Getting started with IoT

Data repository
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The edge IoT nodes of a network are limited in storage size
and processing constraints. In an IoT application, you may
have thousands of nodes collecting data. The solution is to
move this data on to a database storage either locally (privately)
hosted or on a cloud storage platform where it can be
processed from a centralised location.
Traditionally, most IoT devices will push all data up to the data
repository, but with the emergence of edge computing, only
the processed data or data of interest may be sent.
The cloud
‘The cloud’ is a term used to describe a global network of
powerful servers which are designed to store and manage data,
run applications and deliver content or a service. The largest
providers of these cloud services are Amazon, Microsoft, IBM
and Google. The cloud has replaced the need for companies
to run expensive physical servers on-site and offers server-like
services, with users paying when the services are used. Large
amounts of data can be stored inexpensively in the cloud.
Analysis and post
processing
When data arrives in the cloud, a typical task would be for
a software application to
• Unpack the data
• Extract the values from each sensor (for example,
temperature, humidity)
• Check that these values are within acceptable ranges.
Processing in the cloud
IoT devices normally send data to the cloud for processing.
Its huge processing power enables the execution of complex
algorithms, machine learning and artificial intelligence to
extract maximum value from the data.
Benefits of cloud processing
• Huge processing power can perform complex tasks
• Data analytics can be performed on incoming data
to detect trends or abnormalities
Analysis of the data
Cost of
storage
Ongoing
costs
Privacy
of data
Security
of data
Analysis of the data is where the real value is unlocked and
many IoT companies build their value proposition around this.
For example, a business manufactures and sells hardware for
sensing the movement of people or traffic through an area.
Analytics are performed on the captured data. These analytics
can be used to detect trends or anomalies in the movement
of people or traffic, which becomes a “service” they can sell to
improve the efficiency of other systems.
Scalability
Considerations
when choosing
between local
data storage
options and the
cloud
Data storage
location
Ease of
access
Tools for
development
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Visualisation and
presentation
The last stage of the process is to present the information in a
meaningful way.
Depending on the requirements of the user, this could be as
simple an action as ringing a buzzer or sending an alert by SMS
that there is an abnormality.
More frequently, it is a web page, or dashboard, with a series of
graphs showing real-time information from the network. Many
cloud platforms now include tools to visualise data instead of
creating separate traditional web pages to display it.
This may also include an automated feedback loop or manual
two-way communication - the user may wish to input into
the system to control the sensors or an actuator based on the
information they have received.
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