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Industrial Internet of Things (IIoT) - Conceptual Architecture

Posted by Ketan Puri (View Profile | View All Posts) | July 13, 2016 6:29 AM


The popularity of Internet of Things (IoT) is growing rapidly. More and more devices (things) are getting connected to the internet every day. The value potential through these connected devices is enormous. We have witnessed just a fraction of its potential yet. Many startups are in process of building data driven value products, solutions or services that can disrupt the traditional operational procedures. Major cloud vendors have also ventured into it, providing IoT as a key offering in their product stack.

Industrial IoT extends the general concept of IoT to an industrial scale. Every industry has their own set of devices, home grown or proprietary applications with limited interfaces and for some even network bandwidth is of a major concern. Considering the challenges and limitations, varying from industry to industry, there is no single solution that fits all. Every industry is unique in itself with varied set of use cases and require custom tailoring.

This article will talk about the conceptual architecture for an Industrial Internet of Things (IIoT), agnostic of technology or solution.

Below are the key components of any typical IIoT landscape


a) Industrial Control Systems (ICS)

These 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.

b) Devices

These are 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.

They interface with the ICS, Transient Data Stores, Channels, and Processors

c) Transient Store

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.

They generally come as an attached or shared storage to  the devices .

d) Local Processors

These are 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 etc.

They generally feeds data into the remote applications deployed at the industrial sites. At times these are integrated with the devices itself for data processing. 

e) Applications (Local, Remote, Visualization)

These are 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.

f) Channels

These are the mediums for data exchange between devices and outside world. It includes satellite communication, routers, network protocols (Web based or TCP)   etc.

g) Gateways

These provide communications across multiple networks and protocols enabling data interchange between distributed IIoT components. It includes protocol translators, intelligent signal routers etc.

h) Collectors

These are 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.

i) Processors

These are 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.

j) Permanent Data Store and Application Data Store

These are 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.

k) Models

There are 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.

The models are fed back into the data stores, processors, applications, and gateways

l) Security

Security 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 etc. 

m) Computing Environments

These vary from industry to industry depending upon their business landscape and nature of the business (Retail, Health Care, Manufacturing, Oil and Gas, Utilities etc.)

  • 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   


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