Big Data Challenges in IoT and Cloud

This research report discusses the challenges of big data in IoT and cloud computing and how they can be resolved. It highlights the importance of merging these technologies and provides techniques to overcome the challenges. The report also includes an annotated bibliography of 10 peer-reviewed journals.

BIG DATA CHALLENGES IN IOT AND CLOUD
ABSTRACT – IoT as well as cloud computing are considered as the two most significant technologies that have the
responsibility to bring subsequent changes in the technological world. A proper adoption of such technologies is expected
to make life better in future. Big data, on the other hand is responsible for bringing new methods to reduce the
complexities of managing bulk amount of data. Moreover, it even allows to explore these data and undertake necessary
steps for eradicating the issues. However, in spite of having several advantages, there are few disadvantages as well present
in the big data. These challenges can be easily removed after merging the technology with cloud computing and Internet of
Things. Furthermore, a proper synchronization mechanism is enhanced to remove the challenges. This research report
has provided a proper description of big data challenges within IoT and cloud with relevant details. The final part of the
research report has provided an annotated bibliography of 10 peer reviewed journals.
Key Words: Cloud Computing, Big Data, Interoperability, Internet of Things, Devices
1. INTRODUCTION (15 MARKS)
Internet of Things or simply IoT is the proper arrangement
of various interlinked or interrelated devices of computing,
mechanical technologies, digital technologies that help in
providing a unique identification number and that has a major
ability to transmit the data in the network and not involving
human interactions (O'Leary, 2013). It is one of the most
significant requirements that make the entire process of system
complexity extremely simple in respect to others. A proper
connectivity, network protocols and communication protocols
are used to make this technology successful.
Big data is the most significant requirement that helps in
better analysis of the data by resolving the complexities of data
management to a higher level (Bonomi, Milito, Natarajan &
Zhu, 2014). Cloud computing technology is a vital technology
that is extremely helpful in data transfer in the safest and
secured manner. It helps in removing the redundant data
efficiently. This research report will be providing a brief
description of the challenges of big data technology and how
these could be resolved with IoT and cloud computing.
2. BACKGROUND/LITERATURE REVIEW (40 MARKS)
2.1. Internet of Things as well as Challenges of Big Data in
IoT
IoT comprises of a perfect extension of the Internet
connection beyond any type of standardized device like
desktop laptop, tablet and smart phone. All of the significant
processes can also communicate or interact on the Internet
connectivity so that they could be monitored and controlled in a
better manner. Bessis and Dobre (2014) state that, one of the
basic features of the IoT technology is that there are less
chances of data loss. The main issues of IoT are given below:
i) Streamlining of Processes: IoT does not allow
streamlining of processes and thus it is required to consider this
particular challenge so that the business does not face any issue
(Yang, Huang, Li, Liu & Hu, 2017).
ii) Data Capturing: IoT does not provide any scope for data
capturing and thus it is extremely important to involve with
such a technology that has the ability to capture the data like
big data.
iii) Lack of Data Privacy: Often it is observed that there is
a loss of data within IoT and this is mainly because of the
disparate data sources that are being eventually integrated
within the procedure. According to Cai, Xu, Jiang and
Vasilakos (2017), a proper combination of IoT as well as big
data can be effective for eradicating the issues to a major level.
iv) Validation of Data: It is yet another important and
significant issue, which is common for IoT. Without
involvement of a proper technology, IoT cannot guarantee data
validation under any condition.
2.2 Big Data Challenges within Cloud Computing
Both cloud computing as well as IoT technologies have
some of the major and distinctive advantages. However, in
spite of these benefits, when they are combined with big data,
few challenges are faced such as:
i) Higher Data Growth: As per Dinh, Lee, Niyato and
Wang. (2013), there is always a chance of excessive data
growth due to the complexity of linkage to storage. Since data
are stored in the database, it becomes quite important to deal
with the data related issues as the infrastructure might be a
failure. Technologies such as Hadoop and Spark are required
for dealing with the issue of high data growth.
ii) Insight Generation: A proper generation of insights is
the next significant issue of big data technology within cloud
computing that is extremely common in today’s technological
world. Furthermore, the significant establishment of the data
driven significance and creating innovation and disruptions is
also common here (Paul, Ahmad, Rathore & Jabbar, 2016).
