Big Data Challenges in IoT and Cloud

BIG DATA CHALLENGES IN IOT AND CLOUD
ABSTRACT – Cloud computing as well as IoT could be referred to as the significant technologies that are eventually
taking complete charge of individual lives in today’s world. It is highly expected that the proper and efficient adoption of
technologies of IoT and cloud computing is getting much higher and is also expected to rise in near future. Hence, it makes
the two technologies for being considered majorly as the most important parts of future based Internet. The technology of
big data can substantially be introduced as a technology paradigm that has opened several important opportunities and
thus data is needed to be explored and enabled research within each and every sector. The specific technology of big data
provides opportunities to the respective organizations for better analysis of insights of confidential data and even for
mending in a method that it might provide proper results such as identification of major challenges and then providing
suitable solutions for them. There are various important capabilities to perform the analysis, pre-processing, proper
management, security and collection. Although, the technology of big data comprises of a significant role for data
processing as well as resolving several issues related to data mining, transportation, data storage, processing of resources
and many more. For dealing with such issues, IoT and cloud computing are being utilized and after merging them, proper
goals and objectives could be achieved. This research report has detailed about big data challenges and how these could be
resolved with cloud computing as well as IoT.
Key Words: Cloud Computing, IoT, Big Data, Data Lakes, Optimized Algorithms
1. INTRODUCTION (15 MARKS)
IoT could be referred to as the proper arrangement of
various interconnected mechanical or digital technologies and
computing devices as well as human beings that are
eventually provided with a proper UID or unique identifier
and even the major ability to transmit data over subsequent
network by not needing any kind of major interactions from
the machines, human being and computerized systems (Talia,
2013). Several significant organizations are present, which
have deployed the technology of internet of things within
their business to use and apply it for better execution of
business operations. Moreover, Internet of Things even
ensures that the client is satisfied under every circumstance
and hence improving the entire procedure for decision
making for that specific organization. Furthermore, the entire
value of business without much complexities. IoT is referred
to as a natural extension for data acquisition and supervisor
controlling that is also termed as SCADA and it is the most
significant category for software application program to
eventually monitor and control the several procedures
(Fernández et al., 2014). It is also extremely helpful for
gathering of data from any type of remote location to monitor
the equipment and condition.
There is a complete ecosystem of Iot and it consists of
respective web based smart devices that can use few
embedded sensors, communication hardware and processors
to complete three distinct business operations of collection,
sending as well as acting of data, which are acquired from
various environments (Baek, Vu, Liu, Huang & Xiang,
2015). A significant analysis of Internet of Things is
extremely important for the case since they are much more
efficient in technological field. Few IoT devices are
eventually responsible for completing the works and not
intervening human beings; thus allowing the individuals to
perfectly interact with several devices. Several instructions
are also given to them to access these data without much
complexity (Hashem et al., 2015). The communication as
well as network protocols and Internet connection, which is
use with such web enabled device is solely dependent over
IoT based applications.
The analytics for big data evolves as the most important
key for better analysis of the Internet of Things based data
from various connected devices, which are quite helpful to
undertake a proper initiative to improvise the process of
decision making (Reed & Dongarra, 2015). A subsequent role
of this big data after merging with IoT technology is to
progress the larger amounts of data over the basis of real time
and then storing it with several technologies of storage. Cloud
computing technology is the next vital as well as subsequent
technologies to transfer sensitive data. The subsequent
convergence of the technologies of IoT, cloud as well as big
data is responsible to provide business value for all
organizations. The following research report will be outlining
a proper discussion about challenges as well as opportunities
that an organization obtain from the integration of these three
technologies.
2. BACKGROUND/LITERATURE REVIEW (40 MARKS)
2.1. Internet of Things
As per Liu, Yang, Zhang and Chen (2015), the internet of
things involves a subsequent extension of connections within
the Internet beyond the standardized devices like smart phones
and tablets. Each of these process could also communicate and
interrelate over the Internet connections and these processes
can also be remotely monitored and controlled. Several
subsequent application of the IoT devices and these could be
broadly divided into commercial, consumers, and industrial and
infrastructure spaces (Ahmed et al., 2017). For consumer
application, the most efficient and effective applications are
wearable technology, health care, smart home, home
automation and many more. One of several major features of
the technology internet of things could be referred to as being
extremely safe as well as secured and hence chance of data loss
is quite less.
