WO2020030485A1

WO2020030485A1 - Cloud-based switching of sensor usage for extended lifetime of luminaire-integrated sensors

Info

Publication number: WO2020030485A1
Application number: PCT/EP2019/070580
Authority: WO
Inventors: Ruben Rajagopalan; Harry Broers; Maurice Herman Johan Draaijer; Jan EKKEL
Original assignee: Signify Holding B.V.
Priority date: 2018-08-10
Filing date: 2019-07-31
Publication date: 2020-02-13

Abstract

Implementations set forth herein allow an operational lifetime of a sensor (114) of a luminaire (108) to be preserved by deactivating the sensor and allowing the luminaire to rely on extrapolated data for a substantial portion of an operational lifetime of the luminaire. The extrapolated data can be provided by a parametric model (118) that is generated based on a correlation between a sensor output from the sensor of the luminaire and another sensor output from a separate sensor (126). Additionally, an estimated operational lifetime (308) for the luminaire can be provided using data associated with environmental conditions (204, 206, 208) and/or properties of the luminaire. The sensor of the luminaire can be inactive before the lifetime of the luminaire reaches or exceeds the estimated operational lifetime for the luminaire. When the lifetime of the luminaire reaches or exceeds the estimated operational lifetime, or the parametric model needs recalibrating, the sensor of the luminaire can be activated.

Description

Cloud-Based Switching of Sensor Usage for Extended Lifetime of Luminaire-Integrated Sensors

TECHNICAL FIELD

The described embodiments generally relate to predicting lifetimes of luminaire components. More particularly, but not exclusively, the embodiments relate to systems, methods, and apparatuses for scheduling sensor usage based on cloud-data to extend lifetimes of luminaire components.

BACKGROUND

Lighting systems that extend over large geographic areas can prove difficult to maintain because unpredictable malfunctions can occur at any portion of a system.

Furthermore, when a malfunction does occur, scheduling and performing repairs can be time consuming when maintenance crews must travel to distant locations to reach a problematic luminaire. Although maintenance of a single luminaire may not be complicated to undertake, oftentimes, groups of luminaires can fail unpredictably as a result of the luminaires being exposed to similar environmental conditions over their lifetime. Such unpredictable group failures can make the task of performing repairs exceedingly daunting.

SUMMARY

The present disclosure is directed to systems, methods, and apparatuses for preserving an operational lifetime of computing device components by delaying activation and/or deactivation of such computing device components. The activation of the computing device components can be delayed according to a comparison of operational lifetime of the computing device component and the computing device. For instance, the computing device can be a luminaire that is connected over a network to a remote computing device, such as a server, and the computing device component can be a first sensor that is integral to the luminaire. In some implementations, the luminaire can include one or more sensors including those that measure temperature, humidity, vibrations, power, air quality, light level, pressure, precipitation, air electrolyte composition, traffic conditions, changes in electrical conditions of the power grid or the luminaire, sound, images, and/or any other property or condition that can be measured by a sensor. When the luminaire has been installed at a particular location ( e.g ., as a street light that illuminates a highway), a time when the luminaire is installed can be a start time for an operational lifetime of the luminaire and the first sensor of the luminaire. The first sensor can provide a sensor output to the luminaire and/or a remote device, such as a remote server. The sensor output can be used as a basis from which to generate data about particular properties of the luminaire and/or environmental conditions of the luminaire. However, because the first sensor may have a substantially shorter operational lifetime compared to the luminaire, the operational lifetime of the first sensor should be preserved. In order to preserve the operational lifetime of the first sensor, the remote computing device and/or the luminaire can calibrate the first sensor with a second sensor, and rely on resulting

extrapolated data while the first sensor is at least temporarily deactivated. In other words, once the calibration has been completed and a parametric model has been created to generate the extrapolated data, the first sensor can be deactivated while the second sensor continues to operate and provide a separate sensor output to the parametric model to generate extrapolated data. The second sensor can be in communication with the remote computing device and/or the luminaire, and the parametric model can be accessible to the remote computing device and/or the luminaire in order to provide the extrapolated data.

By correlating data from the first sensor and the second sensor, the extrapolated data provided by the parametric model can be reliably used in lieu of the first sensor being deactivated. The remote computing device and/or the luminaire can compare the extrapolated data to the sensor output from the second senor to determine whether the extrapolated data is deviating from the second sensor output, or the second sensor output is deviating from the extrapolated data. When one of the second sensor output and the extrapolated data have deviated from the other up to, or exceeding, a particular value (e.g., a threshold, percentage, deviation metric), the remote computing device and/or the luminaire can cause the first sensor to be activated in order to recalibrate the parametric model and/or the second sensor. For example, when the extrapolated data deviates from second sensor data generated based on an output of the second sensor, the remote computing device can cause the luminaire to activate the first sensor in order that the remote computing device can adjust the parametric model to correct for the deviation.

Eventually, during an operational lifetime of the luminaire, the first sensor can be activated for continual operation when an operational lifetime of the first sensor reaches or exceeds a particular threshold (e.g., an estimate operational lifetime of the luminaire). An estimated operational lifetime of the luminaire can be used as the threshold for activating the first sensor, and the estimated operational lifetime can be generated by the remote computing device and/or one or more luminaires. For instance, the remote computing device can track data associated with operational and/or environmental conditions of one or more luminaires and estimate the operational lifetime of the luminaire based on the tracked data. For example, a luminaire that has experienced multiple electrical surges may have a shorter operational lifetime than another luminaire that has not experienced any electrical surges. Furthermore, a luminaire that has been operational in an area of high heat and high humidity may have a shorter operational lifetime than another luminaire that has been operating in an area with relatively less heat and less humidity. In some implementations, data used to generate the estimate for operational lifetime of one or more luminaires can be based on data from a sensor that is integral to a luminaire and/or data from a sensor that is separate from the luminaire (e.g. the second sensor that is in communication with the remote computing device). Furthermore, or optionally, the estimated operational lifetime can be static or dynamic over time, according to how the tracked data changes over time.

