Intel Corporation

Eve M Schooler

Hassnaa Moustafa

Srikathyayani Srikanteswara

Ned M Smith

Maruti Gupta Hyde

Jessica C McCarthy

Jeffrey Sedayao

David John Zage

Rath Vannithamby

Nageen Himayat

Satish Chandra Jha

Christian Maciocco

S M Iftekharul Alam

Sebastian Schoenberg

Andrew Stephen Brown

Kathiravetpillai Sivanesan

Shao-Wen Yang

Katalin Klara Bartfai-Walcott

Venkatesan Nallampatti Ekambaram

Kuilin Clark Chen

Yi Zhang

Sung Ho Lee

David E Cohen

Vallabhajosyula S Somayazulu

Gabriel Arrobo Vidal

Stepan Karpenko

Ren Wang

Deepak Shashidhar Vembar

Yen-kuang Chen

Zongrui Ding

Ravikumar Balakrishnan

Arun Raghunath

David Ott

Christina R Strong

Francesc Guim Bernat

Kshitij Arun Doshi

Dario Sabella

Wendy C Wong

Nina Taft

Sunil K Cheruvu

Shivani A Sud

Frederic Giroire

Jaideep Chandrashekar

Qian Li

Tianyou Li

Moreno Ambrosin

Maria E Ramirez Loaiza

Gang Shen

Radhika Loomba

Jeffery R Foerster

Perumal Kuppuudaiyar

Lisa Sherin

Sanjana K Kamath

David E Ott

Jessica Mccarthy

In one embodiment, a road side unit (RSU) establishes a data offload session with a vehicle in the vicinity of the RSU based on a session establishment request sent by the vehicle, and stores data received from the vehicle during the data offload session in its memory. The RSU generates storage record information (including identifying information for the RSU) for the stored data, and transmits the storage record information to the vehicle.

Data offload and time synchronization for ubiquitous visual computing witness

Systems and techniques for information centric network (ICN) interworking are described herein. For example, a request may be received at a convergence layer of a node. Here, the request originates from an application on the node. A network protocol, from several available to the node, may be determined to transmit the request. The node then transmits the request via the selected network protocol.

Information centric network interworking techniques

Various aspects of methods, systems, and use cases for verification and attestation of operations in an edge computing environment are described, based on use of a trust calculus and established definitions of trustworthiness properties. In an example, an edge computing verification node is configured to: obtain a trust representation, corresponding to an edge computing feature, that is defined with a trust calculus and provided in a data definition language; receive, from an edge computing node, compute results and attestation evidence from the edge computing feature; attempt validation of the attestation evidence based on attestation properties defined by the trust representation; and communicate an indication of trustworthiness for the compute results, based on the validation of the attestation evidence. In further examples, the trust representation and validation is used in a named function network (NFN), for dynamic composition and execution of a function.

Calculus for trust in edge computing and named function networks

In one embodiment, an apparatus comprises a memory and a processor. The memory is to store sensor data captured by one or more sensors associated with a first device. Further, the processor comprises circuitry to: access the sensor data captured by the one or more sensors associated with the first device; determine that an incident occurred within a vicinity of the first device; identify a first collection of sensor data associated with the incident, wherein the first collection of sensor data is identified from the sensor data captured by the one or more sensors; preserve, on the memory, the first collection of sensor data associated with the incident; and notify one or more second devices of the incident, wherein the one or more second devices are located within the vicinity of the first device.

Ubiquitous witness for vehicle incidents

A named function network (NFN) system includes a routing node, a function generation node, and a server node. The routing node receives requests for new functions, the requests including data values for generating the new functions. The function generation node receives the data values from the routing node and generates a new function for the NFN using the data values. The server node receives a request from the routing node to execute the new function, executes the new function, and transmits results of the execution to the routing node.

