Internet of Things A General Overview Between Arch
Internet of Things A General Overview Between Arch
Internet of Things A General Overview Between Arch
* Correspondence: francesco.pascale@polimi.it
Abstract: In recent years, the growing number of devices connected to the internet has increased
significantly. These devices can interact with the external environment and with human beings
through a wide range of sensors that, perceiving reality through the digitization of some parameters
of interest, can provide an enormous amount of data. All this data is then shared on the network
with other devices and with different applications and infrastructures. This dynamic and ever-
changing world underlies the Internet of Things (IoT) paradigm. To date, countless applications
based on IoT have been developed; think of Smart Cities, smart roads, and smart industries. This
article analyzes the current architectures, technologies, protocols, and applications that characterize
the paradigm.
Keywords: Internet of Things; machine to machine; smart vehicle; e-health; smart building; smart
home; smart city; smart agriculture; Industry 4.0
1. Introduction
Citation: Lombardi, M.; Pascale, F.;
Santaniello, D. Internet of Things: A The Internet of Things (IoT) paradigm refers to a system of devices, interconnected
General Overview between Archi- with each other, equipped with computational capacity (smart objects), identifiable and
tectures, Protocols and Applications. enabled to transfer data over a network, without a required human interaction [1]. The
Information 2021, 12, 87. concept behind this paradigm is the pervasive presence of smart devices, which by coop-
https://doi.org/10.3390/info12020087 erating with each other and interacting with human beings achieve common goals [2].
Although this technology has started to be widely used only in recent years, it is
Academic Editor: Ruggero Lanotte possible to see traces of it already many years ago, even with theoretical hints. To give an
Received: 30 December 2020
example, in 1991, Mark Weiser wrote an article on ubiquitous computing: it is a model of
Accepted: 17 February 2021
human–machine interaction in which information processing is integrated within every-
Published: 19 February 2021
day objects rather than within individual personal computers [3]. One of the first real ap-
plications of a system similar to the described above can be found in the industrial sector,
Publisher’s Note: MDPI stays neu-
tral with regard to jurisdictional
where realized machines were able to exchange information about their state inde-
claims in published maps and insti-
pendently. These systems were called machine to machine (M2M). The machines formed
tutional affiliations. a closed system, and the primary purpose of the information exchange was to make the
monitoring and management of the machines more efficient and less expensive. Com-
pared to the current meaning of IoT, there was a lack of awareness of the potential that
data could provide if reused in a broader context, for example, when aggregated with
Copyright: © 2021 by the authors. Li- other systems connected through a network [4].
censee MDPI, Basel, Switzerland. The term “Internet of Things” was first used in 1999 by Kevin Ashton during a
This article is an open access article Procter and Gamble presentation [5]. In this presentation, Ashton explained the possible
distributed under the terms and con- benefits of using RFID technology in goods management. By equipping the goods with
ditions of the Creative Commons At- special devices, they could “communicate” information of interest (status, traceability,
tribution (CC BY) license (http://crea- etc.). In this way, “things” and people could provide information about their status and
tivecommons.org/licenses/by/4.0/). the surrounding world, but in a much more efficient way.
The actual birth of the IoT dates back, according to Cisco estimates, to a period be-
tween 2008–2009, when for the first time, the number of connected objects exceeded the
world population. In 2010, the number of such objects had almost doubled compared to
that time, reaching about 12.5 billion. Since those years, IoT, thanks to continuous techno-
logical developments and considerable investments by companies, has become increas-
ingly widespread in everyday life.
According to IoT-analytics estimates, there are currently about 20 billion connected
objects globally, and the IoT sector generates a market of about $150 billion. In 2024, con-
nected objects will exceed 30 billion, and the market value will be about 1 billion. As with
any new technology trend, there are three possible categories of challenges for IoT to over-
come: business, society, and technology [6–8].
