Industrial Control Systems (ICS)
provide first hand view of events across industrial systems to the field staff
to manage the industrial operations. They are generally deployed at industrial
sites and includes Distributed Control Systems (DCS), Programmable Logic
Controllers (PLCs), Supervisory Control and Data Acquisition (SCADA) systems
and other industry specific control systems.
industry specific components that interfaces with digital or analog systems and
expose data to the outside digital world. They provide machine to machine,
human to machine and vice versa capability for ICS to exchange information
(real-time or near real-time) enabling other components of the IIoT landscape.
It includes sensors, interpreters, translators, event generators, loggers etc.
interface with the ICS, Transient Data Stores, Channels, and Processors
This is a
temporary optional data store that is connected to a device or an ICS. Its
primary purpose is to ensure data reliability during outages and system
failures including networks. It includes
attached storage, flash, discs etc.
generally come as an attached or shared storage to the devices .
d) Local Processors
low latency data processing systems located near or at the industrial sites. They provide fast processing of the small data. It includes data filters, rule based
engines, event managers, data processors, algorithms, routers, signal detectors
generally feeds data into the remote applications deployed at the industrial
sites. At times these are integrated with the devices itself for data processing.
Applications (Local, Remote, Visualization)
deployed on site or offshore to meet business specific needs. They provide
insights/views of the field operations in real time (for the operators), real
time and historical (for business users and other IT) staff
enabling them to make effective and calculated decisions. It includes web based applications, tools to
manipulate the data, manage devices, interact with other systems, alerts,
notifications, visualizations, dashboards etc.
the mediums for data exchange between devices and outside world. It includes
satellite communication, routers, network protocols (Web based or TCP) etc.
provide communications across multiple networks and protocols enabling data
interchange between distributed IIoT components. It includes protocol
translators, intelligent signal routers etc.
data gatherers that collect and aggregate data from gateways leveraging
standard protocols. It can be custom built or off-the-self products that vary
from industry to industry. For example, OPC data, event stream management
systems, application adapters, brokers etc.
the core of any IIoT solution. Their function is primarily to cater to specific
business needs. It includes stream processors, complex event processing,
signal detection, scoring analytical models, data transformers, advance
analytical tools, executers for machine training algorithms, ingestion pipelines etc.
Permanent Data Store and Application Data Store
the long term data storage systems generally linked to an IIoT solution. They
act as a historians for the device data along with data from other sources. They feed data
into the processors for advanced analytics and model building. It includes
massively parallel processing (MPPs) data stores, on-cloud/on-prem data
repositories, data lakes providing high performance and seamless data access to both
business and IT. For example historians, RDBMS, open source data stores etc.
two type of models that are widely used in the IIoT solutions i.e. Data Models
and Analytical Models. The data models defines a structure to the data while the
analytical models are custom built for catering to industry specific use cases.
Models play an important role in any IIoT solution. They provide a perspective
to the data. Models are generally built by leveraging the data in the
permanent data stores, human experience, and industry standards. The
analytical models are trained leveraging historical data sets or through
machine based training process. Some examples of the analytical models are
clustering, regression, mathematical, statistical etc. Some examples of data
models are Information models, semantic models, Entity relationships mapping,
JSON, XML/XSD etc.
models are fed back into the data stores, processors, applications, and
is the most important aspect of any IIoT application. It runs through entire
pipeline from source to the end consumption. It is very critical for
small, medium and large data driven digital enterprises dealing with their data
in IIoT world. It includes data encryption, user access, authentication,
authorization, user management, network, firewalls, redaction, and masking
vary from industry to industry depending upon their business landscape and
nature of the business (Retail, Health Care, Manufacturing, Oil and Gas,
Fog Computing - Bringing analytics near
to the devices/source
Cloud Computing - Scaling analytics
globally across the enterprise
On-Prem Computing - Crunching data in
existing high performance computing centers
Hybrid Computing - Mix of on-cloud,
on-prem and fog computing optimizing operations tailored for specific industrial business needs