Accelerating the Industrial Internet of Things
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Hewlett Packard Enterprise (HPE) Case Studies IIC - Edge Intelligence Testbed
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IIC - Edge Intelligence Testbed

Hewlett Packard Enterprise (HPE)
IIC - Edge Intelligence Testbed
IIC - Edge Intelligence Testbed
IIC - Edge Intelligence Testbed
Equipment & Machinery
Product Development
A test environment is needed for algorithms and architectures that meets a common set of requirements for many testbeds (see "Testbed in Depth")

A test facility that can be configured into complex edge compute environments, in order to further the state-of-the-art in edge analytics and algorithms
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*This is an IIC testbed currently in progress.*

Hewlett Packard Enterprise, Real-Time Innovations

Vertical industry testbeds

Many emerging industrial IoT applications require coordinated, real-time analytics at the "edge", using algorithms that require a scale of computation and data volume/velocity previously seen only in the data center. Frequently, the networks connecting these machines do not provide sufficient capability, bandwidth, reliability, or cost structure to enable analytics-based control or coordination algorithms to run in a separate location from the machines.

Industrial Internet Consortium members Hewlett-Packard and Real-Time Innovation have joined together on the Edge Intelligence Testbed. The primary objective of the Edge Intelligence Testbed is to significantly accelerate the development of edge architectures and algorithms by removing the barriers that many developers face: access to a wide variety of advanced compute hardware and software configurable to directly resemble state-of-the-art edge systems at very low cost to the tester/developer.
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Emerging (technology has been on the market for > 2 years)
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More rapid development of testbeds and industrial IoT innovation
Horizontal applications are standardized (e.g., asset tracking). Vertical applications are tailored to specific needs (e.g., delivery fleet management).
APIs are the market enabler for IoT. They allow users to manage devices, enable data transfer between software, and provide access capabilities.
Middleware integrates the diverse components of an IoT application by structuring communication, workflows, and business rules.
IoT analytics includes real-time or edge computing and batch analysis. Analytics can be behavioral, descriptive, predictive, or prescriptive.
Visualization solutions use dashboards, alerts, events, maps, and other tools to present easily comprehensible data to end users.
Data management solutions capture, index and store data in traditional database, cloud platforms, and fog systems for future use.
Security software provides encryption, access control, and identity protection to IoT solutions from data collection through end-user applications.
System integrators link IoT component subsystems, customize solutions, and ensure that IoT systems communicate with existing operational systems.
IoT data management consultancies help to make sense of big data, decide which data to maintain and for how long, and troubleshoot IT issues.
IoT software consultancies support the development of data analytics, visualization solutions, and platforms, as well as integration into embedded systems.
Examples of business consulting services include go-to-market design and execution, business model development, channel development, and corporate M&A.
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