A method and system for utilizing fault information of a first system device in a first communication system to change a second communication system is provided. A first fault update is received from the first communication system at a cloud-based fault management service. The first fault update includes a request to update first system device fault information in a common database. The common database is updated by the cloud-based fault management service with the first fault update. The cloud-based fault management service performs smart analytics utilizing the first fault update to change the second communication system.

A method and system for utilizing fault information of a first system device in a first communication system to change a second communication system is provided. A first fault update is received from the first communication system at a cloud-based fault management service. The first fault update includes a request to update first system device fault information in a common database. The common database is updated by the cloud-based fault management service with the first fault update. The cloud-based fault management service performs smart analytics utilizing the first fault update to change the second communication system.

A personal area network that includes a wearable electronic device, a system and methods of using the personal area network that includes a wearable electronic device. The wearable electronic device can act as an aggregator of the data that is being acquired by the one or more sensors and from other devices that are within wireless signal range of the personal area network in order to send some or all of the data over a wireless low power wide area network to remote locations within a larger network for subsequent processing, user notification, analysis of location-determination, contact tracing or the like. Data may flow in a bidirectional manner between the wearable electronic device and at least some of the other devices within the personal area network. In one form, the aggregated data may be used to provide information related to one or more operational parameters of an industrial asset and, if necessary, take control-based action in order to adjust one or more such operational parameters. In one form, a communication network formed by the wearable electronic device is used within an industrial setting in order to perform such data acquisition, processing and related control.

Aspects of the subject disclosure may include, for example, a mobile assistant device that obtains information regarding device locations of a plurality of devices; the mobile assistant device is enabled to move among at least a portion of the device locations. The mobile assistant device detects an indication of a need for maintenance, repair, and/or user attention, relating to a target device. The mobile assistant device moves to a location of the target device; obtains device information regarding a condition of the target device; and reports the device information to equipment of the user and/or a controller of the mobile assistant device. The mobile assistant device engages with the user and/or equipment of the user to provide instructions to the equipment of the user for resolving an issue relating to the target device, and can physically engage with the target device to resolve the issue. Other embodiments are disclosed.

A method includes polling, via a service specific manager operating on a software container in a cloud infrastructure, usage of different application resources and parameters for each service of a plurality of services provided in the cloud infrastructure to yield respective polled data for each service, collating, at the service specific manager, the respective polled data for each service to yield a collation, and based on the collation, deriving a respective weight for each service which a container manager can use to create multiple instances of a new service. The method further includes communicating the respective weight for each service to the container manager and determining, via the container manager, whether to scale up or scale down container services based on the respective weight for each service.

A system may include sensor device comprising a sensor configured to measure sensor data indicating an operational parameter of industrial automation equipment associated with an industrial automation process. The system may also include communication circuitry configured to transmit the sensor data. Additionally, the system includes a processor configured to receive the sensor data. Further, the system includes a non-transitory computer-readable medium comprising computer-executable instructions that, when executed, are configured to cause the processor to perform operations including identifying an operational state of the industrial automation equipment based on the sensor data. The operations may also include determining a discrepancy between the sensor data and the operational state. Further, the operations may include modifying an operation of the processor from a first operational mode to a second operational mode of a plurality of operational based on the comparison.

In one embodiment, a supervisory service for a software-defined wide area network (SD-WAN) obtains telemetry data from one or more edge devices in the SD-WAN. The service trains, using the telemetry data as training data, a machine learning-based model to predict tunnel failures in the SD-WAN. The service receives feedback from the one or more edge devices regarding failure predictions made by the trained machine learning-based model. The service retrains the machine learning-based model, based on the received feedback.

Systems and methods for identifying Passive Intermodulation (PIM) products are disclosed. Some embodiments use RAN Performance Measurement (PM) counters, of actual DL Traffic Load to correlate with UL noise and interference counters. By using counters, the DL traffic is correlated to the increase in noise and interference in order to determine which DL carrier combinations are causing degradation to which UL carriers. In this way, aggressor-victim grouping can be identified through normal downlink traffic load with uplink interference to identify passive intermodulation products. This can be done for all aggressor-victim groups within a radio base station site or cluster of sites. Also, some embodiments enable estimating the maximum PIM interference created by the aggressor carriers. This helps operators to quantify the impact of PIM and can enable them to take counter measures. These embodiments are Radio Access Technology (RAT) agnostic and operator agnostic for the aggressors within a site.

In one embodiment, a network assurance service that monitors a wireless network identifies a set of wireless network anomalies detected in the wireless network that are associated with a set of one or more network measurements. The network assurance service classifies the set of wireless anomalies as radio-related or backend-related. The network assurance service, when the set of wireless anomalies are classified as radio-related, determines that the wireless anomalies are recurring for a particular wireless access point in the network. The network assurance service initiates a change to the wireless network in part to move clients in the wireless network from the particular wireless access point to another wireless access point in the network.

This disclosure describes techniques that include determining the health of one or more routing engines included within a router. In one example, this disclosure describes a method that includes performing, by a first routing engine included within a router, routing operations, wherein the router includes a plurality of routing engines, including the first routing engine and a second routing engine; receiving, by a computing system, data including health indicators associated with the first routing engine; applying, by the computing system, a machine learning model to the data to determine, from the health indicators, a health status of the first routing engine, wherein the machine learning model has been trained to identify the health status from the health indicators; and determining, by the computing system and based on the health status of the first routing engine, whether to switch routing operations to the second routing engine from the first routing engine.