The present invention generally relates to systems and methods for detecting and/or isolating any causes of defective and/or partially defective IoT device or individual sensor device(s). In embodiments the present invention generally relates to fixing, replacing, and/or troubleshooting IoT devices and/or individual sensor device(s) that are defective and/or partially defective.

A method for managing a computer network is provided, which comprises: performing data collection at at least one network node of the computer network belonging to a set of one or more network nodes corresponding to a first depth level of a routing tree that represents nodes of the computer network and edges respectively corresponding to neighboring relations between two nodes of the computer network, the data collection comprising: receiving first data collection configuration data generating second data collection configuration data for collecting data from at least one child node in the routing tree of the at least one network node of the computer network, wherein the second data collection configuration data comprises scheduling data for collecting data from each of the at least one child node, and collecting data from each of the at least one child node according to the second data collection configuration data.

A method for managing a computer network is provided, which comprises: performing data collection at at least one network node of the computer network belonging to a set of one or more network nodes corresponding to a first depth level of a routing tree that represents nodes of the computer network and edges respectively corresponding to neighboring relations between two nodes of the computer network, the data collection comprising: receiving first data collection configuration data generating second data collection configuration data for collecting data from at least one child node in the routing tree of the at least one network node of the computer network, wherein the second data collection configuration data comprises scheduling data for collecting data from each of the at least one child node, and collecting data from each of the at least one child node according to the second data collection configuration data.

A method and system to interpret sensor transmission patterns to analyze anomalies in a smart environment include obtaining a map of the smart environment, the map including an indication of obstructions and openings. The method includes determining an initial location of each sensor of a plurality of sensors in the smart environment. Each sensor emits a transmission after each detection. The method also includes identifying an initial transmission pattern associated with each sensor, and identifying a change in the initial transmission pattern of a sensor among the plurality of sensors. The change is interpreted to determine whether the change in the initial transmission pattern of the sensor among the plurality of sensors is due to movement or obstruction of the sensor. Action is taken based on a determination that the sensor among the plurality of sensors is obstructed or removed.

A method and system to interpret sensor transmission patterns to analyze anomalies in a smart environment include obtaining a map of the smart environment, the map including an indication of obstructions and openings. The method includes determining an initial location of each sensor of a plurality of sensors in the smart environment. Each sensor emits a transmission after each detection. The method also includes identifying an initial transmission pattern associated with each sensor, and identifying a change in the initial transmission pattern of a sensor among the plurality of sensors. The change is interpreted to determine whether the change in the initial transmission pattern of the sensor among the plurality of sensors is due to movement or obstruction of the sensor. Action is taken based on a determination that the sensor among the plurality of sensors is obstructed or removed.

Systems and methods are described to associate data from different nodes of a distributed ledger system. The nodes can generate transaction notifications, log data, and/or metrics data. At least some of the data generated by the nodes can be obtained by a data intake and query system via a distributed ledger system monitor. The data from the distributed ledger system can be stored in the data intake and query system and correlated. Based on an association between at least some of the data of the first node and at least some of the data of the second node, the data intake and query system can determine at least a partial history of a transaction in the distributed ledger system, relationships between components of the distributed ledger system, and/or an architecture of the distributed ledger system.

Systems and methods for increasing a user's task performance and personal wellbeing resulting from caring for indoor plants are disclosed. Internet-of-Things sensors and devices monitor measurements relating to environmental factors and a user's care patterns associated with designated indoor plants. User-performance sensors and devices measure the user's wellbeing and the user's task performance, both in caring for the plants and in other productivity-driven tasks. Recommendations for the plant care and prescriptions for the users are generated using machine-learning models and provided to the user through notifications to alter the user's behavior in a desirable way, both in plant care and in real-world task performance.

Improved systems, methods, and architectures to enhance decision making in Smart Cities and Smart Regions. A system includes an index structure including a first hierarchical data structure including a first hierarchical score based on a plurality of first-level elements, each of the plurality of first-level elements having a respective weighting, and a second hierarchical data structure including a plurality of second hierarchical scores based on a plurality of second-level elements, each of the plurality of second-level elements having a respective weighting, such that the first hierarchical score is based on the plurality of second hierarchical scores through an index factor; and a computer-implemented regional monitor engine to manage local access to a plurality of external data sources to coordinate writes to the index structure.

Improved systems, methods, and architectures to enhance decision making in Smart Cities and Smart Regions. A system includes an index structure including a first hierarchical data structure including a first hierarchical score based on a plurality of first-level elements, each of the plurality of first-level elements having a respective weighting, and a second hierarchical data structure including a plurality of second hierarchical scores based on a plurality of second-level elements, each of the plurality of second-level elements having a respective weighting, such that the first hierarchical score is based on the plurality of second hierarchical scores through an index factor; and a computer-implemented regional monitor engine to manage local access to a plurality of external data sources to coordinate writes to the index structure.

The present disclosure relates to a method for verifying the production process of field devices, including a step of accessing a service platform on which data from field devices, including identification data, the respective type of field device, configuration data, containing application-specific data, environment information of the field devices or parameter data, data relating to the production date of a respective field device and repair or troubleshooting cases of the field devices are stored. The method also includes steps of detecting anomalies by statistically evaluating the repair or troubleshooting cases stored on service platform and creating a notification in the event of a detected anomaly, supplying the data of the field devices and the notifications to a machine learning or prognosis system, and evaluating the data of the field devices and the notifications by means of the machine learning or prognosis system for forecasting series errors of the field devices.