ArticlePDF Available

Abstract and Figures

Cloud computing and Internet of Things (IoT) are two very different technologies that are both already part of our life. Their adoption and use are expected to be more and more pervasive, making them important components of the Future Internet. A novel paradigm where Cloud and IoT are merged together is foreseen as disruptive and as an enabler of a large number of application scenarios.In this paper, we focus our attention on the integration of Cloud and IoT, which is what we call the CloudIoT paradigm. Many works in literature have surveyed Cloud and IoT separately and, more precisely, their main properties, features, underlying technologies, and open issues. However, to the best of our knowledge, these works lack a detailed analysis of the new CloudIoT paradigm, which involves completely new applications, challenges, and research issues. To bridge this gap, in this paper we provide a literature survey on the integration of Cloud and IoT. Starting by analyzing the basics of both IoT and Cloud Computing, we discuss their complementarity, detailing what is currently driving to their integration. Thanks to the adoption of the CloudIoT paradigm a number of applications are gaining momentum: we provide an up-to-date picture of CloudIoT applications in literature, with a focus on their specific research challenges. These challenges are then analyzed in details to show where the main body of research is currently heading. We also discuss what is already available in terms of platforms-both proprietary and open source-and projects implementing the CloudIoT paradigm. Finally, we identify open issues and future directions in this field, which we expect to play a leading role in the landscape of the Future Internet.
Content may be subject to copyright.
Accepted Manuscript
Integration of cloud computing and Internet of Things: A survey
Alessio Botta, Walter de Donato, Valerio Persico, Antonio Pescap´
e
PII: S0167-739X(15)00301-5
DOI: http://dx.doi.org/10.1016/j.future.2015.09.021
Reference: FUTURE 2851
To appear in: Future Generation Computer Systems
Received date: 22 November 2014
Revised date: 15 September 2015
Accepted date: 17 September 2015
Please cite this article as: A. Botta, W. de Donato, V. Persico, A. Pescap´
e, Integration of cloud
computing and Internet of Things: A survey, Future Generation Computer Systems (2015),
http://dx.doi.org/10.1016/j.future.2015.09.021
This is a PDF file of an unedited manuscript that has been accepted for publication. As a
service to our customers we are providing this early version of the manuscript. The manuscript
will undergo copyediting, typesetting, and review of the resulting proof before it is published in
its final form. Please note that during the production process errors may be discovered which
could affect the content, and all legal disclaimers that apply to the journal pertain.
Vision and motivations for the integration of Cloud Computing and Internet of Things (IoT).
Applications stemming from the integration of Cloud Computing and IoT.
Hot research topics and challenges in the integrated scenario of Cloud Computing and IoT.
Open issues and future directions for research in this scenario.
Highlights (for review)
Integration of Cloud Computing
and Internet of Things: a Survey
Alessio Botta, Walter de Donato, Valerio Persico, Antonio Pescap´e
University of Napoli Federico II (Italy)
NM2 SRL, Italy
{a.botta, walter.dedonato, valerio.persico, pescape}@unina.it
Abstract
Cloud computing and Internet of Things (IoT) are two very different tech-
nologies that are both already part of our life. Their adoption and use is
expected to be more and more pervasive, making them important compo-
nents of the Future Internet. A novel paradigm where Cloud and IoT are
merged together is foreseen as disruptive and as an enabler of a large number
of application scenarios.
In this paper, we focus our attention on the integration of Cloud and
IoT, which is what we call the CloudIoT paradigm. Many works in litera-
ture have surveyed Cloud and IoT separately and, more precisely, their main
properties, features, underlying technologies, and open issues. However, to
the best of our knowledge, these works lack a detailed analysis of the new
CloudIoT paradigm, which involves completely new applications, challenges,
and research issues. To bridge this gap, in this paper we provide a literature
survey on the integration of Cloud and IoT. Starting by analyzing the basics
of both IoT and Cloud Computing, we discuss their complementarity, detail-
ing what is currently driving to their integration. Thanks to the adoption
of the CloudIoT paradigm a number of applications are gaining momentum:
we provide an up-to-date picture of CloudIoT applications in literature, with
a focus on their specific research challenges. These challenges are then ana-
lyzed in details to show where the main body of research is currently heading.
We also discuss what is already available in terms of platforms – both propri-
etary and open source – and projects implementing the CloudIoT paradigm.
Finally, we identify open issues and future directions in this field, which we
expect to play a leading role in the landscape of the Future Internet.
Preprint submitted to Journal of Future Generation Computer SystemsSeptember 14, 2015
*Manuscript
Click here to view linked References
Keywords: Cloud Computing, Internet of Things, Ubiquitous Networks,
Cloud of Things, Pervasive Applications, Smart City, Smart Applications.
1. INTRODUCTION AND MOTIVATION
The Internet of Things (IoT) paradigm is based on intelligent and self
configuring nodes (things) interconnected in a dynamic and global network
infrastructure. It represents one of the most disruptive technologies, enabling
ubiquitous and pervasive computing scenarios. IoT is generally characterized
by real world small things, widely distributed, with limited storage and pro-
cessing capacity, which involve concerns regarding reliability, performance,
security, and privacy. On the other hand, Cloud computing has virtually un-
limited capabilities in terms of storage and processing power, is a much more
mature technology, and has most of the IoT issues at least partially solved.
Thus, a novel IT paradigm in which Cloud and IoT are two complementary
technologies merged together is expected to disrupt both current and Future
Internet [132, 25]. We call this new paradigm CloudIoT.
Reviewing the rich and articulate state of the art in this field, we found
that both topics gained popularity in the last few years (Fig. 1a), and the
number of papers dealing with Cloud and IoT separately shows an increasing
trend since 2008 (Fig. 1b)1. In this paper we review the literature focusing on
the integration of Cloud and IoT, a really promising topic for both research
and industry, witnessed by the more recent and rapidly increasing trend
dealing with Cloud and IoT together (Fig. 1c).
Inspired by well known indications in literature [74], we adopt the research
methodology schematically depicted in Fig. 2. By analyzing a large number
of papers mainly published between 2008 and 2014 in selected venues, (A)
we derive a temporal characterization of the literature – aiming at showing
in a qualitative way the temporal behavior of the research and the common
interest about the CloudIoT paradigm (see Fig. 1) – and build the basis for
the following steps. The characterization of the literature is reported in this
section and is supported by Fig. 1. (B) We introduce a short background on
both Cloud and IoT to provide the readers with the necessary basics and to
tackle the integration of Cloud and IoT (Sec. 2). (C) We present a detailed
1Data have been obtained from Google Trends (https://www.google.com/trends/)
and Scholar (https://scholar.google.com/) web facilities.
2
discussion on the CloudIoT paradigm, highlighting the complementarity of its
components and the main driver to their integration (Sec. 3). (D) We detail
the new application scenarios stemming from the adoption of the CloudIoT
paradigm (Sec. 4). (E) We focus on research challenges (Sec. 5) arising
from the adoption of the CloudIoT paradigm and of interest for the new
applications of Sec. 4. (F) We describe the main platforms (both proprietary
and open source) and research projects in the field of CloudIoT (Sec. 6).
(G) Thanks to the previous six steps, we derive the open issues and future
directions in the field of CloudIoT (Sec. 7). Finally, we close the paper with
conclusion remarks (Sec. 8). Note that in our analysis the terms research
challenge and open issue are used with different meanings. We considered
as challenges (Sec. 5) the research problems generated by the integration
of the two worlds currently receiving attention by the research community.
Those aspects usually are or are claimed to be partially solved or solved with
respect to specific contexts and applications. The main issues related to the
integration that still require research efforts are instead referred to as open
issues. These issues are resumed in Sec. 7, where we also point out some
future research directions. It is worth noting that the differentiation between
challenges and open issues has been deduced from the surveyed literature.
2. BACKGROUND AND BASIC CONCEPTS
In this section we recall the basics of IoT and Cloud and overview the
characteristics essential for their integration.
2.1. Internet of Things
The next wave in the era of computing is predicted to be outside the realm
of traditional desktop [53]. In line with this observation, a novel paradigm
called Internet of Things rapidly gained ground in the last few years. IoT
refers to “a world-wide network of interconnected objects uniquely address-
able, based on standard communication protocols” [12, 14] whose point of
convergence is the Internet. The basic idea behind it is the pervasive pres-
ence around people of things, able to measure, infer, understand, and even
modify the environment. IoT is fueled by the recent advances of a variety of
devices and communication technologies, but things included in IoT are not
only complex devices such as mobile phones, but they also comprise every-
day objects such as food, clothing, furniture, paper, landmarks, monuments,
3
1
10
100
2008 2009 2010 2011 2012 2013
Normalized Google Trend Queries
Year
IoT
Cloud
(a) According to Google research trends.
1
10
100
1000
10000
100000
2008 2009 2010 2011 2012 2013
# of publications
Year
IoT (content)
Cloud (content)
IoT (title)
Cloud (title)
(b) By content and title for Cloud and IoT separately.
1
10
100
1000
10000
100000
2008 2009 2010 2011 2012 2013
# of publications
Year
IoT & Cloud (content)
IoT & Cloud (title)
(c) By content and title for Cloud and IoT together.
Figure 1: Interest and research trends about Cloud and IoT.
works of art, etc. [42, 60]. These objects, acting as sensors or actuators, are
able to interact with each other in order to reach a common goal.
The key feature in IoT is, without doubt, its impact on every-day life
of potential users [12]. IoT has remarkable effects both in work and home
scenarios, where it can play a leading role in the next future (assisted living,
domotics, e-health, smart transportation, etc.). Important consequences are
also expected for business (e.g. logistic, industrial automation, transporta-
tion of goods, security, etc.). According to these considerations, in 2008 IoT
has been reported by US National Intelligence Council as one of the six tech-
4
(A) Literature
Analysis
(B) Background and
Basic Concepts
Drivers for
Integraon
(D) Applicaons
(F) Plaorms,
Services, and
Research Projects
(G) Open
Issues
and Future
Directions
(C) Cloud and IoT:
Figure 2: The research methodology adopted in this work.
nologies with potential impact on US interests towards 2025 [60]. Indeed,
in 2011 the number of interconnected devices overtook the number of peo-
ple [53]. In 2012, the number of interconnected devices was estimated to be
9 billion, and it was expected to reach the value of 24 billions by 2020. Such
numbers suggest that IoT will be one of the main sources of big data [37].
In the following we describe a few important aspects related to IoT.
RFID. In IoT scenario, a key role is played by Radio-Frequency IDentifi-
cation (RFID) systems, composed of one or more readers and several tags.
These technologies help in automatic identification of anything they are at-
tached to, and allow objects to be assigned unique digital identities, to be
integrated into a network, and to be associated with digital information and
services [42]. In a typical usage scenario, readers trigger the tag transmis-
sion by generating an appropriate signal, querying for possible presence of
objects uniquely identified by tags. RFID tags are usually passive (they
do not need on-board power supply), but there are also tags powered from
5
Figure 3: IoT paradigm: an overall view (source: [42]).
batteries [12, 124].
(Wireless) Sensor Networks. Another key component in IoT environ-
ments is represented by sensor networks. For example, they can cooperate
with RFID systems to better track the status of things, getting information
about position, movement, temperature, etc. Sensor networks are typically
composed of a potentially high number of sensing nodes, communicating in
a wireless multi-hop fashion. Special nodes (sinks) are usually employed
to gather results. Wireless sensor networks (WSNs) may provide various
useful data and are being utilized in several areas like healthcare, govern-
ment and environmental services (natural disaster relief), defense (military
target tracking and surveillance), hazardous environment exploration, seis-
mic sensing, etc. [7]. However, sensor networks have to face many issues
regarding their communications (short communication range, security and
privacy, reliability, mobility, etc.) and resources (power considerations, stor-
age capacity, processing capabilities, bandwidth availability, etc.). Besides,
WSN has its own resource and design constraints (that are application- and
environment- specific) and that heavily depend on the size of the monitoring
environment [7]. The scientific community deeply addressed several issues re-
lated to sensor networks at different layers (e.g., energy efficiency, reliability,
robustness, scalability, etc.) [12].
Addressing. Thanks to wireless technologies such as RFID and Wi-Fi, IoT
paradigm is transforming the Internet into a fully integrated Future Inter-
6
net [124]. While Internet evolution led to an unprecedented interconnection
of people, current trend is leading to the interconnection of objects, to create
a smart environment [53]. In this context, the ability to uniquely identify
things is critical for the success of IoT since this allows to uniquely address a
huge number of devices and control them through the Internet. Uniqueness,
reliability, persistence, and scalability represent critical features related to
the creation of a unique addressing schema [53]. Unique identification issues
may be addressed by IPv4 to an extent (usually a group of cohabiting sensor
devices can be identified geographically, but not individually). IPv6, with its
Internet Mobility attributes, can mitigate some of the device identification
problems and is expected to play an important role in this field.
Middleware. Due to the heterogeneity of the participating objects, to their
limited storage and processing capabilities and to the huge variety of appli-
cations involved, a key role is played by the middleware between the things
and the application layer, whose main goal is the abstraction of the func-
tionalities and communication capabilities of the devices. The middleware
can be divided in a set of layers (see Fig. 3): Object Abstraction, Service
Management, Service Composition, and Application [42].
2.2. Cloud
The essential aspects of Cloud computing have been reported in the
definition provided by the National Institute of Standard and Technologies
(NIST) [92]: “Cloud computing is a model for enabling ubiquitous, conve-
nient, on-demand network access to a shared pool of configurable computing
resources (e.g., networks, servers, storage, applications, and services) that
can be rapidly provisioned and released with minimal management effort or
service provider interaction.” Even though the main idea behind Cloud com-
puting was not new, the term started to gain popularity after that Google’s
CEO Eric Shmidt used it in 2006 [130], and over the last few years the
appearance of Cloud computing has hugely impacted IT industry. The avail-
ability of virtually unlimited storage [113] and processing capabilities at low
cost enabled the realization of a new computing model, in which virtualized
resources can be leased in an on-demand fashion, being provided as general
utilities. Large companies (like Amazon, Google, Facebook, etc.) widely
adopted this paradigm for delivering services over the Internet, gaining both
economical and technical benefits.
Cloud Computing is a disruptive technology with profound implications
for the delivery of Internet services as well as for the IT sector as a whole.
7
However, several technical and business-related issues are still unsolved. Spe-
cific issues have been identified for each service models, which are mainly
related to security (e.g., data security and integrity, network security), pri-
vacy (e.g., data confidentiality), and service-level agreements, which could
scare away part of potential users [114]. Moreover, the lack of standard APIs
prevents customers to easily extract code and data from a site to run on
another. More in general, outsourcing infrastructure to a Cloud provider,
public Cloud customers are necessarily exposed to price increases, reliability
problems or even to providers going out of business [10]. Fig. 4 summarizes
the main aspects of Cloud: the main characteristics which make it a success-
ful model, its layered architecture, and the standard service models. In the
following, we describe a few important aspects of Cloud.
