Page 71 - Raspberry Pi as a Foundation for Boosting Computer and Technology Literacy

Internet of Things 5.4
data clustering tasks. The algorithms are contained within specialized
methods and can be effectively implemented through the Wolfram
language (Alva 2021, 273). Furthermore, Mathematica has a compre-
hensive framework for creating custom neural networks. This frame-
work encompasses various functionalities, such as data vectorization,
different types of layers, various activation functions, as well as model
importing, exporting, and visualization capabilities (Alva 2021, 331).
Finally, there is a Wolfram Language neural net framework (Wolfram,
n.d.-b), which provides free access to various types of pre-trained ma-
chine learning models.

5.4 Internet of Things 5.4
The Internet of Things is a collection of interconnected computing de-
vices that are capable of communicating with one another as well as
interacting with the physical world (Rose, Eldridge, and Chapin 2015,
11–12). However, to streamline operations and avoid the need for in-
dividual communication with each component of an IoT system, this
process is centralized and executed on a master node. Thus, the mas-
ter node receives commands from the user, sends them to the specific
IoT appliance, and subsequently relays the appliance’s response back
to the user. It is noteworthy that Raspberry Pi devices are commonly
chosen to take on the role of master nodes due to their affordability
and extensibility.

Applications in industries
Raspberry Pi devices have found applications in industries typically
involved in production, healthcare, robotics, and cloud computing. De-
spite the apparent differences, these industries share a common focus
on seeking pragmatic solutions in several key areas, which involve re-
al-time monitoring, wireless communications, and remote control. In
this sense, they all express interest in the IoT and its potential advan-
tages over manual labour. These benefits encompass increased produc-
tivity, greater operating and cost effectiveness, enhanced data analysis
capabilities, and the convenience of ubiquitous connectivity (Rose,
Eldridge, and Chapin 2015, 9). On the other hand, these advantages
might also give rise to certain challenges, depending on the particu-
lar application and the surrounding context of the IoT system. These
issues are related to security, privacy, standardization, compatibility,
and legal implications (Rose, Eldridge, and Chapin 2015, 45).

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