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This work provides the basis to understand and select Cloud Computing models applied for the development of IoT solutions using Low-Power Wide Area Network (LPWAN). Cloud Computing paradigm has transformed how the industry implement solution, through the commoditization of shared IT infrastructures. The advent of massive Internet of Things (IoT) and related workloads brings new challenges to this scenario demanding malleable configurations where the resources are distributed closer to data sources. We introduce an analysis of existing solution architectures, along with an illustrative case from where we derive the lessons, challenges, and opportunities of combining these technologies for a new generation of Cloud-native solutions.
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Revista de Sistemas de Informa¸ao da FSMA
n. 25 (2020) pp. 56-65
Models of Computing as a Service and IoT: an
analysis of the current scenario with applications
using LPWAN
Wesley dos Reis Bezerra, Ph.D Candidate, PPGCC, UFSC,
Fernando Luiz Koch, Visiting Researcher, PPGCC, UFSC, Carlos Becker Westphall, Prof. Dr., PPGCC,
Resumo—A computa¸ao em nuvem ´e um paradigma
que transformou a forma de entrega de computa¸ao em
sistemas distribu´ıdos. Entretanto ela tem encontrado
alguns desafios com a chegada massiva de dispositivos
IoT, fato que demandou uma evolu¸ao e a cria¸ao
de novos paradigmas baseados na cloud. Neste novo
cen´ario com muitos diferentes paradigmas, ´e necess´ario
uma an´alise de suas caracter´ısticas e desafios para
saber qual o mais adequado a aplica¸ao desejada. Este
trabalho tr´as um levantamento sobre os paradigmas
derivados da cloud e que s˜ao aplic´aveis a IoT, assim
como uma compara¸ao de suas caracter´ısticas e de-
safios; por fim, faz uma an´alise utilizando um cen´ario
hipot´etico de piscicultura com IoT para avaliar os
paradigmas elencados. Atrav´es deste estudo ´e poss´ıvel
ter-se uma base de an´alise dos paradigmas e seus
desafio para um escolha adequada no desenvolvimento
de solu¸oes IoT utilizando LPWAN e contrained devices.
Palavras-chave—Computa¸ao em Nuvem, Low
Power WAN, Internet of Things
Models of Computing as a Service and IoT: an
analysis of the current scenario with applications
using LPWAN
This work provides the basis to understand and
select Cloud Computing models applied for the de-
velopment of IoT solutions using Low-Power Wide
Area Network (LPWAN). Cloud Computing paradigm
has transformed how the industry implement solution,
through the commoditization of shared IT infrastruc-
tures. The advent of massive Internet of Things (IoT)
and related workloads brings new challenges to this
scenario demanding malleable configurations where the
resources are distributed closer to data sources. We
introduce an analysis of existing solution architectures,
along with an illustrative case from where we derive
the lessons, challenges, and opportunities of combining
these technologies for a new generation of Cloud-native
Index Terms—Cloud Computing, Low Power WAN,
Internet of Things
Corresponding author: Wesley dos Reis Bezerra, wesley-
I. Introduction
Cloud Computing is the engine to modern Internet
of Things (IoT) solution development. However, this
paradigm was originally designed for the purpose of shared
IT infrastructure, primarily allowing for business of any
size to trade on-premise infrastructure by a rented re-
sources [1], [2].
As workloads start to migrate to IoT-based solutions,
there are new challenges around distribution, heterogene-
ity, volume, velocity, variety, security, vulnerability and
others [3]–[6]. Hence, there is a need to evolve the Cloud
Computing paradigm with malleability, distribution, and
closer proximity to the data sources. This is the origin of
mixed models like Edge Computing, Fog Computing, Mist
Computing and others.
On the other hand, the utilisation of Low-Power Wide
Area Networks (LPWAN) and publish-subscribe protocols
like AM QP ,MQT T ,S T OM P , and CoAP , is increas-
ingly popular in Cloud-IoT solution design. New chal-
lenges in combining these models revolve around issues
of synchronisation, configuration, security, vulnerability
and others [4], [5], [7]. For instance, more sophisticated
security mechanisms require larger computing capacity,
such as processing, memory utilisation, and power con-
sumption. Engineers must measure the trade-offs between
performance, security, and expected device cost while
designing secure IoT devices and deploying distributed
Cloud Computing configurations.
We argue that these issues can be mitigated by selecting
the appropriate model combination for the solution de-
mand. Solution designers and application developers need
to understand the characteristics and capability of the
diverse configurations and how they align with the system
requirements in hands. Therefore, our research question is:
What is the best Cloud Computing model to be
applied for a given application scenario involving
We introduce an analysis of the existing models of Cloud
Computing applicable for the development of solutions
involving IoT, LPWAN and constrained devices. Our goal
BEZERRA, W.R., KOCH, F.L., WESTPHALL, C.B. / Revista de Sistemas de Informa¸ao da FSMA n. 25 (2020) pp. 56-65
is to provide the basis for researches and solution designers
to compare and select technologies for product develop-
ment. We acknowledge the limitations of this study as this
is an extensive area, permeated by different challenges,
opportunities and requirements. Nevertheless, we deem
this analysis comprehensive enough to offer the basis
for understanding and analysis of the combinations. This
study provides three main contributions to the field:
1) a survey of Cloud Computing paradigms applicable
for this application scenario;
2) an analysis of the challenges and opportunities in
developing Cloud-native applications in this scope;
3) guidance on selecting the best combination of Cloud
Computing model and LPWAN in different applica-
tion scenarios.
This work is organised as follows. Section II provides
an overview of the background scenario. Section III intro-
duces a survey of the Cloud Computing paradigms, sup-
port and challenges when applied for the problem scenario.
Section IV analysis the support of the different paradigms
when applied for an illustrative scenario. We conclude
with a discussion of the lessons learned, challenges and
opportunities in Section V.
II. Background
In this section, we present an overview of the current
scenario involving Cloud Computing, connected devices,
and the challenges and opportunities to combine Cloud-
A. Cloud Computing
Cloud Computing can be described as a a model to
support shared Information Technology (IT) resource ac-
cessible on-demand through a network infrastructure [2].
It encompasses a pool of computational resources, like pro-
cessing, storage, applications, and other services, which are
made available through virtualised environments, accessed
through the global network infrastructure. This form of
computing is increasingly perceived as the 5th utility (after
water, electricity, gas, and telephony), which provides the
basic level of computing service that is considered essential
to meet the everyday needs of the general community [8],
Operationally, Cloud Computing is segmented in three
types of services [2]: Infrastructure as a Service (IaaS),
where the shared resources relate to computing infrastruc-
ture, like Virtual Machines, virtual disks, and others; Plat-
form as a Service (PaaS), where the Cloud infrastructure
provides virtualised operational platforms, and; Software
as a Service (SaaS) where the consumer has access to
shared software running on the Cloud. Each service model
has its pros and cons and their adoption relates to the
business requirements. For instance, the SaaS model has
been widely used by software developing companies to
provide solutions through web platform without the need
to maintain the complete stack – hardware, OS, HTTP
servers, and access; the Cloud SaaS provides all-in-one
service accessible through the Web, also including mainte-
nance, support, security, reliability, elasticity, scalability,
and others.