Thus, insight generation in time is quite difficult here.
iii) Maintenance of Big Data Analytics: It is quite vital and
significant to maintain big data analytics, however, when it is
combined with cloud computing, it is observed that it becomes
quite difficult to maintain it (Cecchinel, Jimenez, Mosser &
Riveill, 2014). Moreover, retaining of the big data talent is the
next important requirement in this particular case.
2.3 Past as well as Present Techniques Used for Removing
of Big Data Challenges within Internet of Things as well as
Cloud Computing
There are few of the most significant important techniques
that could be extremely effective for removing any type of big
data complexities to a high level, irrespective of the fact that
whether it is related to internet of things and cloud computing
(Fernando, Loke & Rahayu, 2013). These techniques, which
were being utilized previously to remove all types of problems
or issues of big data technology within cloud computing as well
as IoT technologies are as follows:

i) Proper Monitoring and Management: A better
maintenance, monitoring and management of data is the first as
well as the most significant technique, which could be used to
eradicate the issue of big data in IoT or cloud (Liu, Yang,
Zhang & Chen, 2015). This specific technique is solely
responsible for checking the acceptance of big data and how
the entire process is being monitored. This is one of the most
primitive methodology that was utilized by the top
management of an organization.
ii) Consulting Big Data: This is the second traditional
method of eradicating the issues related to big data challenges
in an effective way or manner. The proper big data consulting
can be helpful to reduce the entire complexities to a higher
level.
According to O’Driscoll, Daugelaite and Sleator (2013), in
spite of having the traditional techniques, there are few more
techniques that are being used in the present world and these
techniques are given below:
i) Data Lakes: It is termed as the most common and
effective technique that is quite common to eradicate the issues
of big data in cloud or internet of things (Sookhak, Gani, Khan
& Buyya, 2015). Such lakes are responsible for providing
cheaper storages to the data and hence can be analysed
irrespective of time.
ii) Big Data Algorithm and Architecture: This type of
algorithm and architecture make the entire issue free from
complexity (Riggins & Wamba, 2015).
iii) Optimized Algorithm: The third type is optimized
algorithm that reduces consumption of computing power and
hence removing the complexities.
Figure 1: Relation between IoT and Big Data
(Source: Bhatt, Dey & Ashour, 2017)
3. PROBLEM STATEMENT (10 MARKS)
3.1 Big Data in IoT and Cloud
Although, there are several benefits of big data for any
specific organization, some of the major issues are also present
for the technology, when it is being involved with internet of
things and cloud computing (Xu, Huang, Chen & Lee, 2015).
This is the most significant technology, which helps to remove
the complexity of bulk data management and complicated data
set. A proper involvement of big data as well as IoT and cloud
computing can easily produce several significant advantages to
the user and also removes the complexities already faced
(Inukollu, Arsi & Ravuri, 2014). However, there are certain
techniques that are extremely effective to remove each of these
issues effectively.
4. CONCLUSION (5 MARKS)
Therefore, it could be concluded that three technologies of
IoT, cloud computing as well as big data are responsible for
making any process and business operation extremely effective
and efficient in respect to other technologies. There is a high
demand of big data when IoT and cloud are being converged
with the technology. Based on the proper implementation of the
respective technology, a proper amalgamation of the two
technologies is needed to obtain real and valuable analytics to
provide efficiency within the respective technological world. A
proper shift within this dependency of interrelated devices is
important to reduce the existing issues in big data technology.
The most significant challenges within big data majorly include
lack of data security, more growth of data complexity and few
others. Although, these could be eradicated with proper steps,
there are some suitable techniques that help in doing so.