When the first sensor of the luminaire has been activated, the luminaire can disconnect from the remote computing device, or at least no longer receive the extrapolated data from the remote computing device. The first sensor can then be operated continually until the luminaire malfunctions or otherwise requires maintenance in order to continue operating. Should the luminaire receive some amount of maintenance that would extend the operational lifetime of the luminaire, the first sensor can be deactivated, at least if an estimate for the extended operational lifetime exceeds a current operational lifetime of the first sensor. For example, in response to the luminaire receiving life-extending maintenance, the remote computing device can generate another value for estimated operational lifetime. The remote computing device can then compare the current operational lifetime of the first sensor to the other value for the estimated operational lifetime and, if the current operational lifetime of the first sensor is less than the other value for the estimated operational lifetime, the remote computing device can cause the first sensor to be deactivated at the luminaire. Furthermore, in response to the luminaire receiving the life-extending maintenance, the parametric model can be updated according to a recalibration of the first sensor with the second sensor. In this way, the luminaire can operate according to extrapolated data from the parametric model while the first sensor is at least temporarily deactivated.

In some implementations, a method implemented by one or more processors is set forth as including operations such as receiving first data that is based on a first sensor output of a first sensor that is connected to a client device. The first sensor can be at least temporarily activated during an estimated client device operational lifetime in order to provide the first sensor output. The method can also include receiving second data that is based on a second sensor output of a second sensor. The second sensor can be remotely located relative to the client device. The method can further include determining a correlation between the first data and the second data in order to build a model based on the correlation. The model can be configured to simulate the first sensor output based at least in part on the second sensor output. The method can also include causing the client device to at least temporarily deactivate the first sensor, and operate according to the parametric model in lieu of the first data that was based on the first sensor output of the first sensor. The method can further include, when an operational-related event associated with the client device has or is predicted to occur, causing the client device to activate the first sensor for at least temporary operation during a remaining client device operational lifetime.

In some implementations, the client device can be a luminaire and the method can further include determining an operational lifetime estimate for the client device based on at least the second sensor output of the second sensor. The operational-related event can corresponds to a sensor operational lifetime of the first sensor reaching or exceeding the operational lifetime estimate for the client device. The first data can be received by a computing device that is remote from the client device and the model can be a parametric model. The method can also include generating, based on the first sensor output and the second sensor output, the parametric model that characterizes a correlation between the first data and the second data. The parametric model can provide a basis from which to generate the operational lifetime estimate of the client device. The method can further include, when the sensor operational lifetime has not reached or exceeded the operational lifetime estimate for the client device: causing the client device to temporarily limit the first sensor from at least providing the first sensor output, and causing the client device to operate based on an output of the parametric model in lieu of the first sensor output.

In some implementations, the method can include, when the sensor operational lifetime has not reached or exceeded the operational lifetime estimate for the client device: causing the client device to temporarily activate the first sensor, determining that a recalibration of the second sensor should be performed, at least based on a comparison between additional first sensor output and additional second sensor output, and recalibrating the model according to the additional first sensor output from the first sensor and the additional second sensor output from the second sensor. As used herein for purposes of the present disclosure, the term“LED” should be understood to include any electroluminescent diode or other type of carrier

injection/junction-based system that is capable of generating radiation in response to an electric signal. Thus, the term LED includes, but is not limited to, various semiconductor- based structures that emit light in response to current, light emitting polymers, organic light emitting diodes (OLEDs), electroluminescent strips, and the like. In particular, the term LED refers to light emitting diodes of all types (including semi-conductor and organic light emitting diodes) that may be configured to generate radiation in one or more of the infrared spectrum, ultraviolet spectrum, and various portions of the visible spectrum (generally including radiation wavelengths from approximately 400 nanometers to approximately 700 nanometers). Some examples of LEDs include, but are not limited to, various types of infrared LEDs, ultraviolet LEDs, red LEDs, blue LEDs, green LEDs, yellow LEDs, amber LEDs, orange LEDs, and white LEDs (discussed further below). It also should be

appreciated that LEDs may be configured and/or controlled to generate radiation having various bandwidths (e.g., full widths at half maximum, or FWHM) for a given spectrum (e.g., narrow bandwidth, broad bandwidth), and a variety of dominant wavelengths within a given general color categorization.

For example, one implementation of an LED configured to generate essentially white light (e.g., a white LED) may include a number of dies which respectively emit different spectra of electroluminescence that, in combination, mix to form essentially white light. In another implementation, a white light LED may be associated with a phosphor material that converts electroluminescence having a first spectrum to a different second spectrum. In one example of this implementation, electroluminescence having a relatively short wavelength and narrow bandwidth spectrum“pumps” the phosphor material, which in turn radiates longer wavelength radiation having a somewhat broader spectrum.

It should also be understood that the term LED does not limit the physical and/or electrical package type of an LED. For example, as discussed above, an LED may refer to a single light emitting device having multiple dies that are configured to respectively emit different spectra of radiation (e.g., that may or may not be individually controllable). Also, an LED may be associated with a phosphor that is considered as an integral part of the LED (e.g., some types of white LEDs). In general, the term LED may refer to packaged LEDs, non-packaged LEDs, surface mount LEDs, chip-on-board LEDs, T-package mount LEDs, radial package LEDs, power package LEDs, LEDs including some type of encasement and/or optical element (e.g., a diffusing lens), etc. The term“light source” should be understood to refer to any one or more of a variety of radiation sources, including, but not limited to, LED-based sources (including one or more LEDs as defined above), incandescent sources (e.g., filament lamps, halogen lamps), fluorescent sources, phosphorescent sources, high-intensity discharge sources (e.g., sodium vapor, mercury vapor, and metal halide lamps), lasers, other types of electroluminescent sources, pyro-luminescent sources (e.g., flames), candle-luminescent sources (e.g., gas mantles, carbon arc radiation sources), photo-luminescent sources (e.g., gaseous discharge sources), cathode luminescent sources using electronic satiation, galvano-luminescent sources, crystallo- luminescent sources, kine-luminescent sources, thermo-luminescent sources, tribo luminescent sources, sonoluminescent sources, radio luminescent sources, and luminescent polymers.