The business field’s challenges mainly concern the identification of the motivation to
start investing or not in a specific product and the design of a business model to achieve
economic gain. In this category, depending on the use and the type of customer, products
can be divided into three categories:
• Consumer IoT (smartphones, smart car, smartwatch, etc.);
• Commercial IoT (IoT Healthcare, Smart City, etc.);
• Industrial IoT (includes various types of devices for industrial use).
The challenges in society’s field are to identify with the perspective of the customer
who benefits from a product. To do this, it is necessary to consider some elements such as
the constant change of requirements and demands imposed by the customer, the emer-
gence of new devices, customer confidence in specific brands and products, and lack of
knowledge of best practices in terms of privacy and security.
Although the current technologies that belong to the IoT domain can now be defined
as advanced, several areas can be identified that need further development.
IoT needs minimal components to be integrated into everyday objects. The miniatur-
ization and integration of components itself is a field that can expand with the integration
of silicon components into metallic or fabric materials. In addition, there is a need for such
components to quickly harvest the necessary energy from their surroundings and use it
profitably. Smart objects need to withstand harsh conditions, be it humidity, temperature,
or shock and vibration; for their everyday use, they also need to be extremely reliable, and
guarantee very high and consistent quality. Another aspect that is often underestimated
is the ability of smart devices to self-configure and organize themselves. Moreover, it will
be necessary to find standard protocols to identify objects uniquely. Moreover, a critical
field concerns security to find solutions to secure connected objects, preventing cyber-
attacks that can undermine the global growth of the Internet of Things.
fragmentation of possible applications, each of which depends on many very often differ-
ent variables and design specifications. This problem must be added to each supplier’s
tendency to propose its platform for similar applications [9–11]. In Figure 1, it is possible
to see some of most common IoT Architecture used.
• Sensing and actuation: objects can collect information about the surrounding world
and manipulate it through the use of sensors and actuators;
• Embedded information processing: the smart objects are equipped with calculation
capabilities to process the results of the sensors and drive the actuators;
• Localization: objects are aware of their physical location or can be located;
• User interface: objects can communicate appropriately with users via displays or
other interfaces.
Table 1 lists some of the technologies used to implement smart objects’ various fea-
tures [11]. There are many hardware platforms on the market equipped with these fea-
tures, among the many examples are RaspberryPi, Arduino, Beaglebone Black, etc.
In this layer, wireless protocols are particularly important. Compared to those requir-
ing cables, wireless sensors can be installed in hard-to-reach environments and require
less material and human resources for installation. Additionally, in a wireless sensor net-
work, the various nodes can be added or removed easily, and their location can be
changed without reconsidering the structure of the entire network. The choice of a proto-
col to use depends on the network’s size, the power consumption of each node, and the
transmission speed needed in a given application.
In other applications, however, it may be necessary to build a wired network. The
latter enjoys more excellent reliability and higher transmission rates [14]. To give an ex-
ample, it is possible to think of a vehicle’s internal network that connects the various Elec-
tronic Control Units (ECU) that control the mechanical parts of the car (steering, brake,
etc.). In this case, it is essential to have a reliable and fast network, because delays or mal-
functions could have severe consequences for the people on board the car.
however, it could be necessary to add additional layers or adapt the architecture to the
specific application to realize.
In particular, there are two different architectures: the architectures belonging to the
first class are obtained by adapting to the context of IoT existing architectures; the archi-
tectures belonging to the second class are built from scratch. Some examples of architec-
tures can be seen in Table 3.
Figure 2. IETF protocol stack for IoT (left) and TCP/IP stack (right).
Due to the scarcity of computational resources and the heterogeneity of devices and
traffic, new protocols have been introduced that have replaced or flanked the TCP/IP stack
protocols.
In particular, instead of the application-level protocols of the TCP/IP stack, the Con-
strained Application Protocol (CoAP) is used, which represents a lightweight version of
the Hypertext Transfer Protocol (HTTP), suitable to working with devices and sensors
with limited resources. CoAP also uses the User Datagram Protocol (UDP) at the transport
level, which, compared to the Transmission Control Protocol (TCP) used by HTTP, offers
fewer services but is much lighter [21]. Finally, a layer is added (adaptation layer) in which
the IPv6 packet headers, using the IPv6 over Low Power Wireless Personal Area Network
(6LoWPAN) protocol, are encapsulated and compressed so that devices can manage them
with little computing power. These protocols will be discussed in detail in the next section.