Amongst the major techniques, the most suitable and
appropriate techniques to eradicate the big data challenges in
Internet of Things and cloud computing are optimized
algorithm and data lake. This above given research report has
perfectly outline a brief description of issues in the big data
technology and how to reduce these technologies properly.
5. ANNOTATED BIBLIOGRAPHY
Fernando, N., Loke, S. W., & Rahayu, W. (2013).
Mobile cloud computing: A survey. Future generation
computer systems, 29(1), 84-106.
As per Fernando, Loke and Rahayu (2013), the mobile
cloud computing could be extremely vulnerable for the
society and these are required to removed effectively. The
major challenges of mobile cloud computing include
scarcity of the resources and frequent disconnections.
Dinh, H. T., Lee, C., Niyato, D., & Wang, P. (2013). A
survey of mobile cloud computing: architecture,
applications, and approaches. Wireless communications
and mobile computing, 13(18), 1587-1611.
According to Dinh et al. (2013), there are several
distinctive applications, approaches and architecture of
mobile cloud computing for Big Data technology. Cloud
here is responsible for removing the complexities without
any type of issues and with the help of cloud applications,
such issues could be resolved.
O’Driscoll, A., Daugelaite, J., & Sleator, R. D. (2013).
‘Big data’, Hadoop and cloud computing in
genomics. Journal of biomedical informatics, 46(5), 774-
781.
O’Driscoll, Daugelaite and Sleator (2013) stated that
the application of big data, called Hadoop is solely
responsible for having some of the most distinctive and
significant requirements and the presence of cloud
computing makes the entire process much simpler.
O'Leary, D. E. (2013). BIG DATA’, THE ‘INTERNET
OF THINGS’AND THE ‘INTERNET OF
SIGNS. Intelligent Systems in Accounting, Finance and
Management, 20(1), 53-65.
According to O'Leary (2013), IoT is the most
important and noteworthy technology that can act as a
proper requirement to remove any type of big data issues
and complexities majorly.
Bessis, N., & Dobre, C. (Eds.). (2014). Big data and
internet of things: a roadmap for smart

environments (Vol. 546). Basel, Switzerland: Springer
International Publishing.
As per Bessis and Dobre (2014), the interconnection
of big data and internet of things is responsible for
providing smart cities and even to make the smart city
more advanced and effective in respect to other smart
cities.
Cecchinel, C., Jimenez, M., Mosser, S., & Riveill, M.
(2014, June). An architecture to support the collection
of big data in the internet of things. In 2014 IEEE
World Congress on Services (pp. 442-449). IEEE.
Cecchinel, Jimenez, Mosser and Riveill (2014) state
that, a proper architecture or framework is required in the
organization for supporting the interconnection of internet
of things and big data. This type of technology hence could
be quite effective for reducing the complexities of big data
challenges.
Bonomi, F., Milito, R., Natarajan, P., & Zhu, J. (2014).
Fog computing: A platform for internet of things and
analytics. In Big data and internet of things: A roadmap
for smart environments (pp. 169-186). Springer, Cham.
According to Bonomi, Milito, Natarajan and Zhu
(2014), fog computing is one of the major platforms of
Internet of Things that could be extremely effective for
reducing the challenges like data security to a higher level
as it has a combination of cloud computing technology
with it.
Riggins, F. J., & Wamba, S. F. (2015, January).
Research directions on the adoption, usage, and impact
of the internet of things through the use of big data
analytics. In 2015 48th Hawaii International Conference
on System Sciences (pp. 1531-1540). IEEE.
As per Riggins and Wamba (2015), the significant
impact, adoption and usage of Internet of Things with the
proper utilization of big data analytics is extremely
important and vital for understanding the entire concept of
big data analytics without much complexity.
Paul, A., Ahmad, A., Rathore, M. M., & Jabbar, S.
(2016). Smartbuddy: defining human behaviors using
big data analytics in social internet of things. IEEE
Wireless Communications, 23(5), 68-74.
Paul, Ahmad, Rathore and Jabbar (2016) state that,
smart buddy is a concept of the combination of big data
analytics and internet of things and it helps in reducing the
several complexities of data management without much
issues and with utmost efficiency.