The Cloud Computing model introduces a form of dis-
tributed computing boosted by a major business preroga-
tive: trade the fixed cost infrastructure around on-premise
computing by shared IT infrastructure with variable cost
[1]. The business argument is that Cloud clients do not
need to afford computing equity, including machinery and
physical infrastructure, but instead rent this service from
a Cloud provider who also takes care of maintenance,
support, depreciation, Quality of Services, infrastructure,
redundancy, safety, security, and others. Combine with
advances in network communication and scalability of
computing power, this model is rapidly becoming the de
facto infrastructure for Digital Transformation strategies
[10], [11].
B. Distributed Cloud Computing
However, as business start to migrate workloads to IoT-
based solutions, there are many challenges to the Cloud
Computing model to support the new computational de-
mands [3]–[6]. The term IoT was introduced by Kevin
Ashton in 1998 [5] and has been used to designate smart
devices with internet connectivity. IoT is flourishing as an
important tool to solve problems in several areas of knowl-
edge, such as Smart Cities, Smart Buildings, Industry
4.0, Precision Agriculture, Health, Education, Connected
Vehicles, and many others [3], [12], [13]. Each area of
IoT application has its specified demands for network
consumption, latency sensitivity, physical network layer,
distribution, power consumption, and security. Due to this
diversity, a large number of companies have developed
IoT solutions leading to a heterogeneous and fragmented
market [14].
Hence, there is a need to adapt the Cloud paradigm
with malleability, distribution, and closer proximity to the
data sources. This is the origin of mixed models like Edge
Computing, Fog Computing, Mist Computing and others.
There is an increased need for localised processing and
storage in distributed IoT solutions. Even though usually
IoT data is formed by small packages, due to the large
number of devices, they generate large data volumes for
communication, storage, and processing [15]. Therefore,
there is a need to select the proper distributed cloud
computing to support the requirements of specific IoT
solutions [16].
Telemetry protocols have been incorporated into ex-
isting Cloud Computing services to address the issues
of data volume and velocity in IoT configurations. To
cite the main ones, MQTT [17], CoAP [18], DDS [19],
AMQP [20], XMPP [21], STOMP [22] and HTTP19 are
example of popular publish-subscribe protocols New chal-
lenges in combining these models revolve around issues
of synchronisation, configuration, security, vulnerability
and others [4], [5], [7]. For instance, more sophisticated
security mechanisms require larger computing capacity,
BEZERRA, W.R., KOCH, F.L., WESTPHALL, C.B. / Revista de Sistemas de Informa¸ao da FSMA n. 25 (2020) pp. 56-65
such as processing, memory utilisation, and power con-
sumption. Engineers must measure the trade-offs between
performance, security, and expected device cost while
designing secure IoT devices and deploying distributed
Cloud Computing configurations.
C. Challenges and Opportunities
With the IoT bringing so many connected devices, some
concerns come to the forefront, such as aspects of:
security, where flaws are frequently reported by data
manufacturers [4]–[6], system intrusion [23]–[25], and
data volume and data flows pose a problem to IoT
applications, where it is important to understand the
characteristics of the data flow between: (i) small and
simple data streams, e.g. coming from e.g. smart me-
tering applications [26], and; (ii) complex and larger
data flows , e.g. sources from images, patterns and
matrices [27];
limited or heterogeneous networks;
energy consumption, which becomes a major challenge
when applied to constrained devices [28], often de-
signed to perform with a long battery life, e.g. ultra-
long life battery can last for 10 years if the device is
properly operated.
Finally, there is a surge of implementations around
Low Power Wide Area Network (LPWAN), where devices
connect to centralised services through wireless protocols
with limited data transmission and radio link.
III. Survey
As IoT-based solutions start to take hold of the market,
it became clear that Cloud-centred solutions imposed
severe restrictions in terms of latency, heterogeneity, vol-
ume, velocity, variety, security, vulnerability and others.
For that, new concepts of distributed Cloud Computing
started to emerge in the market, each bringing new solu-
tions and challenges of their own.
Computing as a service has gone through several stages
of development. It can be used in virtual automation
through Virtual Machine Environment (VME), the virtu-
alisation of services in Software Define Networks (SDN),
and the distributed models like Fog Computing, Mist
Computing, Mobile Edge Computing, Mobile Cloud Com-
puting, and Superfluid Computing, between others.
In what follows, we introduce these alternative models
and explain how they support application scenarios in-
volving Cloud-IoT-LPWAN. We also interwove an analysis
of the challenges and opportunities in developing Cloud-
native applications in this scope.
A. Cloud Computing-IoT
There are some case where Cloud Computing looks like
the ideal option for IoT. This model provides low cost
processing and storage, availability, well-known program-
ming resources, and others The concept works well in
Fig. 1. Cloud Computing-Centred IoT Architecture
situations where wither there is good connectivity or no
demand for real-time processing. Popular examples are
applications of video security in home automation that
store streams on the Cloud. Li et al [29] presents the
example of Connected Vehicles Cloud Computing where
vehicle-bounded sensor devices connect to Cloud-centred
infrastructure for services. Figure 1 depicts the solution
architecture, composed of:
1) cloud layer, providing centralised processing and
2) service consumers layer, which in the case of IoT
applications service consumers are sensors.
Sensors must be connected either directly through Ap-
plication Program Interfaces (APIs) or through a Protocol
Gateway. All services are performed on the Cloud in-
frastructure, including processing, storage, and any other
add-value service. If location information is needed, the
gateway (or sensor) must include location information
such as pre-configured or from a GPS, as the central
service lacks awareness of location [30].
This architecture presents challenges to support IoT-
based applications mainly due to volume, latency, het-
erogeneity, security, and others. Basu et al [31] list some
important challenges, such as: data segregation, data loca-
tion, data incomplete data, monitoring and data logging,
problems associated with the security of Virtual Machines
and their environment, and even natural disasters. Subra-
manian et al [32] enlist confidentiality and integrity among
the main security challenges.
Other requirements are authentication, auditing, and le-
gal security requirements. The latter is important because
the data is subject to regulations in some countries. Sha
et al [12] describe the challenges around heterogeneous
network technologies, privacy, large scale of systems, and
management of trust. Stergiou et al [33] corroborate to
the narrative, listing key challenges around heterogeneity,
performance, reliability, big data and monitoring.