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The main difference from the architecture described above is that the devices are not
directly accessible and only look at the Internet through the gateway. In this way, the
constraints given by the scarcity of computational resources and heterogeneity must be
managed only at the lowest level, where the connection between the devices and the gate-
way itself is implemented. At the higher level, the standard protocols of the TCP/IP stack
can be used.
provides distributed computing, data storage, and control capabilities. It interferes be-
tween cloud structures and end users to make available files and resources that are usually
accessible only through a network connection. Thanks to the advantage of distributed ar-
chitecture, Edge Computing can provide faster response and greater quality of service
[25]. Building an architecture based on Edge Computing may have more benefits:
• Minimizes latency; many actions are taken very close to where the action is;
• Allows for bandwidth saving, avoiding over dimensioning the band to the Cloud;
• Solves some security issues, because many decisions are taken in a subnet and are
not exposed to the risks arising from the external Internet.
The Edge Computing applications are multiple and differentiated, and it is possible
to monitor or analyze in real time network data of industrial devices. It can be possible,
for example, to have actions machine to machine (M2M), perform actions on other ma-
chines or Human Machine Interaction (HMI), launch alerts, and report to specific users.
In Figure 4, there is an example of fog/edge computing architecture:
The problems related to data transmission delays and security in data management
have led to the development of Edge Computing as a potential solution. Edge Computing,
defined as IT architecture in which computing applications, data-storage, and services are
completely or partially pushed near the end-user [26], could bring a significant improve-
ment in IoT paradigm.
Edge Computing architectures for IoT (ECAs-IoT) include security-based architec-
tures, which also include Software Defined Networking (SDN) based approaches. Ma-
chine learning based architectures include Transferring Trained Models (TTM) and
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Board Computers (SBCs) integrated with sensors and built-in TCP/IP and security func-
tionalities, which are typically used to realize IoT products (e.g., Arduino Yun, Raspberry
PI, Beagle Bone Black, etc.); and RFID sensor network (RSN), which has the possibility of
supporting sensing, computing, and communication capabilities in a passive system.
In the middle of IoT architecture we can find the network layer, which is used to
define routing and provide data transmission support through integrated heterogeneous
networks. Examples of communication protocols used for the IoT are Wi-Fi, Bluetooth,
IEEE 802.15.4, Z-wave, and LTE-Advanced. Some specific communication technologies
also used are RFID, Near Field Communication (NFC), and ultra-wide bandwidth (UWB).
RFID is the first technology used to realize the M2M concept (RFID tag and reader): the
RFID reader transmits a query signal to the tag and receives reflected signal from it, which
in turn is passed to the database, and it identifies objects based on the reflected signals
[33]. RFID tags can be active, passive, or semi-passive/active, and they have a range from
10 cm to 200 m. The NFC works at high frequency band at 13.56 MHz and supports data
rate up to 424 kbps; the applicable range is up to 10 cm [34]. The UWB communication
technology is designed to support communications within a low range coverage area us-
ing low energy and high bandwidth, whose applications to connect sensors have been
increased recently [35]. Another communication technology is Wi-Fi that uses radio waves
to exchange data among things within 100 m range [36]. Bluetooth presents a communi-
cation technology that is used to exchange data between devices over short distances us-
ing short-wavelength radio to minimize power consumption [37]. Bluetooth special inter-
est group (SIG) produced Bluetooth 4.1, which provides Bluetooth Low Energy as well as
high-speed and IP connectivity to support IoT. LTE (Long-Term Evolution) is originally a
standard wireless communication, and it can cover fast-travelling devices and provide
multicasting and broadcasting services. LTE-A (LTE Advanced) [38] is an improved ver-
sion of LTE including bandwidth extension, which supports up to 100 MHz, downlink
and uplink spatial multiplexing, extended coverage, higher throughput, and lower laten-
cies.