A given light source may be configured to generate electromagnetic radiation within the visible spectrum, outside the visible spectrum, or a combination of both. Hence, the terms“light” and“radiation” are used interchangeably herein. Additionally, a light source may include as an integral component one or more filters (e.g., color filters), lenses, or other optical components. Also, it should be understood that light sources may be configured for a variety of applications, including, but not limited to, indication, display, and/or illumination. An“illumination source” is a light source that is particularly configured to generate radiation having a sufficient intensity to effectively illuminate an interior or exterior space. In this context,“sufficient intensity” refers to sufficient radiant power in the visible spectrum generated in the space or environment (the unit“lumens” often is employed to represent the total light output from a light source in all directions, in terms of radiant power or“luminous flux”) to provide ambient illumination (i.e., light that may be perceived indirectly and that may be, for example, reflected off of one or more of a variety of intervening surfaces before being perceived in whole or in part).

The term“controller” is used herein generally to describe various apparatus relating to the operation of one or more light sources. A controller can be implemented in numerous ways (e.g., such as with dedicated hardware) to perform various functions discussed herein. A“processor” is one example of a controller, which employs one or more microprocessors that may be programmed using software (e.g., machine code) to perform various functions discussed herein. A controller may be implemented with or without employing a processor, and also may be implemented as a combination of dedicated hardware to perform some functions and a processor (e.g., one or more programmed microprocessors and associated circuitry) to perform other functions. Examples of controller components that may be employed in various embodiments of the present disclosure include, but are not limited to, conventional microprocessors, application specific integrated circuits (ASICs), and field-programmable gate arrays (FPGAs).

In various implementations, a processor or controller may be associated with one or more storage media (generically referred to herein as“memory,” e.g., volatile and non-volatile computer memory such as RAM, PROM, EPROM, and EEPROM, floppy disks, compact disks, optical disks, magnetic tape, etc.). In some implementations, the storage media may be encoded with one or more programs that, when executed on one or more processors and/or controllers, perform at least some of the functions discussed herein.

Various storage media may be fixed within a processor or controller or may be transportable, such that the one or more programs stored thereon can be loaded into a processor or controller so as to implement various aspects of the present invention discussed herein. The terms“program” or“computer program” are used herein in a generic sense to refer to any type of computer code (e.g., software or machine code) that can be employed to program one or more processors or controllers.

The term“addressable” is used herein to refer to a device (e.g., a light source in general, a lighting unit or fixture, a controller or processor associated with one or more light sources or lighting units, other non-lighting related devices, etc.) that is configured to receive information (e.g., data) intended for multiple devices, including itself, and to selectively respond to particular information intended for it. The term“addressable” often is used in connection with a networked environment (or a“network,” discussed further below), in which multiple devices are coupled together via some communications medium or media.

In one network implementation, one or more devices coupled to a network may serve as a controller for one or more other devices coupled to the network (e.g., in a master/slave relationship). In another implementation, a networked environment may include one or more dedicated controllers that are configured to control one or more of the devices coupled to the network. Generally, multiple devices coupled to the network each may have access to data that is present on the communications medium or media; however, a given device may be“addressable” in that it is configured to selectively exchange data with (i.e., receive data from and/or transmit data to) the network, based, for example, on one or more particular identifiers (e.g.,“addresses”) assigned to it.

The term“network” as used herein refers to any interconnection of two or more devices (including controllers or processors) that facilitates the transport of information (e.g., for device control, data storage, data exchange, etc.) between any two or more devices and/or among multiple devices coupled to the network. As should be readily appreciated, various implementations of networks suitable for interconnecting multiple devices may include any of a variety of network topologies and employ any of a variety of communication protocols. Additionally, in various networks according to the present disclosure, any one connection between two devices may represent a dedicated connection between the two systems, or alternatively a non-dedicated connection. In addition to carrying information intended for the two devices, such a non-dedicated connection may carry information not necessarily intended for either of the two devices (e.g., an open network connection).

Furthermore, it should be readily appreciated that various networks of devices as discussed herein may employ one or more wireless, wire/cable, and/or fiber optic links to facilitate information transport throughout the network.

The term“lifetime” as used herein can refer to an amount of time a device is operational within a specification for the device. For example, the lifetime of a sensor can be an amount of time the sensor is able to provide accurate sensor data that meets a

predetermined specification or degree of accuracy. The lifetime of a luminaire (or one or more components thereof) can be an amount of time the luminaire is able to provide controlled illumination according to a specified control or power input to the luminaire. Alternatively, or additionally, a“lifetime” can be an amount of time since a respective apparatus was installed, and an estimated operational lifetime can be an estimated total amount of time the respective apparatus will be operational from the time it was installed.

It should be appreciated that all combinations of the foregoing concepts and additional concepts discussed in greater detail below (provided such concepts are not mutually inconsistent) are contemplated as being part of the inventive subject matter disclosed herein. In particular, all combinations of claimed subject matter appearing at the end of this disclosure are contemplated as being part of the inventive subject matter disclosed herein. It should also be appreciated that terminology explicitly employed herein that also may appear in any disclosure incorporated by reference should be accorded a meaning most consistent with the particular concepts disclosed herein.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings, like reference characters generally refer to the same parts throughout the different views. Also, the drawings are not necessarily to scale, emphasis instead generally being placed upon illustrating the principles of the invention. FIG. 1 illustrates a system for preserving an operational lifetime of computing device components by delaying activation of such computing device components.

FIG. 2 illustrates a plot of a variety of variables that can be collected by a remote computing device discussed herein.

