, we sam-
pled search data in a five-year moving window between
the mentioned period. On Google Scholar, the total number
of MQTT based research works found between 2000-2004,
2005-2009, 2010-2014, and 2015-2019 are 87, 244, 1,530,
and 14,600 respectively. On Dimensions, the total number
of MQTT based research works found between 2000-2004,
2005-2009, 2010-2014, and 2015-2019 are 16, 45, 281,
and 6,736 respectively. On Scopus, the total number of
MQTT based research works found between 2000-2004,
2005-2009, 2010-2014, and 2015-2019 are 0, 3, 33, and 1,024
respectively. See Figure 6.
Hence, in a 5-year window between 2004 and 2019,
MQTT-based research works show an average annual expo-
nential growth rate percent (see Equation 1)
ln(
Present
Past
No.of years )100 (1)
of 32.02 on Google Scholar search, on Dimensions, it is
33.77%, and in Scopus, MQTT based research works register
an average annual exponential growth rate of 36.39 percent.
See Table 5.
We also investigated ongoing research around other M2M
IoT protocols, namely AMQP and CoAP. As the initial
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TABLE 3. IoT protocols related literature search data, 7th Nov 19.
TABLE 4. MQTT application areas related literature search data, 7th Nov 19.
TABLE 5. Average annual exponential growth in MQTT based research work, 2000-2019.
release years of CoAP and AMQP are 2014 and 2015 respec-
tively 2, we decided to gather the last five years’ research
works surrounding those protocols. Between 2015 and 2019:
12,600 CoAP related articles were found on Google Scholar,
5,984 CoAP based articles were found on Dimensions, and
‘‘715’’ CoAP related articles were found in Scopus. Sim-
ilarly, the total number of AMQP related articles between
2015 and 2019 found on Google Scholar, Dimensions and
Scopus are 4,950, 1,785, and 78 respectively. In terms of the
last five years’ total number of research publications, MQTT
stands tall among other M2M protocols (CoAP, AMQP) with
14,600 number of published articles. See Figure 7.
Considering MQTT application areas, we aggregated the
last 20 years (2000-2019) MQTT related publication data.
On Google scholar, out of 13,570 published articles in numer-
ous domains, 3,870 articles were found to be related to the
healthcare domain, 1,850 articles were related to the agri-
culture domain, 1,560 articles were in the logistics domain,
1,180 articles were in the disaster management domain, and
5,110 articles were in smart city services domain. On Dimen-
sions, out of 8949 published articles 2,050 articles were
found to be related to the healthcare domain, 1,264 articles
were related to the agriculture domain, 1,474 articles fell
into the logistics domain, 1,000 articles were in the disaster
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FIGURE 8. MQTT application areas: 2000-2019.
management domain, and 3,161 articles in smart city ser-
vices domain. On Scopus listing, out 154 published articles,
38 were related to healthcare, 26 were related to agriculture,
19 were related to logistics, only one article was related to
disaster management, and 70 articles were related to smart
city services – see Figure 8.
VI. INTRODUCTION OF MQTT IMPLEMENTATIONS
AND TAXONOMY OF FEATURES
In this section, we present an introduction and detailed com-
parison of some of the currently available MQTT implemen-
tations (brokers and client libraries) for IoT communication.
We compare brokers and client libraries that implement ver-
sions 5.0 and/or 3.1.1 and/or 3.1 of the MQTT Protocol. All
data collected for this comparison are from the respective
official documentation/website/ GitHub links of the MQTT
implementations. The comparison is limited to features of
MQTT protocol and uses the stable version of each imple-
mentation.
A. INTRODUCTION OF MQTT IMPLEMENTATIONS
The MQTT protocol provides a lightweight way of using
a publish/subscribe model for messaging [8]. In this sub-
section, we will introduce some of the widely used MQTT
brokers and client libraries.
Mosquitto [30] is an EPL/EDL licensed open-source mes-
sage broker. It is developed by Eclipse Foundation and sup-
ports 3.1, 3.1.1.1, and 5.0 versions of the MQTT protocol. It is
a single-threaded non-scalable implementation. It is written
in C. It also offers a C library for implementing MQTT
clients.
Bevywise MQTT Route [31] is a commercially licensed,
closed-source MQTT-based message broker developed by
Bevywise Networks. It is built on MQTT 3.1 and 3.1.1.1 spec-
ifications and implements all QoS levels of MQTT. It is a
fixed-threaded (2threads) non-scalable implementation. It is
written in C and Python. It also provides MQTT client
implementation.