In many architectures, it can be possible to find a service layer such as SoA-based
architecture. The service layer is located between the network layer and the application
layer and provides efficient and secure services to objects or applications. In the service
layer, the following enabling technologies should be included to ensure that the service
can be provided efficiently: interface technology, service management technology, mid-
dleware technology, and resource management and sharing technology. The interface
technology must be designed in the service layer to ensure the efficient and secure infor-
mation exchange for communications among devices and applications. To support appli-
cations in IoT, an interface profile (IFP) can be considered as a service standard, which can
be used to facilitate the interactions among services provided by various devices or appli-
cations. To achieve an efficient IFP, universal plug and play should be implemented
[39,40]. Service management can effectively discover the devices and applications, and
schedule efficient and reliable services to meet requests. A service can be considered as an
integrated application, including collection, exchanging, and storage of data, or an asso-
ciation of these behaviors to achieve a special objective [41].
Various heterogeneous networks are integrated to provide data delivery for all ap-
plications in IoT (smart transportation, smart grid, etc.). To reduce the cost, some applica-
tions can share part of the network resources to increase their utilization. In this case, en-
suring that information requested by various applications is delivered on time is a chal-
lenging issue in IoT. Existing resource sharing mechanisms primarily focus on the spec-
trum sharing, which is used to efficiently coordinate multiple networks in the same fre-
quency to maximize the utilization of network resources [42,43].
Applications are on the top of the architecture, exporting all the system’s functional-
ities to the final user. However, this layer is not considered to be part of the middleware,
but exploits all the functionalities of the middleware layer. Using standard web service
protocols and service composition technologies, applications can realize a perfect
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integration between distributed systems and applications. Many IoT standards are pro-
posed to facilitate and simplify application programmers’ and service providers’ jobs. Dif-
ferent groups have been created to provide protocols in support of the IoT. In this para-
graph, some of the most common protocols that can enable a reliable and secure commu-
nication in IoT are presented as shown in Table 4 [44,45].
• IEEE 802.15.4 is a protocol designed for the physical layer and the MAC layer in wire-
less personal area networks (WPANs). This protocol is used to focus on low-rate
WPANs, providing low rate connections of all things in a personal area with low
energy consumption, low rate transmission, and low cost [46].
• Low-power WPANs (LoWPANs) are organized by many low-cost devices connected
via wireless communications (Tan and Koo 2014). In comparison with other types of
networks, LoWPAN has several advantages (small packet sizes, low power, low
bandwidth, etc.). 6LoWPAN protocol (an enhancement of LoWPANs), designed
combining IPv6 and LoWPAN, has several advantages: great connectivity and com-
patibility with legacy architectures, low-energy consumption, ad-hoc self-organiza-
tion, etc.
• ZigBee is a wireless network technology, designed for short-term communication
with low-energy consumption and great reliability. In ZigBee protocol, five layers
are included: physical layer, MAC layer, transmission layer, network layer, and ap-
plication layer.
• The main objective of Z-wave is to provide reliable transmission between a control
unit and one or more end-devices; Z-wave is suitable for networks with low band-
width. Although both ZigBee and Z-wave support the shortrange wireless commu-
nication with low cost and low energy consumption, there are some differences be-
tween them.
• LoRaWAN is a cloud-based MAC (Media Access Control) layer protocol but primar-
ily serves as a network layer protocol for managing communications between
LPWAN (Low Power Wide Area Network) gateways and end-node devices such as
routing protocol, managed by LoRa Alliance. Version 1.0 of the LoRaWAN specifica-
tion was released in June 2015. LoRaWAN defines the communication protocol and
system architecture for the network, while the physical LoRa layer allows long-range
communication link. LoRaWAN is also responsible for managing communication
frequencies, data rates, and power for all devices. Devices in the network are asyn-
chronous and transmit when they have data available for sending. Data transmitted
by a device (called an endpoint) are received by multiple gateways, which forward
data packets to a centralized network server (or network server). The network server
filters out duplicate packets, performs security checks, and manages the network.