2.2 Challenges or Issues of Big Data in IoT
As per Cai, Xu, Jiang and Vasilakos, (2017), there are
various important and significant challenges or issues of the
technology of big data in the technology of IoT and these
challenges are provided below:

i) Process Streaming: This is the first as well as the most
significant issue for big data within IoT, which often occurs in
any business. It is eventually termed as the velocity challenge
and thus event streaming is occurred (Hwang & Chen, 2017).
This process streaming is much more important or vital to
maintain business process efficiency.
ii) Validation of Data: This is the second vital and
significant challenge of big datum after amalgamation of
Internet of Things. The various companies are obtaining
several data pieces with noteworthy systems and the data types
never agree with one another (Zhang, Yang, Chen & Li, 2018).
The complete procedure for getting each of these records may
agree to factor that records and data are safe and secured,
accurate and usable and thus it is referred to as governance of
data. For solving the data governance challenges, this is vital
for combining processes and policies.
iii) Information Security as well as Privacy: This is the
third distinctive and noteworthy challenge of big data that
occurs when combined with cloud computing or IoT.
Mohammadi, Al-Fuqaha, Sorour and Guizani (2018) stated
that, there are some of the disparate sources of data, which are
integrated within the procedure and thus this is being checked
that big data can lead to various issues while integration of
data. The combination of internet of things and technology of
big data technologies subsequently come from various sources
like electronic mail systems, employee created document,
enterprise application and social media system. A proper
amalgamation of the sensitive data as well as integration of
data are properly used to create the most difficult reports
(Biswas & Giaffreda, 2014). The various distinctive vendors
thus offer a subsequent series of for integration tools as well as
ETL to make the complete process quite easier without having
proper solutions for data integration issues.
iv) Data Capturing: It is the fourth noteworthy and vital
challenge of big data that occurs when combined with the
major technologies of IoT and cloud computing. As soon as the
data is being captured, it is absolutely apparent that the
subsequent user would be losing endorsement and
authentication and the respective data is eventually lost and
recovery is almost not possible (Barnaghi, Sheth & Henson,
2013). The data capturing is absolutely dependent on
application type within the organization. With a proper
arrangement of such technologies of the big data and IoT, it
eventually becomes the most significant problem to capture the
respective data and hence the velocity of data is being
decreased to a higher level.
Figure 1: IoT and Big Data
(Source: Zhang, Yang, Chen & Li, 2018)
2.3 Big Data Issues in Technology of Cloud Computing
Malek et al. (2017) stated that, this technology of cloud
computing after the appropriate combination of IoT
substantially gives few of the most important and significant
benefits as well as technological advancement towards the
people to make it more efficient or effective than any other
technology. The most significant and noteworthy challenges of
this technology of the big data technology with IoT and cloud
computing are provided below:
i) Recruiting as well as Retaining of the Talent of Big
Data: With the significant purpose to develop, manage and
then run the applications that mainly generate all types of
insights, several distinctive businesses subsequently need
professionals to deal with the skills as well as knowledge of big
data technology (Mishra, Lin & Chang, 2015). For effectively
and efficiently dealing with the shortages of talent, the
respective organizations consist of some significant
alternatives. At first, several factors increases the budget and
even their retention as well as recruitment efforts. Another
distinctive and important alterative, which is required to be
considered within the subsequent case would be that they
would be offering better opportunities of training to their recent
staff and employees with major attempt to develop the talent,
which is needed by them. The second subsequent and
noteworthy alternative within the scenario is that various
companies are checking for better technologies (Bashir & Gill,
2016). Thus, they are eventually buying various solutions for
big data analytics with self service capability and machine
learning capability. By the proper emergence of this cloud
computing technology as well as IoT technology, the specific
challenge arises and it even becomes extremely problematic for
every user and thus it is quite important and is needed for
removing all type of challenges effectively.
ii) Generating the Insights within a Timely Way: The
second important and significant challenge of the technology of
big datum with the involvement of big datum or internet of
things is the generation of major insights in a timely manner.