B. Fog Computing
Fog Computing is a model of distributed Cloud Comput-
ing designed to cope with the growth of IoT environment
and issues of latency inherent to this configuration [30],
[34]. Yi et al [7] postulate that this model will be the
BEZERRA, W.R., KOCH, F.L., WESTPHALL, C.B. / Revista de Sistemas de Informa¸ao da FSMA n. 25 (2020) pp. 56-65
Fig. 2. Fog Computing Architecture
internet of the future due to the number of existing devices
and the arrival of more devices in the market.
This model is organised in three layers, as depicted in
Figure 2:
1) Cloud with the classic CC model and centralised
2) Fog Nodes are central part of the architecture; these
can be either physical components or virtual com-
ponents and are tightly coupled with smart devices
or network access nodes, providing computational
resources to these devices.
3) Sensor Nodes providing the data sources in the edge
This model is inherently distributed geographically, pro-
viding resources near the data sources where they are
usually most necessary. For example, in case of IoT and
wearable devices, impeding restrictions of processing and
storage in these devices demand for Fog Nodes to deliver
situated resources.
However, the multi-layered nature of this model in-
creases its implementation complexity. One direct impact
of this extra complexity is on security. Yi et al [7] describes
the challenges of Fog Computing around the choice of
virtualisation technologies to be applied, along with the
issues of latency, network management, security, and infor-
mation privacy. Luan et al [30] includes challenges around
application development of applications, scalability, and
DSouza et al [1] mentions that Fog Computing’s multi-
level collaboration brings a new set of problems around
identity management, resource access management, dis-
tributed decision, dynamic load balancing, quality of ser-
vice and security. Vaquero et al [15] mentions that in order
for Fog Computing to become reality, developers must
first solve its many challenges around synchronisation,
device discovery, management, security, standardisation,
accountability, billing of mobile applications.
C. Mist Computing
Mist Computing (MC) plays an important role in the
migrating computation power to the systems’ edge. This
model extents Fog Computing by adding an extra layer
Fig. 3. Mist Computing Architecture
placed closer to the edge, below the Fog Nodes [35]. Figure
3 depicts the architecture:
1) Cloud Computing provide centralised services, when
needed, such as monitoring, updates, central data
repository, offloading processing, and others.
2) Fog Computing provides distributed capabilities and
group control over the mist nodes, bridging with
Cloud Computing services when required; for in-
stance, Fog Nodes may provide resources like re-
gional storage, processing offloading, monitoring,
software updates, and others.
3) Mist Nodes are located at the border, providing lo-
cal resources and processing, with connectivity with
their peers and requesting resources from them.
Mist Nodes are usually implemented through devices
that offer basic computational power, such as Arduinos,
Nodemcus, and other microcontrollers and microchips.
A practical example of Mist Computing implementation
exist in automated vehicles where the multiple elements
are connected to a central car unit (Fog), which can
connected to other cars for collaboration and collective
The key concept is to promote interaction between
Mist Nodes as much as possible, refraining from utilising
centralised services or devices. The architecture can be
conceptualised as a model in which network edge devices
have predictable accessibility and provide their communi-
cation and computational resources as a service [36]. Mist
Nodes can distribute software processes to run on service
providers on their own. That is, Mist Computing favours
a model of computing “a hop away” [37].
Mist Computing brings computing power deeper into
the edge, embedding processing in microcontrollers and
System on Chips [38]. This model can provide a flexible
environment for execution of customised programs [36].
However, Mist Nodes do not have the computational power
of a Fog Node so they must be used in complement.
In situations where applications demand processing or
storage, these requests can be offloaded to Fog Nodes,
which in turn can relay to Cloud Computing.
This architect can be leveraged to extend the capacity
of constrained devices, like mobile sensors, and promote
fast processing at the very edge of the system. Being an
integral part of the edge, Mist Computing provides the
lowest latency possible for an IoT application. However,
BEZERRA, W.R., KOCH, F.L., WESTPHALL, C.B. / Revista de Sistemas de Informa¸ao da FSMA n. 25 (2020) pp. 56-65
it suitable only for a restricted number of application sce-
narios considering the constrained computing capabilities
and implicit distribution.
Preden et al [39] describes the challenges of Mist Com-
puting related to communication complexity and self-
management, as a result of the dependence of a central
component. For Yogi et al [40] list as challenges: low
storage capacity, limited bandwidth, and resistance from
solution developers to adopt the model. Vasconcelos et al
[41] argues that the challenges are common to Fog Com-
puting, and the most characteristic challenge is related to
the complexity brought by the dynamic topology. Suarez-
Albela et al.[42] list challenges related to security, as the
use of security mechanisms such as encryption demand a
require energy consumption by the devices.
D. Mobile Cloud Computing
Fig. 4. Mobile Cloud Computing Architecture
Mobile Cloud Computing (MCC) is about the provision-
ing of data and storage closed to the mobile user. In its
most elementary form it refers to an infrastructure where
both data processing and storage take place outside the
mobile device [7]. The concept of mobile cloud relates to
pervasive devices sharing their support services and het-
erogeneous resources, such as network band, processing,
content, and others [43]. The architecture contains three
layers as depicted in Figure 4:
1) Front-end layer, where either user interfaces or ser-
vices request applications are located, usually run-
ning on mobile devices; this layer works by request-
ing service from external providers, through offload-
ing processing and storage.
2) Middle layer, promotes offloading access to comput-
ing power hosted on servers, wireless network, or
cloudlet [44].
3) Cloud layer, provides the back-end infrastructure to
respond to all requests.
Mobile Cloud Applications can transport computing,
energy and data storage out of phones and into the Cloud.
This allows to create a new range of solutions not only
for mobile phone applications but also a range of other
solution niches [45].
In order to prevent delays in processing the data due
to latency or network bottlenecks, the network segment
between the front-end and the middle layer must sup-
port high throughput. Moreover, the communication link
between the middle layer and the cloud is usually the
Internet, thus subject to intermittent connections, latency,
and security issues. Hence, real-time applications must be
processed in the middle layer. The element responsible for
bringing cloud capabilities close to the mobile device are
the cloudlet [43].
Au et al [46] argues that the key challenges for this
model are related to data and authentication, including:
authentication of mobile users and devices, security in data
communication and storage, data integrity, data search,
and secure data sharing. Leppanen and Riekki [47] enlist
as challenges offloading, scheduling, monitoring, resource
tracking, context awareness, and remote service availabil-
ity. Challenges and issues of heterogeneity in Mobile Cloud
Computing are largerly discussed in [14].
Sekaran, Vikram and Chowdary [48] present the issue of
security and Distributed Denial of Services (DDoS), and
describe different ways to prevent these attacks in MCC.