Data are then forwarded to the application servers. The technology shows high reli-
ability for moderate load; however, it does present some performance issues related
to sending acknowledgments.
• The Sigfox standard is based on ultra narrow band RF communication with very low
consumption. It takes advantage of the 868 MHz frequency and is not subject to con-
cessions. The possible applications are countless: for example, the remote detection
of sensors and meters.
• Narrowband Internet of Things is an LPWAN radio technology standard developed
by 3GPP to enable communication for a wide range of cellular devices and services,
the specifications of which were frozen in 3GPP Release 13, in June 2016. Other 3GPP
IoT technologies include eMTC and EC-GSM-IoT.
• Message Queue Telemetry Transport (MQTT) uses a publish/subscribe technique: it
is a messaging protocol, which is used to collect measured data on remote sensors
and transmit them to servers. MQTT is a simple and lightweight protocol and sup-
ports networks with low bandwidth and high latency.
• Constrained Application Protocol (CoAP) is a messaging protocol based on repre-
sentational state transfer (REST) architecture [47,48]. CoAP has been proposed to
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modify some HTTP functions to meet the requirements for IoT; in fact, it is an appli-
cation layer protocol in the 6LoWPAN protocol stack and aims to enable resources
constrained devices to achieve RESTful interactions.
• Extensible Messaging and Presence Protocol (XMPP) is an instant messaging proto-
col based on XML streaming protocols. XMPP inherits features from XML protocol,
so it has great scalability, addressing, and security capabilities, and it can be used for
multiparty chatting, voice and video streaming, and tele-presence.
• Data distribution service (DDS) is a publish/subscribe protocol for supporting high
performance device-to-device communication. DDS has been developed by object-
manage-group and is a data centric protocol, in which multicasting can be supported
to achieve great QoS and high reliability.
• Advanced Message Queuing Protocol (AMQP) is an open standard message queuing
protocol used to provide message service (queuing, routing, security, reliability, etc.)
in the application layer; it focuses on message-oriented environments and can be con-
sidered as a message-oriented middleware protocol.
Protocol Layer
IEEE 802.15.4 Perception Layer
LoWPANs Network Layer
ZigBee Network Layer
Z-wave Network Layer
LoraWLAN Network Layer
Sigfox Network Layer
NB-IoT Network Layer
Message Queue Telemetry Transport (MQTT) Application Layer
Constrained application protocol (CoAP) Application Layer
Extensible messaging and presence protocol (XMPP) Application Layer
Data distribution service (DDS) Application Layer
Advanced message queuing protocol (AMQP) Application Layer
4. Applications in IoT
As said previously, there are many applications field of the IoT like Big Data, Cloud
Computing, health care, Smart City, Smart Home, Smart Grid, mobile application, cyber
industries, Smart Agriculture, automotives, and many others.
The importance of IoT has gone established with time, especially for those applica-
tions natively interconnected such as mobile application, and it lends itself to all those
pervasive/ubiquitous computing applications [49]. In fact, there is often confusion be-
tween the concept of IoT and Context-Aware Computing, because they seem closely
linked. Although these two concepts are often used simultaneously, they remain two sep-
arate things. IoT is one of the enabling technologies of context-aware computing, and to-
day, a large number of IoT-based pervasive systems take advantage of Context-Aware-
ness as a core feature [50–52].
Another important paradigm that fits with the IoT is Industry 4.0, which represents
the current trend of automation and data exchange in manufacturing technologies. Indus-
try 4.0 includes cyber–physical systems, the Internet of things, and Cloud Computing.
Today, several research works explain this concept. Indeed, Guo-jian Cheng et al. talk
about Industry 4.0 Development and its application for intelligent manufacturing [53].
This concept is strict in relationship with IoT, because to realize the vision of cyber–phys-
ical systems, it iss necessary that all systems are interconnected over the network, and IoT
provides the right technology base. As highlighted by the EU Framework Program for
Research and Innovation (HORIZON 2020) (Horizon 2020 is the financial instrument im-
plementing the Innovation Union, a Europe 2020 flagship initiative aimed at securing
Information 2021, 12, 87 14 of 21
Europe's global competitiveness.), many funds will be invested by the European commu-
nity in IoT and Industry 4.0, because this will soon represent a sure source of work for
companies [54].