Function generation in named function networks

Generally discussed herein are systems, devices, and methods for data management in a reverse content data network (rCDN). A component of the rCDN may include a memory to hold content received from a first sensor device of a plurality of sensor devices of the rCDN and first attributes that describe properties of the content. The component may include processing circuitry to receive second content from a second sensor device of the plurality of sensor devices, the second content including a plurality of second attributes that describe properties of the second content, and forward, in response to a determination, based on the first and second attributes, that there is insufficient space to store the second content on the memory, the second content to a node of the rCDN that is fewer hops away from a backend cloud than the component.

Data management in an edge network

Devices and techniques for Information Centric Network (ICN) packet transmission control are described herein. An interest (or data) packet may be received at an ICN router. Here, the packet includes quality of service (QoS) information. For an interest packet, the ICN router creates a pending interest table (PIT) entry for the packet. The ICN router determines that it does not have a route for the packet. Thus, if it is an interest packet, there is no forward route in a forwarding information base (FIB). If it is a data packet, there is no corresponding PIT entry. However, after extracting the QoS information from the packet, the ICN router broadcasts the packet in accordance with the QoS information.

Information centric network packet transmission control

In one embodiment, an apparatus comprises a memory and a processor. The memory stores visual data captured by one or more sensors. The processor detects one or more first objects in the visual data based on a machine learning model and one or more first reference templates. The processor further determines, based on an object ontology, that the visual data is expected to contain a second object, wherein the object ontology indicates that the second object is related to the one or more first objects. The processor further detects the second object in the visual data based on the machine learning model and a second reference template. The processor further determines, based on an inference rule, that the visual data is expected to contain a third object. The processor further detects the third object in the visual data based on the machine learning model and a third reference template.

Automated semantic inference of visual features and scenes

To address technical problems facing managing multiple sources of information from multiple vehicles, vehicular computing power may be exploited to process such information before sharing with others, which may help reduce network traffic overhead. A technical solution to improve this information processing over vehicular networks by using a hybrid Named Function Network (NFN) and Information Centric Network (ICN), such as in a hybrid NFN/ICN. An NFN may be used to orchestrate computations in a highly dynamic environment after decomposing the computations into a number of small functions. A function may include a digitally signed binary supplied by a car vendor or other trusted authority and executed within a controlled environment, such as a virtual machine, container, Java runtime-environment, or other controlled environment.

Network function execution in information centric networks

In one embodiment, an apparatus comprises a network interface and a processor. The processor is to: receive, via the network interface, a plurality of data streams to be routed over a network, wherein the plurality of data streams correspond to sensor data captured by a plurality of sensors; identify, from the plurality of data streams, a set of related data streams that are contextually related; identify a convergence function to be performed on the set of related data streams, wherein the convergence function is for transforming the set of related data streams into a converged data stream that is smaller in size than the set of related data streams; perform the convergence function to transform the set of related data streams into the converged data stream; and route, via the network interface, the converged data stream to one or more corresponding destinations over the network.

Converged routing for distributed computing systems

Generally discussed herein are systems, devices, and methods for populating a cache in an information-centric network. A device of an ICN can include a content store including published content and attributes of the published content stored thereon, the attributes including at least two of a device from which the content originated attribute, a lineage attribute, and a service level agreement attribute, and content processing circuitry coupled to the content store, the content processing circuitry configured to manage the published content based on the attributes.

Processing and caching in an information-centric network

System and techniques for publisher control in an information centric network (ICN) are described herein. Named data criteria to identify data for a workload may be constructed. A discriminator for potential publishers of the data may be constructed. An interest packet may be transmitted based on the named data criteria and the discriminator and a response to the interest packet received from one of the potential publishers.

Publisher control in an information centric network

Systems and techniques for information-centric network data cache management are described herein. A demand metric may be calculated for a content item requested from an information-centric network (ICN). A resistance metric may be calculated for each cache node of a set of cache nodes in the ICN based on the demand metric. A topology of the set of cache nodes may be evaluated to identify a transmission cost for each cache node of the set of cache nodes. An influencer node may be selected from the set of cache nodes based on the resistance metric for the influencer node and the transmission cost for the influencer node. The content item may be cached in a data cache of the influencer node.

Information-centric network data cache management

Ubiquitous visual computing witness

Growing community of inventors

Portola Valley, CA, United States of America

Eve M Schooler