20
F. Firouzi et al.
3. Edge computing (data element analysis and transformation) – This is also
known as the fog layer because it is the layer where data cleaning, aggregation,
and processing begin. It is the responsibility of this stage to prepare the data
for analysis and storage. One of the methods to mitigate the enormous data
flows is to start the analysis as early as possible. Data is evaluated, possibly
reformatted or reordered, filtered, and checked for warning thresholds. Edge/fog
computing facilitates data processing, data storage, and networking services
between endpoint IoT devices and the center (e.g., cloud or data centers). The
key idea of edge/fog is to process the data and make actions closer to where the
data is created. This can ultimately result in reducing the traffic between IoT
devices and real-time actions (i.e., respond fasters to events).
4. Data accumulation (storage) – This is the layer which prepares data to be stored
in a database, whatever that format is. The key here is that after this layer, the
data is expected to be able to be retrieved based on queries.
5. Data abstraction (aggregation and access) – Another layer that deals with the
data. At this layer, the data is consistent, complete, and validated. In practice,
data is often stored across multiple databases; the task of this layer is to ensure
that the data is able to be queried and a unified, reliable result is returned.
6. Application (reporting, analytics, control) – This is where individual software
applications can query the data to perform specific functions, such as reporting,
monitoring, control of devices, visualizations, and analytics.
7. Collaboration and processes (involves people and business processes) – This
is the layer that makes use of the outputs from the software applications of
the previous layer. Data and conclusions from that data are shared with other
entities or applications. The collaboration of several data sources illuminates new
business practices, makes existing processes more efficient, and opens the doors
to innovation. This is where the benefits of the Internet of Things are largely
realized.
1.2.2 Simplified Reference Model of IoT
A simplified IoT architecture is comprised of the following layers (see Fig. 1.4)
[12, 13]:
• IoT Things Layer – Consists of all IoT sensors and actuators.
• IoT Network Layer – Includes network components such as IoT gateways,
switches, and routers responsible for transmitting data in a timely and dependable
fashion. This layer also includes fog/edge nodes to perform data analysis and
transformation and information processing as quickly and closely to the things
as possible. This is very helpful in real-time applications such as IoT healthcare
to be able to provide low-latency and faster responses to emergencies.
F. Firouzi et al.
3. Edge computing (data element analysis and transformation) – This is also
known as the fog layer because it is the layer where data cleaning, aggregation,
and processing begin. It is the responsibility of this stage to prepare the data
for analysis and storage. One of the methods to mitigate the enormous data
flows is to start the analysis as early as possible. Data is evaluated, possibly
reformatted or reordered, filtered, and checked for warning thresholds. Edge/fog
computing facilitates data processing, data storage, and networking services
between endpoint IoT devices and the center (e.g., cloud or data centers). The
key idea of edge/fog is to process the data and make actions closer to where the
data is created. This can ultimately result in reducing the traffic between IoT
devices and real-time actions (i.e., respond fasters to events).
4. Data accumulation (storage) – This is the layer which prepares data to be stored
in a database, whatever that format is. The key here is that after this layer, the
data is expected to be able to be retrieved based on queries.
5. Data abstraction (aggregation and access) – Another layer that deals with the
data. At this layer, the data is consistent, complete, and validated. In practice,
data is often stored across multiple databases; the task of this layer is to ensure
that the data is able to be queried and a unified, reliable result is returned.
6. Application (reporting, analytics, control) – This is where individual software
applications can query the data to perform specific functions, such as reporting,
monitoring, control of devices, visualizations, and analytics.
7. Collaboration and processes (involves people and business processes) – This
is the layer that makes use of the outputs from the software applications of
the previous layer. Data and conclusions from that data are shared with other
entities or applications. The collaboration of several data sources illuminates new
business practices, makes existing processes more efficient, and opens the doors
to innovation. This is where the benefits of the Internet of Things are largely
realized.
1.2.2 Simplified Reference Model of IoT
A simplified IoT architecture is comprised of the following layers (see Fig. 1.4)
[12, 13]:
• IoT Things Layer – Consists of all IoT sensors and actuators.
• IoT Network Layer – Includes network components such as IoT gateways,
switches, and routers responsible for transmitting data in a timely and dependable
fashion. This layer also includes fog/edge nodes to perform data analysis and
transformation and information processing as quickly and closely to the things
as possible. This is very helpful in real-time applications such as IoT healthcare
to be able to provide low-latency and faster responses to emergencies.