A Horizontal Taxonomy for the Industrial IoT
The key factor here is not addition or changes in which device is used. It is more a function of not
knowing which types of devices may be involved.
These systems must implement a different way to discover information. Instead of searching for
data, it is more efficient to automate the process by building runtime maps of devices and their
data relationships. The choice of “20” different devices is arbitrary. The point: when there are
many different configurations for many devices, mapping them at runtime becomes an important
architectural need. Each device requires some sort of server or manager that locally configures
attached sub-devices, and then presents that catalog to the rest of the system. This avoids
manual gymnastics.
Figure 8: IIoT Device Integration Challenge
Large systems assembled in the field from a large variety of “devices” face a challenge in understanding and
discovering interacting devices and their relationships. The most common example applications are in
manufacturing. These applications benefit from a design that offers the ability for remote applications and
human interfaces to “browse” the system, thus discovering data sources and relationships.
3.6
Distribution Focus
Metric: Fan out > 10
Architectural Impact: Must use one-to-many connection technology
We define “fan out” as the number of data recipients that must be informed upon change of a
single data item. Thus, a data item that must go to 10 different destinations each time it changes
has a fan out of “10”.
Fan out impacts architecture because many protocols work through single 1:1 connections. Most
of the enterprise world works this way, often with TCP, a 1:1 session protocol. Examples include
connecting a browser to a web server, a phone app to a backend, or a bank to a credit card
company. While these systems can achieve significant scale, they must manage a separate
connection to each endpoint. When many data updates must go to many endpoints, the system
IIC Journal of Innovation
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