Layered Architecture and Service Models. The architecture of Cloud
can be split into four layers: datacenter (hardware), infrastructure, platform,
and application [130]. Each of them can be seen as a service for the layer
above and as a consumer for the layer below. In practice, Cloud services can
be grouped in three main categories: Software as a Service (SaaS), Platform
as a Service (PaaS), and Infrastructure as a Service (IaaS). SaaS refers to
the provisioning of applications running on Cloud environments. Applica-
tions are typically accessible through a thin client or a web browser. PaaS
refers to platform-layer resources (e.g., operating system support, software
development frameworks, etc.). IaaS refers to providing processing, storage,
and network resources, allowing the consumer to control the operating sys-
tem, storage and applications. It has raised the greatest interest so far [40].
Types of Clouds. Different types of Clouds have been identified in the lit-
erature [92, 130], as reported in the following: (i) Private Cloud provisioned
for exclusive use by a single organization, typically owned, managed, and
operated by the organization itself; (ii) Community Cloud – provisioned for
exclusive use by a specific community of consumers that have shared con-
cerns; (iii) Public Cloud – provisioned for open use by the general public;
(iv) Hybrid Cloud – composition of two or more distinct Cloud infrastruc-
tures (private, community, or public); (v) Virtual Private Cloud – alternative
aimed at addressing issues related to public and private Clouds, taking ad-
vantage of virtual private network (VPN) technologies for allowing business
owners to setup required network settings (e.g. security, topology, etc.).
Considering the different issues related to enterprise applications and
Cloud environments (e.g. lowering cost, increasing reliability, etc.), each type
8
Figure 4: Cloud paradigm: an overall view.
of Cloud has his own benefits and drawbacks. Thus selecting a proper Cloud
model depends on the specific business scenario.
Economical advantages. Cloud computing model is attractive since it
frees the business owner from the need to invest in the infrastructure (CAPEX),
renting resources according to needs and only paying for the usage. More-
over, it allows to decrease operating costs (OPEX), as service providers do
not have to provision capacities according to peak load (in fact, resources are
released when service demand is low). Finally, the outsourcing of the service
infrastructure to the Clouds shifts the business risk towards the infrastruc-
ture provider, generally better equipped to manage it.
Technical advantages. In addition to such economical advantages, Cloud
computing guarantees a number of technical benefits, including: energy effi-
ciency, optimization of hardware and software resource utilization, elasticity,
performance isolation, and flexibility.
3. CLOUD AND IOT: DRIVERS FOR INTEGRATION
The two worlds of Cloud and IoT have seen a rapid and independent
evolution. These worlds are very different from each other and, even bet-
ter, their characteristics are often complementary, as Tab. 1 shows. Such
complementarity is the main reason why many researchers have proposed
9
Table 1: Complementary aspects of Cloud and IoT.
IoT Cloud
Displacement pervasive centralized
Reachability limited ubiquitous
Components real world things virtual resources
Computational capabilities limited virtually unlimited
Storage limited or none virtually unlimited
Role of the Internet point of convergence means for delivering services
Big data source means to manage
and are proposing their integration, generally to obtain benefits in specific
application scenarios [8, 4, 51].
In general, IoT can benefit from the virtually unlimited capabilities and
resources of Cloud to compensate its technological constraints (e.g., storage,
processing, communication). To cite a few examples, Cloud can offer an ef-
fective solution for IoT service management and composition as well as for
implementing applications and services that exploit the things or the data
produced by them [83]. On the other hand, Cloud can benefit from IoT by
extending its scope to deal with real world things in a more distributed and
dynamic manner, and for delivering new services in a large number of real life
scenarios. In many cases, Cloud can provide the intermediate layer between
the things and the applications, hiding all the complexity and functionali-
ties necessary to implement the latter. This will impact future application
development, where information gathering, processing, and transmission will
generate new challenges, especially in a multi-cloud environment [41].
In this section we discuss the main CloudIoT drivers, i.e., the motivations
driving toward the integration of Cloud and IoT. Most of the papers in
literature are actually seeing Cloud as the missing piece in the integrated
scenario, i.e. they believe that Cloud fills some gaps of IoT (e.g. the limited
storage). A few others, instead, see IoT filling gaps of Cloud (mainly the
limited scope). In this paper we consider both as CloudIoT drivers and we
start our discussion from the first ones.
Most of these drivers fall in three categories that are communication,
storage, and computation, while a few others are more basic and have impli-
cations in all such categories, i.e. they are transversal. In the following, we
start discussing such transversal drivers, and then detail the ones related to
communication, storage, and processing.
Being IoT characterized by a very high heterogeneity of devices, tech-
nologies, and protocols, it lacks different important properties such as scala-
10
bility, interoperability, flexibility, reliability, efficiency, availability, and secu-
rity. Since Cloud has proved to provide them [47, 34, 115], we identify them
as some of the main transversal CloudIoT drivers. Two other transversal
drivers are the ease of use and the reduced cost obtained by both users and
providers of applications and services [34]. Indeed, Cloud facilitates the flow
between IoT data collection and data processing, and enables rapid setup and
integration of new things, while maintaining low costs for deployment and
for complex data processing [110]. As a consequence, analyses of unprece-
dented complexity [34, 101] are possible, and data-driven decision making
and prediction algorithms can be employed at low cost, providing means for
increasing revenues and reduced risks [129].
Communication. Data and application sharing are two important CloudIoT
drivers falling in the communication category. Thanks to the CloudIoT
paradigm, personalized ubiquitous applications can be delivered through the
IoT, while automation can be applied to both data collection and distribution
at low cost. Cloud offers an effective and cheap solution to connect, track,
and manage any thing from anywhere at any time by using customized por-
tals and built-in apps [110]. The availability of high speed networks enables
effective monitoring and control of remote things [110, 47, 101], their coordi-
nation [47, 101, 115], their communications [47], and real-time access to the
produced data [110].
It is worth mentioning that although Cloud can significantly improve
and simplify IoT communication, it can still represent a bottleneck in some
scenarios: indeed, over the last 20 years data storage density and processor
power increased of a factor of 1018 and 1015 respectively, while broadband
capacity increased only of 104[68]. As a consequence, practical limitations
can arise when trying to transfer huge amounts of data from the edge of the
Internet onto Cloud.
Storage. IoT involves by definition a large amount of information sources
(i.e., the things), which produce a huge amount of non-structured or semi-
structured data [41], which also have the three characteristics typical of Big
Data [134]: volume (i.e., data size), variety (i.e., data types), and velocity
(i.e., data generation frequency). Large-scale and long-lived storage, possible
thanks to the virtually unlimited, low-cost, and on-demand storage capacity
provided by Cloud, represents an important CloudIoT driver. Cloud is the
most convenient and cost effective solution to deal with data produced by
IoT [110] and, in this respect, it generates new opportunities for data aggre-
11
gation [47], integration [129], and sharing with third parties [129]. Once into
Cloud, data can be treated as homogeneous through well-defined APIs [47],
can be protected by applying top-level security [34], and can be directly
accessed and visualized from any place [110].
Computation. IoT devices have limited processing and energy resources
that do not allow complex, on-site data processing. Collected data is usu-
ally transmitted to more powerful nodes where aggregation and processing
is possible, but scalability is challenging to achieve without a proper infras-
tructure. Cloud offers virtually unlimited processing capabilities and an on-
demand usage model. This represents another important CloudIoT driver:
IoT processing needs can be properly satisfied for performing real-time data
analysis (on-the-fly) [110, 34], for implementing scalable, real-time, collab-
orative, sensor-centric applications [47], for managing complex events [110],
and for supporting task offloading for energy saving [125].
Scope. As the things add capabilities, and more people and new types of
information are connected, users spread across the world quickly enter the
Internet of Everything (IoE) [43, 2], a network of networks where billions
of connections create unprecedented opportunities as well as new risks.
The adoption of the CloudIoT paradigm enables new smart services and
applications based on the extension of Cloud through the things [110, 115]
which enable the cloud to deal with a number of new, real-life scenarios,
giving birth to the Things as a Service paradigm [27, 94, 36]. This is another
important driver for CloudIoT. The literature shows how a number of new
paradigms emerging from the integration of Cloud and IoT and related to
this particular driver. They are summarized in Tab. 2. Since no standard
has been clearly defined, there is no sharp distinction among the proposed
acronyms, which in some cases appear to collide. Vehicular Cloud is another
important new paradigm emerging in this area [57].
In conclusion, several motivations are driving the integration of Cloud and
IoT. Some of them are actually related with specific application scenarios.
Section 4 analyzes these application scenarios in details, revealing the main
challenges associated with each of them.
12
Table 2: New Paradigms enabled by CloudIoT: everything as a Service.
XaaS (Acronym) X(Expansion) Description
Things as a service aggregating and abstracting heterogeneous resources
[27, 94, 36] according to tailored thing-like semantics
SaaS [110, 129, 34] Sensing providing ubiquitous access
or S2aaS [104, 72, 71] as a service to sensor data
SAaaS [110] Sensing and Actuation enabling automatic control logics
as a service implemented in the Cloud
SEaaS [110, 34] Sensor Event dispatching messaging services
as a service triggered by sensor events
SenaaS [129] Sensor enabling ubiquitous management
as a service of remote sensors
DBaaS [129] DataBase enabling ubiquitous
as a service database management
DaaS [129] Data providing ubiquitous access
as a service to any kind of data
EaaS [129] Ethernet providing ubiquitous layer-2 connectivity
as a service to remote devices
IPMaaS [129] Identity enabling ubiquitous access to policy
and Policy Management and identity management functionalities
as a service
VSaaS [108] Video Surveillance providing ubiquitous access to recorded video
as a service and implementing complex analyses in the Cloud
4. APPLICATIONS
CloudIoT gave birth to a new set of smart services and applications,
that can strongly impact everyday life (Fig. 5). Many of the applications
described in the following (may) benefit from Machine-to-Machine commu-
nications (M2M) when the things need to exchange information among them-
selves and not only send them towards the cloud [93]. This represents one
of the open issues in this field, as discussed in Sec. 7. In this section we
describe the wide set of applications that are made possible or significantly
improved thanks to the CloudIoT paradigm. For each application we point
out the challenges, which we discuss in detail in Sec. 5.
Healthcare. The adoption of the CloudIoT paradigm in the healthcare field
can bring several opportunities to medical IT, and experts believe that it can
significantly improve healthcare services and contribute to its continuous and
systematic innovation [77]. Indeed, CloudIoT employed in this scenario is
13
Figure 5: Services made possible thanks to the CloudIoT paradigm.
Source: http://siliconangle.com/
able to simplify healthcare processes and allows to enhance the quality of
the medical services by enabling the cooperation among the different entities
involved. Ambient assisted living (AAL), in particular, aims at easing the
daily lives of people with disabilities and chronic medical conditions.
Through the application of CloudIoT in this field it is possible to sup-
ply many innovative services, such as: collecting patients’ vital data via a
network of sensors connected to medical devices, delivering the data to a
medical center’s Cloud for storage and processing, properly managing infor-
mation provided by sensors, or guaranteeing ubiquitous access to, or sharing
of, medical data as Electronic Healthcare Records (EHR) [48, 88, 77].
CloudIoT enables cost effective and high-quality, ubiquitous medical ser-
vices [77, 48]. Pervasive healthcare applications generate a vast amount of
sensor data that have to be managed properly for further analysis and pro-
cessing [39]. The adoption of Cloud represents a promising solution for man-
aging healthcare sensor data efficiently [39] and allows to abstract technical
details, eliminating the need for expertise in, or control over, the technology
infrastructure [6, 88]. Moreover, it leads to the easy automation of the pro-
cess of collecting and delivering data at a reduced cost [77]. It further makes
mobile devices suited for health information delivery, access, and communi-
14
cation, also on the go [97]. Cloud allows to face common challenges of this
application scenario such as: security, privacy, and reliability, by enhanc-
ing medical data security and service availability and redundancy [77, 6].
Thanks to the efficient management of sensor data it is possible to provide
assisted-living services in real-time [44]. Moreover, Cloud adoption enables
the execution (in the Cloud) of secure multimedia-based health services, over-
coming the issue of running heavy multimedia and security algorithms on
devices with limited computational capacity and small batteries [97], and
it provides a flexible storage and processing infrastructure to perform both
online and offline analyses of data streams generated in healthcare Body Sen-
sor Networks (BSNs)2. Thanks to the use of the CloudIoT paradigm BSNs
can be deployed in a community of people and can generate large amounts
of contextual data that are stored, processed, and analyzed in a scalable
fashion [45].
In the healthcare domain, common challenges are related to the lack of
trust in data security and privacy by users (exposure to hacker attacks, viola-
tion of medical data confidentiality, data lock-in and loss of governance, privi-
lege abuse), performance unpredictability (resource exhaustion, data transfer
bottlenecks, impact on real-time services, streaming QoS), legal issues (con-
tract law, intellectual property rights, data jurisdiction) and are still object of
investigation [77, 38, 97]. The lack of specific research related to the adoption
of these technologies in the context of mission critical systems, of deepened
reliability analyses and the limited number of case studies are often defined
as the major obstacles [77, 44, 45].
Smart Cities and Communities. CloudIoT leads to the generation of
services that interact with the surrounding environment, thus creating new
opportunities for contextualization and geo-awareness. Sustainable devel-
opment of urban areas is a challenge of key importance and requires new,
efficient, and user-friendly technologies and services. The challenge is to
harness the collaborative power of ICT networks (networks of people, of
knowledge, of sensors) to create collective and individual awareness about
the multiple sustainability threats which our society is facing nowadays at
social, environmental, and political levels. The resulting collective intelli-
2BSNs have been recently introduced for the remote monitoring of human activities in
the healthcare domains but also in other application domains such as emergency manage-
ment, fitness, and behavior surveillance.
15
gence will lead to better informed decision-making processes and empower
citizens, through participation and interaction, to adopt more sustainable in-
dividual and collective behaviours and lifestyles [58]. CloudIoT can provide
a common middleware for future-oriented smart-city services [13, 115, 28],
acquiring information from different heterogeneous sensing infrastructures,
accessing all kinds of geo-location and IoT technologies (e.g., 3D representa-
tions through RFID sensors and geo-tagging), and exposing information in a
uniform way (e.g., through a dynamically annotated map). Frameworks typ-
ically consist of a sensor platform (with APIs for sensing and actuating) and
a Cloud platform which offers scalable and long-lived storage and processing
resources for the automatic management and control of real-world sensing
devices in a large-scale deployment. Crowdsourced and reputation-based
frameworks also exist: authors of [72, 71] propose a framework implement-
ing the Sensing as a Service paradigm in the context of smart cities and
aimed at public safety. Authors of [9, 107] present an ecosystem for mobile
crowdsensing applications which relies on the Cloud-based publish/subscribe
middleware to acquire sensor data from mobile devices in a context-aware
and energy-efficient manner. Authors of [121] focus on the burden on ap-
plication developers and final users created by the need to deal with-large
scale environments. Since IoT scenario is highly fragmented, sensor virtual-
ization can be employed to reduce the gap between existing heterogeneous
technologies and their potential users, allowing them to interact with sensors
at different layers [106].
A number of recently proposed solutions suggest to use Cloud architec-
tures to enable the discovery, the connection, and the integration of sensors
and actuators, thus creating platforms able to provision and support ubiq-
uitous connectivity and real-time applications for smart cities [94, 105]. For
instance, authors of [76] discuss a concept towards developing a smart city
using an intelligent, energy efficient, public illumination system, which would
also offer ubiquitous communication. Moreover, Cloud-based platforms help
to make it easier for third parties to develop and provide IoT plugins en-
abling any device to be connected to the Cloud [13]. This type of advanced
service model hides the complexity and the heterogeneity of the underly-
ing infrastructure, while at the same time meeting complex requirements
for Cloud, such as high reactivity and timeliness, scalability, security, easy-
configurability, and flexibility [13, 115, 135].
