Use of Machine Learning to Improve the Internet of Things Based Applications | PPT

Use of machine learning to improve the Internet of Things based applications
Research Hypothesis
There are diverse categories of
challenges related with the use of IoT
based devices such as the scalability
issues, interoperability issues, and
data security issues (Lee et al.
2016). Each of these issue is having
a direct adverse impact on the
performance of the IoT devices
(Outchakoucht, Hamza and Leroy
2017).
Machine learning algorithms is very
much useful to improve the capability
of any advanced system these days
such as the Management Information
System (Lee et al. 2018).
The notable determination of this
poster is to identify whether the
challenges of IoT devices can be
resolved using Machine Learning
techniques or not. Secondary data
collection procedure shall be
considered in this research.
Literature Review
Name of the researcher: Please Fill
Contact number of the researcher: Please fill
• Accuracy in the predictive outcome of the IoT based devices is
expected to be enhanced with the help of the advancements
which are done by the machine learning experts.
• The technical challenges of IoT based devices are expected to
get resolved with the help of the collaborative efforts coming
from data mining and machine learning algorithms.
• Accessing algorithm intelligence will be much easier in the
coming years due to the use of machine learning language.
Alam, F., Mehmood, R., Katib, I. and Albeshri, A., 2016. Analysis of eight data mining algorithms for smarter Internet of
Things (IoT). Procedia Computer Science, 98, pp.437-442
Chin, J., Callaghan, V. and Lam, I., 2017, June. Understanding and personalising smart city services using machine
learning, the internet-of-things and big data. In 2017 IEEE 26th International Symposium on Industrial Electronics (ISIE)
(pp. 2050-2055). IEEE.
Diro, A.A. and Chilamkurti, N., 2018. Distributed attack detection scheme using deep learning approach for Internet of
Things. Future Generation Computer Systems, 82, pp.761-768.
Doshi, R., Apthorpe, N. and Feamster, N., 2018, May. Machine learning ddos detection for consumer internet of things
devices. In 2018 IEEE Security and Privacy Workshops (SPW) (pp. 29-35). IEEE.
Earley, S., 2015. Analytics, machine learning, and the internet of things. IT Professional, 17(1), pp.10-13.
Jeschke, S., Brecher, C., Meisen, T., Özdemir, D. and Eschert, T., 2017. Industrial internet of things and cyber
manufacturing systems. In Industrial internet of things (pp. 3-19). Springer, Cham.
Kumar, Ashish, Saurabh Goyal, and Manik Varma. "Resource-efficient machine learning in 2 KB RAM for the internet of
things." In Proceedings of the 34th International Conference on Machine Learning-Volume 70, pp. 1935-1944. JMLR.
org, 2017.
Kumar, P.M. and Gandhi, U.D., 2018. A novel three-tier Internet of Things architecture with machine learning algorithm for
early detection of heart diseases. Computers & Electrical Engineering, 65, pp.222-235.
Lade, P., Ghosh, R. and Srinivasan, S., 2017. Manufacturing analytics and industrial internet of things. IEEE Intelligent
Systems, 32(3), pp.74-79.
Lee, J., Stanley, M., Spanias, A. and Tepedelenlioglu, C., 2016, December. Integrating machine learning in embedded
sensor systems for Internet-of-Things applications. In 2016 IEEE international symposium on signal processing and
information technology (ISSPIT) (pp. 290-294). IEEE.
Li, H., Ota, K. and Dong, M., 2018. Learning IoT in edge: Deep learning for the Internet of Things with edge computing.
IEEE network, 32(1), pp.96-101.
Luo, X., Liu, J., Zhang, D. and Chang, X., 2016. A large-scale web QoS prediction scheme for the Industrial Internet of
Things based on a kernel machine learning algorithm. Computer Networks, 101, pp.81-89.
Mahdavinejad, M.S., Rezvan, M., Barekatain, M., Adibi, P., Barnaghi, P. and Sheth, A.P., 2018. Machine learning for
Internet of Things data analysis: A survey. Digital Communications and Networks, 4(3), pp.161-175.
Outchakoucht, A., Hamza, E.S. and Leroy, J.P., 2017. Dynamic access control policy based on blockchain and machine
learning for the internet of things. Int. J. Adv. Comput. Sci. Appl., 8(7), pp.417-424.
Introduction to the research
Future Research
