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Role of IoT-Cloud Ecosystem in Smart Cities : Review and Challenges
Ridhima Rani
⇑
, Vijaita Kashyap, Meenu Khurana
Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India
article info
Article history:
Received 14 September 2020
Accepted 3 October 2020
Available online xxxx
Keywords:
Smart city
IoT
Cloud computing
Fog computing
abstract
Smart Cities is one of the most important Internet of Things (IoT) applications. Billions of smart devices
on IoT produce volumes of data directed to cloud for storage and processing. Sending complete data to
cloud is least preferred from resource utilization perspective comprising of bandwidth and storage.
Therefore, cloud computing paradigm limitations conquered by fog computing, acting as a bridge
between IoT and cloud. Further, the limited computational capacity of end-devices in IoT infrastructure
and inherited pros and cons of cloud and fog computing necessitates for all three paradigms to work
together to full fill the needs of sustainable infrastructure for smart city. Keeping in view the need of inte-
grating fog computing paradigm (due to its limited storage and computational capabilities), with IoT and
cloud infrastructure, this article reviews the literature on role of IoT & cloud ecosystem in smart cities
along with parameters of evaluation and future research directions in smart cities.
Ó2020 Elsevier Ltd. All rights reserved.
Selection and peer-review under responsibility of the scientific committee of the National Conference on
Functional Materials: Emerging Technologies and Applications in Materials Science.
1. Introduction
The world population that resides in cities will be rising up to
50%, by the year 2050, and will undoubtedly increase to 70% [1].
With the rise in number of people with more demand for services,
it becomes imperative to think over an idea of building smart cities
by adopting information from communication technologies (ICT)
[2]. Smart-cities are constructed with the integration of electronics,
sensors, networks etc. The latest ICTs are important for a smart city
with smart meters, smart phone technologies, smart hospitals,
artificial intelligence, Radio Frequency Identification, and fog-
computing, cloud-computing, Internet of Things (IoT) as infrastruc-
ture. An IoT network consists of concrete objects immersed with
software, smart sensors, electronics and connections among them
to transfer and exchange the data [3,4]. Due to interconnection of
virtual and physical world through electronic devices in streets,
houses, vehicles, and buildings, IoT-based smart cities offer ser-
vices to public and administration such as surveillance systems,
smart-homes, smart-parking, vehicular-traffic, climate systems,
ecological pollution, smart energy, and smart grids [5]. Nowadays,
development of IoT infrastructure in smart-city applications is fac-
ing many challenges [6]. Therefore, in smart city architecture, sen-
sors are integrated with cloud to analyze the streaming data for
making decisions. IoT and cloud computing paradigm works to
provide inputs and information to the tasks which are executed
by integrated sensors, vehicles, humans and mobiles [7,8]. Cloud-
computing paradigm comprises of pool of resources to provide
storage and processing services to applications on demand. The
main challenges that are still faced by research community in
cloud computing are scalability and elasticity [9]. There are many
issues in cloud computing which hinders various functions and
services (smart healthcare, smart cities, smart home), from signif-
icant profits of cloud computing. These problems include lack of
geographical location-awareness, need of mobility and unreliable
delays [10]. Therefore, fog or edge computing plays vital role to
deal with the cloud computing limitations. Fog computing is a col-
lection of resources such as routers, computers, storage data cen-
ters etc, that reside on the edge of a network within close
proximity of end user. Presumably, cloud expansion near the edge
of the network. The purpose of using fog computing is to reduce
delays, computational cost, unreliable geographical location and
energy consumption issues on data computation and storage while
sending the heavy computations to the central cloud for processing
[11] in smart city, an application of IoT [12] as shown in Fig. 1.
The objective of this paper is to review the articles related to
smart cities in IoT-Cloud paradigm along with parameters of eval-
uation. The article also identifies the challenges for future research
direction for researchers in industry and academia.
https://doi.org/10.1016/j.matpr.2020.10.054
2214-7853/Ó2020 Elsevier Ltd. All rights reserved.
Selection and peer-review under responsibility of the scientific committee of the National Conference on Functional Materials: Emerging Technologies and Applications in
Materials Science.
⇑
Corresponding author.
E-mail address: rdahiya7@gmail.com (R. Rani).
Materials Today: Proceedings xxx (xxxx) xxx
Contents lists available at ScienceDirect
Materials Today: Proceedings
journal homepage: www.elsevier.com/locate/matpr
Please cite this article as: R. Rani, V. Kashyap and M. Khurana, Role of IoT-Cloud Ecosystem in Smart Cities : Review and Challenges, Materials Today: Pro-
ceedings, https://doi.org/10.1016/j.matpr.2020.10.054
This article is divided into the following four parts: Section 1
gives an introduction on role of IoT & cloud ecosystem in smart city
applications and how the ecosystem can be benefitted after its
integration with fog computing. Section 2 reviews the literature,
and Section 3 elaborates on challenges of smart cities & future
research directions. Section 4 concludes the paper after analyzing
the future research directions.
Table 1 depicts the parameters of evaluation used by different
research articles reviewed in the literature.
