Methods and apparatus to trigger calibration of a sensor node using machine learning

At least one non-transitory machine-readable storage medium comprising instructions that, when executed, cause processor circuitry to:

• execute a machine learning model on first data from a first node in a network to forecast a period of interference with a communication ability of the first node;
• cause transmission of a calibration instruction to the first node to cause the first node to communicate with one of a first gateway or a second gateway during the forecasted period of interference;
• and
• cause storage of an indication that the calibration instruction has been transmitted to the first node.

A method for collecting data from a network, the method comprising:

• executing a machine learning model on first data from a first node in the network to forecast a period of interference with a communication ability of the first node;
• transmitting a calibration trigger to the first node to cause the first node to communicate with one of a first gateway or a second gateway during the forecasted period of interference;
• and
• setting a flag to indicate that the calibration trigger has been transmitted to the first node.

An apparatus to collect data from a network including one or more nodes, a first gateway, and a second gateway, the apparatus comprising:

• means for forecasting, using a machine learning model and first data from a first node in the network, a period of interference with a communication ability of the first node;
• and
• means for transmitting a trigger to the first node to cause the first node to communicate with one of the first gateway or the second gateway during the forecasted period of interference;
• and
• the means for forecasting is to set a flag to indicate that the trigger has been transmitted to the first node.