FIG. 3 illustrates a plot of extrapolated data that can be used by a luminaire in lieu of a sensor of the luminaire being temporarily inactive.

FIG. 4 illustrates a plot of different failures rates that correspond to different types of failures that can occur at a network of luminaires.

FIG. 5 illustrates a plot that includes time ranges that can be stored or otherwise accessible to a lighting management system.

FIGS. 6A and 6B illustrate methods for toggling sensor usage according to an estimate of luminaire lifetime that is provided by a parametric model.

FIG. 7 illustrates a method for toggling activity of a sensor of a client device according to whether an operational lifetime of the sensor has reaches or exceeded a threshold associated with an estimated client device operational lifetime.

DETAILED DESCRIPTION

FIG. 1 illustrates a system 100 for preserving an operational lifetime of computing device components by delaying activation of such computing device components. The system 100 can include one or more luminaires 108, which can be installed at numerous different locations in order to provide light for a variety of purposes such as navigating and working. When luminaires 108 are installed along streets 110, their locations can be far from populous areas and/or located relatively high off the ground, making them difficult to maintain. Maintenance of the luminaires 108 can involve scheduling transportation of equipment to such locations, which can be inefficient and costly when luminaires 108 randomly fail or otherwise malfunction. However, the system 100 is capable of delaying operations of particular components of each luminaire 108, such as a first sensor 114, in order preserve an operational lifetime of the first sensor 114 and/or the luminaire 108.

The system 100 can include a network of luminaries 108 that each include one or more sensor modules for tracking data related to environmental conditions and/or operating properties of each luminaire 108. A sensor module can be a hardware and/or software element of a computing device 112 that is part of the luminaire 108. The sensor module can include one or more sensors that can measure temperature, traffic density, humidity, wind speeds, sound, sound pollution, vibration, power grid properties, radiation, air quality, and/or any other metric suitable for use when predicting lifetimes of a device and/or operating a device. Data gathered from the operation of the sensors can be stored in a memory of the computing device 112 of the luminaire 108 and/or transmitted to a remote computing device 116, such as a remote server 102. In some implementations, the sensor module can include a traffic density sensor capable of providing an output corresponding to an estimate of traffic density for a road. The traffic density sensor can be deactivated or activated according to some implementations discussed herein, at least based on an accuracy of the output of the traffic density sensor relative to other traffic services (e.g., a third party news organization that provides traffic monitoring services). When an anomaly (e.g., a traffic jam, a car accident, etc.) is detected by the sensor module, the sensor module can be activated until the anomaly has disappeared, thereby allowing the sensor module to provide traffic density estimates for the luminaire in lieu of the parametric model. Once the anomaly has disappeared and the parametric model can provide more reasonable or accurate results, the sensor module can be deactivated again.

In some implementations, a traffic monitoring service can provide an indication of a traffic anomaly, such as a traffic accident or traffic jam, and in response to receiving the indication, the luminaire can activate the sensor module. Alternatively, or additionally, the indication can be compared with an output of the parametric model so that, when the comparison indicates the parametric model is at least somewhat inaccurate, the luminaire can activate the sensor module to at least temporarily provide more accurate traffic data. Alternatively, or additionally, the traffic monitoring service can provide an indication that a traffic anomaly has occurred, and in response, the luminaire can de-activate the sensor module until a subsequent indication reveals that the traffic anomaly has passed or is otherwise gone. In this way, computational resources for processing outputs of the sensor module can be preserved when traffic may not be especially useful to analyze— such as when all cars on a road are stopped in traffic.

In some implementations, a sensor module can include multiple sensors, and/or a luminaire can be in communication with a different computing device that includes a different sensor module. One or more sensors of the same or different devices can be activated and/or deactivated according to how their combined outputs compare to an output of a remote service or remote computing device. For instance, multiple devices can include sensor modules that can provide a combined output from which air particle measurements can be estimated. When an anomaly of the combined output occurs relative to an output of a comparable air particle measuring service, one or more of the sensors can be deactivated until the anomaly has disappeared.

Each luminaire 108 can communicate over a network connection 106 with one or more remote computing devices, which can be capable of analyzing the data measured by the sensor modules and/or environmental data related to conditions of the luminaires 108.

For example, one or more luminaires 108 can be connected to the remote computing device 116, which can include or be connected to a database that stores data collected by the sensor modules of the luminaires 108. The remote computing device 116 can track data affecting lifetimes of the luminaires 108 in order to estimate an operational lifetime of each luminaire 108 and/or extrapolate data based on a sensor output of each of the first sensor 114 and a second sensor 126. The remote computing device 116 can use the environmental data and operational data of the luminaires 108 to generate such estimates. In some implementations, environmental data can be transmitted by one or more remote servers tasked with collecting environmental data from a variety of sources. These sources can include weather services, which can provide information regarding weather conditions near and far from the luminaires 108. For example, the weather services can provide data related to conditions external to the luminaires 108 such as maximum temperature, mean temperature, minimum temperature, humidity, cloud coverage, percentage of sunshine, precipitation, chances of thunder, chances of lightning, condensation, heat index, vapor pressure, air quality, saltiness of air, wind speed, and/or any other data that can affect an operation of a device.

The remote computing device 116 can calibrate and/or correlate data from the sensor modules of the luminaires 108 and other data received by the remote computing device 116 ( e.g ., data from another other computing device 124 that includes a second sensor 126). By calibrating the data from the sensor modules with data from other sources, a parametric model 118 can be generated at the remote computing device 116. Furthermore, a lifetime prediction engine 120 of the remote computing device 116 can use data from the luminaires 108 and/or other sources (e.g., the other computing device 124) in order to estimate an operational lifetime of each luminaire 108. For example, each sensor module (e.g., which can include the first sensor 114) can include a temperature sensor for tracking a temperature of each luminaire 108. Temperature data from the sensor module can be calibrated with temperature data at the remote computing device 116 to develop the parametric model 118, so that when the first sensor 114 is deactivated, the temperature of the luminaire 108 can be extrapolated and relied upon. The extrapolated data, optionally in combination with environmental data, can then be used as a basis for estimating an operational lifetime of one or more luminaires 108. The first sensor 114 can be activated again in order to recalibrate with the other sources of data ( e.g ., the second sensor 126) and/or operate continuously in response to an operational lifetime of the first sensor 114 reaching or exceeding an estimated operational lifetime of one or more luminaires 108. The parametric model 118 can be based on statistical analysis, machine learning, linear analysis, and/or any other technique for identifying correlations between two or more sets of data.

