Introduction to IOT 21ETC151
MODULE 3
IOT PROCESSING TOPOLOGIES & TYPES
Data Format:
The Internet is a vast space where huge quantities and varieties of data are generated
regularly and flow freely.
The massive volume of data generated by the huge number of users is further enhanced
by the multiple devices utilized by most users.
In addition to these data-generating sources, non-human data generation sources such as
sensor nodes and automated monitoring systems further add to the data load on the
Internet.
This huge data volume is composed of a variety of data such as e-mails, text documents
(Word docs, PDFs, and others), social media posts, videos, audio files, and images, as
shown in Figure.
However, these data can be broadly grouped into two types based on how they can be accessed
and stored:
1) Structured data
2) Unstructured data.
, Introduction to IOT 21ETC151
Structured data:
These are typically text data that have a pre-defined structure.
Structured data are associated with relational database management systems
(RDBMS). These are primarily created by using length-limited data fields such as
phone numbers, social security numbers, and other such information.
Even if the data is human or machine - generated, these data are easily searchable
by querying algorithms as well as human- generated queries.
Common usage of this type of data is associated with flight or train reservation
systems, banking systems, inventory controls, and other similar systems.
Established languages such as Structured Query Language (SQL) are used for
accessing these data in RDBMS.
However, in the context of IoT, structured data holds a minor share of the total
generated data over the Internet.
Unstructured data:
In simple words, all the data on the Internet, which is not structured, is
categorized as unstructured.
These data types have no pre-defined structure and can vary according to
applications and data-generating sources.
Some of the common examples of human-generated unstructured data include
text, e-mails, videos, images, phone recordings, chats, and others
Some common examples of machine-generated unstructured data include sensor
data from traffic, buildings, industries, satellite imagery, surveillance videos, and
others.
This data type does not have fixed formats associated with it, which makes it very
difficult for querying algorithms to perform a look-up.
Querying languages such as NoSQL are generally used for this data type.
, Introduction to IOT 21ETC151
Importance of Processing in IoT
The vast amount and types of data flowing through the Internet necessitate the
need for intelligent and resourceful processing techniques.
This necessity has become even more crucial with the rapid advancements in IoT,
which is laying enormous pressure on the existing network infrastructure globally.
It is important to decide—when to process and what to process?
Before deciding upon the processing to pursue, we first divide the data to be
processed into three types based on the urgency of processing:
1) Very time critical, 2) Time critical 3) Normal.
Very time critical:
Data from sources such as flight control systems, healthcare, and other such sources, which need
immediate decision support, are deemed as very critical. These data have a very low threshold of
processing latency, typically in the range of a few milliseconds.
Time critical:
Data from sources that can tolerate normal processing latency are deemed as time- critical data.
These data, generally associated with sources such as vehicles, traffic, machine systems, smart
home systems, surveillance systems, and others, which can tolerate a latency of a few seconds
fall in this category.
Normal:
Finally, the last category of data, normal data, can tolerate a processing latency of a few minutes
to a few hours and are typically associated with less data-sensitive domains such as agriculture,
environmental monitoring, and others.
MODULE 3
IOT PROCESSING TOPOLOGIES & TYPES
Data Format:
The Internet is a vast space where huge quantities and varieties of data are generated
regularly and flow freely.
The massive volume of data generated by the huge number of users is further enhanced
by the multiple devices utilized by most users.
In addition to these data-generating sources, non-human data generation sources such as
sensor nodes and automated monitoring systems further add to the data load on the
Internet.
This huge data volume is composed of a variety of data such as e-mails, text documents
(Word docs, PDFs, and others), social media posts, videos, audio files, and images, as
shown in Figure.
However, these data can be broadly grouped into two types based on how they can be accessed
and stored:
1) Structured data
2) Unstructured data.
, Introduction to IOT 21ETC151
Structured data:
These are typically text data that have a pre-defined structure.
Structured data are associated with relational database management systems
(RDBMS). These are primarily created by using length-limited data fields such as
phone numbers, social security numbers, and other such information.
Even if the data is human or machine - generated, these data are easily searchable
by querying algorithms as well as human- generated queries.
Common usage of this type of data is associated with flight or train reservation
systems, banking systems, inventory controls, and other similar systems.
Established languages such as Structured Query Language (SQL) are used for
accessing these data in RDBMS.
However, in the context of IoT, structured data holds a minor share of the total
generated data over the Internet.
Unstructured data:
In simple words, all the data on the Internet, which is not structured, is
categorized as unstructured.
These data types have no pre-defined structure and can vary according to
applications and data-generating sources.
Some of the common examples of human-generated unstructured data include
text, e-mails, videos, images, phone recordings, chats, and others
Some common examples of machine-generated unstructured data include sensor
data from traffic, buildings, industries, satellite imagery, surveillance videos, and
others.
This data type does not have fixed formats associated with it, which makes it very
difficult for querying algorithms to perform a look-up.
Querying languages such as NoSQL are generally used for this data type.
, Introduction to IOT 21ETC151
Importance of Processing in IoT
The vast amount and types of data flowing through the Internet necessitate the
need for intelligent and resourceful processing techniques.
This necessity has become even more crucial with the rapid advancements in IoT,
which is laying enormous pressure on the existing network infrastructure globally.
It is important to decide—when to process and what to process?
Before deciding upon the processing to pursue, we first divide the data to be
processed into three types based on the urgency of processing:
1) Very time critical, 2) Time critical 3) Normal.
Very time critical:
Data from sources such as flight control systems, healthcare, and other such sources, which need
immediate decision support, are deemed as very critical. These data have a very low threshold of
processing latency, typically in the range of a few milliseconds.
Time critical:
Data from sources that can tolerate normal processing latency are deemed as time- critical data.
These data, generally associated with sources such as vehicles, traffic, machine systems, smart
home systems, surveillance systems, and others, which can tolerate a latency of a few seconds
fall in this category.
Normal:
Finally, the last category of data, normal data, can tolerate a processing latency of a few minutes
to a few hours and are typically associated with less data-sensitive domains such as agriculture,
environmental monitoring, and others.