Yang, C., Huang, Q., Li, Z., Liu, K., & Hu, F. (2017).
Big Data and cloud computing: innovation
opportunities and challenges. International Journal of
Digital Earth, 10(1), 13-53.
According to Yang, Huang, Li, Liu and Hu (2017), it
is quite important to maintain innovation and creativity so
that the entire architecture does not involve any complexity
regarding data management and data security. IoT is also
included in this architecture.
References:
Bessis, N., & Dobre, C. (Eds.). (2014). Big data and
internet of things: a roadmap for smart
environments (Vol. 546). Basel, Switzerland:
Springer International Publishing.
Bhatt, C., Dey, N., & Ashour, A. S. (Eds.).
(2017). Internet of things and big data
technologies for next generation healthcare (Vol.
23). New York: Springer.
Bonomi, F., Milito, R., Natarajan, P., & Zhu, J. (2014).
Fog computing: A platform for internet of things
and analytics. In Big data and internet of things:
A roadmap for smart environments (pp. 169-186).
Springer, Cham.
Cai, H., Xu, B., Jiang, L., & Vasilakos, A. V. (2017). IoT-
based big data storage systems in cloud
computing: Perspectives and challenges. IEEE
Internet of Things Journal, 4(1), 75-87.
Cecchinel, C., Jimenez, M., Mosser, S., & Riveill, M.
(2014, June). An architecture to support the
collection of big data in the internet of things.
In 2014 IEEE World Congress on Services (pp.
442-449). IEEE.
Dinh, H. T., Lee, C., Niyato, D., & Wang, P. (2013). A
survey of mobile cloud computing: architecture,
applications, and approaches. Wireless
communications and mobile computing, 13(18),
1587-1611.
Fernando, N., Loke, S. W., & Rahayu, W. (2013). Mobile
cloud computing: A survey. Future generation
computer systems, 29(1), 84-106.
Inukollu, V. N., Arsi, S., & Ravuri, S. R. (2014). Security
issues associated with big data in cloud
computing. International Journal of Network
Security & Its Applications, 6(3), 45.
Liu, C., Yang, C., Zhang, X., & Chen, J. (2015). External
integrity verification for outsourced big data in
cloud and IoT: A big picture. Future generation
computer systems, 49, 58-67.
O’Driscoll, A., Daugelaite, J., & Sleator, R. D. (2013).
‘Big data’, Hadoop and cloud computing in
genomics. Journal of biomedical
informatics, 46(5), 774-781.
O'Leary, D. E. (2013). BIG DATA’, THE ‘INTERNET OF
THINGS’AND THE ‘INTERNET OF
SIGNS. Intelligent Systems in Accounting,
Finance and Management, 20(1), 53-65.
Paul, A., Ahmad, A., Rathore, M. M., & Jabbar, S. (2016).
Smartbuddy: defining human behaviors using big
data analytics in social internet of things. IEEE
Wireless Communications, 23(5), 68-74.

Riggins, F. J., & Wamba, S. F. (2015, January). Research
directions on the adoption, usage, and impact of
the internet of things through the use of big data
analytics. In 2015 48th Hawaii International
Conference on System Sciences (pp. 1531-1540).
IEEE.
Sookhak, M., Gani, A., Khan, M. K., & Buyya, R.
(2015). Dynamic remote data auditing for
securing big data storage in cloud
computing (Doctoral dissertation, Fakulti Sains
Komputer dan Teknologi Maklumat, Universiti
Malaya).
Xu, J., Huang, E., Chen, C. H., & Lee, L. H. (2015).
Simulation optimization: A review and
exploration in the new era of cloud computing
and big data. Asia-Pacific Journal of Operational
Research, 32(03), 1550019.
Yang, C., Huang, Q., Li, Z., Liu, K., & Hu, F. (2017). Big
Data and cloud computing: innovation
opportunities and challenges. International
Journal of Digital Earth, 10(1), 13-53.