As stated by Alam et al. (2016), scalability, interoperability and data security are the prime issues in IoT
based devices such as smart televisions, smart appliances, commercial security systems, smart
wearable, and smart city technologies.
According to Chin, Callaghan and Lam (2017), machine learning algorithms (MLA) are very much
essential to address the threat coming from the scalability issue of the IoT devices used in construction
projects. The researcher of this journal highlighted that networking and storage architecture of the IoT
device can also be secured with the help of MLA (Lade, Ghosh and Srinivasan 2017). Supporting the
above discussion as elaborated by Diro and Chilamkurti (2018), interoperability issues in IoT based
applications such as the server issues, infrastructure issues, power supply issues and the file formatting
issues can be addressed in the first place with the help of the MLA. The journal also stated that the use
of public key architecture using MLA can reduce the data security issues in the IoT based applications
(Kumar and Gandhi 2018).
At the same time, as mentioned by Doshi, Apthorpe and Feamster (2018), data security issues in the
IoT based applications such as fake data generation, presence of untrusted mappers, sensitive
information mining, struggle of granular access control, data proven difficulties and security issues of the
database which are linked with the IoT based devices can also be addressed using the MLA. As
mentioned by Earley (2015), continuous improvement of IoT based devices can be maintained with the
help of MLA. The journal helped in understanding that the trends and patters of the IoT devices can also
be analyzed with the help of the MLA (Mahdavinejad et al. 2018).
On the other hand, as mentioned by Jeschke et al. (2017), there are few limitations related with the use
of MLA in IoT based devices as well such as the requirement of requiring larger data sets. The journal
explained that data interpretation ability of the IoT devices might get affected due to the addition of MLA
(Kumar, Saurabh and Manik 2017). The other limitation of MLA in IoT based devices is the high error
susceptibility (Luo et al. 2016).
Research hypothesis is defined as the
type of hypothesis is defined as the type of
statement which helps in understanding
the probable outcome of a research paper
(Kumar and Gandhi 2018). The testable
proposition of a research is also identified
with the help of research hypothesis (Lade,
Ghosh and Srinivasan 2017). There are
diverse categories of research hypothesis
which are considered in research
proposals such as the null hypothesis (H0)
and alternative hypothesis (H1).
The hypothesis of this research are as
followings:
H0: Machine Learning techniques have no
impact on the IoT based devices.
H1: Machine Learning techniques can
improve the IoT based applications.
Overview of Literature Review
Literature Review of this proposal shall
be conducted with the help of
secondary data collection method.
Data presented in the literature review
shall be collected from peer reviewed
journals. The literature review shall be
reviewing how the challenges of the
IoT based applications are addressed
using Machine Learning Algorithms.
References
Data security Scalability issue Interoperability
issue
0
2
4
6
8
10
12
Challenges of IoT addressed by Machine
Learning
2018 2019 2020
• Non probabilistic sampling method shall
be considered in this proposal.
• Hypothesis testing research shall be
considered as the type of investigation in
this proposal.
• Analysis of the collected data shall be
done using quantitative technique.
Methodology
•
Activity Months
Week 1 Week 2 Week 3 Week
4
Week 5 Week
6
Week
7
Selection of topic 
Identification of research
hypothesis
 
Research layout 
Conducting Literature review   
Forming the research plan  
Selecting appropriate
research methodologies
 
Reviewing secondary data  
Interpretation of collected
data sets

Concluding the proposal 
Designing draft proposal  
Proposal submission  
Literature Gap
o Data security issue in IoT can be
solved with the help of IoT security
analytics.
o Avoiding single point of failure in
IoT devices can address the
scalability issues in IoT based
devices.
o Development of a data strategy can
address the interoperability issue in
IoT based applications.