Abstract: A serving gateway device (SGW) can cause establishment of a requested connection to a first PDN for a user device. The SGW can cause suspension of the connection to the first PDN and establishment of a requested connection to a second PDN for the user device based on a requested connection to a second PDN for a user device. The SGW can cause termination of the connection to the second PDN based on the respective end time associated with the second PDN, and cause restoration of the connection to the first PDN after the connection to the second PDN is terminated.

Abstract: A device may receive fiber sensing data identifying vehicles traveling on a roadway associated with a fiber optic network and location data identifying geographical locations of the vehicles traveling on the roadway. The device may process the fiber sensing data, with a machine learning model, to identify a particular vehicle, of the vehicles, that is traveling in a wrong direction on the roadway. The device may process the location data, with the machine learning model, to identify locations of the roadway, a cellular network associated with the roadway, and vehicle devices of the vehicles traveling on the roadway, other than the particular vehicle, and a nearest camera device to the particular vehicle. The device may perform one or more actions based on the locations of the roadway, the cellular network associated with the roadway, and the vehicle devices of the vehicles traveling on the roadway, other than the particular vehicle.

Abstract: A device, method, and system provide for collecting service parameters associated with application sessions associated with a plurality of MEC clusters; obtaining capability information associated with each of the plurality of MEC clusters; generating service profiles for a set of transport networks based on the service parameters and the capability information, wherein each transport network includes at least one of the plurality of MEC clusters; receiving, from a user equipment (UE) device, a request for a MEC service having a minimum service requirement; and selecting, based on the service profiles, a MEC cluster from the plurality of MEC clusters to provide the MEC service, wherein the selected MEC cluster is included in a first transport network that meets the minimum service requirement.

Abstract: A serving gateway device (SGW) can cause establishment of a requested connection to a first PDN for a user device. The SGW can cause suspension of the connection to the first PDN and establishment of a requested connection to a second PDN for the user device based on a requested connection to a second PDN for a user device. The SGW can cause termination of the connection to the second PDN based on the respective end time associated with the second PDN, and cause restoration of the connection to the first PDN after the connection to the second PDN is terminated.

Abstract: A system described herein may provide a technique for the real-time determination of events, objects, focal points, or the like to be captured by one or more cameras in a multi-camera environment. Such determination may be based on “crowdsourced” data from multiple User Equipment (“UEs”). The crowdsourced data may include positioning and/or pose information associated with UEs. The positioning information for a given UE may include location information, and the pose information may include an azimuth angle, magnetic declination, or other suitable information indicating where a particular physical facet of the UE is facing. For example, the pose information may be used to indicate or infer where a camera of the UE is pointed. One or more actuatable cameras may be displaced, rotated, etc. to capture video at one or more identified crowdsourced focal points.

Abstract: A device may receive fiber sensing data identifying vehicles traveling on a roadway associated with a fiber optic network and location data identifying geographical locations of the vehicles traveling on the roadway. The device may process the fiber sensing data, with a machine learning model, to identify a particular vehicle, of the vehicles, that is traveling in a wrong direction on the roadway. The device may process the location data, with the machine learning model, to identify locations of the roadway, a cellular network associated with the roadway, and vehicle devices of the vehicles traveling on the roadway, other than the particular vehicle, and a nearest camera device to the particular vehicle. The device may perform one or more actions based on the locations of the roadway, the cellular network associated with the roadway, and the vehicle devices of the vehicles traveling on the roadway, other than the particular vehicle.

Abstract: A serving gateway device (SGW) can cause establishment of a requested connection to a first PDN for a user device. The SGW can cause suspension of the connection to the first PDN and establishment of a requested connection to a second PDN for the user device based on a requested connection to a second PDN for a user device. The SGW can cause termination of the connection to the second PDN based on the respective end time associated with the second PDN, and cause restoration of the connection to the first PDN after the connection to the second PDN is terminated.

Abstract: A device obtains a data structure from a computing device, and divides the data structure into data segments. The device obtains a list of user devices from the computing device and obtains, from one or more network devices, a respective location and a respective reachability of each user device. The device determines, based on the respective location of each user device, a set of user devices connected to a base station. The device determines a communication capability of the base station, and determines, based on the communication capability, a subset of the set of user devices and one or more sets of the data segments. The device sends, based on the respective reachability of each user device of the subset of user devices, the one or more sets of data segments to the subset of user devices via the base station.

Abstract: Provided are systems and methods that use artificial intelligence and/or machine learning to dynamically allocate services at different times and at different network edge locations within a Multi-Access Edge (“MEC”) enhanced network based on a multitude of factors that change the priorities of the services at the different times and at the different edge locations. For instance, a MEC controller, controlling the allocation of resources at a particular edge location, may modify the allocation of services at that particular edge location at different times based on time and/or location sensitive events that occur at different times and that relate to different services, changing usage patterns that are derived from prior service utilization, and/or categorization of the services as permanent, time insensitive, or other categories of services with permissions to execute at different times from different edge locations.

Abstract: A device may receive fiber sensing data identifying vehicles traveling on a roadway associated with a fiber optic network and location data identifying geographical locations of the vehicles traveling on the roadway. The device may process the fiber sensing data, with a machine learning model, to identify a particular vehicle, of the vehicles, that is traveling in a wrong direction on the roadway. The device may process the location data, with the machine learning model, to identify locations of the roadway, a cellular network associated with the roadway, and vehicle devices of the vehicles traveling on the roadway, other than the particular vehicle, and a nearest camera device to the particular vehicle. The device may perform one or more actions based on the locations of the roadway, the cellular network associated with the roadway, and the vehicle devices of the vehicles traveling on the roadway, other than the particular vehicle.

