# US 11,310,892 B2 · System, methods, and apparatuses for distributed detection of luminaire anomalies

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- Number: US 11,310,892 B2
- Document ID: US11310892B2
- Status: Granted
- Kind: B2
- Application number: 16/769,113
- Filed: 2018-11-20
- Published: 2021-06-10
- Granted: 2022-04-19
- Earliest claimed: 2018-01-26
- Assignees: Signify Holding B.V.
- Applicants: Signify Holding B.V.
- Inventors: Olaitan Philip Olaleye; Tamir Hegazy; Abhishek Murthy
- Web page: https://patentgrep.com/patent/US11310892B2

## CPC

- H04L 12/4625: {Single bridge functionality, e.g. connection of two networks over a single bridge}
- H05B 47/165: following a pre-assigned programmed sequence; Logic control [LC]
- H04W 4/38: for collecting sensor information
- H05B 47/11: by determining the brightness or colour temperature of ambient light
- H05B 47/155: Coordinated control of two or more light sources
- H05B 47/19: via wireless transmission
- H04L 67/12: specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
- H04W 4/33: for indoor environments, e.g. buildings
- Y02B 20/40: Control techniques providing energy savings, e.g. smart controller or presence detection
- H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M)
- H05B: ELECTRIC HEATING; ELECTRIC LIGHT SOURCES NOT OTHERWISE PROVIDED FOR; CIRCUIT ARRANGEMENTS FOR ELECTRIC LIGHT SOURCES, IN GENERAL
- H04W: WIRELESS COMMUNICATION NETWORKS (broadcast communication H04H; communication systems using wireless links for non-selective communication, e.g. wireless extensions H04M1/72)
- Y02B: CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS

## Abstract

The described implementations provided herein relate to systems, methods, and apparatuses for using a network of luminaires to perform distributed computation of sensor data to identify hardware and network anomalies. In some implementations, a method is set forth as including operations such as receiving, from a first luminaire (114, 128, 216), first sensor data (122, 124) in response to a stimulus (204) affecting a network of luminaires (110), and receiving, from a second luminaire (208) in the network of luminaires, second sensor data. The method can also include determining a correlation between the first sensor data and the second sensor data, and modifying a luminaire spatial-temporal model (222) based at least partially on the correlation. The method can also include receiving subsequent sensor data from the first luminaire or the second luminaire, and providing a signal (212) to the first luminaire or the second luminaire based on a comparison of the subsequent sensor data to the luminaire spatial-temporal model.

## Claims

### Claim 1

A method for detecting sensor health implemented by one or more processors, the method comprising:
generating, at a computing device of a first luminaire, first sensor data corresponding to a stimulus affecting a network of luminaires that includes the first luminaire;
receiving, at the computing device of the first luminaire, second sensor data from a second luminaire in the network of luminaires, wherein the second sensor data is generated by the second luminaire in response to the stimulus;
accessing a luminaire spatial-temporal model, wherein the luminaire spatial-temporal model is based on historical sensor data collected from at least the first luminaire and the second luminaire;
determining variance data from the luminaire spatial-temporal model, the variance data based on a difference between the historical sensor data and the first sensor data, and a difference between the historical sensor data and the second sensor data, wherein the variance data includes probabilistic metrics;
determining that at least one probabilistic metric of the probabilistic metrics satisfies a probabilistic threshold; and
when a variance is identified in the variance data: causing a signal to be transmitted based on the variance, wherein the variance corresponds to an amount of latency in a response to the stimulus.

### Claim 2 (depends on 1)

The method of claim 1, wherein the signal causes the first luminaire or the second luminaire to compensate for the variance, wherein compensating for the variance includes limiting an amount of data that is provided from a sensor of the first luminaire or the second luminaire.

### Claim 3 (depends on 1)

The method of claim 1, comprising:
determining that at least one subsequent probabilistic metric of the probabilistic metrics does not satisfy the probabilistic threshold.

### Claim 4 (depends on 1)

The method of claim 1, wherein the first sensor data or the second sensor data includes data from multiple different types of sensors.

