Abstract: The present teaching relates to method, system, medium, and implementations for increasing content insertion opportunities. A prediction input is received that characterizes utilization of content stream utilization channels (CSUCs). Schedule parameters are automatically predicted for the prediction input using prediction models obtained via machine learning based on grouped historic data related to CSUCs, where grouping is based on an operational mode in which the prediction models operate. Using the predicted schedule parameters, insertion opportunity may be identified with respect to CSUCs and insertion schedules are generated specifying insertions of content streams into identified CSUCs at respective insertion times.

Abstract: In some implementations, a radio access network (RAN) intelligent controller (RIC) associated with a core network may identify one or more controls for controlling a RAN at two or more of a cell level, a slicing level, a user equipment (UE) grouping level, or a quality of service (QOS) level. The RIC may transmit, to one or more of a radio unit (RU) associated with the RAN, a distributed unit (DU) associated with the RAN, a centralized unit (CU) associated with the RAN, or a network management system (NMS), signaling associated with the one or more controls.

Abstract: A system described herein may maintain a set of Quality of Service (“QoS”) parameters associated with a particular User Equipment (“UE”). The system may provide the set of QoS parameters to a policy element of a wireless network, wherein the wireless network establishes a first dedicated bearer with the particular UE, which may be associated with the set of QoS parameters. The system may receive, after the first dedicated bearer is established, information indicating an occurrence of a particular event associated with the particular UE, and may output, to a policy element of the wireless network, a request to establish a second dedicated bearer with the particular UE. The wireless network may establish the second dedicated bearer with the particular UE, which may be associated with the set of QoS parameters.

Abstract: In some implementations, a device of a network may receive, from a user equipment (UE), a request associated with enabling the UE to access a network, wherein the request includes a first routing indicator. The device may identify an authentication manager, of the network, that is mapped to the first routing indicator in an entry of a routing table of the network. The device may route the request to the authentication manager of the network to permit the authentication manager to authenticate the UE. The device may purge, based on the request being routed to the authentication manager, the entry to remove the first routing indicator from the routing table. The device may store, after purging the entry, a second routing indicator in the entry to map the second routing indicator to the authentication manager, wherein the second routing indicator is different from the first routing indicator.

Abstract: A device may receive a MAC address and MAC integer of a network device newly provided in a network relative to existing network devices, existing IP addresses associated with the existing network devices, and a list of available IP addresses. The device may convert the available IP addresses into integers, and may select a first available IP address. The device may identify a first integer of the first available IP address, and may combine the first integer and the MAC integer to generate a network device integer. The device may identify an IP address for the network device based on the network device integer, and may process the IP address and the existing IP addresses, with a machine learning model, to determine that the IP address is not duplicative of one of the existing IP addresses. The device may assign the IP address to the network device.

Abstract: A device may receive, before a time period, current capacity data associated with sectors of an FWA network and qualified sector data identifying qualified sectors of FWA devices associated with the FWA network, and may receive, over the time period, FWA data associated with the FWA devices and corresponding sectors. The device may apply rules to the FWA data to generate modified FWA data, and may calculate maximum data volumes during the time period for the FWA devices based on the modified FWA data. The device may assign actual sectors to the FWA devices based on the maximum data volumes, and may compare the qualified sectors and the actual sectors to identify modified sectors. The device may modify the current capacity data based on the modified sectors and to generate modified capacity data associated with the sectors, and may perform actions based on the modified capacity data.

Abstract: A system described herein may provide a technique for selectively adding or forgoing adding shortcut links to a node map using contraction hierarchy techniques based on turn costs. “Useful” turn pairs for a given path through a shortcut link may include a turn into a first node of the shortcut link and a turn out of a second node of the shortcut link, where no alternative lower cost paths (including such “useful” turn pairs) from the first node to the second node are available. The useful turn pairs for a shortcut link may be used to evaluate super shortcut links that includes the shortcut link with identified useful turn pairs. For example, a search for alternative lower cost paths may need not be performed for paths, associated with the super shortcut link, which do not include a turn from the useful turn pairs of the component shortcut link.

Abstract: Systems and methods enable the provisioning of security as a service for network slices. A network device stores definitions of multiple security assurance levels for network slices based on security parameters of assets used in the network slices. The network device stores multiple network slice templates, wherein the multiple network slice templates have different security assurance levels, of the multiple security assurance levels, for a Network Service Descriptor (NSD). The network device receives a request for a network slice with a requested security assurance level, of the multiple security assurance levels, for the NSD, and deploys the network slice using one of the network slice templates that has a security assurance level that corresponds to the requested security assurance level. The network device monitors the security parameters of the assets of the network slice for changes to the security assurance level of the deployed network slice.

Abstract: A system described herein may monitor a set of local Key Performance Indicators (“KPIs”) associated with an edge computing device. The local KPIs may not be provided to a central orchestrator that is communicatively coupled to multiple edge computing devices. The system may monitor application KPIs associated applications implemented by the edge computing device, which may be provided to the central orchestrator. The orchestration models may include conditions associated with the set of local KPIs, and orchestration actions to perform with respect to the particular edge computing based on the conditions. The system may perform orchestration actions on the particular edge computing device based on the monitored local KPIs and application KPIs. The central orchestrator may identify that the application KPIs are below a threshold and perform a second set of orchestration actions with respect to the edge computing device, or one or more other edge computing devices.