EMQ X [32] is developed by EMQ Inc. It is an
Erlang-based open-source message broker/client built on
MQTT 5.0 specifications. It is highly scalable supports clus-
tering. It can run virtually anywhere starting from edge to
cloud. It uses Apache License Version 2.0.
HiveMQ CE (Community Edition) [33] is a Java-based
open-source scalable MQTT broker. It supports MQTT 3.x
and MQTT 5.0. It is developed by HiveMQ GmbH. It is
distributed with Apache License Version 2.0.
HiveMQ [34] is an MQTT based scalable, commercially
licensed messaging platform built on the specifications of 3.x
and 5.0 versions of MQTT protocol. It is written in Java and
developed by HiveMQ GmbH. HiveMQ also has an MQTT
client implementation.
IBM Watson IoT Platform Message Gateway [35] is a
commercially licensed, scalable messaging service based on
MQTT 3.x and 5.0. It offers 40+MQTT client libraries.
JoramMQ [36] is an open-source, scalable message bro-
ker based on MQTT 3.x specifications. It is developed by
ScalAgent. It has both commercial and free distributions
distributed in commercial license and GNU Lesser General
Public License respectively.
flespi [37] is a commercially licensed, scalable IoT plat-
form developed by Gurtam. It is written in C and implements
3.1, 3.1.1, and 5.0 versions of MQTT specification.
PubSub+[38] is an event broker that implements MQTT
3.1.1 specification. It is developed by Solace and available in
both free and commercial versions.
Thingstream [39] is a scalable IoT communications-as-
a-service platform offered by u-blox. It implements MQTT
5.0 specification and offers both MQTT broker and client
library. It is commercially distributed.
VernemQ [40] is an open-source, highly scalable MQTT
broker developed by Octavo Labs AG. It is written in Erlang
and implements MQTT 3.x and 5.0 versions. It uses Apache
License version 2.0.
RabbitMQ [41] is an erlang based message broker that
supports MQTT 3.1.1 version. It is developed by Pivotal
Software. It uses MPL 1.1 license.
Apache ActiveMQ [42] is an open-source, scalable, multi-
protocol message server written in Java and developed by
Apache Software Foundation. It is being distributed in two
flavors- ‘‘Classic’’ and ‘Artemis’’. It uses Apache License
version 2.0. It implements the MQTT 3.1.1 specification.
Adafruit IO [43] is developed by Adafruit. It provides
open-source MQTT client libraries for Python, Ruby, and
Arduino. It implements MQTT 3.1.1 protocol and uses MIT
license for software distribution. net-mqtt is an open-source
MQTT implementation for Haskell language and developed
by Dustin Sallings. It supports 3.x, 5.0 versions of the proto-
col. It uses BSD 3 license.
Eclipse Paho MQTT [44] offers open-source client
libraries in Java, Python, JacaScript, GoLang, C, C++, Rust,
and .Net(C#) languages. Paho C library implements all the
versions of MQTT while libraries in all other languages
only which implement versions 3.1 and 3.1.1 of the MQTT
protocol. It uses Eclipse Public License 1.0, and Eclipse
Distribution License 1.0 (BSD).
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TABLE 6. Comparison of features of MQTT brokers with source availability model and design related properties. Key: ‘‘’’ means unknown.
wolfMQT [45] is an open-source C MQTT client library
for embedded use. It is developed by WolfSSL and available
in GPL v2.0 and Commercial licenses.
Eclipse M2Mqtt [46] is an open-source, C# client library
for .Net and WinRT platforms. It used Eclipse Public
License 1.0.
Machine Head [47] is an open-source clojure based
MQTT library distributed with Creative Commons Attribu-
tion 3.0 Unported License. It is developed by ClojureWerkz.
MQTT-C [48] is an MQTT based, open-source C language
library developed by Liam Bindle. It is distributed with MIT
license.
B. TAXONOMY OF FEATURES OF MQTT
IMPLEMENTATIONS
To compare and analyze features of MQTT protocol
implementations we introduced in the previous subsection,
we define seven taxonomy categories. Tables 6,7,10 and 11
use these categories to compare the source code availability,
the design and implementation, the protocol features, secu-
rity, and finally data visualization support of the overviewed
solutions. In the following paragraphs we define our taxon-
omy elements in detail, then provide a discussion for the
comparison.
Source code availability model: There are two types of
source code availability model: closed-source and open-
source. Under the open-source model source code of a
released software product can be viewed and modified.