Noor et al [49] list as the main challenges security, pri-
vacy, bandwidth control, data transfer, data management,
synchronisation, energy efficiency and heterogeneity.
E. Mobile Edge Computing
Fig. 5. Mobile Edge Computing Architecture [50]
Mobile Edge Computing (MEC) utilises the infrastruc-
ture based on mobile networks to provide connectivity to
edge devices. This architecture has the purpose of bringing
computing resources, mainly processing and storage, near
to the data sources. Services are deployed directly at
base stations or in smart cells, as for instance Femtocells,
Picocells, Nanocells, and others. This approach relies on
the infrastructure provided by mobile phone operators and
it may incur in issues of coverage, high costs of mobile
data, and others.
The architecture is implemented in three layers, as
depicted in Figure 5:
1) edge, usually composed of IoT , mobile computing,
and wearable devices;
2) servers, providing located computing resources aim-
ing at low latency and quick response for service
BEZERRA, W.R., KOCH, F.L., WESTPHALL, C.B. / Revista de Sistemas de Informa¸ao da FSMA n. 25 (2020) pp. 56-65
3) cloud, providing centralised processing and storage.
MEC brings some relevant benefits such as ultra-low
latency, high bandwidth, real-time access to radio network
information, and location services [51]. For the latter,
unlike other models, MEC allows to identify the location
of the data source, thus providing additional support for
security, locality, and auditing e.g. in case of regulated
installations. Moreover, ultra-low latency favours applica-
tions that require quick response in decision-making.
The devices located on the edge of the MEC must have
components that allow them to access the cellular network,
either via macrocells i.e. antennas and other classic devices
or smartcells, which are eager to increase the reach of
cellular networks, by expanding their capacity to provide
connectivity, specially to o rural areas.
This model is becoming a hot topic with the multiplica-
tion of MEC service providers and the arrival 5G technol-
ogy, in solutions to support communication, computing,
control, and content delivery [52]. One the key benefits of
this model for IoT solutions is better coverage, specially
in urban areas. This will allow for like Smart Cities and
Smart Buildings that can directly benefit from MEC and
The server layer brings mobile users close to the benefits
of Cloud Computing around elasticity and virtualisation.
In the MEC model, this is implemented upon virtual-
isation platforms like Network Functions Virtualisation
(NFV), Information-Centric Networks (ICN) and Software
Defined Networks (SDN) [52].
NFV promotes the virtualisation of network functions,
making them work like in the Cloud Computing model.
This is implemented either through dedicated hardware
or off-the-shelf devices [51]. The objective of virtualising
network functions is towards the cost benefits of shared in-
frastructure, preventing e.g. capital costs and operational
costs related to site allocation, cooling, maintenance, and
others. The functioning is based on three concepts: virtu-
alisation, orchestration, and turning all functions into soft-
ware. Common examples of virtualised functions include
firewall, DHCP, NAT, and others
SDN virtualises the network by decoupling control plane
from data plane [51]. As an analogy, NFV visualises
network functions, SDN. The goal is to understand the
allocation and performance of routes, define new ones, con-
nect physical and virtual services, define policies, allocate
IPs, allocate bandwidth, and ensure connectivity. SDN
facilities the process of creating NFV by supporting the
configuration of the connection of functions in the network.
Ahmed and Rehmani [53] list as challenges the fast
development of services at a cost efficient rate, optimised
resource utilisation, facilitate the migration of existing
application, and security. Vassilakis [54] mentions the need
for security and privacy solutions specific to this model
and the possible coexistence with unreliable nodes. Beck
et al. [55], [56] defines some metrics that need to be
met to allow utilisation in some areas, such as energy
consumption, delay, bandwidth and scalability. They also
mention challenges around scalability, mobility awareness,
and utilisation awareness by embedded applications.
Varghese et al. [57] presents a list with five challenges:
(i) ability to provide general purpose computing at the
edge nodes; (ii) node discovery; (iii) task partitioning
and orchestration; (iv) balance demands, processing, and
Quality of Service (QoS) and Quality of Experience(QoE);
and (v) safety. Roman, Lopes and Mambo [58] list as chal-
lenges: infrastructure, virtualisation, resources and tasks,
distribution, mobility and programmability.
F. Emerging Models
There are two trendy technologies emerging in the
market that are worth mentioning at this point: Superfluid
Cloud and Cloud Radio Access Network. We acknowledge
that this list is far from exhaustive and other commercial
and research solutions emerge frequently in the market,
but included a brief description for the reference.
Superfluid Cloud [59] is a multi-tenant model where
virtualised services based on software execute on com-
modity hardware, being shared and deployed across the
network. The idea is to apply low-cost devices with devices
with significant computational power, such as System-on-
Chip, Cubieboard, Raspberry Pi, and others. The most im-
portant characteristics include [51]: recursion; scalability;
separation between state and processing; support for very
small VMs; support for Extended State Finite Machines
(XFSMs), and; on-the-fly monitoring.
Cloud Radio Access Networks virtualise some important
functions of modern telecommunications architecture, low-
ering the cost of deploying and operating mobile networks
[60]. Demestichas et al [61] list challenges around multiple
perspectives of society, economy, users, and operators.
There are also issues on normalisation in order to have a
cohesive, inclusive, and sustainable structure. These chal-
lenges relate to wireless communications in general and
can be mitigated through C-RAN’s centralised structures.
IV. Application Scenarios
In business-critical application that supports decision-
making in near real-time based on information from IoT
sensors, Quality of Service (QoS) can be measured, be-
tween other factors, on aspects of efficiency, accuracy, and
security. This sort of solution cannot compromising QoS
due to e.g. network latency, intermittent communication,
intrusion attacks and things of the sort. It is intuitive
that the system will perform better if the decision-making
processes are closer to actuators and connected through
a reliable communication network, which obviously imply
in infrastructure costs. Hence, solution designers juggle to
reach a balance between QoS and affordability.
Let us consider an application scenario in the field
of Smart Agriculture: a system for monitoring artificial
tanks in fish farming. In this solution, sensors information
about the fish tanks, such as capture water temperature,
oxygenation level, ammonia rates, movement, and other
information about fishery. The information is relayed to a
processing unit for storage, analysis, and recommendation
BEZERRA, W.R., KOCH, F.L., WESTPHALL, C.B. / Revista de Sistemas de Informa¸ao da FSMA n. 25 (2020) pp. 56-65
generation. The recommendation system controls actua-
tors that act upon mechanisms to control conditions, e.g.
turning the oxygenation pumps, releasing food, promoting
water circulation, and providing information and insights
to oversees through dashboards on mobile devices. The
system also provides visualisation through mobile comput-
ing devices to support on-spot decision making.