2. Literature review on role of IoT-Cloud ecosystem in smart
cities
Considering various parameters of evaluation depicted in
Table 1 this section reviews the literature and depicts the tools
and techniques, and findings of various research articles in Table 2
along with a pictorial representation of articles reviewed in Fig. 2
with their year of publication.
Qian et al. [13] proposed another framework Hybrid IoT that
supports well-organized transmission, caching and computation
of big data generated by dispersed and substantial IoT devices
which are deployed in smart cities. Ultra dense networking based
computation offloading and hierarchical multiple-access between
cloud and medium access control layer supports smart city vision.
Fan et al. [14] uses a pseudo random-number generator and
quadratic- residuals, and guarantees the security of planned vali-
dation scheme, based on Cloud for RFID healthcare system. The
proposed scheme also ensures conflict to certain attacks and data
privacy in mobile communications.
Garg et al. [15] proposed a model for surveillance in transporta-
tion system. They overcome the problem of security threats in aer-
ial vehicles by using the real-time analytics and applications at
different levels. They have used the probabilistic data structure
approach to detect the cyber-threats in smart vehicles. The ultra
aerial vehicles operate as a data provider by collecting the data
from vehicles and aggregators gives the security while transferring
the load to edge devices. The authors guarantee the security of the
strange movements of the vehicles in real time.
Lin et al. [16] studied the architecture of drones and analyze the
security and privacy of the unmanned aerial vehicles. The author
focused on lightweight cryptography to find the solution for data
leakage, confidentiality of data, and ease of access of data.
Kumar et al. [17] have proposed a model for energy saving for
next generation smart cities. They have used a cloud based infras-
tructure for making decisions to save the energy for different
Fig. 1. Smart City with Cloud and Fog Layers.
Table 1
Parameters of evaluation for smart cities in IoT-cloud paradigm.
#Ref. Computation delay Computation cost Resource allocation Bandwidth Security Energy
[13] UU✗✗✗U
[14] ✗UU ✗U✗
[15] UUU ✗U✗
[16] ✗✗✗ ✗U✗
[17] UUU U✗U
[18] ✗✗✗ ✗U✗
R. Rani, V. Kashyap and M. Khurana Materials Today: Proceedings xxx (xxxx) xxx
2
gadgets. They focused on reducing the overload on the main grid
by providing continuous DC capacity to all low-voltage machines
with less reliance on the main grid. With this reason the overall
burden of energy on the building during peak hours is diminished
by making it self-sustainable related to the demands in energy.
Dener, M. [19] surveyed different research articles which are
describing the role of cloud computing in providing storage, com-
puting, database and numerous application services for access over
the internet. These services provided by cloud are helpful for the
integration and exchange of information among different systems
in smart city.
Khattak et al. [20] presents a novel framework that integrates
vehicular-networking clouds with IoT, and referenced as VCoT.
The article gives a deep insight on role of IoT-VC (VCoT) for differ-
ent real-world applications like, intelligent traffic light, smart
homes, and smart city for automation and general control along
with associated challenges.
Daniel et al. [21] proposed a management policy that maintains
as short operational latency as possible and minimum latency of
requests in an IoT enabled architecture for smart cities services
based on cloud. The author focused on special cases such as rush
hours, outages and considered predictability and periodicity
assumptions as well for proving that operational latency is not
unreasonably long in comparison to analyzed articles.
Perera et al. [22] elaborates on the need of integration of fog and
cloud computing. Keeping in view key benefits and characteristics
of fog computing like latency management, higher availability,
minimizing big data based on priority with the supported use case
scenarios, the author suggested a sustainable infrastructure for
smart cities based on IoT.
Kaur et al. [23] proposed architecture, dependent on cloud and
IoT for the advancement of smart cities. The author focused on the
different characteristic of cloud and deployed them with IoT to
make smart cities better. Their method is basically for reducing
the cost of infrastructure and investment in smart city. The author
took case- study of Dubai smart city with some applications based
scenario and proposed architecture of healthcare in smart city.
Elhoseny et al. [24] framed a smart cities system in which the e-
learning approach is familiarized. It is possible to change in learn-
ing method to the progression of technologies like cloud, internet
of things & big data, but it is a challenging task to make a smart
learning system for smart cities. A smart-learning framework
based on big-data and able to work in smart environment is
proposed.
Massobrio et al. [25] presented an analysis of big-data based on
smart city using cloud computing infrastructure. Map Reduced
parallel model is used and implemented using the Hadoop frame-
work. Basically, they focused on two cases one is public transport
services and another is the estimation of origin to destination
matrix. The former one is using old locations data and latter one
is using the data from ticket sales. The practical result shows that
the model is supporting huge volume of data proficiently.
Dingfu Jiang [26] proposed a data-aggregation algorithm based
on Markov-chain for solving data transmission issues during com-
munication. Solution to the problem achieved by sending the data
again. Practically, it has been proved that sending data again meets
the real needs of smart cities by sharing and exchanging the infor-
mation via various sensors.
