Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may detect, while transmitting data using at least one first bandwidth on at least one sidelink channel to at least one other UE, one or more failures of a listen-before-talk (LBT) procedure within an amount of time. The UE may transmit, to at least one of a base station or the at least one other UE, a report indicating the one or more failures. Numerous other aspects are described.

Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may detect, while transmitting data using at least one first bandwidth on at least one sidelink channel to at least one other UE, one or more failures of a listen-before-talk (LBT) procedure within an amount of time. The UE may transmit, to at least one of a base station or the at least one other UE, a report indicating the one or more failures. Numerous other aspects are described.

A method includes processing a user input for generating a non-deterministic finite automata tree (NFAT) correlation policy. The user input indicates one or more of a static condition or a dynamic condition for inclusion in the NFAT correlation policy. The static condition includes a comparison between a defined entity and a first fixed parameter. The dynamic condition includes a comparison between the defined entity and a variable parameter. An applicable NFAT element is generated that includes at least one of the NFAT correlation policy generated based on a determination that the user input indicates the static condition or a NFAT template generated based on a determination that the user input indicates the dynamic condition. Event data received from a network device is processed to detect a status of a network entity associated with a communication network based on the applicable NFAT element.

A method includes processing a user input for generating a non-deterministic finite automata tree (NFAT) correlation policy. The user input indicates one or more of a static condition or a dynamic condition for inclusion in the NFAT correlation policy. The static condition includes a comparison between a defined entity and a first fixed parameter. The dynamic condition includes a comparison between the defined entity and a variable parameter. An applicable NFAT element is generated that includes at least one of the NFAT correlation policy generated based on a determination that the user input indicates the static condition or a NFAT template generated based on a determination that the user input indicates the dynamic condition. Event data received from a network device is processed to detect a status of a network entity associated with a communication network based on the applicable NFAT element.

Methods, systems, and devices are provided herein for a mechanism to identify link down reasons. As described herein, a first port of a first peer device may be determined to have unexpectedly changed to a port down state. Subsequently, a topology file may be referenced to identify a second port of a second peer device with which the first peer device is intended to have a link if not for the first port being in a port down state. In some examples, port settings of the first port may be compared with port settings of the second port. If a port setting for the first port mismatches an associated port setting for the second port, an alert message may be transmitted to a network administrator indicating this mismatch as a possible reason for the first port being in the port down state.

A fault recovery method and apparatus, and a storage medium are provided, and belong to the field of Internet technologies. In the method, network composition information and abnormal event information of a target network are obtained, where the network composition information includes a network topology of the target network and device information of a plurality of network devices on the target network, and the device information includes one or more of interface configuration information, protocol configuration information, and service configuration information; and then a possible root cause of a fault of the target network is determined based on the network composition information and the abnormal event information, where the possible root cause of the fault is used to determine a corresponding fault recovery plan.

A network disruption detection engine gathers and stores network routing path update messages that routinely modify routing tables used by internet routers for transporting message traffic between a source and a destination. Routing path messages continually maintain a network transport infrastructure both for intranets used for particular entities such as corporations, and internet traffic between arbitrary source and destination nodes. Major disruptions or outages typically result in an increase in routing path messages, typically focused on a particular set, region or network entity where the disruption occurred, as other routing entities seek to avoid the troubled region. Analysis of this sudden activity of routing messages and extracting message content about the network region they seek to avoid allows identification and queries of a widespread network outage.

A network disruption detection engine gathers and stores network routing path update messages that routinely modify routing tables used by internet routers for transporting message traffic between a source and a destination. Routing path messages continually maintain a network transport infrastructure both for intranets used for particular entities such as corporations, and internet traffic between arbitrary source and destination nodes. Major disruptions or outages typically result in an increase in routing path messages, typically focused on a particular set, region or network entity where the disruption occurred, as other routing entities seek to avoid the troubled region. Analysis of this sudden activity of routing messages and extracting message content about the network region they seek to avoid allows identification and queries of a widespread network outage.

The invention relates to a system for monitoring and controlling a dynamic network such as an oil, gas, or water pipeline. The system includes a plurality of sensors for measuring aspects of a state of the network with each sensor being associated with a segment of the network and connected to a virtual sensor which accumulates and pre-processes measurements from the sensors for each segment of the network. The system further includes a network topology processor for storing the topology of the network and relating sensors and virtual sensors to segments of the network and neighbouring sensors and virtual sensors in accordance with the topology and a reinforcement learning artificial neural network (ANN) based nonlinear state estimation and predictive control model which uses measurements from the sensors and virtual sensors to model the state of the network and estimate sequential states of the network.

Embodiments provide a fault detection method and device. The method includes obtaining first detection logic, where the first detection logic includes a plurality of detection events and indicates a logical relationship between the plurality of detection events, a single detection event includes identification information of a to-be-detected device, a detection instruction, and identification information of a fault event, and identification information of to-be-detected devices that is in the plurality of detection events is partially the same; in response to the obtained first detection logic, successively executing corresponding detection instructions on to-be-detected devices in single detection events based on the logical relationship between the plurality of detection events, and receiving an execution result returned by the to-be-detected device; and if it is detected that the execution result includes the identification information of the fault event, determining that the fault event exists. In this application, the fault detection device automatically queries statuses of a plurality of to-be-detected devices, and quickly determines the fault event, so that a fault that occurs in an application scenario can be accurately located.