Abstract: Systems and techniques for location management are described herein. In an example, a system may include at least one processor and at least one memory with instructions stored thereon that when executed by the processor, cause the processor to obtain data originating from one or more sensors proximate to the location. A trained activity-based detection model may identify an activity at the location and perform a determination of a service to be offered at the location based on the detected activity. The system may then send a message to a user offering the service to the user, and in response to receiving an authorization accepting the service from the user, cause the service to be implemented at the location, which may include classifying the service as a service type, matching the service type to a service provider, and sending a notification to the service provider.

Abstract: Systems and techniques for mobility-as-a-service for user experience are described herein. An orchestration log may be maintained that includes current orchestration data. An orchestration backup record may be generated that includes alternate MaaS nodes on the MaaS network. It may be determined that connectivity is lost to a first orchestration container hosted by a first MaaS node. An orchestration container is generated using the orchestration log to maintain orchestration functionality. An available second MaaS node is identified from the alternate MaaS nodes. The orchestration container may be transferred to the second MaaS node.

Abstract: Disclosed herein are systems and methods for vehicle-occupancy-based and user-preference-based smart routing, and autonomous volumetric-occupancy measurement. In an embodiment, a system is configured to receive from a user device associated with a user, a routing-options request for routing options between two locations, and to responsively identify one or more routing options between the two locations based at least in part on occupancy data for a vehicle that would be utilized for at least a portion of at least one of the identified routing options. The occupancy data is based on an output of an automated occupancy-measurement system onboard the vehicle. The system is also configured to provide the one or more identified routing options to the user device. In some embodiments, the occupancy data is obtained using volumetric-occupancy measurement. Some embodiments relate to volumetric-occupancy measurement conducted by autonomous mesh nodes.

Abstract: Systems, apparatus, articles of manufacture (e.g., computer readable media), and methods are disclosed to implement task-oriented communications for networked control systems. Examples disclosed herein are to determine a criticality of a data packet of a data flow, different packets of the data flow having different respective criticalities, the data flow associated with an application. Disclosed examples are also to perform a quality of service (QoS) operation associated with the data packet based on the criticality of the data packet. For example, the QoS operation is to be performed after generation of the data packet and before reception of the data packet by a device that is to implement the application.

Abstract: Various systems and methods for improving connectivity of a Mobility-as-a-Service (MaaS) node are described herein, including categorizing MaaS communication traffic of a MaaS node into different levels of priority and controlling duplication or repetition of the MaaS communication traffic using the categorized level of priority of the MaaS communication traffic.

Abstract: A 3GPP LTE protocol enhancement realizes the full benefit of discontinuous reception (DRX) in Long Term Evolution networks by coordinating and aligning DRX operations for conserving power and timing overhead. A dual connectivity enabled User Equipment (UE) comprising a processor and transceiver is configured to align DRX configuration between counterpart Evolved Node Bs (eNB)s, wherein counterpart eNBs are a Master eNB (MeNB) and a Secondary eNB (SeNB) simultaneously connected to the UE, communicate system frame timing and system frame number (SFN) information between the counterpart eNBs, align DRX start offset (drxStartOffset) values for the counterpart eNBs according to the communicated system frame timing and SFN information to compensate for offsets in system frame timing, and allow the start of a DRX ON duration at specific frame or sub-frame times determined by the drxStartOffset values, after the expiration of a DRX inactivity timer.

Abstract: A resource management framework may be used to improve performance of dominant and non-dominant resources for edge multi-tenant applications. The resource management framework may include an admission control mechanism, which may be used to balance disproportionate resource allocations by controlling allocation of unconstrained resources proportional to the requested dominant resources based on resource availability. The admission control mechanism may provide ongoing monitoring of dominant and non-dominant resource utilization, such as using a hybrid centralized-distributed telemetry collection approach. The resource management framework may also include a lightweight resource monitoring and policy enforcement mechanism on distributed networking elements to reduce or eliminate the exploitations of non-dominant resources.

Abstract: Systems and methods for dynamically controlling an infrastructure item are disclosed. The systems and methods may include receiving environmental data. The environmental data may capture behavior of a crossing user. An intent of the crossing user may be determined based on the environmental data. A setting of the infrastructure item may be changed based on the intent of the crossing user.

Abstract: Various systems and methods for providing opportunistic placement of compute in an edge network are described herein. A node in an edge network may be configured to access a service level agreement related to a workload, the workload to be orchestrated for a user equipment by the node; modify a machine learning model based on the service level agreement; implement the machine learning model to identify resource requirements to execute the workload in a manner to satisfy the service level agreement; initiate resource assignments from a resource provider, the resource assignments to satisfy the resource requirements; construct a resource hierarchy from the resource assignments; initiate execution of the workload using resources from the resource hierarchy; and monitor and adapt execution of the workload based on the resource hierarchy in response to the execution of the workload.

