INFS5095 Assignment 1: Technology Review of IoT Data Management

This report, created for an INFS5095 assignment, explores the application of cognitive computing in managing and exploiting data generated by the Internet of Things (IoT). The introduction provides a brief overview of IoT and its potential benefits, followed by a detailed explanation of cognitive computing, including its functionalities and relationship with artificial intelligence. The report outlines the benefits of using cognitive computing with IoT data, emphasizing its ability to facilitate better decision-making and increase productivity in various industries. It also addresses the limitations of cognitive computing, such as its challenges in risk analysis and the need for extensive training data. The report concludes by illustrating how cognitive computing can be applied with IoT data, highlighting the role of machine learning and predictive analysis in enhancing data management and decision-making processes. The report also includes references to relevant sources and discussions on the future potential of cognitive computing in the context of the IoT.

Big Data Basics
INFS 5095
2019
Student's Assignment Guide
Assignment 1 – Technology Review
(Internal and External/Online)
Technology Review for managing
or exploiting IoT Data
[Your Name]
[Date]
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Contents
Introduction...........................................................................................................................................3
About Internet of Things (IoT) Data.......................................................................................................3
About [your chosen technology]...........................................................................................................3
Benefits of Using [your chosen technology] with IoT Data....................................................................3
Limitations to Using [your chosen technology] with IoT Data...............................................................3
Using [your chosen technology] with IoT Data......................................................................................3
References.............................................................................................................................................3
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Introduction
In this report the discussion will be related to Internet of Things commonly known as IOT. The report
consists of a brief description of basically IOT is and how it can be implemented for the betterment
of mankind. This report also describes about cognitive computing, its benefits with the IOT, its
limitations and how in future, IOT and cognitive computing can be combined in order to get benefits
from these advanced technology. The report analysis the benefit of the cognitive computing and its
relation with the internet of things.
About Internet of Things (IoT) Data
Internet of things can be defined as a extension of the Internet connectivity in the physical devices
and in the everyday objects. Embedded with that of electronics, the Internet connectivity, and other
hardware forms such as sensors, these devices can communicate and also interact among each
other with the help of Internet and also these devices can be remotely controlled. Considering the
consumer markets IoT techniques are considered to be more synonymous with that of the products
that are basically pertaining with that of the concept of the “smart home”, that is basically all
devices starting from that of lighting fixtures, thermostats , home based security systems , cameras
and other household appliances as well which basically supports or more prominently shares the
common ecosystems, and which can be basically controlled with the help of a controlled device that
is associated to that of the ecosystems such as the smartphones and also the smat kind of the
speakers. Although the systems that are based on the IoT has gained huge popularity but there are
certain departments where there is a drawback in this systems, especially in the privacy and the
security departments that are basically related to the devices (Chiang and Zhang 2016).
About Cognitive Computing
Cognitive computing systems are the types of systems that basically uses the computer based
models in order to simulate the human cognitions techniques or processes in order to find a solution
to the complex problems in which the answer may be considered to be ambiguous and also
uncertain (Lee and Lee 2015). The term which is known as the cognitive computing is often used
with that of the artificial intelligence, this phrase is commonly associated to that of the IBM cognitive
systems. Cognitive systems is basically used to synthesize the information from the numerous
information sources. In order to achieve this , the cognitive systems basically includes the self
learning techniques that basically takes the help of the data mining , the pattern recognition and the
natural language processing in order to mimic how the human brain basically works (Centenaro et
al. 2016). The cognitive systems must be flexible in order to learn the changing of the information
and the goals that has to be achieved. The systems must have the capability to digest the real timing
data and adopt to the change in the environments. The human computer interaction is considered
top be a critical component of the cognitive systems (Yao 2016).
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Benefits of Using Cognitive Computing with IoT Data
Cognitive Computing means that the computers will have the ability to work in the complex
problems. The necessity of the cognitive computing in the field of IoT arises from the importance of
the information in the modern businesses. The necessities for the cognitive computing in the
Internet of Things arise from the importance of the data in the modern businesses. In smart IoT
places of future, everyone starting from the startups to the enterprises will be using the data in
order to take the effective decisions by the use of the facts rather than their own instincts. The
cognitive computing techniques basically uses the data and then responds to the changes within it in
order to take the better decisions that is based on the past experiences, compared to that of the rule
based decision making systems. The cognitive computing uses the information in order to respond
to the changes and to also take the better decisions in the basis of the learning experience , which is
basically compared to that of the rule based decision systems. In the recent future Internet of Things
will be powered by the cognitive computing will be leading to the increased in the productivity. As
there is a introduction of a more autonomous systems in the industries of the IoT, businesses have
to develop certain skills in order to develop the necessary skills in order to take the advantages of
the expansion of the potentials (Noor 2015). Cognitive computing in the field of IoT will be leading to
the products that will be able to make a instant and an autonomous business related decisions
without any kind of interventions of the humans. Starting from the interaction of the customers to
that of the manufacturing and the maintenance of the equipment’s, processes or the steps that once
was requiring a guess work and the reactive management as well will now be having a face based
and a more proactive solutions. Coming to the banking industries that has several usage of the
cognitive computing specially in the fraud detection departments. Previously the detection of the
faults were based on the rule analysis (Huh, Cho and Kim 2017). But with the introduction of the
cognitive computing the rules has just become a very smaller part of a more comprehensive wholes
which allows the banks to understand the customers spending habits and also allows to put a freeze
on the card if the pattern of the usage detects that are card is used by the customers in a fraud
manner (Ramu, Reddy and Jayanthi 2018).
