Intel Corporation

Kirk Smith

Rita H Wouhaybi

Mark D Yarvis

Robert Chavez

John B Vicente

Ansuya Negi

Sharad K Garg

Aaron R Berck

Casey T Rathbone

Xubo Zhang

Ron Kuruvilla Thomas

Mandeep Shetty

Atul N Hatalkar

Roderick E Kronschnabel

Matthew J Schneider

Chris D Lucero

Steven M Brown

Jeremy Ouillette

Carlos Cordeiro

Markus Dominik Mueck

Bahareh Sadeghi

Srikathyayani Srikanteswara

Hani H Elgebaly

Mohamed El-Refaey

Hazem Abdelmegeed

Norhan M Osman

Karl Kempf

Beverly Toperzer

Martin W Braun

Daniel E Rivera

Wenlin Wang

Various systems and methods for implementing a software defined industrial system are described herein. For example, an orchestrated system of distributed nodes may run an application, including modules implemented on the distributed nodes. In response to a node failing, a module may be redeployed to a replacement node. In an example, self-descriptive control applications and software modules are provided in the context of orchestratable distributed systems. The self-descriptive control applications may be executed by an orchestrator or like control device and use a module manifest to generate a control system application. For example, an edge control node of the industrial system may include a system on a chip including a microcontroller (MCU) to convert IO data. The system on a chip includes a central processing unit (CPU) in an initial inactive state, which may be changed to an activated state in response an activation signal.

Distributed software-defined industrial systems

Various systems and methods may be used to implement a software defined industrial system. For example, an orchestrated system of distributed nodes may run an application, including modules implemented on the distributed nodes. The orchestrated system may include an orchestration server, a first node executing a first module, and a second node executing a second module. In response to the second node failing, the second module may be redeployed to a replacement node (e.g., the first node or a different node). The replacement mode may be determined by the first node or another node, for example based on connections to or from the second node.

Distributed dynamic architecture for error correction

Various systems and methods are provided for implementing a software defined industrial system. In an example, self-descriptive control applications and software modules are provided in the context of orchestratable distributed systems. The self-descriptive control applications may be executed by an orchestrator or like control device, configured to: identify available software modules adapted to perform functional operations in a control system environment; identify operational characteristics that identify characteristics of execution of the available software modules that are available to implement a control system application; select a software module for execution based on the operational configuration and the operational characteristics identified in the manifest; and cause the execution of the selected software module in the control system environment based on an application specification for the control system application.

Self-descriptive orchestratable modules in software-defined industrial systems

Various systems and methods may be used to implement a software defined industrial system. For example, an edge control node of the industrial system may include a system on a chip including a microcontroller (MCU) to convert IO data. The system on a chip includes a central processing unit (CPU) in an initial inactive state to receive an activation signal from, for example, an orchestration server, and change to an activated state in response to receiving the activation signal.

Scalable edge compute in a distributed control environment

Certain embodiments herein are directed to determining an allocation of wireless spectrum, which may include one or more frequencies for communicating content, among operator networks that provide wireless (for example, cellular) services to their customers. Information associated with a supply of wireless spectrum and a demand for wireless spectrum may be analyzed to determine an allocation of the wireless spectrum. Various techniques may be used to determine the allocation, such as various types of auctions, mathematical or statistical techniques, and/or heuristic approaches that may consider various factors. Upon determining an allocation of the wireless spectrum, configuration information associated with the wireless spectrum may be determined and sent to one or more devices associated with respective operator networks, where the configuration information may be used to configure the operator network to utilize the allocated wireless spectrum.

Dynamic allocation of wireless spectrum for utilization by wireless operator networks

This disclosure is directed to spectrum reclaiming in a leased spectrum system. A spectrum exchange coordinator (SEC) may be interposed between a spectrum owner and at least one operator to facilitate the leasing and reclaiming of spectrum. Upon occurrence of a trigger, the SEC may then determine a procedure for allowing the spectrum owner to reclaim the leased spectrum. The spectrum owner may be allowed to reclaim the leased spectrum in full based on an agreement. If not previously agreed to, the spectrum owner may be allowed to reclaim at least part of the spectrum. These operations may include a dual risk evaluation to determine how reclaiming the leased spectrum will affect quality of service (QoS). Partial or gradual reclaiming may then allow the leased spectrum to be reclaimed while minimizing impact. Examples of gradual reclaiming may be based on a time domain, frequency domain, or cellular basis.

Spectrum reclaiming in a leased spectrum system

Entering a demand prediction for a discrete component product manufactured in a supply chain into a model predictive controller, entering inventory information for an inventory in the supply chain that is associated with the discrete component product into the model predictive controller, and controlling the inventory with the model predictive controller based on the demand prediction is disclosed.

Managing supply chains with model predictive control

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Chandler, AZ, United States of America

Kirk Smith