Common challenges are related to security, reliability, scale, heterogene-
ity and timeliness. Indeed, enabling the necessary resources, storage and
16
computing capabilities for large amounts of heterogeneous and personalized
data (coming from distributed sources) in a transparent and secure manner
and the development and the deployment of various middleware platforms in
a such fragmented scenario (in which different IoT ecosystems are not able
to communicate between them) are not trivial tasks [115]. The involvement
of multiple physical sensors in the scope of service delivery creates additional
challenges associated with real-time interactions, which imposes a need for
studying extensions over real-time operating systems for embedded devices,
as well as how they could be supported in the scope of a Cloud environ-
ment [115]. Moreover, the resulting system has to provide a rapid setup
of deployed sensors and an easy integration of new sensors in the sensing
environment [94].
The blending of IoT resources into the Cloud introduces new resource
management requirements, which are associated with the need to optimize
not only processing, storage and I/O resources, but also sensor reading cy-
cles, multi-sensor queries and shared access to expensive location-dependent
IoT resources [115]. Significant research on sensing, actuation, and IoT is di-
rected towards the efficient semantic annotation of sensor data [94]. Finally,
while cities share common concerns – such as the need to effectively share in-
formation within and between cities and the desire for enhanced cross-border
protocols – they lack a common infrastructure and methodology for collab-
orating, generating operational and regional fragmentation that currently
prevent innovative synergies [13, 115].
Smart Home and Smart Metering. Home networks have been identified
as the environment where users mainly act: CloudIoT has large application
in home environments, where the joint adoption of heterogeneous embedded
devices and Cloud enables the automation of common in-house activities. In-
deed, the merging of computing with physical things, enables the transforma-
tion of everyday objects into information appliances which – interconnected
through the Internet – can expose services through a web interface. Sev-
eral smart-home applications proposed in literature involve (wireless) sensor
networks and realize the connection of intelligent appliances to the Inter-
net in order to remotely monitor their behavior (e.g., to monitor devices’
power usage to improve power usage habits [26]) or remotely control them
(e.g., to manage lighting, heating, and air conditioning [54]). In particu-
lar, smart lighting recently attracted growing attention from the research
community [127, 91]; lighting is responsible for 19% of global use of electri-
17
cal energy, and accounts for about 6% of the total emissions of greenhouse
gases [23]: smart lighting control systems proved to save the energy consumed
for lighting up to 45% [91]. In this scenario, the Cloud is the best candidate
for building flexible applications with only a few lines of code, making home
automation a trivial task [70], and providing necessary resources for tasks
beyond the scope of local networks [95].
Cloud can enable direct interaction of the user to sensors/actuators (i.e.
to support event-based systems) and can satisfy some crucial requirements
such as internal network interconnection (i.e. any digital appliance in smart
home should be able to interconnect with any other), intelligent remote con-
trol (i.e. appliances and services in the smart home should be intelligently
manageable at any time by any device from anywhere), and automation (i.e.
interconnected appliances within the home should implement their functions
via linking to services provided by smart-home oriented Cloud) [126]. Cloud-
based solutions allow to set-up an ubiquitous space where any device can be
individually accessed in a standardized way [70] and to guarantee concur-
rent, multi-user support through the Web. To properly face the potentially
high number of devices and the volume of their communication with the
Cloud, administration and control of devices could be leveraged by deploy-
ing more powerful computing devices, acting as mediators among IoT devices
and Cloud components, for implementing complex functionalities on top of
them, mitigating the frequency of communications with the Cloud.
Several challenges must be resolved when implementing applications in
this context, which are mainly related to the lack of standards and reliability.
Home devices should be web-enabled, and the interaction with them should
be uniform [54], (i.e. a standard web-based interface should be defined for
service description and communication). Moreover, appliance recognition
routines are needed to enable appliances’ easy discovery. Reliability concerns
also exist related to not always reachable devices, device failure, and variable
QoS [70].
Video Surveillance. CloudIoT in the context of intelligent video surveil-
lance leads to easily and efficiently store, manage, and process video contents
originating from video sensors (i.e. IP cameras) and to automatically extract
knowledge from scenes. It has become a tool of the greatest importance for
several security-related applications. Proposed solutions are able to deliver
video streams to multiple user devices through the Internet, by distribut-
ing the processing tasks over the physical server resources on-demand, in a
18
load-balanced and fault-tolerant fashion [49].
As an alternative to in-house, self-contained management systems, com-
plex video analytics require Cloud-based solutions (VSaaS [108]) to properly
satisfy the requirements of video storage (e.g., stored media is centrally se-
cured, fault-tolerant, on-demand, scalable, and accessible at high-speed) and
video processing (e.g., computer vision algorithms and pattern recognition
module execution).
Commonly considered challenges for this kind of applications are mainly
related to the impossibility of using any camera connection and control due
to the limited diffusion of the enabling technology and tools (need of buying
new cameras)[108]. Available devices are characterized by high heterogeneity
due to the lack of properly defined standards and service schemes [108, 49].
Automotive and Smart Mobility. As an emerging technology, IoT is ex-
pected to offer promising solutions to transform transportation systems and
automobile services (i.e. Intelligent Transportation Systems, ITS). The inte-
gration of Cloud with IoT technologies (such as WSNs and RFID) represents
a promising opportunity [57]. Indeed, a new generation of vehicular data-
mining Cloud service can be developed and deployed to bring many business
benefits, such as increasing road safety, reducing road congestion, manag-
ing traffic and parking, performing warranty analysis and recommending car
maintenance or fixing [57].
Numerous vehicles possess powerful sensing, networking, communication,
and data processing capabilities, and can exchange information with each
other (Vehicle to Vehicle, V2V) or exchange information with the roadside
infrastructure such as camera and street lights (Vehicle to Infratructure, V2I)
over various protocols, including HTTP, SMTP, TCP/IP, WAP, and Next
Generation Telematics Protocol (NGTP) [55]. In this context, Ethernet and
IP-based routing (being less expensive and more flexible than related tech-
nologies) are claimed to be very important technologies for future commu-
nication networks in electric vehicles, enabling the link between the vehicle
electronics and the Internet. Indeed they integrate vehicles into a typical
IoT, and meet the demand for powerful communication with Cloud-based
services [55].
The literature proposes several examples of multi-layered, Cloud-based
vehicular data platforms that merge Cloud computing and IoT technologies
to tackle the main current challenges [57]. These platforms aim at provid-
ing real-time, cheap, secure, and on-demand services to customers, through
19
different types of Clouds, which also include temporary vehicular Clouds
(i.e. formed by the vehicles representing the Cloud datacenters [18]) de-
signed to expand the conventional Clouds in order to increase on-demand
the whole Cloud computing, processing, and storage capabilities, by using
under-utilized facilities of vehicles.
Several challenges have been identified in literature related to this appli-
cation scenario. The huge number of vehicles and their dynamically changing
number make system scalability difficult to achieve [57]. Vehicles moving at
various speeds frequently cause intermittent communication impacting per-
formance, reliability, and QoS [18]. The lack of an established infrastructure
makes it difficult to implement effective authentication and authorization
mechanisms [18], with impacts on security and privacy provision [90]. The
lack of global standards, experimental studies, and proper benchmarks on
realistic ITS-Clouds affects interoperability [57].
Smart Energy and Smart Grid. IoT and Cloud can be effectively merged
to provide intelligent management of energy distribution and consumption
in both local- and wide-area heterogeneous environments.
The IoT nodes typically involved in this kind of processes have sensing,
processing, and networking capabilities, but limited resources. Hence, com-
puting tasks can be properly demanded to the Cloud, where more complex
and comprehensive decisions can be made. Cloud adoption leads to increase
the reliability by providing self-healing mechanisms and enables mutual op-
eration and participation of the users, to achieve distributed generation, elec-
tricity quality, and demand response [128]. Cloud computing makes possible
to analyze and process vast amounts of data and information coming from
different sources distributed along wide area networks, for the purpose of
implementation of intelligent control to objects.
Several challenges should be adequately addressed to realize the full po-
tential of such application. Large scale distributed sources raise issues about
heterogeneity, data size and collection rate, latency dynamics, and cost of
security enforcement [128]. Security and privacy concerns inherent in an
information-rich smart grid evironment may be further exacerbated by de-
ployment on Cloud and introduce challenges such as: the integration of data
having diverse ownership, the aggregation of public and private data, or a
longer and wider exposure to attacks [111]. Legal issues can derive from the
distribution of data archival over different jurisdictions [111]. Finally, con-
sumers should gain more confidence in sharing data to help improving and
20
optimizing services offered [111].
Smart Logistics. The adoption of CloudIoT in logistics promotes a new
service mode that is radically changing business paradigms [85]. It enables
new interesting scenarios and allows the easy and automated management
of flows of goods between the point of origin and the point of consumption,
in order to meet specific requirements expressed in terms of time, cost or
means of transport. Moreover, thanks to geo-tagging technologies, it enables
to automatically track goods while in transit.
CloudIoT is proposed to help conventional logistics systems in evolving
into advanced systems, capable of dealing automatically with complexity
and changes. Ideed, logistics resources are heterogeneous (e.g., geographical
distribution, morphological diversity, and self-governing zone). These make
resource sharing and management more complex. Hence, computer aided
software tools supporting the adoption of IoT can experience a bottleneck
in dealing with complexity, dynamics, and uncertainties in their applica-
tions in modern enterprises. The adoption of Cloud computing can help in
overcoming the bottlenecks enabling complex decision-making systems where
automated algorithms can be enforced to retrieve information for assembly
planning [119]. By adopting a scalable and modularized architecture, Cloud
helps to make the system robust, reliable, flexible, and easily expandable.
In this context, important challenges are related to resource heterogene-
ity, and solutions are investigated in terms of logistics virtualization and
service selection [85]. The former is critical for resource sharing and dynamic
allocation and provides flexibility in the use of resources. The latter allows
service requesters and providers to agree on the attributes that govern the in-
teraction and provides the selection of an appropriate web service that meets
certain functional and non-functional criteria.
Environmental Monitoring. The combined use of Cloud and IoT can
contribute to the deployment of a high speed information system between
the entity in charge of monitoring wide-area environments and the sen-
sors/actuators properly deployed in the area. Some applications can be re-
lated to the continuous and long-term monitoring of water level (for lakes,
streams, sewages), gas concentration in air (e.g., in laboratories, deposits),
soil humidity and other characteristics, inclination for static structures (e.g.,
bridges, dams), position changes (e.g., land slides), lighting conditions (e.g.,
to detect intrusions in dark places), infrared radiation for fire or animal de-
tection [78]. Other potential applications of this kind are: agriculture infor-
21
mation transmission and intelligent detection, intelligent cultivation control,
food safety tracking, precision irrigation, forest identification, and tree track-
ing [110].
A Cloud-based data access is able to bridge the latency-energy require-
ments of low power communication segments and the ubiquitous and fast ac-
cess to data for end users (either humans or IoT applications) [78]. Moreover
it allows to manage and process complex events, generated by the real-time
data streamed by sensors.
The main challenges in this field pertain to the potential massive-scale of
the infrastructures. Specifically, environmental dynamism makes it difficult
to provide computational resources that are sufficient to deal with changing
environmental conditions. Moreover, challenges are also related to security,
as threats can be found in information leak due to potential breaches caused
by infected clients or communication channel vulnerabilities.
Finally, research is still needed on the implementation and promotion of
proper communication protocols (such as IPv6 for individually addressing
the things), the setting of various IoT standards for promoting interoperabil-
ity and for scaling the cost of IoT facilities, and the assessment of risks and
uncertainties.
In this section we have carefully surveyed and described a number of
applications arising from the adoption of the CloudIoT paradigm and for
each of them we have pinpointed the related challenges. In Sec. 5 we provide
a detailed discussion of these challenges.
5. CHALLENGES
We have discussed how integrating Cloud and IoT provides several bene-
fits and fosters the birth or improvement of a number of interesting applica-
tions. At the same time, we have seen that the complex CloudIoT scenario
imposes several challenges for each application that is currently receiving
attention by the research community [41]. This section is devoted to the
analysis of such challenges. In the following we first deal with the typical
challenges raised by the application scenarios reported above. We then focus
on other important recurring challenging topics strictly related to CloudIoT.
Security and Privacy. When critical IoT applications move towards the
Cloud, concerns arise due to the lack of, e.g., trust in the service provider,
22
CloudIoT
Video
Surveillance
Smart Cities
and
Communities
Smart Energy
and
Smart Grid
Automotive
and Smart
Mobility
Healthcare
Smart
Logistics
Environmental
Monitoring
Smart
Home and
Smart
Metering
Heterogeneity
Large
Scale
Security
Reliability Performance
Privacy
Legal and
Social
Aspects
Figure 6: Application scenarios driven by the CloudIoT paradigm and related challenges.
knowledge about service level agreements (SLAs), and knowledge about the
physical location of data. Accordingly, new challenges require specific atten-
tion [16, 111, 114]. Such a distributed system is exposed to several possible
attacks (e.g., session riding, SQL injection, cross site scripting, and side-
channel) and important vulnerabilities (e.g., session hijacking and virtual
machine escape). Multi-tenancy can also compromise security and lead to
sensitive information leakage. Moreover, public key cryptography cannot be
applied at all layers due to the computing power constraints imposed by the
things. These are examples of topics that are currently under investigation
in order to tackle the big challenge of security and privacy in CloudIoT.
Heterogeneity. A big challenge in CloudIoT is related to the wide hetero-
geneity of devices, operating systems, platforms, and services available and
possibly used for new or improved applications.
Cloud platforms heterogeneity is also a non-negligible concern. Cloud
services typically come with proprietary interfaces, causing resource integra-
23
tion and mash-up to be properly customized based on the specific providers.
This issue can be exacerbated when users adopt multi-cloud approaches, i.e.
when services depend on multiple providers in order to improve application
performance and resilience or vendor lock-in [52]. These aspects are only par-
tially solved by cloud brokering, voluntarily implemented by cloud providers
(in form of federation) or by third parties.
IoT services and applications have typically been conceived as isolated
vertical solutions, in which all system components are tightly coupled to the
specific application context. For each possible application/service, providers
have to survey target scenarios, analyze requirements, select hardware and
software environments, integrate heterogeneous subsystems, develop, provide
computing infrastructure, and provide service maintenance. On the other
hand, also thanks to Cloud service delivery models, CloudIoT should ease
IoT service delivery [84]. However, although PaaS-like models would repre-
sent a generic solution for facilitating the deployment of IoT applications,
their implementation requires tackling the big challenge of heterogeneity.
For instance, the interaction with (management of) huge amounts of highly
heterogeneous things (and the related data produced) has to be properly
addressed into the Cloud at different levels. This challenge involves several
aspects, where solutions are being investigated in terms of unifying platforms
and middleware (described in detail in Section 6), interoperable programming
interfaces [70], means for copying with data diversity [111], etc.