This is a business-critical solution as QoS issues on the
monitor-actuator system directly impact farm’s produc-
tion. For instance, taking too long to open oxygenation
pumps will lead to high fish mortality; not releasing
enough food will lead to malnutrition; providing too much
food leads to food poisoning, and others. Scenarios like
this present clear challenges in terms of volume, latency,
and intermittent access to remote services even by making
use of telemetry protocols, such as MQTT. Hence, solution
design around distributed Cloud Computing must be con-
By applying the Cloud Computing-IoT model, described
in Section III-A, developers have the advantage of having
centralised data from multiple points, thus supporting
insight models that demand correlation of large volumes
of data, creation of dynamic filters, and provide greater
computational and storage resources. Nonetheless, this
model stress the issues of volume, latency, and intermittent
connections around the uploading data links. Hence, this
model is useful for specific requirements, such as the cre-
ation of models that correlate data from multiple sites, and
in specific conditions, e.g. when there is a high-throughput
stable data link to support the upload data link.
On the other hand, the Fog Computing model, intro-
duced in Section III-B, allows for data being stored in
an intermediate node, preventing good part of latency for
data uploading. The system’s intelligence would be allo-
cated in the FN, closer to the edge. Thus, insights, reports,
dashboards, and recommendations would be processed on
the Fog Nodes, based on Analytic models trained on the
Cloud. The process of synchronisation, maintenance, and
model revision and retraining, and retraining, make part
of the Orchestration Strategy and must be defined by
developers during the system setup. This extra step of
complexity is the down side of the model. It requires that
developers are familiar of the new demands and toolkits to
enable the development at an acceptable cost. Moreover,
the data is more expose at the edges, thus highlighting
issues of security and privacy.
Mist Computing, presented in Section III-C, is not ap-
plicable to this scenario as this model does not provide
support to reporting, storing data, and massive data pro-
cessing. This model, if directly applicable, would provide
the best response time in relation to the sensors and
actuators, as processing takes place in the same network
segment where the sensors are positioned. Due to the
limited processing capability, a combination of Mist Com-
puting could be adopted in the architecture to provide
reactive responses for alarming systems, for instance.
Mobile Cloud Computing, introduced in Section III-D,
allows for low-cost sensors to be integrated in the solution.
This model requires the implementation of cloudlet on the
same local wireless network. However, due to extensive
data distribution, the compilation and access to reports
would depend on an auxiliary system installed on a Cloud
Computing system and it would require a synchronisation
process to upload data from the cloudlets. This setup
implies in higher implementation costs, eventual latency
on generating the reports, and maintenance complexity.
Mobile Edge Computing, explained in Section III-E,
provides us with a scenario very close to CF. However,
it is applicable when there is no possibility of using a
cheaper wireless link, with the cellular network option
being the only viable one. This can happen due to the
sensed tank being remotely distant from a wifi access point
but still covered by the mobile network. It is observed that
transmission over the mobile network requires greater use
of energy and, consequently, shorter battery life.
When considering the utilisation of LP-WAN in the
scenario, this technology favors the use of Fog Computing
model. That is, when the solution demands higher power
networks such as WiFi and wired network, one could not
apply thecloudlets. If applying a centralised node , such
as in the Cloud Computing-IoT model, the solution would
require a gateway to bridge between the LPWAN segment
and the Internet. If the solution involves a number of
constrained devices, then any model that require large
message exchange, such as Cloud Computing and Mobile
Cloud Computing are not the best choice for the scenario.
Hence, we conclude that Fog Computing model is the
intermediate solution for this problem scenario. By pro-
viding proximity to the edge for the data source, it allows
for lower latency and fastest response time suitable in
mission critical situations. Mobile Edge Computing also
prevents itself as a viable solution in this scenario, due to
similarities with Fog Computing. However, the approach
implies in higher hardware costs.
V. Conclusions
This work brought an overview of emerging paradigms
of distribute cloud computing, describing their architec-
ture, applicability and models. We focused on solutions
around LPWAN and constrained devices and related the
restrictions and characteristics of both models.
We concluded that Fog Computing is the most adequate
paradigm for the proposed scenario, providing the desired
features of distribution, orchestration, and normalised in-
terfaces. However, the Mobile Edge Computing model pro-
vides similar characteristics with appealing cost structure.
Thus, both models must be considered when designing IoT
solutions that demand low latency, local processing, high
throughput and other related characteristics.
Moreover, we assessed that aspects of security are fun-
damental in distributed Cloud Computing. We highlight
the often overseen issues of intrusion attacks. As any other
distributed environment, Fog Computing, Mist Comput-
ing, MEC, and others are prone to intrusion attacks due
to their distribution and heterogeneity nature. It requires
methods for distributed intrusion detection and reaction
BEZERRA, W.R., KOCH, F.L., WESTPHALL, C.B. / Revista de Sistemas de Informa¸ao da FSMA n. 25 (2020) pp. 56-65
allowing for real-time security and intrusion prevention
[23], [24]. Vieira et al [25] introduces a method to apply
Big Data for fast intrusion detection and reaction, claiming
that the longer it takes to react to intrusion attacks, the
more likely are them to succeed. We believe that these
solutions will become increasingly more relevant with the
widespread of IoT and distributed Cloud Computing.
As future work, we propose the use of network sim-
ulators to evaluate the protocols to be used together
with the paradigms. Simulations could also be applied
to evaluate message protocols and security issues. This
will allow you to assess what network protocol is most
suitable for LPWAN and CD in the proposed scene. In
addition, there are potential advancements in distributed
processing and swarm computing to be considered and
integrated in the models. For example, in Assuncao et al
[62] we introduced a view of grids of agents as a implemen-
tations were very distributed and interconnected acting
elements would implement required services. The raise of
Fog Computing and Mist Computing catalyse the need
for that kind of infrastructure, creating an opportunity
for future research and development. Finally, situation
aware solutions such as presented in [63], a context-aware
content delivery implementation, will demand for extended
coordination and orchestration in Fog Computing and
Mobile Edge Computing, calling for research and develop-
ment in context-aware orchestration in distributed cloud
computing environments.
[1] C. Dsouza, G.-J. Ahn, and M. Taguinod, “Policy-
driven security management for fog computing: Pre-
liminary framework and a case study,”in Proceedings
of the 2014 IEEE 15th International Conference on
Information Reuse and Integration (IEEE IRI 2014),
IEEE, 2014, pp. 16–23.
[2] P. Mell, T. Grance, et al., “The nist definition of
cloud computing,” 2011.
[3] M. Aazam and E.-N. Huh, “Fog computing and
smart gateway based communication for cloud of
things,” in 2014 International Conference on Future
Internet of Things and Cloud, IEEE, 2014, pp. 464–
[4] OWASP top 10 internet of things,, 2018.