### Claim 5 (depends on 4)

The method of claim 4, further comprising:
determining a change in correlation between data from the multiple different types of sensors, wherein the at least one probabilistic metric is based on the change in correlation.

### Claim 6 (depends on 1)

The method of claim 1, wherein the historical sensor data includes gradient distribution data for sensors integral to the network of luminaires.

### Claim 7 (depends on 1)

The method of claim 1, comprising:
causing the luminaire spatial-temporal model to be modified according to the signal, wherein the signal is based on the amount of latency in the response of the first luminaire or the second luminaire to the stimulus.

### Claim 8

A computing device, comprising:
one or more processors; and
memory connected to the one or more processors, the memory comprising instructions that, when executed by the one or more processors, cause the one or more processors to perform operations that include:
receiving, from a first luminaire, first sensor data in response to a stimulus affecting a network of luminaires that the one or more processors are in communications with;
receiving, from a second luminaire, second sensor data in the network of luminaires, wherein the second sensor data is generated by the second luminaire in response to the stimulus;
determining a correlation between the first sensor data and the second sensor data;
modifying a luminaire spatial-temporal model based at least partially on the correlation between the first sensor data and the second sensor data, wherein the luminaire spatial-temporal model is based on gradient distribution data for multiple types of sensors integrated into the network of luminaires, and the multiple types of sensors include at least a passive infrared sensor;
receiving subsequent sensor data from the first luminaire or the second luminaire; and
providing a signal to the first luminaire or the second luminaire based on a comparison of the subsequent sensor data to the modified luminaire spatial-temporal model.

### Claim 9 (depends on 8)

The computing device of claim 8, wherein determining the correlation includes generating a probabilistic metric, and the operations further include:
determining that the probabilistic metric satisfies a probabilistic threshold.

### Claim 10 (depends on 8)

The computing device of claim 8, wherein the first sensor data or the second sensor data include data from multiple types of sensors.

### Claim 11 (depends on 10)

The computing device of claim 10, wherein determining the correlation includes determining a change in correlation between data from the multiple types of sensors.

### Claim 12

A system, comprising:
one or more processors; and
memory configured to store instructions that, when executed by the one or more processors, cause the one or more processors to perform operations that include:
generating first sensor data at the one or more processors, wherein the one or more processors are in communication with a network of luminaires;
receiving second sensor data from a luminaire in the network of luminaires, wherein the second sensor data is generated by the luminaire;
comparing the first sensor data and the second sensor data to a luminaire spatial-temporal model to identify a variance between the first sensor data and the luminaire spatial-temporal model or the second sensor data and the luminaire spatial-temporal model, wherein the luminaire spatial-temporal model is based on historical sensor data collected from adjacent luminaires in the network of luminaires and wherein the variance corresponds to a probabilistic metric;
determining that the probabilistic metric satisfies a probabilistic threshold; and
when the variance is identified, causing the one or more processors, or the luminaire, to operate to compensate for the variance, wherein the variance corresponds to a probabilistic metric.

### Claim 13 (depends on 12)

The system of claim 12, wherein the operations further include:
determining that a subsequent probabilistic metric does not satisfy the probabilistic threshold.

### Claim 14 (depends on 12)

The system of claim 12, wherein the first sensor data or the second sensor data include data from multiple types of sensors.

### Claim 15 (depends on 14)

The system of claim 14, wherein the operations further include:
determining a change in correlation between data from the multiple different types of sensors.

### Claim 16 (depends on 12)

The system of claim 12, wherein the operations further include:
modifying the luminaire spatial-temporal model based on the one or more processors, or the luminaire, operating to compensate for the variance, wherein the luminaire spatial-temporal model is stored in the memory.

### Claim 17 (depends on 12)

The system of claim 12, wherein compensating for the variance data includes limiting an amount of data that is provided from a sensor.

### Claim 18 (depends on 12)

The system of claim 12, wherein the luminaire spatial-temporal model identifies gradient distributions for the historical sensor data collected from the adjacent luminaires.

### Claim 19 (depends on 12)

The system of claim 12, further comprising a lighting device, wherein the first sensor data and the second sensor data are based on an amount of light detected at an area illuminated by the network of luminaires.