Performance. Often CloudIoT applications introduce specific performance
and QoS requirements at several levels (i.e. for communication, compu-
tation, and storage aspects) and in some particular scenarios meeting re-
quirements may not be easily achievable. In particular, obtaining stable
acceptable network performance to reach the Cloud is a main challenge,
considering that broadband increase did not follow storage and computa-
tion evolution [68, 110]. In fact, in several scenarios (e.g., when mobility
is required) provisioning of data and services needs to be performed with
high reactivity [57, 94]. Since timeliness may be heavily impacted by unpre-
dictability issues real-time applications are mainly susceptible to performance
challenges [115, 110]. Usability and user experience also can be affected by
poor QoS (e.g., when multimedia streaming is needed)[77].
Reliability. When CloudIoT is adopted for mission-critical applications, re-
liability concerns typically arise e.g., in the context of smart mobility, vehicles
are often on the move and the vehicular networking and communication is
24
often intermittent or unreliable. When applications are deployed in resource-
constrained environments a number of challenges related to device failure or
not always reachable devices exists [57].
From the one hand, Cloud capabilities help to overcome some of these
challenges (e.g., Cloud enhances the reliability of the devices by allowing to
offload heavy tasks and thus to increase devices’ battery duration or offering
the possibility of building a modularized architecture) [128, 119]; on the
other hand, it introduces uncertainties related to data center virtualization
or resource exhaustion [110, 77]. The lack of reliability analyses and of the
development of specific case studies exacerbate the challenge.
Large Scale. CloudIoT allows to design novel applications aimed at inte-
grating and analyzing information coming from real-world (embedded) de-
vices [105, 78, 19, 94, 110, 121]. Some of the depicted scenarios implicitly
require the interaction with a very large number of these devices, usually
distributed across wide-area environments. The large scale of the result-
ing systems makes typical challenges harder to overcome (e.g., requirements
about storage capacity and computational capability for further processing
become arduous to be satisfied when facing long-lived data collected at high-
rate). Moreover the distribution of the IoT devices makes monitoring tasks
harder since they have to face latency dynamics and connectivity issues.
Legal and Social Aspects. These are two important challenges, partly
related. Legal aspects are extremely important and actual in the current re-
search for specific application scenarios. Think, for example, to a CloudIoT
service based on user-provided data. In this case, on the one hand, the ser-
vice provider has to conform to different international laws. And, on the
other hand, users have to be provided with incentives in order to contribute
to data collection. In more general terms, Social aspects are of interest for
research and are currently considered an interesting challenge because, often,
the investment into omnipresent Internet-capable devices is not reasonable
in every scenario. It is more convenient to give the opportunity to users
to participate in submitting data that represent a thing [11]. The authors
of [32] have identified a set of issues to be addressed by any system which
incorporates humans as a source of sensor data, in order to remain trusted by
its users (such as integrating the qualitative observations generated by hu-
mans with the machine-generated quantitative observations, or the need to
characterize and manage data quality, reliability, reputation, and trustwor-
thiness). Users could also be empowered with new building blocks and tools:
25
accelerators, frameworks, and toolkits that enable the participation of users
in IoT as done in the Internet through Wikis and Blogs [33]. Such tools and
techniques should enable researchers and design professionals to learn about
user work, giving users an active role in technology design. To achieve this,
these tools should allow users to easily experiment various design possibilities
in a cost-effective way. Related to this challenge, researchers are trying to
provide adequate tools for implementing a cooperative prototyping approach,
where users and designers explore together applications and their relations.
Besides the challenges reported above, other important aspects are cur-
rently of large interest for the research community. They partly intersect
with the challenges reported above, but they require a separate discussion as
they involve a large body of research on their own.
Big Data. With an estimated number of 50 billion devices that will be
networked by 2020, specific attention must be paid to transportation, stor-
age, access, and processing of the huge amount of data they will produce.
Thanks to the recent development in technologies, IoT will be one of the
main sources of big data, and Cloud will enable to store it for long time and
to perform complex analyses on it. The ubiquity of mobile devices and sen-
sor pervasiveness, indeed call for scalable computing platforms (every day 2.5
quintillion bytes of data are created) [37]. Handling this data conveniently is
a critical challenge, as the overall application performance is highly depen-
dent on the properties of the data management service [37]. For instance,
cloud-based methods for Big Data summarization based on the extraction
of semantic feature are actually under investigation [69]. Hence, following
the NoSQL movement, both proprietary and open source solutions adopt
alternative database technologies for big data [31]: time-series, key-value,
document store, wide column stores, and graph databases. Unfortunately,
no perfect data management solution exists for the Cloud to manage big
data [129]. Moreover, data integrity is an important factor, not only for its
impact on the qualities of service, but also for security and privacy related
aspects expecially on outsourced data[87].
Sensor Networks. Sensor networks have been defined as the major en-
abler of IoT [129] and as one of the five technologies that will shape the
world, offering the ability to measure, infer, and understand environmental
indicators, from delicate ecologies and natural resources to urban environ-
ments [53]. Recent technological advances have made efficient, low-cost, and
26
low-power miniaturized devices available for use in large-scale, remote sens-
ing applications [5]. Moreover, smartphones, even though limited by power
consumption and reliability, come with a variety of sensors (GPS, accelerom-
eter, digital compass, microphone, and camera), enabling a wide range of
mobile applications in different domains of IoT. In this context, the timely
processing of huge and streaming sensor data, subject to energy and network
constraints and uncertainties, has been identified as the main challenge [131].
Cloud provides new opportunities in aggregating sensor data and exploiting
the aggregates for larger coverage and relevancy, but at the same time af-
fects privacy and security [131]. Furthermore, being lack of mobility a typical
aspect of common IoT devices, the mobility of sensors introduced by smart-
phones as well as wearable electronics represents a new challenge [103].
Monitoring. As largely documented in the literature, monitoring is an
essential activity in Cloud environments for capacity planning, for managing
resources, SLAs, performance and security, and for troubleshooting [3]. As
a consequence, CloudIoT inherits the same monitoring requirements from
Cloud, but the related challenges are further affected by volume, variety, and
velocity characteristics of IoT.
Fog Computing. Fog computing is an extension of classic Cloud computing
to the edge of the network (as fog is a cloud close to the ground). It has been
designed to support IoT applications characterized by latency constraints
and requirement for mobility and geo-distribution [20, 133, 1]. Even though
computing, storage, and networking are resources of both the Cloud and the
Fog, the latter has specific characteristics: edge location and location aware-
ness implying low latency; geographical distribution and a very large number
of nodes in contrast to centralized Cloud; support for mobility (through wire-
less access) and real-time interaction (instead of batch processes); support
for interplay with the Cloud. Authors of [89] proposed an analysis showing
how building Fog computing projects is challenging. Indeed, the adoption of
Fog-based approaches requires various specific algorithms and methodologies
dealing with reliability of the networks of smart devices, and operating under
specific conditions that ask for fault tolerant techniques.
6. PLATFORMS, SERVICES, AND RESEARCH PROJECTS
In this section we describe open source and proprietary platforms for
implementing the new vision and paradigms of Sec. 3 and the new CloudIoT
applications of Sec. 4. Also, we survey research projects focused on the
27
Table 3: Challenges pertaining CloudIoT applications.
Challenges
Privacy
Legal and Social Aspects
Large Scale
Security
Reliability
Performance
Heterogeneity
Applications
Smart Home and Smart Metering X X X
Video Surveillance X X X X
Healthcare X X X X X X X
Smart Cities and Communities X X X X X X
Smart Energy and Smart Grid X X X X X X
Automotive and Smart Mobility X X X X
Smart Logistics X X X X
Environmental Monitoring X X X X X
CloudIoT paradigm, which we believe are an example of the research efforts
funded in this important area.
6.1. Platforms
The design of CloudIoT platforms can lead to develop intelligent infras-
tructures, enabling smart applications to benefit from cloud-based frame-
works [81]. These platforms would enable the new paradigms reported in
Tab. 2 and discussed in Sec. 3. Based on such platforms, end-users would be
able to leverage intelligent providers’ sensing and actuating infrastructures,
rather than having to deploy sensing infrastructures by themselves – which
proved to be a time-consuming and tedious task that dramatically slows in-
novation [65]. The resulting virtualization of sensing resources should also
provide a mean to customize the virtual sensing infrastructure in order to
adapt to the different applications.
While the literature reports efforts in defining a generic high-level archi-
tecture to deal with the integration of Cloud and IoT [36, 67] – e.g., Software
defined IoT (SDIoT) – there are several open source and proprietary plat-
forms available for Cloud and IoT integration. Most of them are aimed at
solving one of the main issues in this field that is related to the heterogeneity
of things and Clouds. These platforms try to bridge this gap implementing a
middleware towards the things and another towards the Cloud, and they typ-
ically provide an API towards the applications. Other platforms are instead
28
bound to specific hardware devices or Clouds. Tab. 4 reports the main char-
acteristics of these platforms and services. In the following we review both
types of platforms, starting with the ones belonging to the former group.
IoTCloud [100] is an open source project aimed at integrating the things
(smart phones, tablets, robots, web pages, etc.) with backends for managing
sensors and their messages and for providing an API to applications inter-
ested in these data. The platform has been showcased with video sensors
(i.e., IP cameras) on the FutureGrid Cloud testbed [46]. The software is
available online through the code sharing platform github. OpenIoT [98]
is another open source effort fostered by a research project financed by the
EU. The project aims at providing a middleware to configure and deploy
algorithms for collection and filtering messages by things, while at the same
time generating and processing events for interested applications. The au-
thors of [81] discuss the infrastructural functional modules and the design
principles of the middleware for enabling the dynamic, self-organizing for-
mulation of optimized IoT applications in cloud environments. Among the
main focuses of OpenIoT are the mobility aspects of IoT for energy-efficient
orchestration of data collection and transmission to the Cloud. The project
web site [98] contains different videos showing possible applications in sev-
eral scenarios. Also this software is available online through github. There
are also projects specifically aimed at creating toolkits for the interaction of
IoT and Cloud. For example, IoT Toolkit (run by a Silicon Valley based
organization called OSIOT) [64] aims at developing a toolkit that allows to
glue the several protocols available for the things, for the Cloud, and for the
applications.
As for the proprietary solutions (e.g. platforms typically bound to spe-
cific things or Clouds), Postscapes publishes a catalog of projects, events,
interviews, and company/job listings within the industry [61]. Interesting
examples include open source projects run by private companies. For exam-
ple, the open source project of NimBits [96] provides a set of software to be
installed on private or public Clouds (mainly Google App Engine) to create
a PaaS that collects data from things and triggers computations or alerts
when specific conditions are verified. The company provides also a Cloud
service for running this software that is free of charge but has some usage
limitations. On the other side, there are companies that provide things ready
to be integrated in Clouds. For example, openPicus [62] is an Italian com-
pany which builds things (e.g. small sensors equipped with WiFi or GPRS
connectivity) using an open hardware approach. The idea is to build very
29
cheap products that have full TCP/IP stack implemented and HTTP server
on-board, which allows to interact with them using simple RESTful APIs.
Finally, there are also several services (es. Xively [122], Open.Sen.se [99],
ThingSpeak [118], CloudPlugs [29], Carriots [22]) that allow to collect data
from things and to store these data on the Cloud offered by the service
provider. These services typically provide an API and different example ap-
plications to use the data collected by the things, which range from specific,
proprietary things to open, and widely distributed ones (e.g. Arduino). This
is the most common market trend is this area, allowing service providers to
offer free subscriptions and make business out of data provided by the users.
Starting from these services, companies have created toolkits for their inte-
gration in CloudIoT frameworks. For example, NetLab [63] is a toolkit for
interaction among physical and digital objects (e.g. controlling video movies
through arduino). NetLab has created two widgets called CouldIn and Cloud-
Out that allow to interact with several CloudIoT services. In particular, they
allow to periodically send data from things to these services or to periodically
retrieve data from these services. Compliant services include Xively (formerly
COSM and Pachube), Open.Sen.se, and ThingSpeak. Hardware producers
have also started to launch Cloud services where clients can upload their
data. For example, at Synapse they created a component of their operating
system (SNAP) that allow to send data to private and public Cloud and
to manage the related tasks (operation, administration, maintenance, and
provisioning) [116]. Recently Intel has also launched an initiative [59] that
provides a software library for Galileo/Edison platforms (compatible with
arduino) and a private Cloud where data can be stored by things based on
Galileo/Edison platforms and accessed by applications though a public API.
The sources of software as well as the designs of the hardware are released
to the public (i.e. open source and open hardware).
6.2. Research Projects
ClouT [28], which stands for “Cloud of things”, is a research project run
by industrial and research partners as well as city administrations from Eu-
rope and Japan. The partners aim at developing infrastructure, services,
tools and applications for municipalities and their stakeholders (citizens, ser-
vice developers, etc.) to create, deploy, and manage user-centric applications
based on IoT and Cloud integration. Target applications include enhanced
public transportation, increased citizen participation through mobile devices
30
Table 4: Platforms, services and research projects.
Proprietary things
Open things
Private cloud
Public cloud
Free
Open source
Application API
Last update
Ready to use
IoTCloud X X n/a n/a X X X Oct. 14 X
OpenIoT X X X X X X X Oct. 14 X
IoT Toolkit X X n/a n/a X X n/a Dec. 13
NimBits X X X partly X X n/a Nov. 14 X
openPicus Xn/a n/a n/a n/a Xn/a n/a
Xively X X X X X n/a X
Open.Sen.se X X X X X n/a X
ThingSpeak X X X X X n/a X
CloudPlugs X X X X X n/a X
Carriots X X X X X n/a X
NetLab X X X X X n/a Oct. 14 X
Intel IoT
Analytics X X X X X X Nov. 14 X
Synapse IoT
Cloud X X X X Nov. 14 X
ClouT X X X X n/a n/a Xn/a
(e.g. to photograph and record situations of interest to city administrators),
safety management, city event monitoring, and emergency management.
IoT6 is a European research project on the future Internet of Things.
It aims at exploiting IPv6 and related standards (e.g., 6LoWPAN, CORE,
COAP) to overcome current shortcomings (e.g. in terms of fragmentation)
in the area of IoT research and development. Its main objectives are to
research, design, and develop a highly scalable IPv6-based service-oriented
architecture to achieve interoperability, mobility, Cloud computing integra-
tion, and intelligence distribution among heterogeneous things, applications,
and services.
The OpenIoT project, cited before for its open source platform, aims
at creating an open source middleware for getting information from het-
erogeneous things, hiding the differences among these objects. The project
explores efficient ways to use and manage Cloud environments for things and
resources (such as sensors, actuators, and smart devices) and offering utility-
based (i.e., pay-as-you-go) IoT services. Authors of [106] present a federation
of Future Internet of Things IoT-LAB (FIT IoT-LAB) integrated with Ope-
nIoT, providing a very large scale infrastructure facility suitable for testing
small wireless sensor devices and heterogeneous communicating objects.
31
Several projects target research issues related to IoT and do not explicitly
mention issues related to their integration with the Cloud. However, we
report them here because they often mention data collection and elaboration
platforms that are very likely being Clouds (right now or in the next years).
These project include Smart Santander [112], The Cooperative ITS Corridor
from Rotterdam to Vienna [30], and WISEBED [120].