The concept of IoT is often associated with Smart City and Smart Home. In fact, Smart
City has defined as an urban development vision that integrate multiple information and
communication technologies to improve the quality of life [55]. The assets that are in-
cluded and managed in this vision are multiple; for example, we can consider local de-
partments information systems, schools, libraries, transportation systems, hospitals,
power plants, water supply networks, waste management, and law enforcement. Smart
Home is defined as a modern home that has appliances, lighting, and/or electronic devices
that can be controlled remotely by the owner, often through mobile. As evidence, these
two concepts integrate with the IoT paradigm and are enabled by it. K. E. Skouby and P.
Lynggaard suggest a four-layer model that joins and interfaces these elements by deploy-
ing technologies such as 5G, Internet of Things, Cloud of Things, and distributed artificial
intelligence [56]. Talari et al. provide an inclusive review on the concept of the smart city
besides their different applications, benefits, and advantages. In this work, it can be pos-
sible find a review of IoT technologies and their capabilities to merge into and apply to
the different parts of smart cities [57].
5G represents a new era technology that will allow us to surf at speeds much higher
than the current ones; in fact, if 4G technology provided an average of 10 Mbps per user,
5G will make us navigate at a minimum of 100 Mbps [58]. This will certainly give more
possibilities for mobile devices and a greater impetus to the development of new planning
strategies such as smart cities and all that follows, such as having more and more objects
connected to each other and able to exchange information. Obviously, this great range of
additional opportunities is undoubtedly a great point in favor of this technology.
Another important and challenging application for IoT is the Smart Grid system. The
Smart Grid aims to use energy power in a safe and correct way in which the power supply
system can distribute electricity avoiding waste. For these reasons, today the use of Smart
Grid and Smart Micro Grid is very widespread. An example of this application is pre-
sented by Clarizia et al., who proposed an architecture of a real-time energy management
system that provides several Smart Meters [59]. This application monitors connected loads
and communicate with a Smart Concentrator via CAN Bus that stores the data forwarded
from a single Smart Meter, in order to make this information available for http request/re-
sponse. Li et al. present the design and implementation of a novel co-simulator and eval-
uate the effectively IoT-aided algorithms for scheduling the jobs of electrical appliances
[60].
During last year, health care support systems are growing up, and these are essential
for the medical support, with an IoT communication framework as the main enabler for
distributed worldwide health care applications. Vitali and Pernici have proposed an ap-
proach to a health-care scenario enriched with IoT devices; these techniques allow the
discovery of interconnections between processes and external factors which have an im-
pact on them [61]. Ahouandjinou et al. have explored the opportunities for IoT to realize
the vision of the future of health care to attainment a new monitoring status system for
patients of the Intensive Care Unit (ICU), to improve medical care service performances
[62].
Today, to support IoT, it is important to consider all the substructures that are used
to support the IoT devices. In particular, Cloud Computing is one of technologies that is
more applied and better lends itself to be used in IoT. It is important for example for data
storing and sharing between various IoT devices, such as communication from and to
sensor and actuator or mobile device. Galache et al. presents the Cloud project, whose
main aim is making citizens aware of city resources and helping them to use and care
these resources by means of smart IoT services in the Cloud [63]. Carrillo et al. present a
framework used to provide the needed computational power to the Smart Building by
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using Cloud Computing to have all computational power as well as control monitor ca-
pabilities in the cloud [64].
As its definition, Big Data represents large and complex data sets, which include
analysis, capture, data curation, search, sharing, storage, transfer, visualization, querying,
updating, and information privacy. In last few years, the IoT challenge consists in manag-
ing and analyzing the entire large amount of data generated by sensors all around the
world. This is the reason why the concept of Big Data is often associated with that of IoT.