FIG. 2 illustrates a plot 200 of a variety of variables that can be monitored by a remote computing device discussed herein. Specifically, plot 200 illustrates example data that can correspond to environmental variables monitored by one or more sensors in communication with the remote computing device. For example, plot 200 can include humidity data 204 over a period of time (“t”), temperature data 206 over a period of time, and precipitation data 208 over a period. The values (“ Value”) for this data can have some relationship to sensor data provided by sensors embedded in luminaires that are also experiencing the monitored environmental conditions. For example, as provided in plot 300 of FIG. 3, remote temperature“( Temp.)” data 302 collected by a separate sensor can have some correlation to local temperature data 304 measured by a sensor of a sensor module of a luminaire (e.g., Luminaire 108). The correlation can be identified by a lifetime prediction engine, which can be embodied as software operating at the remote computing device. The correlation can be approximately a one to one correlation in some cases. However, because the generation of local temperature measurements can depend on whether a sensor is on or off, the lifetime prediction engine can identify patterns of when the luminaire is on or off. Therefore, luminaire lifetime predictions can also be based on patterns related to when the luminaire is on or off.

Furthermore, correlations can be based on data taken from a local sensor and multiple remote sensors of different types. The lifetime prediction engine can convert one or more correlations into a parametric model whose input is the data from a sensor and whose output is extrapolated data 306. The extrapolated data 306 can be generated after the parametric model has been created and before an estimated operational lifetime 308 of the luminaire has been reached. The extrapolated data 306 can be used by the luminaire and/or any other device that can rely on the extrapolated data 306 for particular operations. In some embodiments, one or more other variables can be used to identify correlations between sensor data from the luminaire and sensor data from a sensor that is separate from the luminaire.

For example, humidity data 204 can be provided by a remote service, while a sensor of a luminaire also measures humidity. A parametric model corresponding to the humidity data can be derived so that, when the sensor of the luminaire is inactive (prior to the estimated operational lifetime 308), the parametric model can be used thereafter to generate extrapolated data 306 corresponding to humidity.

Data can be collected by a database of the remote computing device, which can relate the remote services data and/or the sensor data to luminaire lifetimes. The data can be analyzed by the lifetime prediction engine to determine trends in luminaire lifetimes compared to the data in the database. For example, the lifetime prediction engine can determine that a luminaire will fail after a threshold number of temperature cycles have occurred at the luminaire. The number of temperature cycles can be tracked using sensor data from the luminaire, and/or from extrapolated sensor data generated before the estimated operational lifetime 308 of the luminaire has been reached or exceeded. Alternatively, the lifetime prediction engine can determine that the luminaire will fail after experiencing a threshold amount of humidity for at least a particular period of time. The humidity experienced by the luminaire can be tracked using the sensor data from the luminaire, and from extrapolated sensor data generated before the estimated operational lifetime 308 of the luminaire has been reached or exceeded. Once the luminaire has experienced the threshold number of temperature cycles and/or the threshold amount of humidity (i.e., the estimated operational lifetime 308 of the luminaire has been reached or exceeded), the remote computing device can provide an indication to the luminaire to activate the sensor of the luminaire for collecting local data from the sensor (e.g., local temperature data 304). In this way, the luminaire would rely on extrapolated data 306 prior to the lifetime of the local sensor (e.g., first sensor) reaching or exceeding the estimated operational lifetime 308, and rely on the data provided by the local sensor (e.g., local temperature data 304) after the lifetime of the local sensor has reached or exceeded the estimated operational lifetime 308.

FIG. 4 illustrates a plot 400 of different failures rates that correspond to different types of failures that can occur at a network of luminaires. In some embodiments, a remote computing device can track the occurrence of different types of failures in order to accurately make predictions about when a luminaire will fail. For example, the plot 400 can include failure rate data corresponding to installation-based failure 404. Installation-based failure can correspond to a time period that includes the installation of a luminaire and/or shortly thereafter. During installation and immediately after installation, a luminaire can be more susceptible to certain types of environmental influences, such as conditions related to a power grid and/or environmental conditions such as lightning strikes. Therefore, a prediction engine of the lighting management system can use power grid data to make estimates about luminaire lifetime during a range of time that includes the initial installation. In other words, the prediction engine can determine how long the luminaire has been in operation and then select the data or function corresponding to the amount of time the luminaire has been installed before making a prediction about the lifetime of the luminaire. In some

implementations, one or more sensors of one or more luminaires, respectively, can be employed to collect data that can be used to determine a probability of a lightning strike. When the determined probability satisfies a threshold, according to some implementations discussed herein, the one or more sensors of the one or more luminaires can be at least temporarily deactivated. The sensors can be deactivated until a parametric model, the also provides a probability of lightning strike, indicates that the probability no longer satisfies the aforementioned threshold. In some implementations, the sensor can be deactivated, or the luminaire can be deactivated using galvanic decoupling via one or more relays, in order to prevent a high voltage or hazardous condition at the luminaire.

