Abstract: A computer device may include a memory configured to store instructions and a processor configured to execute the instructions to determine a device status associated with the wireless communication device and determine that a machine learning process is to be performed based on the determined device status. The processor may be further configured to execute the instructions to select a machine learning model based on the determined device status; select one or more data inputs based on the determined device status; and perform the machine learning process using the selected machine learning model and the selected one or more data inputs.

Abstract: A method, a device, and a non-transitory storage medium are described in which a privacy management service is provided. The privacy management service may be included in an in-home device. The privacy management service may multiple levels of privacy relating to traffic received from end devices that are connected to an external network via the in-home device. The privacy management service may include a smart speaker service. The privacy management service may allow a user to configure a privacy level of an end device. The privacy management service may include machine learning logic that may filter sensitive information included in received traffic.

Abstract: Systems and methods described herein provide an intelligent MEC resource scheduling service. A network device in a MEC network stores, in a memory, threshold values indicating overload conditions for resource usage by a first MEC cluster; monitors resource usage in the first MEC cluster; determines, based on the monitoring, when one of the threshold values is reached; identifies available resources in a second MEC cluster; and re-directs, based on the identifying, at least some of the resource usage from the first MEC cluster to the second MEC cluster.

Abstract: A method, a device, and a non-transitory storage medium are described in which a packet size service is provided. The packet size service may calculate a payload size for packets that include application service data. The packet size service may inform an end device and an application service layer network of the calculated payload size. The packet size service may obtain quality of service information pertaining to an application service, and other information as a basis for the calculation. The packet size service may use a machine learning system to calculate the payload size.

Abstract: A device determines information concerning the device and sends the information concerning the device to a first device. The device receives, from the first device, information concerning a user device, and receives, from a second device, a request concerning the user device accessing the device. The request includes information identifying the device and information identifying the user device. The device generates a request response by validating the user device for access to the device based on the request and sends, to the second device, the request response to facilitate a communication session to be established between the user device and the device. The device communicates with the user device via the communication session.

Abstract: Systems and methods described herein provide an intelligent MEC resource scheduling service. A network device in a MEC network stores, in a memory, threshold values indicating overload conditions for resource usage by a first MEC cluster; monitors resource usage in the first MEC cluster; determines, based on the monitoring, when one of the threshold values is reached; identifies available resources in a second MEC cluster; and re-directs, based on the identifying, at least some of the resource usage from the first MEC cluster to the second MEC cluster.

Abstract: A method, a device, and a non-transitory storage medium are described in which a privacy management service is provided. The privacy management service may be included in an in-home device. The privacy management service may multiple levels of privacy relating to traffic received from end devices that are connected to an external network via the in-home device. The privacy management service may include a smart speaker service. The privacy management service may allow a user to configure a privacy level of an end device. The privacy management service may include machine learning logic that may filter sensitive information included in received traffic.

Abstract: Systems and methods described herein provide an intelligent MEC resource scheduling service. A network device in a MEC network stores, in a memory, threshold values indicating overload conditions for resource usage by a first MEC cluster; monitors resource usage in the first MEC cluster; determines, based on the monitoring, when one of the threshold values is reached; identifies available resources in a second MEC cluster; and re-directs, based on the identifying, at least some of the resource usage from the first MEC cluster to the second MEC cluster.

Abstract: A method, a device, and a non-transitory storage medium are described in which a packet size service is provided. The packet size service may calculate a payload size for packets that include application service data. The packet size service may inform an end device and an application service layer network of the calculated payload size. The packet size service may obtain quality of service information pertaining to an application service, and other information as a basis for the calculation. The packet size service may use a machine learning system to calculate the payload size.

Abstract: Systems and methods described herein provide an intelligent MEC resource scheduling service. A network device in a MEC network stores, in a memory, threshold values indicating overload conditions for resource usage by a first MEC cluster; monitors resource usage in the first MEC cluster; determines, based on the monitoring, when one of the threshold values is reached; identifies available resources in a second MEC cluster; and re-directs, based on the identifying, at least some of the resource usage from the first MEC cluster to the second MEC cluster.

Abstract: A management device may identify a client device, of a plurality of client devices, in a local network, and may configure one or more remote connection parameters for a connection between the client device and a remote device, configure one or more local connection parameters for a connection between the client device and another client device, of the plurality of client devices, in the local network, configure one or more monitoring parameters for monitoring the client device, and configure one or more alarm parameters for the client device. The management device may manage the client device based on the one or more remote connection parameters, the one or more local connection parameters, the one or more monitoring parameters, and the one or more alarm parameters.

Abstract: A device determines information concerning the device and sends the information concerning the device to a first device. The device receives, from the first device, information concerning a user device, and receives, from a second device, a request concerning the user device accessing the device. The request includes information identifying the device and information identifying the user device. The device generates a request response by validating the user device for access to the device based on the request and sends, to the second device, the request response to facilitate a communication session to be established between the user device and the device. The device communicates with the user device via the communication session.

Abstract: A computer device may include a memory configured to store instructions and a processor configured to execute the instructions to determine a device status associated with the wireless communication device and determine that a machine learning process is to be performed based on the determined device status. The processor may be further configured to execute the instructions to select a machine learning model based on the determined device status; select one or more data inputs based on the determined device status; and perform the machine learning process using the selected machine learning model and the selected one or more data inputs.

Abstract: A computer device may include a memory configured to store instructions and a processor configured to execute the instructions to determine a device status associated with the wireless communication device and determine that a machine learning process is to be performed based on the determined device status. The processor may be further configured to execute the instructions to select a machine learning model based on the determined device status; select one or more data inputs based on the determined device status; and perform the machine learning process using the selected machine learning model and the selected one or more data inputs.

Abstract: A management device may identify a client device, of a plurality of client devices, in a local network, and may configure one or more remote connection parameters for a connection between the client device and a remote device, configure one or more local connection parameters for a connection between the client device and another client device, of the plurality of client devices, in the local network, configure one or more monitoring parameters for monitoring the client device, and configure one or more alarm parameters for the client device. The management device may manage the client device based on the one or more remote connection parameters, the one or more local connection parameters, the one or more monitoring parameters, and the one or more alarm parameters.

Abstract: A computer device may include a memory configured to store instructions and a processor configured to execute the instructions to determine a device status associated with the wireless communication device and determine that a machine learning process is to be performed based on the determined device status. The processor may be further configured to execute the instructions to select a machine learning model based on the determined device status; select one or more data inputs based on the determined device status; and perform the machine learning process using the selected machine learning model and the selected one or more data inputs.