7. OPEN ISSUES AND FUTURE DIRECTIONS
Thanks to the analyses we have done in Sec. 3, in Sec. 4, and in Sec. 5,
here we resume the main issues related to CloudIoT still requiring research
efforts and point out some future directions [7, 82].
7.1. Open Issues
Standardization. The lack of standards is actually considered as a big issue
towards CloudIoT by a large number of researchers. Currently most things
are connected to the Cloud through web-based interfaces, which are able to
reduce the complexity for developing such applications [109]. However, they
are not specifically designed for efficient machine-to-machine communications
and introduce overhead in terms of network load, delay, and data processing.
Moreover, interoperability is still an issue, because both the Cloud and the
Things implement non-standard heterogeneous interfaces [35]. Even though
the scientific community has provided multiple contributions to the deploy-
ment and standardization of IoT and Cloud paradigms, a clear necessity of
standard protocols, architectures and APIs is being demanded in order to
facilitate the interconnection among heterogeneous smart objects and the
creation of enhanced services, which realize the CloudIoT paradigm [115].
Power and Energy Efficiency. Recent CloudIoT applications involve fre-
quent data transmission from the things to the Cloud, which, in turn, may
rely on smartphones as gateway [83]. Such process quickly drains battery ca-
pacity on both the things and the gateway limiting the continuous operation
to 24 hours or less. The literature shows that, in the field strictly related to
the integration of Cloud and IoT, obtaining energy efficiency in both data
processing and transmission is an important open issue. However, signifi-
cant research effort has already been spent for what concerns the Cloud and
IoT separately. For handling such issue, several directions have been pro-
posed: more efficient data transmission and compression technologies [38];
32
data caching mechanisms for reusing collected data in time-tolerant applica-
tions [123]; middleware to handle adverse situations and to compress data in
case of continuous and long-duration monitoring of data [75]. These open is-
sues have therefore implications also in terms of communication technologies,
which are described later on in this section.
Big Data. In a previous section we have described big data as an impor-
tant research topic, tightly coupled with CloudIoT and with several related
challenges. Even if several contributions have been provided, we consider
big data as an important open issue, where research is still strongly neces-
sary. CloudIoT involves the management and processing of huge amounts
of data and events coming from different locations and heterogeneous source
types, where most applications require complex operations to be performed
in real-time [56, 117]. On the one hand, this means properly synchronizing
events coming from remote sources and reconstructing and correlating their
semantics in order to infer the situation meaningful for the specific applica-
tion. On the other hand, it means to process in real time huge amounts of
multimedia data and to derive in time the information necessary to trigger
relevant services and assist the user in his current location.
Security and Privacy. As for the previous case, we consider security and
privacy to be both a research challenge that is receiving a lot of attention
as well as an open issue where more effort is still required. While many
users are already concerned about privacy and security in Cloud-based ap-
plications, since CloudIoT brings data coming from the real world into the
Cloud and enables triggering actions into the real world, such concerns are
much more relevant. As for privacy, providing properly designed authoriza-
tion roles and policies while transparently guaranteeing that only authorized
individuals have access to sensitive data is still a challenge, especially when
data integrity must be ensured in response to authorized changes [80]. Re-
garding security, it remains challenging to cope with different threats from
hackers [73]: malware can be injected into physical sensors to produce tam-
pered data; raw or processed data can be stolen/tampered on the Cloud;
compromised gateways can cause security breaches to the CloudIoT system;
the communication channels are vulnerable to side-channel information leak.
Intelligence. The centralization into the Cloud of real-time data coming
from heterogeneous things enables enhanced decision-making capabilities by
using sophisticated information selection and fusion mechanisms. Although
research efforts have been spent in this field [102], maximizing the intelligence
33
in this context is still an open challenge.
Integration Methodology. While CloudIoT solutions have been already
built around specific applications, little effort has been spent to derive a
common methodology to integrate Cloud and IoT systems. Since sets of ap-
plications have common requirements, several standardized workflows could
be defined. Moreover, a generic and flexible platform could be the starting
point for implementing such workflows more easily.
Pricing and Billing. Different entities involved in CloudIoT systems have
their own customer and service management, and methods of payments and
pricing. Moreover, the cost for deploying things is decreasing, while the cost
to keep them connected to the Cloud increases exponentially. Hence, setting
the price for integrated services, distributing it among the different entities,
and managing the payment process are still open issues in the CloudIoT
integrated scenario.
Network Communications. CloudIoT involves several heterogeneous net-
work technologies, where many applications require continuity in the trans-
mission of data and overall consumption of bandwidth increases dramatically.
On the one hand, the efficiency of the access management for enabling conti-
nuity and for optimizing the bandwidth usage is still an open issue [50]. On
the other hand, current bandwidth limitation cannot support the increasing
trend [123], and additional research work is necessary to improve the alloca-
tion methods at huge scales. Moreover, many CloudIoT applications (e.g.,
healthcare) require fault-tolerant and reliable continuous transfer of data
from the things to the Cloud. For instance, a patient wearing body sensors
may be out of coverage area from the gateway (e.g., a smartphone). Thus,
scenarios intrinsically prone to connection failures require specific support in
order to avoid accumulation of errors [17].
Novel solutions for network communications are of paramount importance
in both human-centric and M2M contexts. The proliferation of mobile de-
vices, and the increasing usage of Cloud computing, along with the increasing
demand for multimedia services, are changing the life style of users and creat-
ing new opportunities to providers and clients. Indeed, multimedia data will
account for up to 90% of all the Internet traffic in a few years, and most of
the content will be created and accessed by mobile things (e.g., smartphones
or tablets) carried by humans or placed in vehicles [24]. The integration of
human-centric multimedia networking into Internet Cloud computing envi-
ronments allows mobile users to have new multimedia experiences not previ-
34
ously available. For instance, the Cloud can be configured to perform a set
of important tasks and services for mobile multimedia users and networks,
ranging from assessing the video quality level and load balancing to mul-
timedia transcoding and redundancy/error correction schemes. This novel
mobile multimedia era imposes new challenges for networks, contents, ter-
minals, and humans, and must overcome problems associated, for instance,
with high congestion, low scalability, fast battery consumption, and poor user
experience. CloudIoT involves M2M communications among many hetero-
geneous devices with different protocols [16], which depend on the specific
application scenario. M2M communication can be seen as an advanced form
of sensor networks, where the ultimate goal is to provide comprehensive con-
nections among all smart devices. However, the communication framework
from sensor networks faces difficulties in satisfying the requirements of recent
scenarios such as ITS, where each smart device can play more than one of
the possible roles: sensor, decision maker, and action executor [86]. M2M
largely benefited from the advances in wireless communication technologies,
such as wearable and implantable biosensors, along with recent developments
in the embedded computing, intelligent systems, and Cloud computing ar-
eas [93]. In the literature, a few realizations of M2M communications have
also been proposed, leveraging for example, Bluetooth (IEEE 802.15.1), Zig-
bee (IEEE 802.15.4), or WiFi (IEEE 802.11b and p) technologies. However,
there is still no consensus on the network architecture of a general scenario
for M2M communications. Managing the things in a uniform fashion in a
heterogeneous scenario, while providing required performance still represents
an open issue [21, 15]. The majority of applications do not involve mobility:
in stationary scenarios, IoT often adopts IEEE 802.15.4/6LoWPAN solu-
tions [103]. On the other hand, scenarios such as vehicular networks mostly
adopt IEEE 802.11p. However, being WiFi and Bluetooth the most widely
used radio technologies for wireless networks, their adoption for IoT appli-
cations is increasing: they represent a cheaper solution, most mobile devices
already support them (e.g., smart phones), and both standards are becoming
more and more low power. In some other cases, when power constraints are
less critical, GPRS is still used for Internet connectivity, but it results in
a very costly solution (e.g., multiple SIM cards are necessary) [103]. Re-
cently, serious attention was attracted by the standardization progress of
LTE-Advanced, and the impacts of introducing M2M communications into
LTE-Advanced are now under considerable study in 3GPP [86]. We be-
lieve that research on network communications for CloudIoT is still needed
35
in order to provide effective and efficient solutions.
SLA Enforcement. CloudIoT users require things-generated data to be
transferred and processed according to application dependent constraints,
which can be strict in case of critical scenarios. Guaranteeing a certain
QoS level about Cloud resources might not be always possible for a single
provider, thus relying on multiple Cloud providers might be necessary to
avoid SLA violations. However, dynamically selecting the best combination
of Cloud providers still represents an open issue because of costs, time and
heterogeneity of QoS management support [79].
Storage. Storage solutions have been frequently considered in this paper.
For example, we have already considered them as a driver for the integration
of Cloud and IoT. However, the literature considers this as a still open issue
as current solutions may not provide the necessary support for future appli-
cations. For example, the storage of data transferred from the things to the
Cloud involves some engineering issues still requiring research efforts. While
data has to be properly timestamped to enable server-side reconstruction and
processing, transfers require proper timing in order to avoid excessive bursti-
ness of network and processing load [17]. One possible direction to address
such issues involves the introduction of predictive storage and caching [66].
Scalability and Flexibility. CloudIoT requires efficient mechanisms to
match collected data and events to appropriate applications and services.
Providing flexible subscription schemas and events management while guar-
anteeing scalability with respect to things and users is still considered an
open issue [79].
7.2. Future Directions
In order to enable the full potential of CloudIoT, additional research effort
is expected in several directions:
Properly identifying, naming, and addressing things will be necessary
to support both the huge number of things and their mobility. While
IPv6 could be the proper solution, its large-scale adoption is still an
ongoing process and additional research is necessary to both speed up
this slow process in specific scenarios (e.g. access networks) and to cope
with new mobility and scalability requirements.
Solutions for detecting environmental changes based on IoT data will
enable the delivery of enhanced context-based services, helping to pro-
vide the best service depending on the situation. Such opportunity
36
will incentivate the research of more effective algorithms for delivering
personalized contents and ads.
Large scale support for multi-networking (e.g., multihoming, multi-
path, multicast), connection handover and roaming will be mandatory
for improving network reliability and guaranteeing continuous connec-
tivity, QoS, redundancy, and fault tolerance. In this context solutions
based on Software Defined Networking are also envisaged.
Many applications of CloudIoT would benefit from efficient and flexi-
ble mechanisms for creating logically isolated network partitions over
globally distributed network infrastructures, which could be another
important driver for research in network virtualization and software-
defined networking fields.
Converging towards a common open service platform environment for
providing APIs to develop third-party CloudIoT-based applications
will enable new business opportunities and drive research efforts in
the direction of defining standard protocols, libraries, languages, and
methodologies for CloudIoT.
8. CONCLUSION
The integration of Cloud Computing and Internet of Things represents
the next big leap ahead in the Future Internet. The new applications arising
from this integration – we called CloudIoT– open up new exciting directions
for business and research.
In this paper, we surveyed the literature in order to identify the comple-
mentary aspects of Cloud and IoT and the main drivers for integrating them
into a unique environment. Since the adoption of the CloudIoT paradigm
enabled several new applications, we derived the main research challenges of
interest for each of them. We further analyzed such challenges in order to
identify current research directions. Finally, we surveyed available platforms
and projects by comparing their main aspects and identified open issues and
future research directions in this field.
Thanks to the CloudIoT paradigm everyday life and activities will be po-
tentially improved for everyone: smart cities will enable more efficient public
37
services and promote new business opportunities, ubiquitous healthcare ap-
plications will improve the quality of life for many patients, etc. These new
application scenarios pose important research challenges such as the het-
erogeneity of involved devices and technologies; the required performance,
reliability, scalability and security; privacy preservation; legal and social as-
pects. The open issues of CloudIoT paradigm pertain mainly power and
energy efficiency, SLA enforcement, pricing and billing, security and privacy.
The envisioned future directions include the identification of the definitive
solution for naming and addressing things, the large scale support for multi-
networking, and the convergence toward a common open service platform
environment.
ACKNOWLEDGEMENTS
This work is partially funded by the MIUR projects: PLATINO (P ON 01 01007),
SMART HEALTH (P ON 04a2C), S2-MOVE (P ON 04a3 00058), SIRIO (P ON 01 02425),
and art. 11 DM 593/2000 for NM2 SRL.
REFERENCES
[1] Aazam, M., Huh, E.-N., Aug 2014. Fog computing and smart gateway
based communication for cloud of things. In: Future Internet of Things
and Cloud (FiCloud), 2014 International Conference on. pp. 464–470.
[2] Abdelwahab, S., Hamdaoui, B., Guizani, M., Rayes, A., 2014. Enabling
smart cloud services through remote sensing: An internet of everything
enabler. Internet of Things Journal, IEEE 1 (3), 276–288.
[3] Aceto, G., Botta, A., de Donato, W., Pescap`e, A., 2013. Cloud moni-
toring: A survey. Computer Networks 57 (9), 2093–2115.
[4] Aitken, R., Chandra, V., Myers, J., Sandhu, B., Shifren, L., Yeric, G.,
2014. Device and technology implications of the internet of things. In:
VLSI Technology (VLSI-Technology): Digest of Technical Papers, 2014
Symposium on. pp. 1–4.
[5] Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., Cayirci, E., 2002.
Wireless sensor networks: a survey. Computer networks 38 (4), 393–
422.
38
[6] Alagoz et al., F., 2010. From cloud computing to mobile Internet, from
user focus to culture and hedonism: the crucible of mobile health care
and wellness applications. In: ICPCA 2010. IEEE, pp. 38–45.
[7] Alamri, A., Ansari, W. S., Hassan, M. M., Hossain, M. S., Alelaiwi,
A., Hossain, M. A., 2013. A survey on sensor-cloud: architecture, ap-
plications, and approaches. International Journal of Distributed Sensor
Networks 2013.
[8] Alhakbani, N., Hassan, M. M., Hossain, M. A., Alnuem, M., 2014.
A framework of adaptive interaction support in cloud-based internet
of things (iot) environment. In: Internet and Distributed Computing
Systems. Springer, pp. 136–146.
[9] Antonic, A., Roankovic, K., Marjanovic, M., Pripuic, K., Zarko, I.,
Aug 2014. A mobile crowdsensing ecosystem enabled by a cloud-based
publish/subscribe middleware. In: Future Internet of Things and Cloud
(FiCloud), 2014 International Conference on. pp. 107–114.
[10] Armbrust, M., Fox, A., Griffith, R., Joseph, A. D., Katz, R., Konwin-
ski, A., Lee, G., Patterson, D., Rabkin, A., Stoica, I., et al., 2010. A
view of cloud computing. Communications of the ACM 53 (4), 50–58.
[11] Atkins et al., C., 2013. A Cloud Service for End-User Participation
Concerning the Internet of Things. In: Signal-Image Technology &
Internet-Based Systems (SITIS), 2013 International Conference on.
IEEE, pp. 273–278.
[12] Atzori, L., Iera, A., Morabito, G., 2010. The internet of things: A
survey. Computer Networks 54 (15), 2787–2805.
[13] Ballon, P., Glidden, J., Kranas, P., Menychtas, A., Ruston, S., Van
Der Graaf, S., 2011. Is there a need for a cloud platform for european
smart cities? In: eChallenges e-2011 Conference Proceedings, IIMC
International Information Management Corporation.