Under the closed-source model source code is not made
available in the public domain [49]. Various licenses are
available for commercial and non-commercial software
distributions and redistributions. A software license is a
legal document that provides abiding guidelines for the
use and distribution of software [50].
Source code metrics: In this taxonomy element we
chose source code metrics to present and compare vari-
ous quality features of the source code of the examined
MQTT brokers. Poor software quality makes the code
difficult to read and understand, harder to guess the
functionality or check for undefined symbols, and reuse
the code. It adversely affects the overall productivity
of the employees and the profitability of the company.
In this work, we used a source code analyzer called
CLOC [51] to calculate certain quality metrics. CLOC
is a command line tool that takes file, directory, and/or
archive names as inputs and recognizes applied pro-
gramming languages to develop the software, counts the
number of files, blank lines, comment lines, and lines of
code. CLOC works with only open-source applications.
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TABLE 7. Comparison of the examined open-source MQTT brokers with software metrics.
So in this comparison, we have only listed open-source
MQTT brokers.
Design and Implementation: This category focuses
on software development related artifacts such as the
programming language used to develop the application,
supported operating systems or platforms, latest stable
release, and date of release, cross-compilation ability,
and the entity or company or individual behind the devel-
opment of the application.
Protocol Features: In this category, we compare vari-
ous MQTT implementations according to the supported
MQTT features such as QoS, Retain Flag, Persistent
Session, Shared Subscriptions, Last Will and testament,
Error Log, Built-in Gateway, MQTT Version(3.x/5.0),
and availability of MQTT-SN Support, etc. ‘‘Retain
Flag’’ when enabled (set to true) the broker stores the
last retained message and the corresponding QoS for that
topic. ‘‘Persistent Session’’ presents an ongoing connec-
tion to an MQTT message broker). ‘‘Shared subscrip-
tions’’ is an MQTT V5 feature that allows MQTT clients
to share a single subscription on the broker. ‘‘last Will
and testament’’ is used to notify subscribers of an unex-
pected disconnection of the publisher. MQTT-SN refers
to MQTT for sensor networks [52]. ‘‘Built-in gateway’
refers to an MQTT gateway that acts as an intermediary
between sensors/devices and any IoT platform.
Security: Under this category, we list various security-
related features implemented by various MQTT bro-
kers and client libraries such as Authentication,
MQTT Over TSL/SSL, TCP, WS/WSS, thread secu-
rity, etc. For MQTT over TSL/SSL communications
the port 8883 is reserved. MQTT over Websockets
allows receiving MQTT data directly through a web
browser.
Cloud offerings: In this category, various MQTT imple-
mentations are compared in terms of Bare Metal avail-
ability, Cloud Hosting availability and Docker Support.
Data visualization support: The following features
form this category: UI and dashboard, Custom UI,
ElasticSearch Integration, Tableau Support, and Elas-
ticSearch integration. ‘‘Tableau’’ is a data visualization
service and ElasticSearch integration’. It is a distributed
RESTful search engine built for the cloud.
C. DISCUSSION
In this subsection, we provide further discussions on the com-
parison of the analyzed MQTT brokers and client libraries.
In the previous subsections, we provided short introductions
of the main properties of the overviewed works, defined the
taxonomy elements used to categorize them, and provided
classifications with comparison tables. Though the tables
provide detailed information on the properties of MQTT
implementations, we summarise some of the most valuable
findings from these comparisons.
FIGURE 9. The ratio of the programming languages used to implement
MQTT brokers.
Concerning the compared MQTT brokers, Table 6shows
that almost 50% of investigated solutions are open-source,
and 50% of the tools are closed-source in nature. Concerning
the implementation of the reviewed brokers, Figure 9shows
that almost 57.2 % of the solutions are written in C and
Java where the contribution of each language amounts to
28.60%, 14.30% of solutions are written in Erlang, C++, and
Python: each language shares of 9.50% of solutions, almost
4.8% of reviewed solutions are written in JavaScript and the
percentage share of Go-based solutions also amounts to the
same 4.8% of the solutions. Table 7details the measured
source code metrics of considered open-source MQTT bro-
kers. We can see that ActiveMQ has the highest number of
lines of code with a good number of comments. In terms of
number of comment lines to lines of code ratio, ActiveMQ
is followed by HiveMQ CE, EMQX, VerneMQ, RabbitMQ,
and Mosquitto, respectively. Table 8shows that almost all
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TABLE 8. Comparison of features of MQTT brokers with protocol specific properties. Keys: ‘‘Yes’’ means supported, ‘‘No’’ means not supported, ‘‘’’
means unknown.
