A process of administering electronic device registration and authentication includes receiving an access request; comparing the credentials to credential data within a database; scanning for available communications channels; reporting available communications channels to an activation administrator server via one of the channels; and initiating activation of an activatable electronic device. The access request includes credentials and attribute data. The device can self-administer the process by an available means of communication. The device includes a memory, communications interfaces, and a processor. The processor identifies a unique attribute of the device and obtains and populates a form to generate an activation request including the device attribute, information associated with the device, and an intended usage. The activation request is forwarded to the server using the interface. The processor receives activation information and stores the information to the memory; activates the device; and generates a reporting message.

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.

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.

The disclosure relates to a system and method of configuring and validating multi-vendor and multi-region Internet-of-Things (IoT) devices using reinforcement learning. In some embodiments, the method includes generating a matching table for each of a plurality of IoT sensors based on a plurality of sensor attributes extracted from a product data associated with an IoT sensor; acquiring an identification information and operational information associated with the IoT sensor and a set of neighboring IoT sensors for each of the plurality of IoT sensors; identifying an appropriate set of IoT sensors from the plurality of IoT sensors, based on a user requirement, the matching table, the identification information and the operational information, using a Reinforcement Learning (RL) model; and dynamically configuring each of the appropriate set of IoT sensors based on a vendor type.

The disclosure relates to a system and method of configuring and validating multi-vendor and multi-region Internet-of-Things (IoT) devices using reinforcement learning. In some embodiments, the method includes generating a matching table for each of a plurality of IoT sensors based on a plurality of sensor attributes extracted from a product data associated with an IoT sensor; acquiring an identification information and operational information associated with the IoT sensor and a set of neighboring IoT sensors for each of the plurality of IoT sensors; identifying an appropriate set of IoT sensors from the plurality of IoT sensors, based on a user requirement, the matching table, the identification information and the operational information, using a Reinforcement Learning (RL) model; and dynamically configuring each of the appropriate set of IoT sensors based on a vendor type.

Systems and methods described herein provide for generating behavior profiles for a plurality of end devices associated with an end user; receiving, from the end user, a request for an over-the-air (OTA) update for the plurality of end devices; generating an OTA update campaign plan based on the behavior profiles responsive to the request; providing the OTA update campaign plan to the end user; receiving, from the end user, a modified request for the OTA update; and generating an OTA update campaign schedule responsive to the modified request.

Systems and methods described herein provide for generating behavior profiles for a plurality of end devices associated with an end user; receiving, from the end user, a request for an over-the-air (OTA) update for the plurality of end devices; generating an OTA update campaign plan based on the behavior profiles responsive to the request; providing the OTA update campaign plan to the end user; receiving, from the end user, a modified request for the OTA update; and generating an OTA update campaign schedule responsive to the modified request.

Methods and systems for autonomous vehicle recharging or refueling are disclosed. Autonomous vehicles may be automatically refueled by routing the vehicles to available fueling stations when not in operation, according to methods described herein. A fuel level within a tank of an autonomous vehicle may be monitored until it reaches a refueling threshold, at which point an on-board computer may generate a predicted use profile for the vehicle. Based upon the predicted use profile, a time and location for the vehicle to refuel the vehicle may be determined. In some embodiments, the vehicle may be controlled to automatically travel to a fueling station, refill a fuel tank, and return to its starting location in order to refuel when not in use.

A method for creating resources and a corresponding registration method, server, client device, and computer readable storage medium. The method for creating resources includes: receiving a registration request; determining a registration type corresponding to the registration request; creating a resource corresponding to the registration type on the basis of the registration type.

According to an aspect of an embodiment, a method may include obtaining a JavaScript Object Notation (JSON) schema that corresponds to legacy data. The legacy data may include a plurality of legacy data points corresponding to device features of a legacy device. The plurality of legacy data points may be delimited according to a legacy data format. The JSON schema may include a plurality of property definitions corresponding to the legacy data points. The JSON schema may additionally include a legacy object that describes the legacy data format in a manner that allows for processing of the legacy data using the JSON schema. The method may also include processing the legacy data using the plurality of property definitions and the legacy object included in the JSON schema in a manner that allows the legacy device to be used as a web-of-things Thing.