[14] Bassi, A., Horn, G., 2008. Internet of Things in 2020: A Roadmap for
the Future. European Commission: Information Society and Media.
39
[15] Bernaschi, M., Cacace, F., Pescape, A., Za, S., 2005. Analysis and
experimentation over heterogeneous wireless networks. In: Tridentcom.
IEEE.
[16] Bhattasali, T., Chaki, R., Chaki, N., 2013. Secure and trusted cloud of
things. In: India Conference (INDICON), 2013 Annual IEEE. IEEE,
pp. 1–6.
[17] Biswas, J., Maniyeri, J., Gopalakrishnan, K., Shue, L., Eugene, P. J.,
Palit, H. N., Siang, F. Y., Seng, L. L., Xiaorong, L., 2010. Processing of
wearable sensor data on the cloud-a step towards scaling of continuous
monitoring of health and well-being. In: Engineering in Medicine and
Biology Society (EMBC), 2010 Annual International Conference of the
IEEE. IEEE, pp. 3860–3863.
[18] Bitam, S., Mellouk, A., 2012. ITS-cloud: Cloud computing for Intel-
ligent transportation system. In: Global Communications Conference
(GLOBECOM), 2012 IEEE. IEEE, pp. 2054–2059.
[19] Bo, Y., Wang, H., 2011. The application of cloud computing and the
internet of things in agriculture and forestry. In: Service Sciences
(IJCSS), 2011 International Joint Conference on. IEEE, pp. 168–172.
[20] Bonomi, F., Milito, R., Zhu, J., Addepalli, S., 2012. Fog computing and
its role in the internet of things. In: Proceedings of the First Edition
of the MCC Workshop on Mobile Cloud Computing. MCC ’12. ACM,
New York, NY, USA, pp. 13–16.
URL http://doi.acm.org/10.1145/2342509.2342513
[21] Botta, A., Pescap´e, A., Ventre, G., 2008. Quality of service statis-
tics over heterogeneous networks: Analysis and applications. European
Journal of Operational Research 191 (3), 1075–1088.
[22] Carriots, 2014. https://www.carriots.com.
[23] Castro, M., Jara, A., Skarmeta, A., March 2013. Smart lighting solu-
tions for smart cities. In: Advanced Information Networking and Ap-
plications Workshops (WAINA), 2013 27th International Conference
on. pp. 1374–1379.
40
[24] Cerqueira, E., Lee, E., Weng, J.-T., Lim, J.-H., Joy, J., Gerla, M., 2014.
Recent advances and challenges in human-centric multimedia mobile
cloud computing. In: Computing, Networking and Communications
(ICNC), 2014 International Conference on. IEEE, pp. 242–246.
[25] Chao, H.-C., 2011. Internet of things and cloud computing for future
internet. In: Ubiquitous Intelligence and Computing. Lecture Notes in
Computer Science.
[26] Chen, S.-Y., Lai, C.-F., Huang, Y.-M., Jeng, Y.-L., 2013. Intelligent
home-appliance recognition over IoT cloud network. In: Wireless Com-
munications and Mobile Computing Conference (IWCMC), 2013 9th
International. IEEE, pp. 639–643.
[27] Christophe, B., Boussard, M., Lu, M., Pastor, A., Toubiana, V., 2011.
The web of things vision: Things as a service and interaction patterns.
Bell Labs Technical Journal 16 (1), 55–61.
[28] Cloud Project, 2014. http://clout-project.eu/.
[29] CloudPlugs, 2014. http://cloudplugs.com/.
[30] Cooperative ITS corridor, 2014. http://www.bmvi.de/
SharedDocs/EN/Anlagen/VerkehrUndMobilitaet/Strasse/
cooperative-its-corridor.pdf?__blob=publicationFile.
[31] Copie, A., Fortis, T.-F., Munteanu, V. I., 2013. Benchmarking Cloud
Databases for the Requirements of the Internet of Things. In: 34th In-
ternational Conference on Information Technology Interfaces, ITI 2013.
pp. 77–82.
[32] Corsar, D., Edwards, P., Velaga, N., Nelson, J., Pan, J., 2011. Short
paper: addressing the challenges of semantic citizen-sensing. In: 4th
International Workshop on Semantic Sensor Networks, CEUR-WS. pp.
90–95.
[33] Cvijikj, I. P., Michahelles, F., 2011. The Toolkit Approach for End-user
Participation in the Internet of Things. In: Architecting the Internet
of Things. Springer, pp. 65–96.
41
[34] Dash, S. K., Mohapatra, S., Pattnaik, P. K., 2010. A Survey on Appli-
cation of Wireless Sensor Network Using Cloud Computing. Interna-
tional Journal of Computer science & Engineering Technologies 1 (4),
50–55.
[35] Dinh, H. T., Lee, C., Niyato, D., Wang, P., 2013. A survey of mobile
cloud computing: architecture, applications, and approaches. Wireless
communications and mobile computing 13 (18), 1587–1611.
[36] Distefano, S., Merlino, G., Puliafito, A., 2012. Enabling the cloud of
things. In: Innovative Mobile and Internet Services in Ubiquitous Com-
puting (IMIS), 2012 Sixth International Conference on. IEEE, pp. 858–
863.
[37] Dobre, C., Xhafa, F., 2014. Intelligent services for big data science.
Future Generation Computer Systems 37, 267–281.
[38] Doukas, C., Maglogiannis, I., 2011. Managing wearable sensor data
through cloud computing. In: Cloud Computing Technology and Sci-
ence (CloudCom), 2011 IEEE Third International Conference on.
IEEE, pp. 440–445.
[39] Doukas, C., Maglogiannis, I., 2012. Bringing iot and cloud comput-
ing towards pervasive healthcare. In: Innovative Mobile and Internet
Services in Ubiquitous Computing (IMIS), 2012 Sixth International
Conference on. IEEE, pp. 922–926.
[40] Dukaric, R., Juric, M. B., 2013. Towards a unified taxonomy and ar-
chitecture of cloud frameworks. Future Generation Computer Systems
29 (5), 1196–1210.
[41] European Commission, 2013. Definition of a research and innovation
policy leveraging Cloud Computing and IoT combination. Tender spec-
ifications, SMART 2013/0037.
[42] Evangelos A, K., Nikolaos D, T., Anthony C, B., 2011. Integrating
RFIDs and Smart Objects into a UnifiedInternet of Things Architec-
ture. Advances in Internet of Things 2011.
[43] Evans, D., 2012. The internet of everything: How more relevant and
valuable connections will change the world. Cisco IBSG, 1–9.
42
[44] Forkan, A., Khalil, I., Tari, Z., 2014. Cocamaal: A cloud-oriented
context-aware middleware in ambient assisted living. Future Gener-
ation Comp. Syst. 35, 114–127.
[45] Fortino, G., Parisi, D., Pirrone, V., Fatta, G. D., 2014. Bodycloud: A
saas approach for community body sensor networks. Future Generation
Comp. Syst. 35, 62–79.
[46] Fox, G., von Laszewski, G., Diaz, J., Keahey, K., Fortes, J., Figueiredo,
R., Smallen, S., Smith, W., Grimshaw, A., 2013. Futuregrid – A re-
configurable testbed for cloud, hpc and grid computing. Contemporary
High Performance Computing: From Petascale toward Exascale, Com-
putational Science. Chapman and Hall/CRC.
[47] Fox, G. C., Kamburugamuve, S., Hartman, R. D., 2012. Architec-
ture and measured characteristics of a cloud based internet of things.
In: Collaboration Technologies and Systems (CTS), 2012 International
Conference on. IEEE, pp. 6–12.
[48] Gachet, D., de Buenaga, M., Aparicio, F., Padr´on, V., 2012. Integrating
internet of things and cloud computing for health services provisioning:
The virtual cloud carer project. In: Innovative Mobile and Internet
Services in Ubiquitous Computing (IMIS), 2012 Sixth International
Conference on. IEEE, pp. 918–921.
[49] Gao, F., 2013. VSaaS Model on DRAGON-Lab. International Journal
of Multimedia & Ubiquitous Engineering 8 (4).
[50] Ge, F., Lin, H., Khajeh, A., al et, 2010. Cognitive radio rides on the
cloud. In: Proceedings of the IEEE Military Communications Confer-
ence. IEEE, pp. 1448–1453.
[51] Gomes, M. M., Righi, R. d. R., da Costa, C. A., 2014. Future di-
rections for providing better iot infrastructure. In: Proceedings of the
2014 ACM International Joint Conference on Pervasive and Ubiquitous
Computing: Adjunct Publication. UbiComp ’14 Adjunct. pp. 51–54.
[52] Grozev, N., Buyya, R., 2014. Inter-cloud architectures and application
brokering: taxonomy and survey. Software: Practice and Experience
44 (3), 369–390.
43
[53] Gubbi, J., Buyya, R., Marusic, S., Palaniswami, M., 2013. Internet of
Things (IoT): A vision, architectural elements, and future directions.
Future Generation Computer Systems 29 (7), 1645–1660.
[54] Han, D.-M., Lim, J.-H., 2010. Smart home energy management sys-
tem using IEEE 802.15. 4 and zigbee. Consumer Electronics, IEEE
Transactions on 56 (3), 1403–1410.
[55] Hank, P., M¨uller, S., Vermesan, O., Van Den Keybus, J., 2013. Au-
tomotive ethernet: in-vehicle networking and smart mobility. In: Pro-
ceedings of the Conference on Design, Automation and Test in Europe.
EDA Consortium, pp. 1735–1739.
[56] Hassan, M. M., Song, B., Huh, E.-N., 2009. A framework of sensor-
cloud integration opportunities and challenges. In: Proceedings of the
3rd international conference on Ubiquitous information management
and communication. ACM, pp. 618–626.
[57] He, W., Yan, G., Xu, L. D., May 2014. Developing vehicular data
cloud services in the iot environment. Industrial Informatics, IEEE
Transactions on 10 (2), 1587–1595.
[58] HORIZON 2020 WORK PROGRAMME 2014-2015., 2014.
Industrial leadership. leadership in enabling and industrial
technologies. information and communication technologies.
http://ec.europa.eu/research/participants/portal/doc/
call/h2020/common/1617606-part_5_i_ict_v2.0_en.pdf.
[59] Intel IoT Analytics, 2014. https://software.intel.com/en-us/
intel-iot-developer-kit-cloud-based-analytics-user-guide.
[60] Intelligence, S. C. B., 2008. Disruptive civil technologies. Six technolo-
gies with potential impacts on US interests out to 2025.
[61] Internet of Things Cloud, 2014. http://postscapes.com/
internet-of-things-cloud.
[62] IoT-a, 2014. http://www.iot-a.eu/.
[63] IoT Cloud Services, 2014. http://www.netlabtoolkit.org/
learning/tutorials/iot-cloud-services/.
44
[64] IoT Toolkit, 2014. http://iot-toolkit.com/.
[65] Irwin, D., Sharma, N., Shenoy, P., Zink, M., 2011. Towards a virtual-
ized sensing environment. In: Testbeds and Research Infrastructures.
Development of Networks and Communities. Springer, pp. 133–142.
[66] Islam, M. M., Hassan, M. M., Lee, G.-W., Huh, E.-N., 2012. A survey
on virtualization of wireless sensor networks. Sensors 12 (2), 2175–2207.
[67] Jararweh, Y., Al-Ayyoub, M., Benkhelifa, E., Vouk, M., Rindos, A.,
et al., 2015. Sdiot: a software defined based internet of things frame-
work. Journal of Ambient Intelligence and Humanized Computing
6 (4), 453–461.
[68] Jeffery, K., 2014. Keynote: CLOUDs: A large virtualisation of small
things. In: The 2nd International Conference on Future Internet of
Things and Cloud (FiCloud-2014).
[69] Ji, Y.-K., Kim, Y.-I., Park, S., 2014. Big data summarization using
semantic feture for iot on cloud.
[70] Kamilaris et al., A., 2011. The smart home meets the web of things.
International Journal of Ad Hoc and Ubiquitous Computing.
[71] Kantarci, B., Mouftah, H., 2014. Trustworthy sensing for public safety
in cloud-centric internet of things.
[72] Kantarci, B., Mouftah, H. T., 2014. Mobility-aware trustworthy crowd-
sourcing in cloud-centric internet of things. In: Computers and Com-
munication (ISCC), 2014 IEEE Symposium on. IEEE, pp. 1–6.
[73] Kapadia, A., Myers, S., Wang, X., Fox, G., 2011. Toward securing
sensor clouds. In: Collaboration Technologies and Systems (CTS), 2011
International Conference on. IEEE, pp. 280–289.
[74] Kitchenham, B., 2004. Procedures for performing systematic reviews.
Keele, UK, Keele University 33, 2004.
[75] Kumar, L. D., Grace, S. S., Krishnan, A., Manikandan, V., Chinraj, R.,
Sumalatha, M., 2012. Data filtering in wireless sensor networks using
neural networks for storage in cloud. In: Recent Trends In Information
45
Technology (ICRTIT), 2012 International Conference on. IEEE, pp.
202–205.
[76] Kumar, N., March 2013. Smart and intelligent energy efficient pub-
lic illumination system with ubiquitous communication for smart city.
In: Smart Structures and Systems (ICSSS), 2013 IEEE International
Conference on. pp. 152–157.
[77] Kuo, A. M.-H., 2011. Opportunities and challenges of cloud computing
to improve health care services. Journal of medical Internet research
13 (3).
[78] Lazarescu, M., 2013. Design of a wsn platform for long-term environ-
mental monitoring for iot applications. Emerging and Selected Topics
in Circuits and Systems, IEEE Journal on 3 (1), 45–54.
[79] Le, X. H., Lee, S., Truc, P. T. H., Vinh, L. T., Khattak, A., Han, M.,
Hung, D. V., Hassan, M., Kim, M., Koo, K.-H., Lee, Y.-K., Huh, E.-
N., Jan 2010. Secured wsn-integrated cloud computing for u-life care.
In: Consumer Communications and Networking Conference (CCNC),
2010 7th IEEE. pp. 1–2.
[80] Le, X. H., Lee, S., True, P. T. H., Khattak, A. M., Han, M., Hung,
D. V., Hassan, M. M., Kim, M., Koo, K.-H., Lee, Y.-K., et al., 2010.
Secured wsn-integrated cloud computing for u-life care. In: Proceed-
ings of the 7th IEEE conference on Consumer communications and
networking conference. IEEE Press, pp. 702–703.
[81] Le Tu’n, A., Quoc, H. N. M., Serrano, M., Hauswirth, M., Soldatos, J.,
Papaioannou, T., Aberer, K., 2012. Global sensor modeling and con-
strained application methods enabling cloud-based open space smart
services. In: Ubiquitous Intelligence & Computing and 9th Interna-
tional Conference on Autonomic & Trusted Computing (UIC/ATC),
2012 9th International Conference on. IEEE, pp. 196–203.
[82] Lee, G. M., Crespi, N., 2010. Shaping future service environments with
the cloud and internet of things: networking challenges and service
evolution. In: Leveraging applications of formal methods, verification,
and validation. Springer, pp. 399–410.
46
[83] Lee, K., Murray, D., Hughes, D., Joosen, W., Nov 2010. Extending sen-
sor networks into the cloud using amazon web services. In: Networked
Embedded Systems for Enterprise Applications (NESEA), 2010 IEEE
International Conference on. pp. 1–7.