[5] S. Deep, X. Zheng, and L. Hamey, “A survey
of security and privacy issues in the internet of
things from the layered context,” arXiv preprint
arXiv:1903.00846, 2019.
[6] E. Bertin, D. Hussein, C. Sengul, and V. Frey,
“Access control in the internet of things: A survey
of existing approaches and open research questions,”
Annals of Telecommunications, pp. 1–14, 2019.
[7] S. Yi, Z. Hao, Z. Qin, and Q. Li, “Fog computing:
Platform and applications,” in 2015 Third IEEE
Workshop on Hot Topics in Web Systems and Tech-
nologies (HotWeb), IEEE, 2015, pp. 73–78.
[8] J. Gubbi, R. Buyya, S. Marusic, and M.
Palaniswami, “Internet of things (iot): A vision,
architectural elements, and future directions,”
Future generation computer systems, vol. 29, no. 7,
pp. 1645–1660, 2013.
[9] R. Buyya, J. Broberg, and A. M. Goscinski, Cloud
computing: Principles and paradigms. John Wiley &
Sons, 2010, vol. 87.
[10] C. Matt, T. Hess, and A. Benlian, “Digital transfor-
mation strategies,” Business & Information Systems
Engineering, vol. 57, no. 5, pp. 339–343, 2015.
[11] S. J. Berman, “Digital transformation: Opportu-
nities to create new business models,” Strategy &
Leadership, 2012.
[12] K. Sha, W. Wei, T. A. Yang, Z. Wang, and W. Shi,
“On security challenges and open issues in internet
of things,” Future Generation Computer Systems,
vol. 83, pp. 326–337, 2018.
[13] M. R. Ramli, S. Bhardwaj, and D.-S. Kim, “Toward
reliable fog computing architecture for industrial
internet of things,” 2019.
[14] Z. Sanaei, S. Abolfazli, A. Gani, and R. Buyya,
“Heterogeneity in mobile cloud computing: Taxon-
omy and open challenges,” IEEE Communications
Surveys & Tutorials, vol. 16, no. 1, pp. 369–392,
[15] L. M. Vaquero and L. Rodero-Merino, “Finding your
way in the fog: Towards a comprehensive defini-
tion of fog computing,” ACM SIGCOMM Computer
Communication Review, vol. 44, no. 5, pp. 27–32,
[16] F. Bonomi, R. Milito, P. Natarajan, and J. Zhu,
“Fog computing: A platform for internet of things
and analytics,” in Big data and internet of things:
A roadmap for smart environments, Springer, 2014,
pp. 169–186.
[17] O. Standard,“Mqtt version 3.1. 1,”URL http://docs.
oasis-open. org/mqtt/mqtt/v3, vol. 1, 2014.
[18] Z. Shelby, K. Hartke, and C. Bormann, “The con-
strained application protocol (coap),” 2014.
[19] G. Pardo-Castellote, B. Farabaugh, and R. Warren,
“An introduction to dds and data-centric communi-
cations,” RTI, Aug, vol. 26, 2005.
[20] O. Standard, “Oasis advanced message queuing pro-
tocol (amqp) version 1.0,” International Journal of
Aerospace Engineering Hindawi www. hindawi. com,
vol. 2018, 2012.
[21] P. Saint-Andre, “Extensible messaging and presence
protocol (xmpp): Core,” 2011.
[22] STOMP Protocol Specification version 1.2,
[23] A. Schulter, J. A. Reis, F. Koch, and C. B. West-
phall, “A grid-based intrusion detection system,”
in International Conference on Networking, Inter-
national Conference on Systems and International
Conference on Mobile Communications and Learn-
BEZERRA, W.R., KOCH, F.L., WESTPHALL, C.B. / Revista de Sistemas de Informa¸ao da FSMA n. 25 (2020) pp. 56-65
ing Technologies (ICNICONSMCL’06), IEEE, 2006,
pp. 187–187.
[24] A. Schulter, F. Navarro, F. Koch, and C. B. West-
phall, “Towards grid-based intrusion detection,” in
2006 IEEE/IFIP Network Operations and Manage-
ment Symposium NOMS 2006, IEEE, 2006, pp. 1–4.
[25] K. Vieira, F. L. Koch, J. B. M. Sobral, C. B.
Westphall, and J. L. de Souza Le˜ao, “Autonomic
intrusion detection and response using big data,”
IEEE Systems Journal, 2019.
[26] M. R. Chowdhury, S. Tripathi, and S. De,“Adaptive
multivariate data compression in smart metering
internet of things,” IEEE Transactions on Industrial
Informatics, 2020.
[27] K. Muhammad, T. Hussain, M. Tanveer, G. Sannino,
and V. H. C. de Albuquerque, “Cost-effective video
summarization using deep cnn with hierarchical
weighted fusion for iot surveillance networks,”IEEE
Internet of Things Journal, 2019.
[28] B. Girgenti, P. Perazzo, C. Vallati, F. Righetti,
G. Dini, and G. Anastasi, “On the feasibility of
attribute-based encryption on constrained iot de-
vices for smart systems,” in 2019 IEEE International
Conference on Smart Computing (SMARTCOMP),
IEEE, 2019, pp. 225–232.
[29] H. Li, R. Lu, J. Misic, and M. Mahmoud, “Security
and privacy of connected vehicular cloud comput-
ing,” IEEE Network, vol. 32, no. 3, pp. 4–6, 2018.
[30] T. H. Luan, L. Gao, Z. Li, Y. Xiang, G. Wei, and
L. Sun, “Fog computing: Focusing on mobile users
at the edge,” arXiv preprint arXiv:1502.01815, 2015.
[31] S. Basu, A. Bardhan, K. Gupta, P. Saha, M. Pal, M.
Bose, K. Basu, S. Chaudhury, and P. Sarkar, “Cloud
computing security challenges & solutions-a survey,”
in 2018 IEEE 8th Annual Computing and Commu-
nication Workshop and Conference (CCWC), IEEE,
2018, pp. 347–356.
[32] N. Subramanian and A. Jeyaraj, “Recent security
challenges in cloud computing,” Computers & Elec-
trical Engineering, vol. 71, pp. 28–42, 2018.
[33] C. Stergiou, K. E. Psannis, B. B. Gupta, and Y.
Ishibashi, “Security, privacy & efficiency of sustain-
able cloud computing for big data & iot,” Sustain-
able Computing: Informatics and Systems, vol. 19,
pp. 174–184, 2018.
[34] R. Buyya and S. N. Srirama, Fog and edge comput-
ing: principles and paradigms. John Wiley & Sons,
[35] M. Iorga, L. Feldman, R. Barton, M. J. Martin,
N. S. Goren, and C. Mahmoudi, “Fog computing
conceptual model,” Tech. Rep., 2018.
