Abstract: A method for addressing anomalies in an Internet of things (IOT) device, the IOT device being a part of a mobile cluster is described. The method comprises determining whether the IOT device produces an anomaly based at least on a first machine learning model, determining adjustments for recommending in functioning of the IOT device based at least on a second machine learning model in response to a determination that the IOT device produces the anomaly, sending the recommended adjustments to one or more user devices that are a part of the mobile cluster for loading the recommended adjustments on to the mobile cluster, and receiving a selection of one or more of the recommended adjustments from the one or more user devices for facilitating addressal of the anomaly in the IOT device.

Abstract: Methods for operating an edge computing unit in a network for data aggregation and status communication with respect to microservices includes extracting data from data sources and converting the extracted data into a pre-defined data set. Data of the pre-defined data set is aggregated into one or more classes based on a prediction performed through a machine learning inferencing or a work flow engine. An irregularity of operation pertaining to the microservices within the aggregated data is identified based on a predetermined-criteria. An alert pertaining to the detected irregularity is generated and communicated to a client device.

Abstract: Embodiments for real-time self-adaptive tuning and control of a device using machine learning are disclosed. For example, a method includes receiving real-time data for a plurality of parameters of the device from a plurality of sources associated with the device and selecting at least one machine learning model from a plurality of machine learning models based on the received real-time data. The method further includes predicting at least one control set point based on the at least one selected machine learning model. The at least one predicted control set point of the device is adjusted for the real-time self-adaptive tuning and control of the device.

Abstract: Embodiments for managing real-time alerts using machine learning are disclosed. For example, a method includes receiving real-time data for one or more parameters of a device for which an alert is to be generated, from one or more sources associated with the device, and selecting a first machine learning model from a plurality of machine learning models based on the received real-time data. The method further includes determining at least one anomaly in the device based on the selected first machine learning model and predicting an impact of the determined at least one anomaly based on a second machine learning model of the plurality of machine learning models. Furthermore, the method includes generating the alert for the device in real-time based on the predicted impact of the determined at least one anomaly and receiving feedback on the generated alert in real-time.