Abstract: A device, method, and system provide for collecting service parameters associated with application sessions associated with a plurality of MEC clusters; obtaining capability information associated with each of the plurality of MEC clusters; generating service profiles for a set of transport networks based on the service parameters and the capability information, wherein each transport network includes at least one of the plurality of MEC clusters; receiving, from a user equipment (UE) device, a request for a MEC service having a minimum service requirement; and selecting, based on the service profiles, a MEC cluster from the plurality of MEC clusters to provide the MEC service, wherein the selected MEC cluster is included in a first transport network that meets the minimum service requirement.

Abstract: A device may receive fiber sensing data identifying vehicles traveling on a roadway associated with a fiber optic network and location data identifying geographical locations of the vehicles traveling on the roadway. The device may process the fiber sensing data, with a machine learning model, to identify a particular vehicle, of the vehicles, that is traveling in a wrong direction on the roadway. The device may process the location data, with the machine learning model, to identify locations of the roadway, a cellular network associated with the roadway, and vehicle devices of the vehicles traveling on the roadway, other than the particular vehicle, and a nearest camera device to the particular vehicle. The device may perform one or more actions based on the locations of the roadway, the cellular network associated with the roadway, and the vehicle devices of the vehicles traveling on the roadway, other than the particular vehicle.

Abstract: A system described herein may provide a technique for the real-time determination of events, objects, focal points, or the like to be captured by one or more cameras in a multi-camera environment. Such determination may be based on “crowdsourced” data from multiple User Equipment (“UEs”). The crowdsourced data may include positioning and/or pose information associated with UEs. The positioning information for a given UE may include location information, and the pose information may include an azimuth angle, magnetic declination, or other suitable information indicating where a particular physical facet of the UE is facing. For example, the pose information may be used to indicate or infer where a camera of the UE is pointed. One or more actuatable cameras may be displaced, rotated, etc. to capture video at one or more identified crowdsourced focal points.

Abstract: A device, method, and system provide for collecting service parameters associated with application sessions associated with a plurality of MEC clusters; obtaining capability information associated with each of the plurality of MEC clusters; generating service profiles for a set of transport networks based on the service parameters and the capability information, wherein each transport network includes at least one of the plurality of MEC clusters; receiving, from a user equipment (UE) device, a request for a MEC service having a minimum service requirement; and selecting, based on the service profiles, a MEC cluster from the plurality of MEC clusters to provide the MEC service, wherein the selected MEC cluster is included in a first transport network that meets the minimum service requirement.

Abstract: A system described herein may provide a technique for the real-time determination of events, objects, focal points, or the like to be captured by one or more cameras in a multi-camera environment. Such determination may be based on “crowdsourced” data from multiple User Equipment (“UEs”). The crowdsourced data may include positioning and/or pose information associated with UEs. The positioning information for a given UE may include location information, and the pose information may include an azimuth angle, magnetic declination, or other suitable information indicating where a particular physical facet of the UE is facing. For example, the pose information may be used to indicate or infer where a camera of the UE is pointed. One or more actuatable cameras may be displaced, rotated, etc. to capture video at one or more identified crowdsourced focal points.

Abstract: A device, method, and system provide for collecting service parameters associated with application sessions associated with a plurality of MEC clusters; obtaining capability information associated with each of the plurality of MEC clusters; generating service profiles for a set of transport networks based on the service parameters and the capability information, wherein each transport network includes at least one of the plurality of MEC clusters; receiving, from a user equipment (UE) device, a request for a MEC service having a minimum service requirement; and selecting, based on the service profiles, a MEC cluster from the plurality of MEC clusters to provide the MEC service, wherein the selected MEC cluster is included in a first transport network that meets the minimum service requirement.

Abstract: A device may receive, from a user device, a request to activate an extended reality experience. The device may obtain access network information relating to a set of access networks available to the user device and user device information relating to the user device. Based on the access network information and the user device information, the device may determine an access network to use for the extended reality experience. The device may determine, based on the access network, a first portion of the extended reality experience to execute locally or a second portion of the extended reality experience to execute remotely.

Abstract: Provided are systems and methods that use artificial intelligence and/or machine learning to dynamically allocate services at different times and at different network edge locations within a Multi-Access Edge (“MEC”) enhanced network based on a multitude of factors that change the priorities of the services at the different times and at the different edge locations. For instance, a MEC controller, controlling the allocation of resources at a particular edge location, may modify the allocation of services at that particular edge location at different times based on time and/or location sensitive events that occur at different times and that relate to different services, changing usage patterns that are derived from prior service utilization, and/or categorization of the services as permanent, time insensitive, or other categories of services with permissions to execute at different times from different edge locations.

Abstract: A device may receive, from a user device, a request to activate an extended reality experience. The device may obtain access network information relating to a set of access networks available to the user device and user device information relating to the user device. Based on the access network information and the user device information, the device may determine an access network to use for the extended reality experience. The device may determine, based on the access network, a first portion of the extended reality experience to execute locally or a second portion of the extended reality experience to execute remotely.

Abstract: A serving gateway device (SGW) can cause establishment of a requested connection to a first PDN for a user device. The SGW can cause suspension of the connection to the first PDN and establishment of a requested connection to a second PDN for the user device based on a requested connection to a second PDN for a user device. The SGW can cause termination of the connection to the second PDN based on the respective end time associated with the second PDN, and cause restoration of the connection to the first PDN after the connection to the second PDN is terminated.