WIRELESS HOME IDENTIFICATION AND SENSING PLATFORM

1 . An integrated occupancy sensing system, comprising:

one or more radio frequency identification (RFID) sensor nodes, each of the one or more RFID sensor nodes including at least one of (1) an image sensor, (2) an acoustic energy sensor, (3) a temperature sensor, (4) an illuminance sensor, or (5) a relative humidity sensor; and

one or more base station units, each of which is configured to be connected to a power source, wherein:

when the one or more base station units are connected to a power source the one or more base station units are configured to emit a continuous wave carrier signal,

the one or more RFID sensor nodes are configured to receive and reflect the continuous wave carrier signal, and

the one or more base station units are also configured to:

receive the reflected signal from the one or more RFID sensor nodes; and

based on the reflected signal, infer a likelihood of human occupancy.

2 . The integrated occupancy sensing system of claim 1 , wherein at least one of the one or more RFID sensor nodes further includes a photovoltaic cell, and the at least one RFID sensor node is powered by a combination of the continuous wave carrier signal and the photovoltaic cell.

3 . The integrated occupancy sensing system of claim 1 , wherein at least one of the one or more RFID sensor nodes does not include an energy storage component.

4 . The integrated occupancy sensing system of claim 1 , wherein each of the one or more RFID sensor nodes includes an identical motherboard that provides power and communication to a corresponding RFID sensor node.

5 . The integrated occupancy sensing system of claim 4 , wherein:

at least one of the one or more RFID sensor node includes (1) a temperature sensor, (2) an illuminance sensor, and (3) a relative humidity sensor and a computer-readable storage that stores a machine-learned AI model for inferring likelihood of human occupancy based on data generated by the temperature sensor, the illuminance sensor, and the relative humidity sensor, and

the machine learned AI model is a trained spatiotemporal pattern network (STPN).

6 . The integrated occupancy sensing system of claim 4 , wherein each of the one or more RFID sensor nodes further includes one or more daughterboards, each of which provides a specific sensing modality.

7 . The integrated occupancy sensing system of claim 6 , wherein:

at least one of the one or more RFID sensor nodes includes an image sensor and a computer-readable storage that stores a machine-learned model for inferring likelihood of human occupancy based on data generated by the image sensor, and

the machine-learned model is a trained convolutional neural network.

8 . The integrated occupancy sensing system of claim 6 , wherein:

at least one of the one or more RFID sensor nodes includes an acoustic energy sensor and a computer-readable storage that stores a machine-learned AI model for inferring likelihood of human occupancy based on data generated by the acoustic energy sensor, and

the machine-learned AI model is a trained random forest classifier.

9 . The integrated occupancy sensing system of claim 6 , wherein at least one of the one or more RFID sensor node includes (1) a temperature sensor, (2) an illuminance sensor, (3) a relative humidity sensor, and (4) either an image sensor or an acoustic energy sensor, and a computer-readable storage that stores a machine-learned AI model for inferring likelihood of human occupancy based on data generated by the temperature sensor, the illuminance sensor, and the relative humidity sensor, and

the machine-learned AI model is a trained spatiotemporal pattern network (STPN).

10 . The integrated occupancy sensing system of claim 1 , wherein at least one of the base station units is configured to:

detect an electromagnetic interference signal within an electric distribution system of a building caused by electrical devices in the building; and

infer the likelihood of human occupancy based on the electromagnetic interference signal.

11 . The integrated occupancy sensing system of claim 1 , wherein:

the at least one base station unit also includes a computer readable storage that stores a machine learned AI model configured to infer an overall likelihood of human occupancy based on the inferences of occupancy received from the one or more RFID sensor nodes, and

the machine learned AI model is trained using an autoregressive logistic regression technique.

12 . A method for detecting human occupancy with a wireless sensing platform, the method comprising:

emitting from one or more base station units a continuous wave carrier signal, the one or more base station units configured to be connected to a power source, wherein:

the continuous wave carrier signal is configured to be received by one or more radio frequency identification (RFID) sensor nodes that are configured to receive and reflect the continuous wave carrier signal, the one or more RFID sensor nodes each including at least one of (1) an image sensor, (2) an acoustic energy sensor, (3) a temperature sensor, (4) an illuminance sensor, or (5) a relative humidity sensor; and

receiving, at the one or more base station units, the reflected signal from the one or more RFID sensor nodes; and

inferring, based on the reflected signal, a likelihood of human occupancy.

13 . The method of claim 12 , wherein each of the one or more RFID sensor nodes comprises an identical motherboard that provides power and communication to a corresponding RFID sensor node.

14 . The method of claim 13 , further comprising:

receiving from at least one of the one or more RFID sensor nodes (1) a temperature sensor reading, (2) an illuminance sensor reading, and (3) a relative humidity sensor reading; and

inferring, using a machine learned AI model that is a trained spatiotemporal pattern network (STPN), a likelihood of human occupancy based on data generated by the temperature sensor, the illuminance sensor, and the relative humidity sensor.

15 . The method of claim 13 , wherein each of the one or more RFID sensor nodes further includes one or more daughterboards, each of which provides a specific sensing modality.

16 . The method of claim 15 , further comprising:

receiving from at least one of the one or more RFID sensor nodes an image sensor reading; and

inferring, using a trained convolutional neural network, a likelihood of human occupancy based on data generated by the image sensor.

17 . The method of claim 15 , further comprising:

receiving from at least one of the one or more RFID sensor nodes an acoustic energy sensor reading; and

inferring, using a trained random forest classifier, a likelihood of human occupancy based on data generated by the acoustic energy sensor.

18 . The method of claim 15 , further comprising:

receiving from at least one of the one or more RFID sensor nodes (1) a temperature sensor, (2) an illuminance sensor reading, (3) a relative humidity sensor reading, and (4) either an image sensor reading or an acoustic energy sensor reading; and

inferring, using a trained spatiotemporal pattern network (STPN), a likelihood of human occupancy based on data generated by the one or more RFID sensor nodes.

19 . The method of claim 12 , further comprising:

detecting an electromagnetic interference signal within an electric distribution system of a building caused by electrical devices in the building; and

inferring the likelihood of human occupancy based on the electromagnetic interference signal.

20 . The method of claim 12 , further comprising:

inferring, using an autoregressive logistic regression technique, an overall likelihood of human occupancy based on the inferences of occupancy received from multiple RFID sensor nodes.