Storing data at edges or cloud storage with high security

    公开(公告)号:US12204675B2

    公开(公告)日:2025-01-21

    申请号:US17498945

    申请日:2021-10-12

    Abstract: The disclosed technology is directed towards partitioning data and distributing the data to different storage locations, which facilitates better data security. For example, a large database of source data can be partitioned into a small enabler partition and one or more large partitions, in which a full set of the partitions is needed to reconstruct the source data to its original state. The large partition can be maintained at an edge computing facility to reduce latency, or at a cloud computing facility to reduce storage expenses, with the smaller enabler partition only accessed when needed to reconstruct the data. A database is partitioned into a group of partitions, and the group of partitions is distributed to separate storage facilities. The separate storage and computing facilities/nodes are accessed to obtain datasets of the group of partitions, and merged to reconstruct the source data.

    INTELLIGENT SUPPORT FRAMEWORK USABLE FOR ENHANCING RESPONDER NETWORK

    公开(公告)号:US20230062010A1

    公开(公告)日:2023-03-02

    申请号:US17463174

    申请日:2021-08-31

    Abstract: Resources associated with a responder communication network and a communication network can be managed in an effective manner In connection with an event, a resource management component (RMC) can analyze network-related data associated with the networks and external data relating to the event or a geographic area related thereto. In connection with the event, based on the analysis, RMC can desirably manage the resources, in part, by determining locations or adjustments for portable base stations, sensors, and/or devices associated with the responder communication network to facilitate high quality communication of information, determining traffic routes and other path planning for vehicles or personnel, creating network slices for high quality communication of information, and/or performing monitoring and intelligent troubleshooting with regard to the networks. RMC can employ artificial intelligence or machine learning techniques and models to facilitate making desired predictions or inferences relating to the event or networks.

    Creating and using cell clusters
    5.
    发明授权

    公开(公告)号:US11576054B2

    公开(公告)日:2023-02-07

    申请号:US17106335

    申请日:2020-11-30

    Abstract: Concepts and technologies are disclosed for creating and using cell clusters. Cellular network data associated with a cellular network can be obtained. The cellular network data can include configuration data associated with a cell of the cellular network and a performance indicator associated with the cellular network. A number of cell clusters to be generated can be determined and the cell clusters can be generated. The cell clusters can include a cell cluster that can represent multiple cells including the cell. A model that represents the cell cluster can be trained. An input cluster that represents multiple inputs can be generated. The inputs can be associated with the multiple cells and the input cluster can include a value. The value can be provided as input to the model to obtain a predicted output associated with the cell cluster.

    Optimal Routes for Vehicular Communications

    公开(公告)号:US20220136846A1

    公开(公告)日:2022-05-05

    申请号:US17086362

    申请日:2020-10-31

    Abstract: Concepts and technologies disclosed herein are directed to determining optimal routes for vehicular communications. According to one aspect disclosed herein, a route optimization system can obtain a quality of service (“QoS”) requirement, an origin location, and a destination location. The route optimization system also can obtain a key performance indicator (“KPI”). The route optimization system can select a route optimization model to be used for the QoS requirement. The route optimization system can determine, based upon the route optimization model and the key performance indicator, an optimized route from the origin location to the destination location that satisfies the quality of service requirement. The optimized route can be sent to a vehicle-to-everything (“V2X”)-enabled device for use in navigating from the origin location to the destination location while receiving a QoS that satisfies the QoS requirement.

    System and method for low latency edge computing

    公开(公告)号:US11204853B2

    公开(公告)日:2021-12-21

    申请号:US16416730

    申请日:2019-05-20

    Abstract: Aspects of the subject disclosure may include, for example, a method in which a processing system receives data at an edge node of a network that also includes regional nodes and central nodes. The processing system also determines a latency criterion associated with an application for processing the data; the application corresponds to an application programming interface. The method also includes processing the data in accordance with the application, monitoring a latency associated with the processing, and determining whether the latency meets the latency criterion. The processing system dynamically assigns data processing resources so that the latency meets the latency criterion; the resources include computation, network and storage resources of the edge node, a central node, and a regional node in communication with the edge node and the central node. Other embodiments are disclosed.

    AUTOMATIC AND REAL-TIME CELL PERFORMANCE EXAMINATION AND PREDICTION IN COMMUNICATION NETWORKS

    公开(公告)号:US20240022938A1

    公开(公告)日:2024-01-18

    申请号:US17864663

    申请日:2022-07-14

    CPC classification number: H04W24/10 H04L41/16 H04B17/318 G06K9/6262 G06K9/6298

    Abstract: Aspects of the subject disclosure may include, for example, a method performed by a processing system; the method includes receiving a plurality of values of key performance indicators (KPIs) relating to performance of a cell on a communication network. The plurality of values of the KPIs includes labeled training data for training a machine learning (ML) model for the performance of the cell. The method further includes iteratively executing, using the labeled training data, a training procedure for the ML model; and testing the trained ML model. The labeled training data corresponds to ground truth data that may include a training data set, a validation data set and a test data set. The trained ML model, when deployed on a communication network, receives as input near-real time data regarding the performance of the cell and provides as output predictions of the performance of the cell. Other embodiments are disclosed.

    SYSTEM AND METHOD FOR LOW LATENCY EDGE COMPUTING

    公开(公告)号:US20230085361A1

    公开(公告)日:2023-03-16

    申请号:US17991340

    申请日:2022-11-21

    Abstract: Aspects of the subject disclosure may include, for example, a method in which a processing system receives data at an edge node of a network that also includes regional nodes and central nodes. The processing system also determines a latency criterion associated with an application for processing the data; the application corresponds to an application programming interface. The method also includes processing the data in accordance with the application, monitoring a latency associated with the processing, and determining whether the latency meets the latency criterion. The processing system dynamically assigns data processing resources so that the latency meets the latency criterion; the resources include computation, network and storage resources of the edge node, a central node, and a regional node in communication with the edge node and the central node. Other embodiments are disclosed.

    Creating and Using Cell Clusters
    10.
    发明申请

    公开(公告)号:US20210314789A1

    公开(公告)日:2021-10-07

    申请号:US17106335

    申请日:2020-11-30

    Abstract: Concepts and technologies are disclosed for creating and using cell clusters. Cellular network data associated with a cellular network can be obtained. The cellular network data can include configuration data associated with a cell of the cellular network and a performance indicator associated with the cellular network. A number of cell clusters to be generated can be determined and the cell clusters can be generated. The cell clusters can include a cell cluster that can represent multiple cells including the cell. A model that represents the cell cluster can be trained. An input cluster that represents multiple inputs can be generated. The inputs can be associated with the multiple cells and the input cluster can include a value. The value can be provided as input to the model to obtain a predicted output associated with the cell cluster.

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