The plot 400 can also include a random failure rate 406 and a wear out rate 408. The random failure rate 406 can correspond to failures that occur without any relationship to the operational lifetime of the luminaire, but otherwise contribute to the overall failure rate 402 of the luminaire. The wear out failure rate 408 can correspond to failures that can occur as a result of degradation caused by regular use of the luminaire. The prediction engine can predict lifetimes of luminaires according to the random failure rate 406 and/or wear out failure rate 408. For example, the prediction engine can predict lifetimes for luminaires based at least partially on the random failure rate 406, regardless of the operational lifetimes of the luminaires. Furthermore, the prediction engine can identify data that contributes to wear out failure rate 408 when making predictions about luminaire lifetimes. For example, temperature and humidity can contribute to wear out failure rates 408. Therefore, extrapolated sensor data, corresponding to sensor data acquired after a sensor has failed, can be used as a basis for predicting luminaire lifetimes after installation of the luminaires.

FIG. 5 illustrates a plot 500 that includes time ranges that can be stored or otherwise accessible to a remote computing device. The plot 500 can include a trend line 502 corresponding to a failure rate for one or more luminaires that are in communication with the remote computing device. The trend line 502 can be based on data collected about the lifetime of similar luminaires. The remote computing device can use the data to identify time ranges corresponding to certain types of luminaire failures. For example, time range 504 can correspond to a time when installation-based failure rate can be most prominent. During this time, the prediction engine of the remote computing device can use data related to conditions that most influence installation-based failure to predict how long a luminaire will be operational. Furthermore, time range 506 can correspond to a time period after installation and before the end of a normal lifetime for a luminaire when the luminaire can succumb to random failure. Time range 508 can correspond to a time period that includes the end of a normal lifetime for a luminaire when the luminaire can fail as a result of wear out. During time range 506 or time range 508, a prediction engine of the remote computing device can select data that corresponds to environmental data that can influence random failure or wear out, respectively. The selected environmental data can then be used to make predictions about how long the luminaire will remain operational.

FIGS. 6A and 6B illustrate methods 600 and 610 for toggling sensor usage according to an estimate of luminaire lifetime that is provided by a parametric model. The method 600 can continue to method 610 according to continuation element“A,” provided in FIGS. 6 A and 6B. The methods 600 and 610 can be performed by one or more computing devices, applications, and/or any other apparatus or module capable of interacting with a lighting device. The method 600 can include an operation 602 of receiving first data that is based on a sensor output of a first sensor that is connected to a device. The first sensor can be, but is not limited to, any sensor that can be responsive to an environmental condition or property of one or more apparatuses. For instance, the first sensor can be, but is not limited to, a temperature sensor, an audio sensor, a humidity sensor, a GPS transmitter, an infrared sensor, a barometer, a current sensor, an ohmmeter, a voltage sensor, a moisture sensor, and/or any other device that can provide a sensor output. The device can be any computing device, such as a luminaire, and can be connected to multiple other devices directly and/or over a network. The first data can be generated by one or more processors that are part of the device, or a separate remote device, in response to receiving the sensor output.

The method 600 can further include an operation 604 of receiving second data that is based on another sensor output of a second sensor that is remotely located relative to the device. The second sensor can be integrated with, or remotely located from, the device e.g ., the luminaire). For instance, the second sensor can be a distance from the device and be responsive to the same and/or different properties or conditions that the first sensor is responsive to. For example, the first sensor can be responsive to changes in pressure and the second sensor can be responsive to changes in temperature. Alternatively, or additionally, the first sensor and the second sensor can be responsive to the same conditions or properties such as temperature. The second data can be generated and/or received by a remote device that is integrated with or separate from the second sensor. For instance, the remote device can be a network server that is in communication with the first sensor and/or the second sensor.

The method 600 can also include an operation 606 of determining a correlation between the first data and the second data to build a model. The model can be a parametric model that is generated using historical sensor outputs from the first sensor and the second sensor. For instance, in order to generate the parametric model, a correlation between a first sensor output of the first sensor and a second sensor output of the second sensor can be identified. The correlation can be used to predict values for the first sensor output when the first sensor output is not available ( e.g ., the first sensor has been

deactivated). In this way, because a sensor may not have an operational lifetime as long as a luminaire, the operational lifetime of the first sensor can be preserved by deactivating the first sensor and relying on the other sensor output from the second sensor. The first sensor can be temporarily and/or periodically deactivated as long as a total time that the first sensor has been installed and/or operational is less than or equal to an estimated operational lifetime of the luminaire.

The method 600 can optionally include an operation 608 of determining an estimated lifetime for the luminaire based at least on the sensor output from the second sensor. The estimate for luminaire lifetime can be based on one or more properties of the luminaire and/or environmental conditions of the luminaire. For instance, the estimated lifetime for a luminaire can be based on data generated based on operations of one or more luminaires that are connected to a common network with the luminaire. Alternatively, or additionally, the estimate for luminaire lifetime can be a static or dynamic value that is generated based on one or more functions having one or more different inputs. For example, an estimate for luminaire lifetime can be based on operating specifications of the luminaire, such as, but not limited to, average power consumed by the luminaire per time period (e.g., per day or month). The estimate for luminaire lifetime can then be used as a basis from which to activate or deactivate the first sensor of the luminaire.

The method 600 can proceed to method 610, as indicated by continuation element“A,” illustrates at both FIGS. 6A and 8B. The method 610 can include an operation 612 of determining whether an operational-related event has occurred or is predicted to occur. For instance, an operational-related event can include a sensor operational lifetime reaching or exceeding the estimated luminaire lifetime. The sensor operational lifetime can be a period of time from a time at which the luminaire or the first sensor was installed to the present time. When the sensor operational lifetime reaches or exceeds the estimated luminaire lifetime ( e.g ., is greater than or equal the estimated luminaire lifetime), the method 610 can proceed to operation 614 of causing the device (e.g., the luminaire) to activate the first sensor for continual operation during a remaining device operational lifetime. In this way, operability of the first sensor can be preserved during the estimated lifetime of the luminaire, so that the first sensor can be more reliably operated after the estimated lifetime of the luminaire. Alternatively, or additionally, the operational-related event can include when an output of the parametric model exhibiting an anomaly. For instance, an anomaly exhibited by the parametric model can include the output of the parametric model being different from an output of a sensor (e.g., a sensor of a luminaire, and/or a remote sensor relative to a luminaire) by a threshold amount (e.g., an N percent difference, where any is any positive number). Alternatively, or additionally, the operational-related event can be a predicted event related to a condition that can affect a functionality of the client device (e.g., a luminaire), such as a lightning strike, power surge, and/or any other event that could be harmful to the client device. The operational-related event can be predicted or identified based on an output of the parametric model, thereby allowing the client device and/or a remote computing device to determine whether the operational-related event has or will occur.