TABLE 9. Comparison of features of MQTT brokers with security related
properties.
solutions support most QoS categories, retain flag, LWT,
and persistent connection features. RabbitMQ supports only
QoS 0, and 1. Of all the reviewed broker solutions, up to
61.53% support the shared subscription feature, 57.14% of
the solutions have already adopted MQTT 5.0, and 69.23% of
the solutions have not implemented MQTT-SN, yet. Table 9
compares features of MQTT brokers with security-related
properties. It shows that all the brokers have enabled security
features through authentication, MQTT over TSL/SSL, and
WS/WSS. Table 10 compares features of MQTT brokers
with cloud and data visualization related properties. Of all
reviewed solutions, around 64.28% of the tools support cloud
hosting, 85.71 % of the solutions have Docker container
availability, 92.3% have UI and dashboard. Only a very few
number of solutions support bare metal, ElasticSearch and
Tableau services integration.
For the compared MQTT client libraries, Table 11 shows
that almost 83.33% of solutions are open-source in nature.
Concerning the implementation of the reviewed brokers,
Figure 10 shows that almost 22.9% of the solutions are writ-
ten in C, 17.1% solutions are in Java. C and Java-based solu-
tions are followed by C++(11.4%), Python(11.4%), Erlang
(8.6%), C#(5.7%), PHP(5.7%), Perl(5.7%), Ruby (5.7%),
Go (2.9%), JavaScript (2.9%) based solutions. Paho MQTT
and Thingstream support the highest number of programming
languages, i.e C, C++, Java, JavaScript, Python, and Go.
Among all the overviewed library solutions, HiveMQ MQTT
client, net-mqtt, MQTT-C, Mosquitto, and EMQ X support
thread-safety feature. MQTT client libraries, like net-mqtt,
Paho-MQTT, wolfMQTT, MQTT-C, Mosquitto, and EMQ
X have support cross-compilation. Almost all the library
solutions support most QoS categories.
From this comparison, we found that ‘Docker container’,
‘MQTT over WS/ WSS Support’, ‘authentication’, ‘REST
API integration’, ‘MQTT Over TSL/SSL’, ‘TCP Support’,
‘Retain Flag’, ‘persistent session’, ‘UI and Dashboard’, ‘Last
Will and testament’ and ‘QoS’ are the most supported fea-
tures by all the brokers. We also found that RabbitMQ
does Not support QoS2 subscriptions. RabbitMQ automat-
ically downgrades QoS 2 to QoS1 [41]. Some of the least
supported features are, ‘MQTT-SN Support’, ‘built-in gate-
way’,’localization support’, ‘bare-metal’, ‘Tableau support’,
‘rule engine’, and ‘Error Log’, etc.
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TABLE 10. Comparison of features of MQTT brokers with cloud and data visualization related properties, Keys: ‘‘Yes’’ means supported, ‘‘No’’ means not
supported, ‘‘’’ means unknown.
TABLE 11. Comparison of features of MQTT client libraries. Key: ‘‘Yes’’ means supported, ‘‘No’’ means not supported, ‘‘’’ means unknown.
VII. SUMMARY OF RESEARCH TRENDS, OPEN
ISSUES AND CHALLENGES
From our survey, it is quite evident that MQTT is one of
the most widely used IoT protocol solutions. The lightweight
designing principle of MQTT enables data-exchange in a
plain-text format that represents a security threat. Hence,
several MQTT broker implementations enable encryption as
a separate feature on the top of TLS. It results in some
performance overhead as discussed in [22]. Currently, many
MQTT brokers use the CONNECT control type message
packet to enable authentication. Brokers require clients to
send usernames and passwords with the CONNECT message
for the validation of the connection failing which connection
is refused. We found that enhancing security for MQTT is an
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FIGURE 10. The ratio of the programming languages used to implement
MQTT client libraries.
ongoing effort. Various authorization methods are continually
being developed, experimented, and tried out to make the
security aspects of MQTT better.
Confidentiality is paramount for securing a system [53],
and this can be achieved by encrypting the messages to
be published at the application layer. The encryption can
either be implemented as a client-to-broker model or end-to-
end model. In a client-to-broker type model of encryption,
brokers decrypt the messages being transmitted on a topic
head, encrypt them and send to the clients that are subscribed
to that specific topic. This process requires more comput-
ing power and energy. Whereas in an end-to-end encryption
model, a broker does not require to decrypt the information
being transmitted on various topic heads. The broker just
functions as a post office to send the message to its appro-
priate recipients. This process involves the consumption of
less computing power and energy [21]. The commercial and
technical interest in Wireless Sensor Networks (WSNs) is
on a rise [54]. A typical Wireless Sensor Network consists
of a large number of battery-operated sensors and actuators
having limited computational resources. These devices need
to communicate with each other.
