Identifying Internet of Things (IoT) devices with packet flow behavior including by using machine learning models is disclosed. Information associated with a network communication of an IoT device is received. A determination of whether the IoT device has previously been classified is made. In response to determining that the IoT device has not previously been classified, a determination is made that a probability match for the IoT device against a behavior signature exceeds a threshold. Based at least in part on the probability match, a classification of the IoT device is provided to a security appliance configured to apply a policy to the IoT device.

Systems and methods for data collection for an industrial heating process are disclosed. The system according to one embodiment can include a plurality of data collectors, including a swarm of self-organized data collector members, wherein the swarm of self-organized data collector members organize to enhance data collection based on at least one of capabilities and conditions of the data collector members of the swarm, and wherein the plurality of data collectors is coupled to a plurality of input channels for acquiring collected data relating to the industrial heating process, and a data acquisition and analysis circuit for receiving the collected data via the plurality of input channels and structured to analyze the received collected data using a neural network to monitor a plurality of conditions relating to the industrial heating process.

Systems and methods for data collection for an industrial heating process are disclosed. The system according to one embodiment can include a plurality of data collectors, including a swarm of self-organized data collector members, wherein the swarm of self-organized data collector members organize to enhance data collection based on at least one of capabilities and conditions of the data collector members of the swarm, and wherein the plurality of data collectors is coupled to a plurality of input channels for acquiring collected data relating to the industrial heating process, and a data acquisition and analysis circuit for receiving the collected data via the plurality of input channels and structured to analyze the received collected data using a neural network to monitor a plurality of conditions relating to the industrial heating process.

A method includes receiving, by a mobility management entity (MME), a redirection request message from an access and mobility management function (AMF) node, where the redirection request message includes key-related information. The method also includes generating, by the MME, an encryption key and an integrity protection key based on the key-related information. The redirection request message is used to request to hand over a voice service from a packet switched (PS) domain to a circuit switched (CS) domain.

A method includes receiving, by a mobility management entity (MME), a redirection request message from an access and mobility management function (AMF) node, where the redirection request message includes key-related information. The method also includes generating, by the MME, an encryption key and an integrity protection key based on the key-related information. The redirection request message is used to request to hand over a voice service from a packet switched (PS) domain to a circuit switched (CS) domain.

An agricultural data collection framework is provided in a system and method for tracking and managing livestock, and for analyzing animal conditions such as health, growth, nutrition, and behavior. The framework uses ultra-high frequency interrogation of RFID tags to collect individual animal data across multiple geographical locations, and incorporates artificial intelligence techniques to develop machine learning base models for statistical process controls around each animal for evaluating the animal condition. The framework provides a determination of normality at an individual animal basis or for a specific location, and generates alerts, predictions, and a targeted processing or application schedule for prioritizing and delivering resources when intervention is needed.

An agricultural data collection framework is provided in a system and method for tracking and managing livestock, and for analyzing animal conditions such as health, growth, nutrition, and behavior. The framework uses ultra-high frequency interrogation of RFID tags to collect individual animal data across multiple geographical locations, and incorporates artificial intelligence techniques to develop machine learning base models for statistical process controls around each animal for evaluating the animal condition. The framework provides a determination of normality at an individual animal basis or for a specific location, and generates alerts, predictions, and a targeted processing or application schedule for prioritizing and delivering resources when intervention is needed.

Various issues with existing congestion and overload control mechanisms are recognized and described herein. Described herein, in accordance with various embodiments, are various mechanisms in which core networks, such as 3GPP networks for example, and an M2M service layer can coordinate and share information to efficiently and intelligently manage each other's congestion and overload states.

Various issues with existing congestion and overload control mechanisms are recognized and described herein. Described herein, in accordance with various embodiments, are various mechanisms in which core networks, such as 3GPP networks for example, and an M2M service layer can coordinate and share information to efficiently and intelligently manage each other's congestion and overload states.

Technology is disclosed for a Third Generation Partnership Project (3GPP) management system operable for peer-to-peer (P2P) edge computing in a fifth generation (5G) computing network. The 3GPP management system can be configured to: identify a user plane function (UPF) based on quality of service (QoS) requirements. The 3GPP management system can be configured to request, from an edge computing management system, deployment of an application server (AS). The 3GPP management system can be configured to request a network functions virtualization (NFV) orchestrator (NFVO) to connect the UPF and the AS based on the QoS requirements.