Abstract: A device may receive time series data, and may convert the time series data into binary data. The device may calculate Hamming distances for the binary data, and may translate the time series data to vectors that capture patterns over a time period of the time series data. The device may calculate vector Euclidean distances for the vectors, and may calculate Euclidean distances for the time series data. The device may select weights for the Hamming distances, the vector Euclidean distances, and the Euclidean distances, and may apply the weights to the Hamming distances, the vector Euclidean distances, and the Euclidean distances. The device may process the time series data, the weighted Hamming distances, the weighted vector Euclidean distances, and the weighted Euclidean distances, with a clustering model, to generate clusters for the time series data, and may perform one or more actions based on the clusters.

Abstract: In some implementations, a device may generate a user equipment (UE) route selection policy (URSP) rule that indicates a route selection descriptor and a traffic descriptor, and one of the route selection descriptor or the traffic descriptor indicates a core network (CN) type associated with the URSP rule. The device may transmit the URSP rule.

Abstract: In some implementations, an access and mobility management function (AMF) may receive subscribed network slice selection assistance information (NSSAI) that indicates location information associated with a slice and time information associated with the slice. The AMF may determine an allowed NSSAI based on one or more of the location information, the time information, a static policy, or a dynamic policy. A access and mobility policy control function (AM-PCF) may create the dynamic policy to determine a location and a time at which the subscribed NSSAI may be used. The AMF may transmit, to a user equipment (UE), an indication of the allowed NSSAI.

Abstract: Disclosed are systems and methods for a robust Self-Organizing Network (SON) framework that quantifies SON applications' control and management of a network into key performance indicators (KPI) that are leveraged to determine the impact of a SON application's effectiveness in regulating network parameters, which then dictates how the SON application operates. The disclosed framework is configured to receive multiple data streams from existing data sources, determine the performance of a node on a network, and then automatically perform SON operations based therefrom. The disclosed framework can utilize this information to predict additional and/or future opportunities for SON automation on the network, which can be based on an aggregate determination of a net performance gain (NPG) of the SON applications. The framework can leverage the NPG to modify, configure and/or further maintain network operations so as to improve control and management of the network and the applications operating thereon.

Abstract: According to disclosed embodiments, as discussed below, a cell broadcasting framework is provided including receiving a request from a Network Function (NF) consumer by a service communication proxy (SCP) on a network, the request including a set of parameters, identifying a plurality of target NF producers corresponding to the set of parameters, transmitting the request to the plurality of target NF producers, receiving a plurality of successful request responses corresponding to some or all of the plurality of target NF producers, transmitting one of the plurality of successful request response to the NF consumer, and discarding all other successful request responses.

Abstract: A vehicle routing platform may store, in a first data structure, a value at a logical index for each pair of locations of a plurality of pairs of locations. The logical index, for a pair of locations, indicates whether a travel time has been determined. The vehicle routing platform may store, in a second data structure, an offset index for each predetermined logical index of one or more predetermined logical indexes of the first data structure. The offset index, for a predetermined logical index, may be based on a quantity of logical indexes preceding the predetermined logical index in the first data structure. The vehicle routing platform may store, in a third data structure, travel times between pairs of locations of a portion of the plurality of pairs of locations. The vehicle routing platform may determine a travel time, between a pair of locations, using the data structures.

Abstract: A method for cloud edge network process automation control includes receiving, at a multi-access edge computing device, a factory application defining an automated process; and identifying process modules associated with the factory application. The method further includes determining resources required by the process modules associated with the factory application; and assigning, based on the determining of the resources, the process modules to devices for execution within the automated process. The method further includes providing distributed orchestration information to the devices, where the distributed orchestration information includes a status of the process modules; and controlling networked plant devices for middle latency conditions based upon the distributed orchestration information.

Abstract: Disclosed are systems and methods for improving interactions with and between computers in hosting and/or providing systems supported by or configured with personal computing devices, servers and/or platforms. The systems interact to instantiate local and persistent core device instances within or across platforms, which can be used to improve the quality of data processing used in processing interactions between or among processors in such systems. The disclosed systems and methods provide systems and methods for cloud hosted device instances. The systems and methods provide a platform for creating cloud hosted device instances, each of which provides compute and memory resources for a device user. The systems and methods provide a core cloud service to centrally manage device data and deploy (and redeploy) local (or “lightweight”) device instances close to a device.

Abstract: A device may receive a video and corresponding camera information associated with a camera that captured the video, and may select an object in the video and a wire model for the object. The device may adjust an orientation, location, or size of the wire model to align the wire model on the object in a frame of the video, based on the corresponding camera information and to generate an adjusted wire model. The device may identify the object in another frame of the video, and may align the adjusted wire model on the object in the other frame. The device may interpolate the adjusted wire model for the object for intermediate frames of the video between the first and other frames, and may generate three-dimensional annotations for the video based on the adjusted wire models. The device may train a machine learning model based on the three-dimensional annotations.

Abstract: A system described herein may identify a particular value that was wirelessly received by a User Equipment (“UE”) at a first time. The particular value may have been recorded to a blockchain, along with a second time, at which the particular value was outputted by a base station of a wireless network. The particular value may include a randomly generated number. The system may determine a location of the base station from which the particular value was outputted. The location of the UE may be determined based on a delay time associated with the particular value, which may be determined based on a difference between the first and second times. The location of the UE may further be determined (e.g., using triangulation techniques) based on values outputted by other base stations, for which the blockchain includes records indicating times at which such values were outputted by the other base stations.

Abstract: Disclosed are embodiments for injecting sidecar proxy capabilities into non-sidecar applications, allowing such non-sidecar applications to communicate with a service mesh architecture. In an embodiment, a method comprises receiving a request to instantiate a proxy for a non-sidecar application at a service mesh gateway (SMG). The SMG then instantiates the proxy in response to the request and broadcasts network information of the non-sidecar application to a mesh controller deployed in a containerized environment. Finally, the SMG (via the proxy) transmits data over a control plane that is communicatively coupled to the mesh controller.