Abstract:
System and techniques for information centric network (ICN) protocol for federated learning are described herein. An interest packet may be received on a first interface to start a federated learning round. Here, the interest packet includes a participant criterion and a federated learning round expiration. An entry, that includes the federated learning round expiration, is created in a pending interest table (PIT) for the interest packet. The interest packet is forwarded, in accordance with a forwarding information base (FIB), to a set of interfaces before the federated learning round expiration. When a data packet from a node, that meeting the participant criterion, is received in response to the interest packet, the data packet is forwarded on the first interface in accordance with the PIT entry.
Abstract:
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.
Abstract:
A method and system to facilitate mobile relay and group mobility in a wireless network. In one embodiment of the invention, the wireless network facilitates a mobile relay station that has logic to switch communication with a first base station to a second base station while maintaining logical communication with its associated mobile station(s). By doing so, the handover of the mobile relay station from a source base station to a target base station is transparent to the mobile station(s) that are connected with the mobile relay station in one embodiment of the invention.
Abstract:
Systems and techniques for cross-layer automated fault tracking and anomaly detection are described herein. Anomaly data may be obtained from a plurality of layers of a network. Elements of the anomaly data may be identified that correspond to a data flow of an application executing on the network. An artificial intelligence model may be trained using the elements of the anomaly data to generate an impact score for the application. The impact score may be generated for the application by evaluating current network metrics using the artificial intelligence model. An operational component of the network may be modified based on the impact score.
Abstract:
Methods, systems, and computer programs are presented for implementing Personalized Mobility as a Service (PMaaS) to improve transportation services delivery. One storage medium includes instructions for detecting, by a mobility as a service (MaaS) system, a request for a trip from a user device of a user. The storage medium further includes instructions for mapping, using a model executing on the machine, the user to a persona from a plurality of persona models. Each persona model has one or more characteristics associated with users of the MaaS system. Further yet, the storage medium includes instructions for determining trip parameters for the trip based on the persona mapped to the user, the trip parameters defining one or more trip segments for the trip, and instructions for providing trip parameters to the user device.
Abstract:
Disclosed are embodiments for adjusting a vehicle stopping point. The vehicle stopping point is a point between a route of the vehicle and a second route. in some embodiments, an adjustment to the stopping point is determined based on ranking secondary routes that are adjusted based on the adjusted vehicle stopping point. Tanking of the secondary routes is based, in sonic embodiments, on a score of segment(s) included in the secondary routes. In some cases, the ranking of the segments considers safety information associated with each of the segments.
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:
Systems and techniques for node-density aware interest packet forwarding in a dynamic ad hoc information centric network (ICN) are described herein. For example, a next interest packet to forward may be obtained at a network node. A time period to hold the next interest packet before forwarding may be calculated based on node density in the network. The node may then broadcast the next interest packet upon expiration of a timer set to the time period and started when the next interest packet was obtained.
Abstract:
A method and system to facilitate mobile relay and group mobility in a wireless network. In one embodiment of the invention, the wireless network facilitates a mobile relay station that has logic to switch communication with a first base station to a second base station while maintaining logical communication with its associated mobile station(s). By doing so, the handover of the mobile relay station from a source base station to a target base station is transparent to the mobile station(s) that are connected with the mobile relay station in one embodiment of the invention.
Abstract:
Techniques for non-linear distributed multitask support vector machines are disclosed. In the illustrative embodiment, a coordinator node sends initial parameters (or a random number generator along with model choice) for a global model to participant nodes. Each participant node performs a round of training based on the common global model parameters, the model models, and local data. Each participant node determines updated parameters for the global model and updated parameters for a local model. Each participant node sends an update of the parameters to the global model to the coordinator node, while keeping the parameters of the local model private. The coordinator node aggregates the updates from the participant nodes, updates the global model parameters, and sends them back to the participant nodes. The process can repeat until a desired error level is reached.