H04L41/14

Scalable Event Driven Auto-Diagnosis System

A method for scalable event driven auto-diagnosis systems includes obtaining a data packet configured for transmission across a network from a source address to a destination address. The method includes obtaining a list of changes to the network. The method also includes analyzing, based on a network model, the data packet using a plurality of analyzers. The method includes correlating the list of changes to the network and the analysis of the data packet. The method further includes determining, based on the correlation between the list of changes to the network and the analysis of the data packet, a configuration status of the network. The method also includes reporting the configuration status to a user.

Scalable Event Driven Auto-Diagnosis System

A method for scalable event driven auto-diagnosis systems includes obtaining a data packet configured for transmission across a network from a source address to a destination address. The method includes obtaining a list of changes to the network. The method also includes analyzing, based on a network model, the data packet using a plurality of analyzers. The method includes correlating the list of changes to the network and the analysis of the data packet. The method further includes determining, based on the correlation between the list of changes to the network and the analysis of the data packet, a configuration status of the network. The method also includes reporting the configuration status to a user.

Network device and medical system for the detection of at least one network problem

A network device (100) detects a network problem in a medical system (105). A reception module (110) receives current medical system process data. A monitoring module (120) detects predefined events (124) based on the process data and triggers a detection signal (132) output in the presence of a predefined event. A sending module (130) sends the detection signal to a predefined device address (134) via a network (140). The predefined events include: a predefined plurality of unsuccessful password entry attempts within a predefined first time period; an unsuccessful encryption within an encryption protocol framework; a predefined plurality of outputs via the network triggered via the network within a predefined second time period; an output of a signal, which is to be carried out, has been unsuccessful; and a predefined number of messages have been received within the framework of a service discovery within a predefined third time period.

Network device and medical system for the detection of at least one network problem

A network device (100) detects a network problem in a medical system (105). A reception module (110) receives current medical system process data. A monitoring module (120) detects predefined events (124) based on the process data and triggers a detection signal (132) output in the presence of a predefined event. A sending module (130) sends the detection signal to a predefined device address (134) via a network (140). The predefined events include: a predefined plurality of unsuccessful password entry attempts within a predefined first time period; an unsuccessful encryption within an encryption protocol framework; a predefined plurality of outputs via the network triggered via the network within a predefined second time period; an output of a signal, which is to be carried out, has been unsuccessful; and a predefined number of messages have been received within the framework of a service discovery within a predefined third time period.

Generating space models from map files

A map file includes two-dimensional or three-dimensional geometric data items collectively representing layout of a building. The map file is parsed and the geometric data items are analyzed to identify building elements including rooms, floors, and objects of the building, and to identify containment relationships between the elements. A space model having a space graph is constructed. The space graph includes nodes that correspond to the respective building elements and links forming relationships between nodes that correspond to the identified containment relationships. Each node may include node metadata, rules or code that operate on the metadata, and a node type that corresponds to a type of physical space. Some nodes may include user representations or device representations that represent physical sensors associated therewith. The representations may receive data from the respectively represented sensors, and the sensor data becomes available via the space model.

Network node memory utilization analysis

Systems, methods, and computer-readable media analyzing memory usage in a network node. A network assurance appliance may be configured to query a node in the network fabric for a number of hardware level entries, stored in memory for the node, that are associated with a concrete level network rule. The network assurance appliance may identify a logical level network intent associated with the concrete level network rule, identify a logical level component of the logical level network intent, and attribute the number of hardware level entries to the logical level component.

Network node memory utilization analysis

Systems, methods, and computer-readable media analyzing memory usage in a network node. A network assurance appliance may be configured to query a node in the network fabric for a number of hardware level entries, stored in memory for the node, that are associated with a concrete level network rule. The network assurance appliance may identify a logical level network intent associated with the concrete level network rule, identify a logical level component of the logical level network intent, and attribute the number of hardware level entries to the logical level component.

Method and apparatus for renewing subscription for network data analysis in wireless communication system

The present disclosure relates to a communication method and system for converging a 5th-Generation (5G) communication system for supporting higher data rates beyond a 4th-Generation (4G) system with a technology for Internet of Things (IoT). The present disclosure may be applied to intelligent services based on the 5G communication technology and the IoT-related technology, such as smart home, smart building, smart city, smart car, connected car, health care, digital education, smart retail, security and safety services. The disclosure provides a method for renewing a subscription for network data collection and analysis in a wireless communication system.

Method and apparatus for renewing subscription for network data analysis in wireless communication system

The present disclosure relates to a communication method and system for converging a 5th-Generation (5G) communication system for supporting higher data rates beyond a 4th-Generation (4G) system with a technology for Internet of Things (IoT). The present disclosure may be applied to intelligent services based on the 5G communication technology and the IoT-related technology, such as smart home, smart building, smart city, smart car, connected car, health care, digital education, smart retail, security and safety services. The disclosure provides a method for renewing a subscription for network data collection and analysis in a wireless communication system.

Control method, related device, and system

A control method includes sending, by a controller, a created context-aware model to a context-aware engine. The context-aware model is used to define a preset control performed when target data meets a trigger condition and to instruct the context-aware engine to send indication information to the controller when the context-aware engine determines that the target data meets the trigger condition. The preset control is used to implement a context-aware function. The indication information is used to indicate that the target data meets the trigger condition. The method also includes receiving, by the controller, the indication information. The method further includes performing, by the controller, the preset control based on the indication information.