Sezer et al. propose a combined framework that brings Big Data, IoT, and semantic web
together to build an augmented framework. They provide a realistic use case that demon-
strates how the model can implement the desired functionality and achieve the goals of
such a model [65]. Bashir et al. present an IoT Big Data Analytics IBDA framework for the
storage and analysis of real time data generated from IoT sensors deployed inside the
smart building, developed by using Python and the Big Data Cloudera platform [66].
Another important concept associated with IoT is Smart Agriculture. Starting from
concept of precision agriculture, that is an integrated system of methodologies and tech-
nologies designed to increase crop production, quality, and productivity of a farm, we
have the definition of Smart Agriculture. It is based on the aim “do the right thing, in the
right place at the right time, with the right amount”, respecting the real needs of plants.
Indeed, it defines the scope of application solutions aimed at the monitoring, manage-
ment, and optimization of different processes related to agriculture. Baranwal et al. pro-
pose an integrated approach based on IoT device, capable of analyzing the sensed infor-
mation and then transmitting it to the user [67]. This device can be controlled and moni-
tored from remote location, and it can be implemented in agricultural fields, grain stores,
and cold stores for security purpose. This study is oriented to accentuate the methods to
solve problems like identification of rodents, threats to crops, and delivering real time
notification based on information analysis and processing without human intervention.
Kapoor et al. describe an approach to combine loT and image processing to determine the
environmental factor or man-made factor (pesticides/fertilizers), which is specifically hin-
dering the growth of the plant [68].
No less important than the previous ones are the Blockchains. Blockchains represent
the reference infrastructure for the operation of this network of “intelligent objects”. Many
think that distributed trust technology is the only technology capable of ensuring scala-
bility, respect for privacy, and reliability for growing IoT environments [69,70].
Blockchains are a candidate for the role of key application for the IoT. The technology
in question can be used to track billions of connected devices, allowing the processing of
the transactions they produce and the coordination between physical devices. This decen-
tralized approach would eliminate the failure points of traditional networks, facilitating
the creation of a more resilient ecosystem in which smart devices can operate. Further-
more, the cryptographic algorithms used by Blockchains would allow to increase the pro-
tection of private consumer data [71].
One of the most interesting applications is the Cognitive IoT based on Blockchain as
shown in [72]. Cognitive IoT is the use of cognitive computing technologies in combina-
tion with data generated by connected devices and the actions those devices can perform
[73,74]. In this way, in a computer, system understanding means being able to take in large
volumes of both structured and unstructured data and derive meaning from them—that
is, establish a model of concepts, entities, and relationships. This provides a wealth of
major and interesting applications.
The sensors’ rapid spread and their produced data also affected the industry of au-
tomotive. In fact, for a few years it is possible to see a fast increase of technologies on
board of a car both for control and assistance systems than for monitoring and diagnostic
systems. In this scenario, IoT offers an important support for automotives, since it fur-
nishes the best way to help the automobile manufacturers thanks to its paradigm imple-
mentations. It is possible to affirm that the Automotive Internet of Things (IoT) is an
emerging research field that applies the IoT to the intelligent transport system [75]. With
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the introduction of smartphones, cloud, Edge Computing, and mobile Internet, the auto-
motive ecosystem is shifting toward the Internet of Vehicles (IoV) [76,77]. These technol-
ogies have brought the car to become a very smart vehicle, so now the automobile is con-
sidered an integrated and complicate ecosystem of objects that cooperates with each other.
