Machine Learning in Internet of Things (IOT)

ITC560 - ASSIGNMENT 3

Purpose of the Report
There are many devices that were so convenient for everyone with the requirements for smarter
and more autonomous. So, the IOT has more power for converting the visions into the real form.
The main purpose of the IOT is for developing the simple lifestyle and the smarter environment
by money, energy, and to save time. By this technology, many types of expenses will be reduced
in the different industries (Mahdavinejad, et al., 2018). There are vast investments and some
studies were running on the IOT that will help to make the IOT trending or the value within
recent years. The IOT includes the bulk of the connected devices which was used to transfer the
information to one another for optimizing the performances, without any input or human
awareness and the actions that will be occurred automatically. IOT contains four types of major
components:
- System monitoring
- Analysis of data
- Network processing
- Sensors
Figure 1: Internet of Things (IoT)
IOT was integrated with the several connectivities and the technologies that were sufficient and
also necessary for its functioning conditions. Though, the communication protocols were the
constituents of IoT technologies that will be enhanced for the future perspective (Salamone, et
al., 2018). By 2020, data measurements can be estimated with the total count of devices that

were connected with the internet where been used between 20 to 50 billion. So, these counts
were grown and the technologies have become mature and the volume of the published data will
be increased. The technology of the devices that were Internet-connected was referred to as IOT
(Internet of Things). This will be continued for extending the recent internet activities for
providing the interactions and the connectivity between cyber and the physical world.
Additionally, for increasing the volume of data, IOT generates the big data that was
characterized by the velocity in the terms of location and the time dependencies. It characterized
with the variation of data quality and the multiple modalities (Sun, et al., 2019).
Analysis and the intelligent processing of big data were the key points to develop the smart
applications of IoT. There were various kinds of machine technologies or the methods were
dealing with challenges that were presented by the IOT data elements that were considered in the
smart cities mainly with the use cases (Punithavathi, et al., 2019). Machine learning components
were mainly affecting the taxonomy of the algorithms of the machine learning which explains
the different kinds of techniques that were applied for extracting the data or the information at a
higher level. The challenges and the potential of machine learning are for the data analytics of
IOT. After applying the SVM Machine to the Aarhus traffic data of smart city were presented for
the detailed explorations (Banerjee, et al., 2019).
Topic
For the report, the topic named “Machine Learning in IOT”. Machine learning was called the
application of AI as Artificial Intelligence. It provides to systemized the ability for learning
automatically and also make improvement from the experience without been programmed
explicitly. The main focus of the machine learning is on the development of the computer
programs which will access the data or the information. Machine learning is so important
because of the new computing technologies such as wireless networks, Big Data, Artificial
Intelligence, and many more (Xiao, et al., 2018).
The iterative or the main aspect of machine learning was so necessary because of the models that
were exposed to the new data or information. They were also able to adapt independently. With
the previous computations, they will able to learn for producing the results, repeatable decisions,
and reliability. Machine learning is the science that was basically used to gain the fresh or the
new momentum. There were many algorithms of Machine learning which were appropriate for
making decisions and the processing on the generating the smart data from the IoT things or the
applications. These algorithms were defined below:
- Decision Trees: It was the support tool for making decisions with the possible
consequences that include the outcomes of the chance events, utility and the costs of
resources.