[36] M. Liyanage, C. Chang, and S. N. Srirama,“Mepaas:
Mobile-embedded platform as a service for dis-
tributing fog computing to edge nodes,” in 2016
17th International Conference on Parallel and Dis-
tributed Computing, Applications and Technologies
(PDCAT), IEEE, 2016, pp. 73–80.
[37] D. Vasconcelos, R. Andrade, V. Severino, and J. D.
Souza, “Cloud, fog, or mist in iot? that is the ques-
tion,” ACM Transactions on Internet Technology
(TOIT), vol. 19, no. 2, pp. 1–20, 2019.
[38] B. R. Swain, J. J. Sahoo, A. Prasad, and D. T.
Selvam, “Rise of fluid computing: A collective effort
of mist, fog and cloud,” 2019.
[39] J. S. Preden, K. Tammem¨
ae, A. Jantsch, M. Leier,
A. Riid, and E. Calis, “The benefits of self-awareness
and attention in fog and mist computing,” Com-
puter, vol. 48, no. 7, pp. 37–45, 2015.
[40] M. K. Yogi, K. Chandrasekhar, and G. V. Kumar,
“Mist computing: Principles, trends and future di-
rection,” arXiv preprint arXiv:1709.06927, 2017.
[41] D. Vasconcelos, V. Severino, J. Neuman, R. An-
drade, and M. Maia, “Bio-inspired model for data
distribution in fog and mist computing,” in 2018
IEEE 42nd Annual Computer Software and Applica-
tions Conference (COMPSAC), IEEE, vol. 2, 2018,
pp. 777–782.
[42] M. Su´arez-Albela, P. Fraga-Lamas, and T. M.
Fern´andez-Caram´es, “A practical evaluation on rsa
and ecc-based cipher suites for iot high-security
energy-efficient fog and mist computing devices,”
Sensors, vol. 18, no. 11, p. 3868, 2018.
[43] I. Stojmenovic, “Fog computing: A cloud to the
ground support for smart things and machine-to-
machine networks,” in 2014 Australasian telecom-
munication networks and applications conference
(ATNAC), IEEE, 2014, pp. 117–122.
[44] Y. Zhang, D. Niyato, and P. Wang, “Offloading in
mobile cloudlet systems with intermittent connec-
tivity,” IEEE Transactions on Mobile Computing,
vol. 14, no. 12, pp. 2516–2529, 2015.
[45] H. T. Dinh, C. Lee, D. Niyato, and P. Wang, “A sur-
vey of mobile cloud computing: Architecture, appli-
cations, and approaches,” Wireless communications
and mobile computing, vol. 13, no. 18, pp. 1587–1611,
[46] M. H. Au, K. Liang, J. K. Liu, R. Lu, and J. Ning,
“Privacy-preserving personal data operation on mo-
bile cloud—chances and challenges over advanced
persistent threat,” Future Generation Computer Sys-
tems, vol. 79, pp. 337–349, 2018.
[47] T. Lepp¨
anen and J. Riekki, “Energy efficient oppor-
tunistic edge computing for the internet of things,”in
Web Intelligence, IOS Press, vol. 17, 2019, pp. 209–
[48] K. Sekaran, G. R. Vikram, and B. Chowdary, “De-
sign of effective security architecture for mobile
cloud computing to prevent ddos attacks,” 2019.
[49] T. H. Noor, S. Zeadally, A. Alfazi, and Q. Z. Sheng,
“Mobile cloud computing: Challenges and future re-
search directions,”Journal of Network and Computer
Applications, vol. 115, pp. 70–85, 2018.
[50] O. Salman, I. Elhajj, A. Kayssi, and A. Chehab,
“Edge computing enabling the internet of things,” in
BEZERRA, W.R., KOCH, F.L., WESTPHALL, C.B. / Revista de Sistemas de Informa¸ao da FSMA n. 25 (2020) pp. 56-65
2015 IEEE 2nd World Forum on Internet of Things
(WF-IoT), IEEE, 2015, pp. 603–608.
[51] G.-a. Zhang, J.-y. Gu, Z.-h. Bao, C. Xu, and S.-b.
Zhang, “Joint routing and channel assignment algo-
rithms in cognitive wireless mesh networks,” Trans-
actions on Emerging Telecommunications Technolo-
gies, vol. 25, no. 3, pp. 294–307, 2014.
[52] Y. Mao, C. You, J. Zhang, K. Huang, and K. B.
Letaief, “A survey on mobile edge computing: The
communication perspective,”IEEE Communications
Surveys & Tutorials, vol. 19, no. 4, pp. 2322–2358,
[53] E. Ahmed and M. H. Rehmani, Mobile edge comput-
ing: Opportunities, solutions, and challenges, 2017.
[54] V. Vassilakis, I. P. Chochliouros, A. S. Spiliopoulou,
E. Sfakianakis, M. Belesioti, N. Bompetsis, M. Wil-
son, C. Turyagyenda, and A. Dardamanis,“Security
analysis of mobile edge computing in virtualized
small cell networks,” in IFIP International Con-
ference on Artificial Intelligence Applications and
Innovations, Springer, 2016, pp. 653–665.
[55] M. T. Beck, M. Werner, S. Feld, and S. Schimper,
“Mobile edge computing: A taxonomy,” in Proc. of
the Sixth International Conference on Advances in
Future Internet, Citeseer, 2014, pp. 48–55.
[56] M. T. Beck and M. Maier, “Mobile edge computing:
Challenges for future virtual network embedding
algorithms,” in Proc. The Eighth International Con-
ference on Advanced Engineering Computing and
Applications in Sciences (ADVCOMP 2014), Cite-
seer, vol. 1, 2014, p. 3.
[57] B. Varghese, N. Wang, S. Barbhuiya, P. Kilpatrick,
and D. S. Nikolopoulos, “Challenges and opportuni-
ties in edge computing,” in 2016 IEEE International
Conference on Smart Cloud (SmartCloud), IEEE,
2016, pp. 20–26.
[58] R. Roman, J. Lopez, and M. Mambo, “Mobile edge
computing, fog et al.: A survey and analysis of
security threats and challenges,” Future Generation
Computer Systems, vol. 78, pp. 680–698, 2018.
[59] F. Manco, J. Martins, K. Yasukata, J. Mendes, S.
Kuenzer, and F. Huici, “The case for the superfluid
cloud,” in 7th {USENIX}Workshop on Hot Topics
in Cloud Computing (HotCloud 15), 2015.
[60] M. Peng, C. Wang, V. Lau, and H. V. Poor,
“Fronthaul-constrained cloud radio access networks:
Insights and challenges,” IEEE Wireless Communi-
cations, vol. 22, no. 2, pp. 152–160, 2015.