Trying to summarize, the applications of most significant interest then turn out to be:
• Smart Wearable: are wearable devices, low energy consumption, equipped with sen-
sors and networked to collect data on users in order to monitor daily physical activity
(to keep track of progress) or other information useful for health (heartbeat, quantity,
and quality of sleep, etc.) to identify possible problems on time and do prevention;
• Smart Vehicle and Connected Vehicle: systems that were once mechanical (steering,
brakes, etc.) have been replaced by electronic control units (ECUs) able to communi-
cate with each other and with the outside world. This is to efficiently exploit the fuel
and increase the driver’s safety by monitoring parameters of interest thanks to elec-
tronics. The internet connection can be used to monitor traffic (and then choose the
most appropriate route to get to a specific destination or find parking quickly), send
signals in case of failure, receive timely assistance, and have access to infotainment
services and many other applications. In this case, the ECUs form a network of smart
objects inside the car, but also the car itself, seen in a broader context (like a smart
city), becomes a smart object;
• e-Health: the goal is to monitor patients’ health through devices (which can also be
placed inside the human body) to make prevention and make diagnoses and treat-
ments even when patients are far from the hospital. Additionally, monitoring the
demand for healthcare services makes it possible to invest efficiently in specific areas
of healthcare;
• Smart Building and Smart Home: smart objects can be used to monitor the structural
integrity of buildings (and thus ensure more excellent safety), environmental param-
eters (such as temperature or humidity), or the presence of people in specific places.
All this to make the environment comfortable and at the same time efficiently man-
age light, electricity, heating, etc.;
• Smart City: a network of sensors can be used to efficiently manage water resources,
transport, energy, waste collection, etc., which would reduce pollution and waste and
increase the comfort of citizens. Two possible examples could be intelligent parking
management and public lighting management. In the first case, citizens could avoid
wasting time looking for a free parking space, and the pollution caused by the car
would be reduced; in the second case, the lighting could be managed according to
the transit of pedestrians and vehicles, which would allow energy savings;
• Smart Metering and Smart Grid: thanks to the monitoring of the electricity grid, it is
possible to manage the distribution and generation of energy (even that generated
by small generators such as wind or photovoltaic plants scattered throughout the
territory);
• Smart Agriculture (or precision agriculture): thanks to a network of sensors and ac-
tuators, you can monitor the health and the actual needs of crops to exploit resources
(water, fertilizers, etc.) in an efficient and targeted way;
• Smart Factory (or Industry 4.0): by integrating new technologies in production pro-
cesses, working conditions could be improved (an example could be the support of a
robot to the human operator) as well as safety and productivity in an industry.
Information 2021, 12, 87 17 of 21
6. Conclusions
This article has provided a current overview of architectures, technologies, protocols,
and applications that characterize the Internet of Things (IoT) paradigm. In particular, the
main architectures used in the IoT domain have been described based on their reference
layers. The leading enabling technologies and most common protocols are IEEE 802.15.4,
LoWPANs, ZigBee, Z-wave, and LoraWLAN, and Sigfox No-IoT, MQTT, CoAP, XMPP,
DDS, and AMQP have been discussed. The main application areas have been identified,
such as Big Data, Cloud Computing, health care, Smart City, Smart Home, Smart Grid,
mobile application, cyber industries, Smart Agriculture, and automotive.
The global IoT market is growing exponentially. The statistics show that it reached
745 billion dollars in 2019. However, to realize its growth, it is unnecessary to analyze
data, statistics, and market forecasts. In fact, it is before our eyes how important connected
devices have become and how emerging technologies such as the Industrial Internet of
Things, smart homes, smart cities, smart factories, and smart metering are already a real-
ity.
Information 2021, 12, 87 18 of 21
The possibilities for the development of the Internet of Things are endless. Much will
depend on the programmatic lines that global market manufacturers will adopt to make
the connected devices as compatible as possible and thus increase the degree of interop-
erability and integration without neglecting the aspects related to safety. The analysis
companies’ forecasts are optimistic: in 2023, the IoT market globally should reach 318 bil-
lion dollars.
Author Contributions: Conceptualization: M.L., F.P. and D.S.; methodology: M.L., F.P. and D.S.;
formal analysis: M.L.; investigation: F.P.; resources: D.S.; data curation: F.P.; writing—original draft
preparation: M.L. and D.S.; writing—review and editing: F.P.; visualization: M.L. and D.S.; super-
vision: F.P. All authors have read and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Informed consent was obtained from all subjects involved in the
study.
Data Availability Statement: MDPI Research Data Policies
Acknowledgments: Not applicable.
Conflicts of Interest: The authors declare no conflict of interest.
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