Optimization of virtual sensing in a multi-device environment

A computer-implemented method producing an inductive virtual sensor model of a target device within an ecosystem of devices, the method comprising:

• grouping devices in the ecosystem of devices into a type-of hierarchy of devices;
• determining a measure of similarity between each node in the type-of hierarchy;
• identifying a first subset of devices in the ecosystem of devices, wherein each device in the first subset of devices comprises a target sensor and a plurality of additional sensors;
• collecting target sensor data from the target sensor of each device in the first subset of devices;
• collecting additional sensor data from the additional sensors of each device in the first subset of devices;
• training a predictive model to predict the target sensor data based on the additional sensor data;
• determining a first node in the type-of hierarchy corresponding to the first subset of devices;
• determining a second node in the type-of hierarchy, wherein the measure of similarity between the first node and the second node is above a predetermined threshold;
• identifying a second subset of devices in the ecosystem of devices that (a) correspond to the second node in the type-of hierarchy;
• (b) lack the target sensor;
• and (c) comprise the plurality of additional sensors;
• and
• distributing the predictive model to each device in the second subset of devices.

A computer-implemented method producing an inductive virtual sensor model of a target device within an ecosystem of devices, the method comprising:

• identifying, by a computing system, a first subset of devices in the ecosystem of devices, wherein each device in the first subset of devices comprises a target sensor and a plurality of additional sensors;
• collecting, by the computing system, target sensor data from the target sensor of each device in the first subset of devices;
• collecting, by the computing system, additional sensor data from the additional sensors of each device in the first subset of devices;
• generating, by the computing system, a listing of possible combinations of the additional sensors;
• applying a heuristic search algorithm to the listing of possible combinations to identify an optimal combination of the additional sensors with respect to (i) number of sensors and (ii) ability to predict the target sensor data based on the additional sensor data;
• training, by the computing system, a predictive model to predict the target sensor data based on the additional sensor data corresponding to the optimal combination of the additional sensors;
• identifying, by the computing system, a second subset of devices in the ecosystem of devices lacking the target sensor, wherein each device in the second subset of devices comprises the optimal combination of the additional sensors;
• and
• distributing, by the computing system, the predictive model to each device in the second subset of devices.