Abstract: A method and system for tracking and targeting particular energy sources in an electrical grid is provided. A processor stores information regarding units of produced energy as transactions in a blockchain. A processor receives, from an endpoint device of a plurality of endpoint devices connected to a power grid, a request for a unit of energy represented in the blockchain. A processor sends an indication, to the endpoint device, that the endpoint device is permitted to consume the unit of energy from the power grid. A processor updates the blockchain to record the consumption of the unit of energy.

Abstract: A method and system for tracking and targeting particular energy sources in an electrical grid is provided. A processor stores information regarding units of produced energy as transactions in a blockchain. A processor receives, from an endpoint device of a plurality of endpoint devices connected to a power grid, a request for a unit of energy represented in the blockchain. A processor sends an indication, to the endpoint device, that the endpoint device is permitted to consume the unit of energy from the power grid. A processor updates the blockchain to record the consumption of the unit of energy.

Abstract: Provided are techniques for network managed rules for machine access. A request to access a machine is received from an operator. One or more machine access rules are identified for a machine and an operator who will operate the machine for a company in a location, where each of the one or more machine access rules includes variable criteria. Data for the variable criteria of the one or more machine access rules is obtained from one or more of: a machine profile of the machine, an operator profile of the operator, a company profile of the company, and environmental data for the location. The data is applied to the variable criteria of the one or more machine access rules to determine whether to provide the operator with access to the machine at the location. An indication of whether the operator has access to the machine at the location is determined.

Abstract: Provided are techniques for grouping devices as a virtual device for providing better quality of device data. A group of devices in an Internet of Things (IoT) network is identified based on one or more attributes of each of the devices. The group of devices is provided as a single virtual device by: collecting data from each of the devices in the group, transforming the data, and using the transformed data to modify a device.

Abstract: Provided are techniques for network managed rules for machine access. A request to access a machine is received from an operator. One or more machine access rules are identified for a machine and an operator who will operate the machine for a company in a location, where each of the one or more machine access rules includes variable criteria. Data for the variable criteria of the one or more machine access rules is obtained from one or more of: a machine profile of the machine, an operator profile of the operator, a company profile of the company, and environmental data for the location. The data is applied to the variable criteria of the one or more machine access rules to determine whether to provide the operator with access to the machine at the location. An indication of whether the operator has access to the machine at the location is determined.

Abstract: Provided are techniques for grouping devices as a virtual device for providing better quality of device data. A group of devices in an Internet of Things (IoT) network is identified based on one or more attributes of each of the devices. The group of devices is provided as a single virtual device by: collecting data from each of the devices in the group, transforming the data, and using the transformed data to modify a device.

Abstract: Provided are techniques for grouping devices as a virtual device for providing better quality of device data. A group of devices in an Internet of Things (IoT) network is identified based on one or more attributes of each of the devices. The group of devices is provided as a single virtual device by: collecting data from each of the devices in the group, transforming the data, and using the transformed data to modify a device.

Abstract: In a method for providing an automatic learned response in a network, a collection system observes user responses to the incoming system indicators and to parameter types and associated parameter values used in the user responses. The collection system creates alert event entries to includes the incoming system indicators, confidence thresholds, the user responses, and the parameter types and associated parameter values used in the user responses. When the collection system receives new system indicators, the collection system determines whether the new system indicators match the system indicators in one or more alert event entries. When the new system indicators match the system indicators in one or more alert event entries and the confidence level exceeds the confidence threshold, the collection system automatically creates a new response based on the user response, the parameter types, and the associated parameter values in the matching alert event entries.

Abstract: Provided are techniques for grouping devices as a virtual device for providing better quality of device data. A group of devices in an Internet of Things (IoT) network is identified based on one or more attributes of each of the devices. The group of devices is provided as a single virtual device by: collecting data from each of the devices in the group, transforming the data, and using the transformed data to modify a device.

Abstract: In a method for providing an automatic learned response in a network, a collection system observes user responses to the incoming system indicators and to parameter types and associated parameter values used in the user responses. The collection system creates alert event entries to includes the incoming system indicators, confidence thresholds, the user responses, and the parameter types and associated parameter values used in the user responses. When the collection system receives new system indicators, the collection system determines whether the new system indicators match the system indicators in one or more alert event entries. When the new system indicators match the system indicators in one or more alert event entries and the confidence level exceeds the confidence threshold, the collection system automatically creates a new response based on the user response, the parameter types, and the associated parameter values in the matching alert event entries.

Abstract: Disclosed aspects relate to Internet of Things (IoT) data maturity management. A set of IoT data may be ingested by a data maturity engine. A set of maturity factors which indicates a fitness for utilization of the set of IoT data may be determined. The determining may occur with respect to the set of IoT data by the data maturity engine. A data management operation may be identified by the data maturity engine based on the set of maturity factors for the set of IoT data. The data management operation may be identified to manage the set of IoT data. The data management operation may be carried-out to manage the set of IoT data.

Abstract: Disclosed aspects relate to Internet of Things (IoT) data maturity management. A set of IoT data may be ingested by a data maturity engine. A set of maturity factors which indicates a fitness for utilization of the set of IoT data may be determined. The determining may occur with respect to the set of IoT data by the data maturity engine. A data management operation may be identified by the data maturity engine based on the set of maturity factors for the set of IoT data. The data management operation may be identified to manage the set of IoT data. The data management operation may be carried-out to manage the set of IoT data.