[61] P. Demestichas, A. Georgakopoulos, D. Karvounas,
K. Tsagkaris, V. Stavroulaki, J. Lu, C. Xiong, and
J. Yao, “5g on the horizon: Key challenges for the
radio-access network,” IEEE vehicular technology
magazine, vol. 8, no. 3, pp. 47–53, 2013.
[62] M. D. Assuncao, F. L. Koch, and C. B. Westphall,
“Grids of agents for computer and telecommunica-
tion network management,” Concurrency and Com-
putation: Practice and Experience, vol. 16, no. 5,
pp. 413–424, 2004.
[63] M. D. De Assuncao, F. L. Koch, and M. A. S.
Netto, Content delivery infrastructure with non-
intentional feedback parameter provisioning, US
Patent 9,590,875, 2017.
Wesley dos Reis Bezerra is a Professor at the Federal Institute of
Santa Catarina, Campus Rio do Sul, and PhD candidate at PPGCC,
UFSC. He obtained his Master in Engineering and Knowledge Man-
agement from EGC, UFSC. He holds a Bachelor of Information
System from INE, UFSC, and a Bachelor in Business Administration
from Est´acio de S´a University.
Fernando Luiz Koch is a Visiting Researcher at PPGCC, UFSC,
and Data & AI Solution Architect with IBM. He dedicated the past
25 years of his career in advancing Data & AI and Cloud Computing.
He holds a PhD in Computer Sciences (2009) from the Utrecht
University, with a thesis about the use of AI to improve Mobile
Services; M.Sc. and B.Sc. in Computer Sciences from INE, UFSC.
He published 6 co-edited books, 35+ patents and 70+ papers.
Carlos Becker Westphall Carlos Becker Westphall is Full Profes-
sor at the Federal University of Santa Catarina since 1993, acting
as leader of the Network and Management Group, founder and
supervisor of the Network and Management Laboratory. PhD in
Informatics, specializing in ”Network and Service Management” at
the Universit´e de Toulose (Paul Sabatier), France in 1991. Master
in Computer Science in 1988 and Electrical Engineer in 1985, both
from UFRGS. He is the author and / or co-author of more than 470
publications. In 2011 he received the ”Awarded IARIA Fellow”. He
is (and was) a member of the Editorial Board of more than a dozen
journals. Serves (and has served) as a member of the organizing and
/ or program committee for hundreds of conferences. Has experience
in Computer Science and Telecommunications, with emphasis on
Administration and Management of Networks and Services, acting
mainly on the following themes: security, autonomic computing,
cloud computing and Internet of Things. He founded the LANOMS
conference (Latin American Network Operations and Management
Symposium). He also provided services: for IEEE acting on CNOM
(Committe on Network Operation and Management); for IFIP acting
in ”WG6.6 - Management of Networks and Distributed Systems”; to
Elsevier as editor of COMNET (Computer Networks Journal); to
Springer as senior editor at JNSM (Journal of Network and Systems
Management) and to IARIA (International Academy, Research, and
Industry Association) as Latin America - IARIA Liaison Board
ResearchGate has not been able to resolve any citations for this publication.
Full-text available
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The latest Internet of Things (IoT) edge-centric architectures allow for unburdening higher layers from part of their computational and data processing requirements. In the specific case of fog computing systems, they reduce greatly the requirements of cloud-centric systems by processing in fog gateways part of the data generated by end devices, thus providing services that were previously offered by a remote cloud. Thanks to recent advances in System-on-Chip (SoC) energy efficiency, it is currently possible to create IoT end devices with enough computational power to process the data generated by their sensors and actuators while providing complex services, which in recent years derived into the development of the mist computing paradigm. To allow mist computing nodes to provide the previously mentioned benefits and guarantee the same level of security as in other architectures, end-to-end standard security mechanisms need to be implemented. In this paper, a high-security energy-efficient fog and mist computing architecture and a testbed are presented and evaluated. The testbed makes use of Transport Layer Security (TLS) 1.2 Elliptic Curve Cryptography (ECC) and Rivest-Shamir-Adleman (RSA) cipher suites (that comply with the yet to come TLS 1.3 standard requirements), which are evaluated and compared in terms of energy consumption and data throughput for a fog gateway and two mist end devices. The obtained results allow a conclusion that ECC outperforms RSA in both energy consumption and data throughput for all the tested security levels. Moreover, the importance of selecting a proper ECC curve is demonstrated, showing that, for the tested devices, some curves present worse energy consumption and data throughput than other curves that provide a higher security level. As a result, this article not only presents a novel mist computing testbed, but also provides guidelines for future researchers to find out efficient and secure implementations for advanced IoT devices.
Video Summarization (VS) has attracted intense attention recently due to its enormous applications in various computer vision domains such as video retrieval, indexing, and browsing. Traditional VS researches mostly target at the effectiveness of VS algorithms by introducing high quality of features and clusters for selecting representative visual elements. Due to the increased density of vision sensors network, there is a trade-off between processing time of VS methods with reasonable and representative quality of the generated summaries. It is a challenging task to generate a video summary of significant importance while fulfilling the needs of IoT surveillance networks with constrained resources. Our study addresses this problem by proposing a new computationally effective solution through designing a deep CNN framework with hierarchical weighted fusion for the summarization of surveillance videos captured in IoT settings. The first stage of our framework designs discriminative rich features extracted from deep CNNs for shot segmentation. Then, we employ image memorability predicted from a fine-tuned CNN model in the framework, along with aesthetic and entropy features to maintain the interestingness and diversity of the summary. Thirdly, a hierarchical weighted fusion mechanism is proposed to produce an aggregated score for effective computation of the extracted features. Finally, an attention curve is constituted using the aggregated score for deciding outstanding keyframes for the final video summary. Experiments are conducted using benchmark datasets for validating the importance and effectiveness of our framework, which outperforms other state-of-the-art schemes.
Cloud computing is an archetype that enables access to a shared pool of computing resources for cloud users in an on-demand or pay-per-use, fashion. Cloud computing offers several benefits to users and organizations, in terms of capital expenditure and savings in operational expenditure. Despite the existence of such benefits, there are some obstacles that place restrictions on the usage of cloud computing. Security is a major issue that is always considered. The lack of this vital feature results in the negative impact of the computing archetype thus resulting in personal, ethical, and financial harm. This paper will focus and explore the security challenges that are faced by cloud entities. These entities include Cloud Service Provider, the Data Owner and Cloud User. Focusing on the crypto-cloud that constitutes of different Communication, Computation, and Service Level Agreement. Studying the causes and effects of various cyber attacks it will provide the necessary upgrades.