Moving Targets: Defining the Edge and Its Architecture

iot edge

Organizations face significant challenges deploying IoT infrastructures and discovering insights from the vast amounts of device-generated data.

While the popular image of edge computing suggests that
it means any processing outside of core corporate systems, it is also somewhat
ambiguous. Working on a laptop, for example, could theoretically be considered
“edge computing.”

For enterprise purposes, there needs to be a more
exacting definition of what edge computing entails, and what part of the
architecture it affects. For the final word on most technology definitions,
many people look to NIST, the National Institute of Standards & Technology.
has something to say about edge computing
as well, pointing to a paper on
the topic published by IEEE, which characterizes the edge as the Internet of
Things and mobile realm, extending inward to virtualization and data centers

See also: ROI From the Edge: Every Industry Has a Different Story

The other authority on all things IT, Gartner,
defines edge
as “part of a distributed computing topology where
information processing is located close to the edge, where things and people
produce or consume that information.”

A separate
Gartner definition
takes it a step further, defines it as a “computing
model that enables and optimizes extreme decentralization, placing nodes as
close as possible to the sources and sinks of data and content. As a
decentralized approach, it is a perfect complement to the hyperscale cloud
providers’ tendency towards centralization, where they take advantage of huge economies
of scale.”

The other piece of understanding edge computing is how it
fits into an architecture. This is critical, as enterprises a roadmap of what
technologies are being brought in to support their business plans, how these
solutions fit together, and the standards and formats that are to be applied.
An edge computing reference architecture can provide enterprises with powerful
edge data processing, enhanced IoT device management, and integration with BI

However, edge architectures is an area that is still a
work in progress, note
the authors of a paper
from the German Research Center for Artificial
Intelligence. The ideal architecture, they note, consists of an additional edge
computing tier between cloud and IoT devices for computing and communication.
“The data produced by the devices themselves are not directly sent to the
cloud or back-end infrastructure, but initial computing is performed on this
tier. This tier is used to aggregate, analyze, and process the data before
sending it into the upper layer, the infrastructure.

An edge architecture needs to be adapted to the nature of
the enterprise it is built around. For example, in
a recent post
, Intel and SAP teamed up to provide a glimpse of IoT edge computing
architecture that expands the reach of existing core enterprise applications.
“Despite the potential of IoT, organizations face significant challenges
for deploying IoT infrastructures and discovering insights from the vast
amounts of device-generated data,” the document’s authors point out. The
reference architecture includes the following components:

  • Data acquisition and device control: An
    architecture should enable “data acquisition and ingestion from various
    sources—such as industrial sensors that are part of the operational domains—and
    provides control from a single dashboard. Data from these various sources is
    ingested into the controlling entities using various protocols.”
  • Data security features between sensors,
    gateways, and the cloud:
    An architecture needs to provide “security-enabled
    capabilities along the entire data path with key, certificate, and identity
    management using Intel® hardware-based
    technologies, the security features allow devices to differentiate data as
    private or shared. The security-enabled flow can allow data to reach only the
    device for which it is intended.”
  • More secure, hands-free device onboarding:
    “New devices, such as physical gateways and qualified sensors, can be
    automatically onboarded and provisioned, eliminating extra deployment tasks
    while improving security.”
  • Edge-data collection, storage, and analysis:
    “Enterprise IoT solutions need an edge platform with robust offline
    capabilities to collect, store, and analyze data.” a platform should
    provide “domain-specific insights, real-time events and actions, reliable
    dashboards, and local business-process execution at the edge.”

This UrIoTNews article is syndicated fromRTInsight