In some implementations, when the sensor operational lifetime does not satisfy the threshold, the method 610 can proceed to operation 616 of causing the device to temporarily limit the first sensor from at least providing the sensor output, and/or causing the device or the first sensor to at least temporarily power off. In this way, the device does not necessarily have to rely on the first sensor for the sensor output, but, rather, can rely on the parametric model to provide data that would otherwise be provided based on the first sensor. Furthermore, the method 610 can proceed to operation 618 of causing the device to operate according to an output of the parametric model in lieu of the sensor output from the first sensor. The output of the parametric model can characterize and/or simulate an output of the first sensor in lieu first sensor being at least temporarily inactive. Alternatively, or optionally, the operation 618 can be causing the other sensor output to be provided to the remote computing device, in order that the remote computing device can use the second sensor output in lieu of the first sensor output.

FIG. 7 illustrates a method 700 for toggling activity of a sensor of a client device according to whether an operational lifetime of the sensor has reaches or exceeded a threshold associated with an estimated client device operational lifetime. The method 700 can be performed by one or more computing devices, applications, and/or any other apparatus or module capable of interacting with an electronic device. The method 700 can include an operation 702 of providing, by a client device that includes a first sensor, first sensor data to a remote computing device that is configured to determine a correlation between the first sensor data and second sensor data. The second sensor data can be provided by a second sensor that is separate from the client device and/or is in communication with the remote computing device. For instance, the client device can be a luminaire and both the first sensor and the second sensor can be environmental sensors, such as temperature sensors. The luminaire can share the data from the first sensor with other luminaires and/or network devices, and/or use the data from the first sensor to make operational decisions, such as how much power to consume at a particular time.

The method 700 can further include an operation 704 of receiving responsive data from the remote computing device. The responsive data can be generated based on a comparison between the first sensor data and the second sensor data. For instance, the responsive data can be sent by the remote computing device in response to the first sensor data and the second sensor data having a determined difference that reaches or exceeds a particular threshold (e.g., greater than or equal to a 5% difference, or any other difference value). Alternatively, or additionally, when the sensors measure different environmental conditions, the responsive data can be provided using one or more functions that provide an indication of a difference between the first sensor data and the second sensor data. The one or more functions can be determined from one or more models that are trained according to data associated with the first sensor and/or the second sensor.

The method 700 can also include an operation 706 of causing, in response to receiving the responsive data, the first sensor to be temporarily inactive during an operational lifetime of the client device. The first sensor can be temporarily deactivated in order to preserve operability of the first sensor by limiting the operations of the first sensor to calibrations with the second sensor, and/or operating after a time, since the first sensor and/or the client device was installed, reaches or exceeds an estimated operational lifetime of the client device. In this way, the client device can rely on extrapolated second sensor data in lieu of the first sensor being deactivated.

The method 700 can further include an operation 708 of receiving additional sensor data that is based on an output of a parametric model in lieu of any further data being provided by the first sensor. In other words, the client device can use the additional data from the parametric model, at least in order to adjust operations according to an output of the parametric model, which can receive sensor output from the second sensor as an input to the parametric model. In this way, because sensors may often malfunction or otherwise become unreliable before their counterpart computing devices, the client device will be able to operate using extrapolated data without risking reducing an operational lifetime of the first sensor.

The method 700 can also include an operation 710 of determining whether an operational-related event has or will occur with respect to the client device. For instance, the operational-related event can be determining an output of the parametric model exhibits an anomaly that causes the output of the parametric model to be outside of a threshold (e.g., 5%) of accuracy with respect to a separate sensor. Alternatively, the operational-related event can be determining an operational lifetime of the first sensor has reached or exceeded an estimated operational lifetime of the client device. The operation 710 can be performed periodically as long as the determination is“NO,” the determination has not occurred, or the operational lifetime of the first sensor has not reached or exceeded the estimated operational lifetime of the client device. When the operational lifetime of the first sensor has reached or exceeded the estimated operational lifetime of the client device, the method 710 can proceed to operation 712. It should be noted that a value for the estimated operational lifetime of the client device can be generated by the remote computing device and/or any other device capable of determining estimates for device lifetime. The operational lifetime for a client device and/or a sensor can be a time period, starting at installation and/or manufacture, and ending at the first failed operation by the client device and/or sensor or the first complete failure of the client device and/or sensor.

The method 700 can further include an operation 712 of causing the client device to activate the first sensor and operate according to further data provided by the first sensor. Previously, before operation 712, the client device would have been operating based on data from the second sensor. However, when the operational lifetime of the first sensor reaches or exceeds the estimated operational lifetime of the client device, the client device can switch from relying on the second sensor to relying on the first sensor for sensor data. This allows the client device to rely on a barely used first sensor at a time when the client device may be most volatile (e.g., after the estimated operational lifetime has passed).