[84] Li, F., V¨ogler, M., Claeßens, M., Dustdar, S., 2013. Efficient and scal-
able IoT service delivery on Cloud. In: Cloud Computing (CLOUD),
2013 IEEE Sixth International Conference on. IEEE, pp. 740–747.
[85] Li, W., Zhong, Y., Wang, X., Cao, Y., 2013. Resource virtualization
and service selection in cloud logistics. Journal of Network and Com-
puter Applications 36 (6), 1696–1704.
[86] Lien, S.-Y., Chen, K.-C., Lin, Y., 2011. Toward ubiquitous massive ac-
cesses in 3gpp machine-to-machine communications. Communications
Magazine, IEEE 49 (4), 66–74.
[87] Liu, C., Yang, C., Zhang, X., Chen, J., 2014. External integrity verifi-
cation for outsourced big data in cloud and iot: A big picture. Future
Generation Computer Systems.
[88] L¨ohr, H., Sadeghi, A.-R., Winandy, M., 2010. Securing the e-health
cloud. In: Proceedings of the 1st ACM International Health Informatics
Symposium. ACM, pp. 220–229.
[89] Madsen, H., Albeanu, G., Burtschy, B., Popentiu-Vladicescu, F., July
2013. Reliability in the utility computing era: Towards reliable fog
computing. In: Systems, Signals and Image Processing (IWSSIP), 2013
20th International Conference on. pp. 43–46.
[90] Marchetta, P., Natale, E., Salvi, A., Tirri, A., Tufo, M., De Pasquale,
D., 2013. Trusted information and security in smart mobility scenarios:
The case of s2-move project. In: Algorithms and Architectures for
Parallel Processing. Springer, pp. 185–192.
[91] Martirano, L., Sept 2011. A smart lighting control to save energy.
In: Intelligent Data Acquisition and Advanced Computing Systems
(IDAACS), 2011 IEEE 6th International Conference on. Vol. 1. pp.
132–138.
47
[92] Mell, P., Grance, T., 2009. The NIST definition of cloud computing.
National Institute of Standards and Technology 53 (6), 50.
[93] Miˇsic, V., Miˇsic, J., 2014. Machine-to-machine communications: Ar-
chitectures, standards and applications.
[94] Mitton, N., Papavassiliou, S., Puliafito, A., Trivedi, K. S., 2012. Com-
bining Cloud and sensors in a smart city environment. EURASIP Jour-
nal on Wireless Communications and Networking 2012 (1), 1–10.
[95] Niedermayer, H., Holz, R., Pahl, M.-O., Carle, G., 2010. On using
home networks and cloud computing for a future internet of things. In:
Future Internet-FIS 2009. Springer, pp. 70–80.
[96] Nimbits, 2014. http://www.nimbits.com/.
[97] Nkosi, M., Mekuria, F., 2010. Cloud computing for enhanced mobile
health applications. In: Cloud Computing Technology and Science
(CloudCom), 2010 IEEE Second International Conference on. IEEE.
[98] Open IoT, 2014. http://www.openiot.eu/.
[99] Open Sense, 2014. http://open.sen.se/.
[100] Open Source IoT Cloud, 2014. https://sites.google.com/site/
opensourceiotcloud/.
[101] Parwekar, P., 2011. From Internet of Things towards cloud of things.
In: Computer and Communication Technology (ICCCT), 2011 2nd
International Conference on. IEEE, pp. 329–333.
[102] Pedersen, T. B., Pedersen, D., Riis, K., 2013. On-demand multidi-
mensional data integration: toward a semantic foundation for cloud
intelligence. The Journal of Supercomputing 65 (1), 217–257.
[103] Pereira, P. P., Eliasson, J., Kyusakov, R., Delsing, J., Raayatinezhad,
A., Johansson, M., 2013. Enabling cloud connectivity for mobile in-
ternet of things applications. In: Service Oriented System Engineering
(SOSE), 2013 IEEE 7th International Symposium on. IEEE.
48
[104] Perera, C., Zaslavsky, A., Christen, P., Georgakopoulos, D., 2014. Sens-
ing as a service model for smart cities supported by internet of things.
Transactions on Emerging Telecommunications Technologies 25 (1),
81–93.
[105] Petrolo, R., Loscr`ı, V., Mitton, N., 2014. Towards a smart city based
on cloud of things. In: Proceedings of the 2014 ACM international
workshop on Wireless and mobile technologies for smart cities. ACM,
pp. 61–66.
[106] Petrolo, R., Mitton, N., Soldatos, J., Hauswirth, M., Schiele, G.,
et al., 2014. Integrating wireless sensor networks within a city cloud.
In: SWANSITY workshop in conjunction with IEEE SECON 2014.
[107] Podnar Zarko, I., Antonic, A., Pripuˇzic, K., 2013. Publish/subscribe
middleware for energy-efficient mobile crowdsensing. In: Proceedings
of the 2013 ACM Conference on Pervasive and Ubiquitous Computing
Adjunct Publication. UbiComp ’13 Adjunct. ACM, New York, NY,
USA, pp. 1099–1110.
URL http://doi.acm.org/10.1145/2494091.2499577
[108] Prati, A., Vezzani, R., Fornaciari, M., Cucchiara, R., 2013. Intelligent
Video Surveillance as a Service. In: Intelligent Multimedia Surveillance.
Springer, pp. 1–16.
[109] Raggett, D., 2010. The web of things: Extending the web into the real
world. In: SOFSEM 2010: Theory and Practice of Computer Science.
Springer, pp. 96–107.
[110] Rao, B. P., Saluia, P., Sharma, N., Mittal, A., Sharma, S. V., 2012.
Cloud computing for Internet of Things & sensing based applications.
In: Sensing Technology (ICST), 2012 Sixth International Conference
on. IEEE, pp. 374–380.
[111] Simmhan, Y., Kumbhare, A. G., Cao, B., Prasanna, V., 2011. An anal-
ysis of security and privacy issues in smart grid software architectures
on clouds. In: Cloud Computing (CLOUD), 2011 IEEE International
Conference on. IEEE, pp. 582–589.
[112] Smart Santander European Research Project, 2014. http://www.
smartsantander.eu.
49
[113] Spillner, J., M¨uLler, J., Schill, A., Jun. 2013. Creating optimal cloud
storage systems. Future Gener. Comput. Syst. 29 (4), 1062–1072.
URL http://dx.doi.org/10.1016/j.future.2012.06.004
[114] Subashini, S., Kavitha, V., 2011. A survey on security issues in service
delivery models of cloud computing. Journal of Network and Computer
Applications 34 (1), 1–11.
[115] Suciu, G., Vulpe, A., Halunga, S., Fratu, O., Todoran, G., Suciu, V.,
2013. Smart Cities Built on Resilient Cloud Computing and Secure
Internet of Things. In: Control Systems and Computer Science (CSCS),
2013 19th International Conference on. IEEE, pp. 513–518.
[116] Synapse Internet of Things Cloud, 2014. https://www.
synapse-wireless.com/snap-components/iot.
[117] Tan, K.-L., 2010. What’s next?: Sensor+ cloud!? In: Proceedings of
the Seventh International Workshop on Data Management for Sensor
Networks. ACM, pp. 1–1.
[118] ThingSpeak, 2014. https://thingspeak.com/.
[119] Wang, C., Bi, Z., Xu, L. D., May 2014. Iot and cloud computing in au-
tomation of assembly modeling systems. Industrial Informatics, IEEE
Transactions on 10 (2), 1426–1434.
[120] WISEBED project, 2014. http://www.wisebed.eu.
[121] Xiao, Y., Simoens, P., Pillai, P., Ha, K., Satyanarayanan, M., 2013.
Lowering the barriers to large-scale mobile crowdsensing. In: Proceed-
ings of the 14th Workshop on Mobile Computing Systems and Appli-
cations. HotMobile ’13. ACM, New York, NY, USA, pp. 9:1–9:6.
URL http://doi.acm.org/10.1145/2444776.2444789
[122] Xively, 2014. https://xively.com/.
[123] Xu, Y., Helal, S., Scmalz, M., 2011. Optimizing push/pull envelopes
for energy-efficient cloud-sensor systems. In: Proceedings of the 14th
ACM international conference on Modeling, analysis and simulation of
wireless and mobile systems. ACM, pp. 17–26.
50
[124] Yan, L., Zhang, Y., Yang, L. T., Ning, H., 2008. The Internet of Things:
from RFID to the next-generation pervasive networked systems. CRC
Press.
[125] Yao, D., Yu, C., Jin, H., Zhou, J., 2013. Energy Efficient Task Schedul-
ing in Mobile Cloud Computing. In: Network and Parallel Computing.
Springer, pp. 344–355.
[126] Ye, X., Huang, J., 2011. A framework for Cloud-based Smart Home.
In: Computer Science and Network Technology (ICCSNT), 2011 Inter-
national Conference on. Vol. 2. IEEE, pp. 894–897.
[127] Ye, X., Huang, J., Dec 2011. A framework for cloud-based smart home.
In: Computer Science and Network Technology (ICCSNT), 2011 Inter-
national Conference on. Vol. 2. pp. 894–897.
[128] Yun, M., Yuxin, B., 2010. Research on the architecture and key tech-
nology of Internet of Things (IoT) applied on smart grid. In: Advances
in Energy Engineering (ICAEE), 2010 International Conference on.
IEEE, pp. 69–72.
[129] Zaslavsky, A., Perera, C., Georgakopoulos, D., 2013. Sensing as a ser-
vice and big data. arXiv preprint arXiv:1301.0159.
[130] Zhang, Q., Cheng, L., Boutaba, R., 2010. Cloud computing: state-of-
the-art and research challenges. Journal of internet services and appli-
cations 1 (1), 7–18.
[131] Zhao, F., 2010. Sensors meet the cloud: Planetary-scale distributed
sensing and decision making. In: Cognitive Informatics (ICCI), 2010
9th IEEE International Conference on. IEEE, pp. 998–998.
[132] Zhou, J., Leppanen, T., Harjula, E., Ylianttila, M., Ojala, T., Yu, C.,
Jin, H., 2013. Cloudthings: A common architecture for integrating the
internet of things with cloud computing. In: CSCWD, 2013. IEEE.
[133] Zhu, J., Chan, D., Prabhu, M., Natarajan, P., Hu, H., Bonomi, F.,
March 2013. Improving web sites performance using edge servers in
fog computing architecture. In: Service Oriented System Engineering
(SOSE), 2013 IEEE 7th International Symposium on. pp. 320–323.
51
[134] Zikopoulos, P., Eaton, C., et al., 2011. Understanding big data: An-
alytics for enterprise class hadoop and streaming data. McGraw-Hill
Osborne Media.
[135] Zissis, D., Lekkas, D., Mar. 2012. Addressing cloud computing security
issues. Future Gener. Comput. Syst. 28 (3), 583–592.
Alessio Botta is a postdoc at the Department of Computer
Engineering and Systems of the University of Napoli Fed-
erico II (Italy). He graduated in Telecommunications Engi-
neering (M.S.) and obtained the Ph.D. in Computer Engi-
neering and Systems, both at University of Napoli Federico
II. His research interests are in the area of networking and, in particular,
in the area of network performance measurement and improvement, with a
specific focus on wireless and heterogeneous systems. Alessio Botta has coau-
thored more than 40 international journal (IEEE Communications Magazine,
IEEE Transactions on Parallel and Distributed Systems, Elsevier Computer
Networks, etc.) and conference (IEEE Globecom, IEEE ICC, IEEE ISCC,
etc.) publications. He has served and serves several technical program com-
mittees of several international conferences (IEEE Globecom, IEEE ICC,
etc.) and he acts as reviewer for different international conferences (IEEE
Infocom, etc.) and journals (IEEE Transactions on Mobile Computing, IEEE
Network, IEEE Transactions on Vehicular Technology, etc.) in the area of
networking. In 2010 he was awarded with the best local paper award at IEEE
ISCC 2010.
Walter de Donato received the M.S. degree in computer
engineering and the Ph.D. in computer engineering and sys-
tems from the from the University of Napoli Federico II,
Italy, where he currently works as a Post-Doc. During his
PhD he visited the College of Computing at Georgia In-
stitute of Technology of Atlanta, Georgia, USA, where he
founded the BISmark project. Since July 2012 he holds a research and de-
velopment manager position at Seven One Solution. He has co-authored
over 20 international journal (Communications of the ACM, IEEE Network,
Elsevier Computer Networks) and conference (SIGCOMM, USENIX ATC,
PAM, Globecom, ...) publications, and he is co-author of a patent. His
current research interests include methodologies, techniques, and distributed
52
architectures for measuring, analyzing, classifying, and monitoring network
traffic. Walter de Donato acted and acts as a reviewer for international con-
ferences (IEEE ICC, IEEE Globecom, ACM Conext, etc.) and journals (Else-
vier Computer Communication, IEEE Transactions on Network and Service
Management, ACM Computing Surveys, IEEE Communications Surveys and
Tutorials, Elsevier Journal of Parallel and Distributed Computing, etc.). He
has received several awards for his research activities: in November 2011
he was awarded the Technologybiz Endorsement Award; in 2012 one of his
papers have been awarded the IRTF ANRP (Applied Networking Research
Prize), he was awarded the Best Poster award at SIGCOMM and the ETIC
AICA-Rotary award.
Valerio Persico is a PhD student at the Department of
Electrical Engineering and Information Technology (DIETI)
of the University of Napoli Federico II (Italy), where he re-
ceived his MS degree in 2013, defending a thesis about a
novel technique for topology discovery of IP networks. He is
a member of the research group called Traffic, working in the area of computer
networks and multimedia and part of the larger COMICS (COMputers for
Interaction and CommunicationS), a research group on networking. His re-
search interests fall in the area of networking and of IP measurements; in par-
ticular his past and present work focuses on traffic classification, IP topology
discovery, IP path tracing, IP alias resolution and cloud monitoring. During
his PhD he coauthored several conference publications, being awarded with
the Best Student Paper Award for his paper Dont Trust Traceroute (Com-
pletely) at CoNext 2013. He also served and serves as peer reviewer for inter-
national conferences and journals such as: IEEE Globecom, IEEE Consumer
Communications and Networking Conference (CCNC), IEEE International
Conference on Communication (ICC), IEEE International COnference on
Cloud Engineering (IC2E) and Elsevier Simulation Modelling Practice and
Theory (SIMPAT).