Kakderi et al. [27] have proposed a method for public/city
establishment based on cloud computing. Basically, the author
focused on the roadmap with the main roadblock when moving
with the ease of services by decision-making. The article also
focused on the acceptance of cloud computing by the government
organization.
Kumar et al. [28] have presented a Bayesian-Coalition-Game
which is based on cloud service for distribution of content in smart
cities. These contents are accessed from anywhere through inter-
net vehicles as they are placed at the cloud. Using the Markov Deci-
sion Process the vehicles act as players in this game and build a
combination among them. They have used various parameters to
evaluate the performance of this model and showed that it is suc-
cessful in smart city for internet of vehicles.
Table2depicts thetools and techniques usedby differentresearch
articles reviewed in the literature along with their findings.
Fig. 2 depicts the diagrammatical view of articles referred in the
review process with years of publication.
3. Challenges and future research directions
Based on the literature review and the parameters of evaluation
depicted in Table 1 this section presents the challenges and future
research directions in designing smart cities using IoT & Cloud
infrastructure.
(i) The author in [13] worked on computation delay and end-
to-end computation cost parameters to improve heteroge-
neous IoT-Network design at physical and medium access
control (MAC) layer. Various resource allocation solutions
still needs to be designed to meet low-latency, massive-
connectivity, high-reliability and high-volume demands for
transfer, calculation and storage of big data. In addition to
that there is a need to design many new ways to access large
IoT devices with limited radio resources, and to provide high
data rate computation-offloading to mobile edge computing
(MEC) servers.
(ii) The author in [14] worked on security and privacy to provide
authenticated RFID based light-eight protocol for smart-
healthcare in cloud computing, using computational costs
and resource allocation as parameters of evaluation. There
are still certain limitations in the proposed approach; there-
fore, it can be improved to provide a secure wireless-
communication in the wireless-body-area-network as an
area of future research for smart healthcare systems with
bandwidth and latency as parameters of evaluation.
(iii) The author in [15] used triple bloom filter and unmanned
aerial vehicles (UAV) to provide security and cyber-threat
detection in intelligent transportation systems (ITS). Compu-
tation cost, security and storage-as-a-resource are used as
parameters of evaluation. No work is done on redundant
data elimination collected by UAVs to achieve more efficient
storage space utilization. So, it can be proposed as future
research direction with bandwidth, improved latency and
computation cost as parameters of evaluation in ITS in smart
city.
(iv) The author in [16] worked on security and privacy using
authentication and protected identity using data-security
solutions. The author worked on security parameter only
0
2
4
6
8
2011 2013 2014 2015 2016 2017 2018 2019 2020
References Reviewed
Year of publicaon
Fig. 2. References with year of publication.
R. Rani, V. Kashyap and M. Khurana Materials Today: Proceedings xxx (xxxx) xxx
3
using Zone service providers (ZSPs) for internet of drones
(IoDs). A lot of work still needs to be done to further enhance
the existing security solutions, like energy efficient intrusion
detection and prevention systems for detecting malicious
cyber-activities is future research direction for IoDs. Simi-
larly, secure data aggregation approaches to reduce commu-
nication cost and energy, is another area of research for IoDs
with confidentiality and access control as parameters of
security.
(v) A DC nanogrid based on cloud for smart cities in [17] is
working on different parameters like bandwidth, resource
allocation (CPU, Memory), energy, migration-cost etc. The
author has not worked for security of these cloud based
DC nanogrids and no machine learning based approach is
yet proposed for efficient management of energy generated
by these nanogrids. So, these two areas can be explored as
future research directions for implementing DC nanogrids
by integrating IoT-Fog paradigm with cloud computing.
(vi) The author in [33] designed architecture for intelligent
transportation in smart city based on big data analytics.
The objective was to process large volume of data in smart
transportation, considering throughput and response time
as parameters of evaluation. As the volume of data increases
with time in smart transportation, we can suggest a future
research area to remove duplicates from this large volume
of data to further improve storage space efficiency in this
architecture.
4. Conclusion
Smart city as an application of IoT and cloud ecosystem is grow-
ing at a rapid rate due to increase in sensing technology and
decrease in cost. Therefore, in such cities to deal with emergency
situations, smart systems are required. Although, the articles
reviewed, elaborates on the cloud, IoT and fog or edge computing
contribution in smart cities. All around discussions and conversa-
tions among specialists and architects, on fields related to smart
cities and IoT advancements should be continually driven, for the
provisioning of explicit plans of action. The challenges that are still
existing and their research directions are depicted. A large portion
of these investigations have neglected the real time systems in
smart cities. Therefore, this limitation persuades us to investigate
the work with more references in the future, to deal with delay
sensitive and real time applications.
CRediT authorship contribution statement
Ridhima Rani: Writing - original draft, Writing - review & edit-
ing, Conceptualization, Methodology. Vijaita Kashyap: Resources,
Visualization. Meenu Khurana: Formal analysis.
Declaration of Competing Interest
The authors declare that they have no known competing finan-
cial interests or personal relationships that could have appeared
to influence the work reported in this paper.
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