Model tiering for IoT device clusters

A method, comprising:

• one or more tier devices connected to a network that comprise a respective data processing model, wherein individual tier devices of the one or more tier devices perform:
• receiving data by the tier device as current data;
• determining by the tier device that received the current data, based on whether an amount of energy that would be required for a data processing model of the edge device to process the current data collected by the edge device is above a threshold amount of energy and also based on a confidence level of one or more previous predictions that were previously generated by a data processing model of the same tier device by processing different data that was previously collected by the same tier device before the current data was received by the tier device and wherein the data processing model is trained based on the same training data that the data processing model was previously trained on to generate the previous predictions, whether to:
• process the current data at the same tier device by using the same data processing model trained based on the same training data that the data processing model was previously trained on to generate the previous predictions in order to generate a new prediction at the tier device based on the current data collected by the tier device, or
• send the current data collected by the tier device to one or more other tier devices connected to the same network;
• and
• in response to the determining:
• processing the current data using the data processing model of the tier device to generate the prediction, or
• sending the current data to the one or more other tier devices.

A non-transitory computer-readable storage medium storing program instructions that, when executed by a tier device connected to a network, cause the tier device to implement:

• receiving data by the tier device as current data;
• determining by the tier device that received the current data, based on whether an amount of energy that would be required for a data processing model of the edge device to process the current data collected by the edge device is above a threshold amount of energy and also based on a confidence level of one or more previous predictions that were previously generated by a data processing model of the same tier device by processing different data that was previously collected by the same tier device before the current data was received by the tier device and wherein the data processing model is trained based on the same training data that the data processing model was previously trained on to generate the previous predictions, whether to:
• process the current data at the same tier device by using the same data processing model trained based on the same training data that the data processing model was previously trained on to generate the previous predictions in order to generate a new prediction at the tier device based on the current data collected by the tier device, or
• send the current data collected by the tier device to one or more other tier devices connected to the same network;
• and
• in response to the determining:
• processing the current data using the data processing model of the tier device to generate the prediction, or
• sending the current data to the one or more other tier devices.