Based on our survey the following open issues are found:
It is evident that MQTT-SN, which is an important
MQTT v.5 feature, needs to be largely adopted by more
broker implementations;
nevertheless, since MQTT-SN is a successor of MQTT,
it also inherits the previous security issues of authen-
tication and encryption, so a little compromise in an
MQTT-SN communication can have an impact on the
whole IoT infrastructure in terms of confidentiality and
accessibility. Solving the security problem in MQTT
would bring to it a great advantage over other available
protocol solutions.
Since big amount of data is being generated by IoT sys-
tems, various brokers may expand their support for var-
ious database programs, AI-enabled search, and MQTT
data analytics.
The MQTT standard architecture defines only one bro-
ker in a system, hence, it is not suitable for edge-based
IoT applications and cannot harness the best out of a
multi-core environment. As the world is rapidly moving
forward in distributed and edge-based computing direc-
tion more research on multi-threaded, scalable MQTT
implementations, edge-based MQTT prototypes and
MQTT-SN security are required to bridge the gap [55].
Finally, we would like to bring attention to privacy issues
of data flows happening within an IoT System. Although
there are some privacy-related works (e.g. [56] and [57])
in IoT in general, there is still a large body of research
needed on protocol-specific privacy solutions.
VIII. CONCLUSION
Things, as basic components for data source, are paramount
in the Internet of Things paradigm. No matter which com-
munication technology is used to deploy and operate an
IoT/M2M network, all of the participating, independent data
generating devices heavily rely on the special messaging
protocols designed for M2M communication within IoT
applications.
In this paper, we introduced the set of various M2M
communication protocols appeared in the past 20 years, and
surveyed the evolution and usage of the MQTT protocol,
which is the most widespread M2M/IoT protocol. We have
comprehensively analyzed some of the important research
works of the current literature to highlight the main features,
advantages, and limitations of the MQTT protocol and its
broker implementations over other IoT protocols. We pre-
sented our findings around the current use of MQTT and
its application areas using various graphs and comparison
tables. We arrived to an in-depth comparative study of the
features of several MQTT brokers and client libraries in
various taxonomy categories, which can be used to empower
researchers and users to select an MQTT implementation
based on their requirements and suitability. We also high-
lighted future research directions that need to address security
and scalability issues to further improve the effectiveness of
these solutions.
Concerning our future works, we plan to perform detailed
performance measurements and usage evaluation of concrete
MQTT broker implementations as an extension to this study.
ACKNOWLEDGMENT
This research was supported by the Hungarian Scientific
Research Fund under the grant number OTKA FK 131793,
and by the University of Szeged Open Access Fund under the
grant number 5053.
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BISWAJEEBAN MISHRA received the M.Sc.
degree in computer sciences from Ravenshaw Uni-
versity, India, in 2010. He is currently a member
of the IoT Cloud Research Group, Department
of Software Engineering, University of Szeged,
Hungary, and a pre-doctoral student with the Doc-
toral School of Computer Science, University of
Szeged. Prior to joining the doctoral programme
in 2015, he had served in the IT industry in India
for five years. His research interests include the
IoT Systems, communication efficiency of the IoT systems, and cloud
computing.
ATTILA KERTESZ is currently an Associate Pro-
fessor with the Software Engineering Department,
University of Szeged, Hungary, leading the IoT
Cloud Research Group of the Department. His
research interests include the federative manage-
ment of the IoT, fog and cloud systems, and data
management issues of distributed systems in gen-
eral. He is currently the Leader of a national
project OTKA FK 131793 financed by the Hungar-
ian Scientific Research Fund, and a work package
leader in the GINOP IoLT project, financed by the Hungarian Government
and the European Regional Development Fund. He is also a Management
Committee member of the INDAIRPOLLNET and CERCIRAS COST
actions. He has also participated in several successful European projects,
including ENTICE EU H2020, COST IC1304, COST IC0805, SHIWA,
S-Cube EU FP7, and the CoreGRID EU FP6 Network of Excellence projects.
He was a member of numerous program committees for European confer-
ences and workshops, and has published over 100 scientific papers, having
more than 1000 independent citations.
201086 VOLUME 8, 2020
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