53
Antonio Pescap´e [SM ’09] received the M.S. Laurea De-
gree in Computer Engineering and the Ph.D. in Computer
Engineering and Systems at University of Napoli Federico
II, Napoli (Italy). He is currently an Associate Professor at
the Department of Electrical Engineering and Information
Technology of the University of Napoli Federico II (Italy)
where he teaches courses in Computer Networks, Computer
Architectures, Programming, and Multimedia and he has also supervised and
graduated more than 100 among BS, MS, and PhD students. His research
interests are in the networking field with focus on Internet Monitoring, Mea-
surements and Management and on Network Security. Antonio Pescap has
co-authored over 140 journal (IEEE ACM Transaction on Networking, Com-
munications of the ACM, IEEE Communications Magazine, JSAC, IEEE
Wireless Communications Magazine, IEEE Networks, etc.) and conference
(SIGCOMM, Conext, IMC, PAM, Globecom, ICC, etc.) publications and he
is co-author of a patent. He has served and serves as workshops and confer-
ences Chair (including IEEE ICC (NGN symposium)) and on more than 90
technical program committees of IEEE and ACM conferences. He serves as
Editorial Board Member of Journal of Network and Computer Applications
and has served as Editorial Board Member of IEEE Survey and Tutorials
(2008-2011) and was guest editor for the special issue of Computer Networks
on Traffic classification and its applications to modern networks. For his re-
search activities he has received several awards, comprising a Google Faculty
Award, several best paper awards and two IRTF (Internet Research Task
Force) ANRP (Applied Networking Research Prize). Antonio Pescap´e has
served and serves as independent reviewer/evaluator of research and imple-
mentation projects and project proposals co-funded by the EU Commission,
Sweden government, several Italian local governments, Italian Ministry for
University and Research (MIUR) and Italian Ministry of Economic Develop-
ment (MISE). Antonio Pescap´e is a Senior Member of the IEEE.
54
... Researchers have recently conducted exploratory studies in this area. For instance, the main research streams have explored the integration of cloud computing with the IoT, anticipating that it will significantly shape the internet and smart cities in the future [14,15]. Similarly, studies of the role played by deep learning in smart cities' mobile networks reveal the potential for more intelligent systems to be developed [16,17]. ...
... Convergence of computer science and technology, data science, and smart cities Table 5 notes several highly cited papers in computer science and technology. For example, Botta et al. [14] studied how cloud computing and IoT can work together, discussing their basic premises, benefits, challenges, and current platforms. This combination is expected to be key for the internet's future and the growth of smart cities. Zhang et al. examined how deep learning can merge with mobile and wireless networks for smart city research and tech deployment [17]. ...
Article
Full-text available
With the construction of smart cities advancing, research on big data and smart cities has become crucial for sustainable development. This study seeks to fill gaps in the literature and elucidate the significance of big data and smart city research, offering a comprehensive analysis that aims to foster academic understanding, promote urban development, and drive technological innovation. Using bibliometric methods and Citespace software (6.2.R3), this study comprehensively examines the research landscape from 2015 to 2023, aiming to understand its dynamics. Under the guidance of the United Nations, global research on big data and smart cities is progressing. Using the Web of Science (WOS) Core Collection as the data source, an exhaustive visual analysis was conducted, revealing various aspects, including the literature output, journal distribution, geographic study trends, research themes, and collaborative networks of scholars and institutions. This study reveals a downward trend despite research growth from 2015 to 2020, focusing on digital technology, smart city innovations, energy management and environmental applications, data security, and sustainable development. However, biases persist towards technology, information silos, homogenised research, and short-sighted strategies. Research should prioritise effectiveness, applications, diverse fields, and interdisciplinary collaboration to advance smart cities comprehensively. In the post-COVID-19 era, using big data to optimise city management is key to fostering intelligent, green, and humane cities and to exploring efficient mechanisms to address urban development challenges in the new era.
... To mitigate these risks, organizations must adopt a security-first approach, particularly in the context of network infrastructures and serverless API environments. This entails implementing the best practices in API design, leveraging machine learning and artificial intelligence to detect threats, and investing in continuous education and skill development for IT teams [15][16][17]. ...
Article
Full-text available
The adoption of Data Networks and Application Programming Interfaces (APIs) has become crucial for small and medium enterprises (SMEs) to streamline operations, improve efficiency, and reduce costs. However, SMEs often face challenges such as resource limitations and security vulnerabilities, which hinder their ability to fully leverage these technologies. This systematic review examines the role of Data Networks and APIs in enhancing operational efficiency within SMEs, focusing on key metrics such as speed, cost reduction, scalability, and security challenges. Following PRISMA 2020 guidelines, we conducted a systematic search across multiple databases including Web of Science, Scopus, IEEE Xplore, and Google Scholar. Studies published between 2014 and 2024, focused on SMEs, and addressing the role of Data Networks and APIs in operational efficiency were included. A total of 49 studies met the inclusion criteria and were analyzed for key outcomes related to operational efficiency, cost-effectiveness, and security risks. The review found that Data Networks and APIs significantly improve operational efficiency by increasing process speed (12% increase), reducing operational costs (8% reduction), and enhancing overall productivity. However, security challenges, particularly related to API vulnerabilities, were a major concern, with cyberat-tacks on APIs increasing by 400% in Q1 2023 alone. Despite these risks, the benefits of implementing Data Networks and APIs in SMEs, particularly in terms of scalability and real-time data processing, were evident across industries. Data Networks and APIs offer substantial improvements in operational efficiency for SMEs, although security remains a significant challenge. Future efforts should focus on developing security frameworks tailored to SMEs while maintaining the operational benefits of these technologies. Further research is needed to explore scalable and secure API models for SMEs.
... Conversely, Apple's cloud provider collapsed in much the same period, preventing thousands of Apple customers from purchasing digital downloads through the Apple Play Store [3]. Different approaches are also run across the architecture, with the system performing the necessary functions and meeting real-time cloud standards [4]. This cloud's requirement forces a large number of scientific universities and businesses to transfer their relevant apps to the online. ...
Article
The widespread utilization Cloud Computing (CC) services for hosting real-time applications have led to the appearance of service dependability as an essential issue in both users and Cloud Service Providers (CSP). Two diverse fault tolerant approaches are provided to improve the cloud service reliability known as proactive and reactive model. Various prevailing approaches consider the coordination problem among the Virtual Machines (VMs) executes parallel processing. Devoid of appropriate VM coordination, the parallel processing outcomes are not so appropriate. To handle this issue, a VM-based cluster allocation model is designed to diminish the total resource consumption by the data centers and network resources. Here, the VM migration process is performed for deteriorating PM to certain optimal PMs. At last, the optimal target selection is handled with an improved optimization approach known as linear Swarm-based intelligence. Here, various metrics are evaluated and compared with other approaches. The experimental outcomes illustrate the efficiency of the anticipated model.
... They are the gadgets that capture information from their surroundings and send it via the Internet of Things gateway to cloud servers [12]. These devices have the ability to deliver a message from the sender to the recipient; it is possible for both the sender and the recipient to be IoT devices in this scenario fig. 1 in below shows these devices. ...
Article
Full-text available
With the expansion of the use of Internet of Things using Internet technology to control and operate highly confidential systems in areas of life and with government facilities such as hospitals or banks, they require high security for their system in both software and hardware to prevent theft by unauthorized persons, and only authorized persons can enter the system. Our proposed work includes an authentication system to enter the Internet of Things system. Where SoC is used to generate True Random Numbers. These numbers are used to reconnect the pins of the devices. Where the encryption of the random code is changed every time an attempt is made to enter the system by unauthorized persons by entering the password and username incorrectly, using SoC (4 Cortex-M chip) by generating a new code instead of changing the connection of the logical controllers. The random numbers are unpredictable, unique and cannot be predicted. TRNs were tested using NIST tests, and they achieved high results. The test results of these numbers also showed that they are unpredictable, unique, have a high entropy value per bit (0.999) and have no correlation with the value (-0.12333). Which means that reconnecting the pins to operate the devices is a difficult process to predict because it depends on numbers generated physically, not mathematically, and cannot be predicted.
Book
Full-text available
This book provides a comprehensive overview of the use of vision attentive technology and artificial intelligence methodologies for functional mobility assessment in elderly populations. Vision Attentive Technology and Functional Mobility Assessment in Elderly Healthcare, begins with a general introduction to vision-attentive technology and its uses in the care of older people. Next it examines functional mobility in senior populations and offers a critique of the methods used today for evaluation. The authors then present several artificial intelligence approaches and vision-aware systems used for screening age related diseases such as Parkinson's disease and sarcopenia. The book also presents the difficulties and possibilities of using visual attentive technology to identify functional impairments caused by aging. This book would be helpful to researchers in the field of healthcare, especially those interested in using technology to enhance patient outcomes. Geriatricians, physical therapists, and occupational therapists who treat older patients will also benefit from reading this book. It will also be helpful to readers who are studying biomedical engineering, artificial intelligence, and healthcare.
Article
Full-text available
Floating Solar Power Plant is one of the energy plants that does not damage nature because solar panels are new renewable energy or EBT and are very effective in supporting world conservation and can be applied in remote areas in Indonesia. This research aims to design and create an IoT-based Floating Solar Power Plant monitoring system. The research methodology used is the experimental method by designing hardware and software monitoring systems, the hardware consists of temperature sensors, voltage sensors, and current sensors connected to a microcontroller. The result of this research is a monitoring system on floating solar panels using esp8266, DHT11, and ACS712. Tests conducted on the monitoring system have an average error value of 1.08% for temperature measurements, these results are based on comparing data on the system and manual measurements with calibrated measuring instruments. Current and voltage measurements have an error value of 4.65% and 2.20%. Based on the test results, it can be said that the monitoring system can work well, because the error value obtained is relatively small in the measurement of temperature, current, and voltage compared to the manual monitoring, from a fairly small error value can be concluded that the monitoring of the floating solar power plant made has worked well.
Article
This study conducts a systematic literature review to address the imperative of digital transformation in the current fast-paced, fiercely competitive economy. It emphasizes the critical role of digital technology adoption for organizations seeking performance enhancement and competitive advantage. Focusing on the manufacturing sector, the research delves into principal challenges surrounding digital transformation, employing an extensive literature study to explore industrial evolution, success-failure dynamics, and performance indicators. The study employs the Antecedents, Decisions, and Outcomes (ADO) framework, offering a structured evaluation of digital transformation’s facets and implications. This work contributes recommendations for future research in the manufacturing sector, aiming at untapped potentials. Studying 86 articles, the paper advances scholarly discourse in digital transformation. The review underscores a robust association between manufacturing firm productivity and digital technology, a result of transformative digital integration.
Conference Paper
Full-text available
Cloud computing can enhance the computing capability of mobile systems by offloading. However, the communication between the mobile device and the cloud is not free. Transmitting large data to cloud consumes much more energy than processing data in mobile device, especially in a low bandwidth condition. Further, some processing tasks can avoid transmitting large data between mobile device and server. Those processing tasks (encoding, rendering) are as the compress algorithm, which can reduce the size of raw data before it is sent to server. In this paper, we present an energy efficient task scheduling strategy (EETS) to determine what kind of task with certain amount of data should be chosen to be offloaded under different environment. We have evaluated the scheduler by using an Android smartphone. The results show that our strategy can achieve 99% of accuracy to choose the right action in order to minimize the system energy usage.
Book
With the number of machine-to-machine (M2M)-enabled devices projected to reach 20 to 50 billion by 2020, there is a critical need to understand the demands imposed by such systems. Machine-to-Machine Communications: Architectures, Technology, Standards, and Applications offers rigorous treatment of the many facets of M2M communication, including its integration with current technology. Presenting the work of a different group of international experts in each chapter, the book begins by supplying an overview of M2M technology. It considers proposed standards, cutting-edge applications, architectures, and traffic modeling and includes case studies that highlight the differences between traditional and M2M communications technology. • Details a practical scheme for the forward error correction code design • Investigates the effectiveness of the IEEE 802.15.4 low data rate wireless personal area network standard for use in M2M communications • Identifies algorithms that will ensure functionality, performance, reliability, and security of M2M systems • Illustrates the relationship between M2M systems and the smart power grid • Presents techniques to ensure integration with and adaptation of existing communication systems to carry M2M traffic Providing authoritative insights into the technologies that enable M2M communications, the book discusses the challenges posed by the use of M2M communications in the smart grid from the aspect of security and proposes an efficient intrusion detection system to deal with a number of possible attacks. After reading this book, you will develop the understanding required to solve problems related to the design, deployment, and operation of M2M communications networks and systems.
Article
To enable full mechanical automation where each smart device can play multiple roles among sensor, decision maker, and action executor, it is essential to construct scrupulous connections among all devices. Machine-to-machine communications thus emerge to achieve ubiquitous communications among all devices. With the merit of providing higher-layer connections, scenarios of 3GPP have been regarded as the promising solution facilitating M2M communications, which is being standardized as an emphatic application to be supported by LTE-Advanced. However, distinct features in M2M communications create diverse challenges from those in human-to-human communications. To deeply understand M2M communications in 3GPP, in this article, we provide an overview of the network architecture and features of M2M communications in 3GPP, and identify potential issues on the air interface, including physical layer transmissions, the random access procedure, and radio resources allocation supporting the most critical QoS provisioning. An effective solution is further proposed to provide QoS guarantees to facilitate M2M applications with inviolable hard timing constraints.
Conference Paper
Smart cities and smart mobility represent two of the most significative real use case scenarios in which there is an increasing demand for collecting, elaborating, and storing large amounts of heterogenous data. In urban and mobility scenarios issues like data trustiness and data and network security are of paramount importance when considering smart mobility services like real-time traffic status, events reporting, fleets management, smart parking, etc. In this architectural paper, we present the main issues related to trustiness and security in the S2-Move project in which the contribution is to design and implement a complete architecture for providing soft real-time information exchange among citizens, public administrations and transportation systems. In this work, we first describe the S2-Move architecture, all the actors involved in the urban scenario, the communication among devices and the core platform, and a set of mobility services that will be used as a proof of the potentialities of the proposed approach. Then, considering both architecture and the considered mobility services, we discuss the main issues related to trustiness and security we should taken into account in the design of a secure and trusted S2-Move architecture.
Article
Internet of Things (IoT) supports a connection between objects and humans, enabling the ubiquitous computing in our daily lives. Future research directions in IoT infrastructure should consider real-time communication and scalability to provide a better experience to the users. We justify this sentence by developing an IoT micro-benchmark, which was evaluated over a real IoT middleware. Considering the observed gaps, this article describes the ideas on redesigning the IoT infrastructure, not imposing any modifications in the users' source code. The modeling combines cloud virtualization and elasticity, service decomposition and multithreading programming. The scientific contribution of the article consists of both a novel IoT infrastructure and the algorithms to control the functioning and scalability of each component.
Article
Data management is a crucial aspect in the Internet of Things (IoT) on Cloud. Big data is about the processing and analysis of large data repositories on Cloud computing. Big document summarization method is an important technique for data management of IoT. Traditional document summarization methods are restricted to summarize suitable information from the exploding IoT big data on Cloud. This paper proposes a big data (i.e., documents, texts) summarization method using the extracted semantic feature which it is extracted by distributed parallel processing of NMF based cloud technique of Hadoop. The proposed method can well represent the inherent structure of big documents set using the semantic feature by the non-negative matrix factorization (NMF). In addition, it can summarize the big data size of document for IoT using the distributed parallel processing based on Hadoop. The experimental results demonstrate that the proposed method can summarize the big data document comparing with the single node of summarization methods.
Article
Video Surveillance as a Service provides service to users using Cloud technology. The DRAGON-Lab supports an opportunity for researchers to use Cloud technology on an open platform, but the services utilize schemes have not been defined. This paper proposed two VSaaS working models, market model and tender/contract model. A comparision testing are given and proven the proposed models are applicable for the DRAGON-Lab, and the market model evaluated have better performance than the tender/contract model. Both of them are justified as reasonable approaches which fulfiled the lacking of this research field.