IIC - Edge Intelligence Testbed
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
Hewlett Packard Enterprise (HPE)Hewlett Packard Enterprise or HPE (formerly HP) makes IT environments more efficient, productive and secure, enabling fast, flexible responses to a rapidly changing competitive landscape. They enable organizations to act quickly on ideas by delivering infrastructure that can be easily composed and recomposed to meet shifting demands, so they can lead in today’s marketplace of disruptive innovation.Year founded: 2015 (1939)Revenue: $53.0 billion (2014)NYSE: HPE
Equipment & Machinery
- CONNECTIVITY PROTOCOLS
*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.
- DATA COLLECTED
- SOLUTION TYPE
- SOLUTION MATURITY
Emerging (technology has been on the market for > 2 years)
- OPERATIONAL IMPACT
Impact #1 [Efficiency Improvement - R&D]
More rapid development of testbeds and industrial IoT innovation
Impact #2 Impact #3
- QUANTITATIVE BENEFIT
- USE CASES
Edge Computing | Edge IntelligenceEdge (fog) computing shifts computing applications, data, and services away from centralized servers to the extremes of a network. This enables analytics and knowledge generation to occur at the source of the data. Industrial IoT companies face challenges turning machine data into business intelligence. Existing cloud-based technologies do not solve problems of data analytics, software deployment, or updates and security for remote devices. Edge or fog computing solves the problem of accessing large amounts of machine-generated data by processing data at the edge of the network and converting it into actionable, useful business information. In an Intelligent Industrial Fog, software can be deployed at various points in the network to not only automate monitoring and control, but also to apply embedded intelligent agents that can adjust device behaviors in relation to ongoing performance variables, reduce running costs by reducing power consumption during off-cycles, or even detect imminent failures and notify technicians to perform preventative maintenance.Edge computing also allows remote software deployment and secure M2M communication. Edge computing leverages resources that are not continuously connected to a network, such as laptops, smartphones, tablets, and sensors. It covers a wide range of technologies, from wireless sensor networks and mobile data acquisition to cooperative distributed peer-to-peer ad hoc networking and processing. Import IoT applications include remote cloud services, distributed data storage and retrieval, and self-healing networks.