Abstract: A computing node in an edge computing network includes a network interface card (NIC), memory storing a plurality of digital object representations of a corresponding plurality of participating entities, and processing circuitry. The processing circuitry detects a message from a participating entity of the plurality. The message is received via the NIC and is associated with a messaging service of the edge computing network. The message is mapped to a service class of a plurality of available service classes based on a service request associated with the message. The message is processed to extract one or more characteristics of the service request. A digital object representation of the plurality of digital object representations is updated based on the one or more characteristics of the service request, the digital object representation corresponding to the participating entity.

Abstract: 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.

Abstract: Various systems and methods for content adaptation based on seat position or occupant position in a vehicle are described herein. An example implementation for content adaptation based on seat position in a vehicle includes: obtaining sensor data, the sensor data including a seat position of a seat in the vehicle; identifying audiovisual content for output to a human occupant in the vehicle; identifying an occupant position of the human occupant, based on the seat position, for a user experience of the output of the audiovisual content; and cause one or more adjustments to the output of the audiovisual content in the vehicle, via an output device, based on the identified position of the human occupant.

Abstract: Systems, apparatus, articles of manufacture, and methods are disclosed for in-network computation and control of network congestion based on in-network computation delays. An example device includes interface circuitry to access a packet including a header having (1) a destination address field to identify a first network address of a destination device capable of performing an action and (2) a workload class field to identify a workload class associated with the action. The example device also includes programmable circuitry to utilize machine-readable instructions to perform the action at the device based on the workload class field, the device having a second network address that is different than the first network address. Additionally, the example programmable circuitry is to modify an indicator field of the packet to indicate that the action has been performed and cause the interface circuitry to forward the packet with the modified indicator field toward the destination device.

Abstract: Systems and techniques for distributed machine learning (DML) in an information centric network (ICN) are described herein. Finite message exchanges, such as those used in many DML exercises, may be efficiently implemented by treating certain data packets as interest packets to reduce overall network overhead when performing the finite message exchange. Further, network efficiency in DML may be improved achieved by using local coordinating nodes to manage devices participating in a distributed machine learning exercise. Additionally, modifying a round of DML training to accommodate available participant devices, such as by using a group quality of service metric to select the devices, or extending the round execution parameters to include additional devices, may have an impact on DML performance.

Abstract: Systems and methods of providing DMRS for a UE are generally described. The DMRS locations in a resource unit of a Physical Resource Allocation of a shared channel are randomly determined, and the DMRS sequences randomly generated before transmission from a master UE to a wearable UE. The DMRS locations are disposed on different subcarriers and symbols in the resource unit and are repeated every k subframes or m resource units within the same subframe. In situations in which the collision/contention probability is relatively small, DMRS in control channels may be used rather than in the shared data channel.

Abstract: Various approaches for deploying and controlling distributed compute operations with the use of infrastructure processing units (IPUs) and similar networked processing units are disclosed. For example, a payload may be received at a networking infrastructure device. Here, the payload is part of a workload that is routed through the networking infrastructure device from a first network node to a second network node. The networking infrastructure device may obtain a workload graph for the workload with vertices of the workload graph specifying functions. The networking infrastructure device may apply a function to the payload in accordance with the workload graph to transform the payload into a processed payload. The processed payload may be transmitted towards the second network node.

Abstract: Various approaches for deploying and controlling distributed compute operations with the use of infrastructure processing units (IPUs) and similar network-addressable processing units are disclosed.

Abstract: Systems and techniques for machine generation of content names in an information centric network (ICN) are described herein. For example, a node may obtain content. An inference engine may be invoked to produce a name for the content. Once the content is named, the node may respond to an interest packet that includes the name of the content. The response is a data packet that includes the content.

Abstract: Systems and methods for managing distributed compute resources are described herein. A system is configured to receive, from an agent operating at a first compute domain of a plurality of compute domains, a request for compute resources; broadcast the request for compute resources to respective agents at the plurality of compute domains; receive a plurality of offers for available compute resources from at least a portion of the plurality of compute domains; transmit, to a selected agent at a selected compute domain of the plurality of compute domains, a commit message to reserve compute resources of the selected compute domain associated with a selected offer of the plurality of offers; and transmit an indication of the commit message to the agent at the first compute domain, wherein the first compute domain is to use the compute resources reserved at the selected compute domain for workloads of the first compute domain.

Abstract: Systems and techniques for distributed machine learning (DML) in an information centric network (ICN) are described herein. Finite message exchanges, such as those used in many DML exercises, may be efficiently implemented by treating certain data packets as interest packets to reduce overall network overhead when performing the finite message exchange. Further, network efficiency in DML may be improved achieved by using local coordinating nodes to manage devices participating in a distributed machine learning exercise. Additionally, modifying a round of DML training to accommodate available participant devices, such as by using a group quality of service metric to select the devices, or extending the round execution parameters to include additional devices, may have an impact on DML performance.