According to Rathore, Ahmad and Paul (2016), there could be
a major decrease within the costs with the help of operational
cost efficiency and hence it becomes a significant problem for

bringing effectiveness within the business. Furthermore, a
proper formation of the information driven culture as well as
conception of the newer avenue for both innovation and
disruption is also possible for this case. The next significant
objective or goal, which the company comprises of before
implementing the technology of big data is to effectively
launch a new product and service offering (Arridha,
Sukaridhoto, Pramadihanto & Funabiki, 2017). It was
extremely competitive and to achieve such offerings, speed is
the most important requirement.
iii) Dealing with Overall Growth and Development of the
Data: This is yet another important and vital challenge, which
is subsequently faced by a user while combing the three
distinctive technologies of big data, cloud and IoT. Thus, an
important challenge in this case is associated to the roper
information analysis and simple storage (De Francisci Morales,
Bifet, Khan, Gama & Fan, 2016). The respective information,
which is being eventually stores in a system of information
technology must be stored and secured in a way that these data
are completely formless and they do not remain in the database.
A perfect and proper unstructured data management can grow
as the most significant challenge and to deal with data growth
type, the several organizations are eventually turning to several
technologies. As soon as the growth of data is coming within
the storage, a hyper as well as congregated infrastructure could
make it extremely easy for scaling the hardware (Zhang et al.,
2017). Furthermore, the technologies can even decrease the
amount of expenses that are associated within the big data
storage. As technology of the cloud computing is being
included with the technology of big data, a proper analysis and
management of data is completed after involvement of few of
the major and significant tools like Hadoop of big data. As per
Wang and Ranjan (2015), the applications of business
intelligence help to find several insights, which are required by
companies.
Figure 2: Big Data in Cloud Computing
(Source: O’Driscoll, Daugelaite & Sleator, 2013)
2.4 Past and Present Techniques Utilized to Remove the Big
Data Challenges in IoT and Cloud Computing
There can be few of the relevant, suitable and the most
important techniques that were used previously for removing
the problems and challenges associated to the technology of big
datum in cloud computing as well as IoT and these techniques
are provided below:
i) Proper Control as well as Regular Monitoring:
According to Perera, Ranjan, Wang, Khan and Zomaya (2015),
one of the most traditional and primitive techniques that was
extremely useful to deal with such issues is proper control and
regular monitoring of data. Each and every recognized issue
related to big data was being removed by this technique. As the
data is solely responsible to bring out effectiveness and
efficiencies within the business, this is ital. for every user to
comprise of a relevant and continuous monitoring as well as
controlling. When these data are not being monitored properly,
there is a higher chance that sensitive data might get lost
(Cecchinel, Jimenez, Mosser & Riveill, 2014). The
organizational top management is responsible for ensuring this
type of controlling and monitoring of data.
ii) Consulting with Big Data Consultants: The big data
consultants can provide better guidance regarding data
management and hence it is required to reduce the total
complexity to better level. Moreover removal of redundant data
is also possible here.
Although, there were some of the major techniques that
were being used traditionally, few current techniques are also
present in today’s world (Cecchinel, Jimenez, Mosser &
Riveill, 2014). These current techniques are provided below:
i) Simplified Architectures as well as Algorithms of Big
Data: One of the major operative and efficient technique that is
being used currently is a simplified architecture and big data
algorithm. According to Suciu, Vulpe, Fratu and Suciu (2015),
a major security to the data is being provided in this case to
properly analyse the data. A regular maintenance as well as
support to big datum algorithm is yet another vital and
noteworthy technique for solving the problems related to
security and up scaling.
ii) Optimized Algorithm: An optimized algorithm is
responsible for reducing the overall consumption of the
respective computing power and hence data related issues are
diminished.
iii) Data Lakes: Data lakes can easily and promptly provide
cheap storage opportunities to the data, which are not at all
needed for being analysed at appropriate moment (Chen, Mao,
Zhang & Leung, 2014).
Amongst the above mentioned past and present techniques,
it is eventually analysed that data lakes could be termed as
extremely effective to resolve every identified big data issue in
cloud and IoT (O’Driscoll, Daugelaite & Sleator, 2013).
Research Area Problem
Addressed
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