Limitations to Using Cognitive Computing with IoT Data
The cognitive fails in the analysis of the risks. It basically fails to analyse the risks that are missing the
unstructured data. This basically includes the socio and the economic factors, the cultural factors ,
the environmental factors and also the peoples. An example can be taken as in a predictive models
discovers a location for the oil explosion. But the country is undergoing a certain change in the
government , the cognitive model has to take the factors in consideration. Thus the intervention of
the humans is necessary in the risk analysis and in the final decision making (Hurwitz, Kaufman and
Bowles 2015).
The cognitive systems basically require a training information in order to completely
understand the processes and to improve. The training processes is the reason for the slow
adoptions. The nursing staffs understands the medical conditions and the processes of using the
cognitive systems makes it worse (Sheth 2016).
Cognitive systems is definitely considered as the next step in computing that are started with
the automation. It basically sets a level that the computer intelligence can even compete with the
human brains but it also faced certain limitations which is basically making it difficult for the artificial
intelligence to be applied in certain situations having a higher level of uncertainty. The problems of
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the complexity grows with the increase of the data sources. It proves to be challenging to implement
, aggregate and to analyse such type of the unstructured data. The organization that is basically
deciding to implement the cognitive computing techniques in their business techniques should
understand the drawbacks and then should proceed accordingly (Wang 2017).
Using Cognitive Computing with IoT Data
Cognitive computing’s technological acquisition with that of the IoT will be able to enable the timely
aggression of the sensors-driving data with plethora of the sources from the external that of the
internal enterprise (Li et al. 2015). Application of the machine learning , neural networks an the
other algorithms will be engendering the intelligent data in order to feed into the predictive analysis
for fully syntheses , time sensitive cognitive data. The expansion of the Internet of things in the
cognitive computing is basically driven by a unique values that can be gained from the cognitive
analytic data. AI is also required in order locate the signals in a huge amounts of the unstructured
data. The predictive analysis is basically required in order to predict the different algorithms in order
to detect the signals. The absorption of the Internet of things basically the best capabilities and also
enables the automation in order to clarify the decision making processes. The internet of things is
anticipated in order to create the real time actions form the data. Machine learning , the deep
learning and the neural networks able to automate the modelling requisites in order to improve the
management of the data. As a result of such kind of automations the foundation for the strategic
decisions will be considered as a lucidity gleaned from the time sensitive information which is
available. Thus from the discussions it is concluded that the cognitive computing will be affecting
mankind in a huge manner and it will be definitely prove to be a boon for the organizations if they
decide to implement this techniques for their betterment (Cormier et al. 2018).
References
Chiang, M. and Zhang, T., 2016. Fog and IoT: An overview of research opportunities. IEEE Internet of
Things Journal, 3(6), pp.854-864.
Centenaro, M., Vangelista, L., Zanella, A. and Zorzi, M., 2016. Long-range communications in
unlicensed bands: The rising stars in the IoT and smart city scenarios. IEEE Wireless
Communications, 23(5), pp.60-67.
Lee, I. and Lee, K., 2015. The Internet of Things (IoT): Applications, investments, and challenges for
enterprises. Business Horizons, 58(4), pp.431-440.
Yao, Y., 2016. Three-way decisions and cognitive computing. Cognitive computation, 8(4), pp.543-
554.
Li, J., Mei, C., Xu, W. and Qian, Y., 2015. Concept learning via granular computing: a cognitive
viewpoint. Information Sciences, 298, pp.447-467.
Sheth, A., 2016. Internet of things to smart iot through semantic, cognitive, and perceptual
computing. IEEE Intelligent Systems, 31(2), pp.108-112.
Hurwitz, J.S., Kaufman, M. and Bowles, A., 2015. Cognitive computing and big data analytics. John
Wiley & Sons.
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Ramu, G., Reddy, P.D.K. and Jayanthi, A., 2018. A Survey of Precision Medicine Strategy Using
Cognitive Computing. International Journal of Machine Learning and Computing, 8(6).
Cormier, M.E., Cox, E.D., Thackrey, W.E., McGlynn, J. and Gardner, H., Scianta Analytics LLC,
2018. System for dispatching cognitive computing across multiple workers. U.S. Patent Application
15/707,904.
Wang, G., 2017. DGCC: data-driven granular cognitive computing. Granular Computing, 2(4), pp.343-
355.
Noor, A.K., 2015. Potential of cognitive computing and cognitive systems. Open Engineering, 5(1).
Huh, S., Cho, S. and Kim, S., 2017, February. Managing IoT devices using blockchain platform. In 2017
19th international conference on advanced communication technology (ICACT)(pp. 464-467). IEEE.
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