While several inventive embodiments have been described and illustrated herein, those of ordinary skill in the art will readily envision a variety of other means and/or structures for performing the function and/or obtaining the results and/or one or more of the advantages described herein, and each of such variations and/or modifications is deemed to be within the scope of the inventive embodiments described herein. More generally, those skilled in the art will readily appreciate that all parameters, dimensions, materials, and configurations described herein are meant to be exemplary and that the actual parameters, dimensions, materials, and/or configurations will depend upon the specific application or applications for which the inventive teachings is/are used. Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the specific inventive embodiments described herein. It is, therefore, to be understood that the foregoing embodiments are presented by way of example only and that, within the scope of the appended claims and equivalents thereto, inventive embodiments may be practiced otherwise than as specifically described and claimed. Inventive embodiments of the present disclosure are directed to each individual feature, system, article, material, kit, and/or method described herein. In addition, any combination of two or more such features, systems, articles, materials, kits, and/or methods, if such features, systems, articles, materials, kits, and/or methods are not mutually inconsistent, is included within the inventive scope of the present disclosure.

All definitions, as defined and used herein, should be understood to control over dictionary definitions, definitions in documents incorporated by reference, and/or ordinary meanings of the defined terms.

The indefinite articles“a” and“an,” as used herein in the specification and in the claims, unless clearly indicated to the contrary, should be understood to mean“at least one.”

The phrase“and/or,” as used herein in the specification and in the claims, should be understood to mean“either or both” of the elements so conjoined, i.e., elements that are conjunctively present in some cases and disjunctively present in other cases.

Multiple elements listed with“and/or” should be construed in the same fashion, i.e.,“one or more” of the elements so conjoined. Other elements may optionally be present other than the elements specifically identified by the“and/or” clause, whether related or unrelated to those elements specifically identified. Thus, as a non-limiting example, a reference to“A and/or B”, when used in conjunction with open-ended language such as“comprising” can refer, in one embodiment, to A only (optionally including elements other than B); in another embodiment, to B only (optionally including elements other than A); in yet another embodiment, to both A and B (optionally including other elements); etc.

As used herein in the specification and in the claims,“or” should be understood to have the same meaning as“and/or” as defined above. For example, when separating items in a list,“or” or“and/or” shall be interpreted as being inclusive, i.e., the inclusion of at least one, but also including more than one, of a number or list of elements, and, optionally, additional unlisted items. Only terms clearly indicated to the contrary, such as“only one of’ or“exactly one of,” or, when used in the claims,“consisting of,” will refer to the inclusion of exactly one element of a number or list of elements. In general, the term “or” as used herein shall only be interpreted as indicating exclusive alternatives (i.e.“one or the other but not both”) when preceded by terms of exclusivity, such as“either,”“one of,” “only one of,” or“exactly one of.”“Consisting essentially of,” when used in the claims, shall have its ordinary meaning as used in the field of patent law.

As used herein in the specification and in the claims, the phrase“at least one,” in reference to a list of one or more elements, should be understood to mean at least one element selected from any one or more of the elements in the list of elements, but not necessarily including at least one of each and every element specifically listed within the list of elements and not excluding any combinations of elements in the list of elements. This definition also allows that elements may optionally be present other than the elements specifically identified within the list of elements to which the phrase“at least one” refers, whether related or unrelated to those elements specifically identified. Thus, as a non-limiting example,“at least one of A and B” (or, equivalently,“at least one of A or B,” or, equivalently “at least one of A and/or B”) can refer, in one embodiment, to at least one, optionally including more than one, A, with no B present (and optionally including elements other than B); in another embodiment, to at least one, optionally including more than one, B, with no A present (and optionally including elements other than A); in yet another embodiment, to at least one, optionally including more than one, A, and at least one, optionally including more than one, B (and optionally including other elements); etc.

It should also be understood that, unless clearly indicated to the contrary, in any methods claimed herein that include more than one step or act, the order of the steps or acts of the method is not necessarily limited to the order in which the steps or acts of the method are recited.

In the claims, as well as in the specification above, all transitional phrases such as“comprising,”“including,”“carrying,”“having,”“containing,”“involving,”“holding,” “composed of,” and the like are to be understood to be open-ended, i.e., to mean including but not limited to. Only the transitional phrases“consisting of’ and“consisting essentially of’ shall be closed or semi-closed transitional phrases, respectively, as set forth in the United States Patent Office Manual of Patent Examining Procedures, Section 2111.03. It should be understood that certain expressions and reference signs used in the claims pursuant to Rule 6.2(b) of the Patent Cooperation Treaty (“PCT”) do not limit the scope

Claims

CLAIMS:

1. A method implemented by one or more processors, the method comprising:

receiving first data that is based on a first sensor output of a first sensor (114) that is connected to a client device (108), wherein the first sensor is at least temporarily activated during an estimated client device operational lifetime in order to provide the first sensor output;

receiving second data that is based on a second sensor output of a second sensor (126), wherein the second sensor is remotely located relative to the client device;

determining a correlation between the first data and the second data in order to build a model (118) based on the correlation, wherein the model is configured to simulate the first sensor output based at least in part on the second sensor output;

causing the client device to at least temporarily deactivate the first sensor, and operate according to the model in lieu of the first data that was based on the first sensor output of the first sensor; and

when an operational-related event associated with the client device has or is predicted to occur:

causing the client device to activate the first sensor for at least temporary operation during a remaining client device operational lifetime,

wherein the client device is a luminaire and the method further comprises: determining an operational lifetime estimate (308) for the client device based on at least the second sensor output of the second sensor, wherein the operational-related event corresponds to a sensor operational lifetime of the first sensor reaching or exceeding the operational lifetime estimate for the client device.

2. The method of claim 1, wherein the first data is received by a computing device (116) that is remote from the client device and the model is a parametric model, and the method further comprises:

generating, based on the first sensor output and the second sensor output, the parametric model that characterizes a correlation between the first data and the second data, wherein the parametric model provides a basis from which to generate the operational lifetime estimate of the client device.

3. The method of claim 1, further comprising:

when the sensor operational lifetime has not reached or exceeded the operational lifetime estimate for the client device:

causing the client device to temporarily limit the first sensor from at least providing the first sensor output, and

causing the client device to operate based on an output of the parametric model in lieu of the first sensor output.

4. The method of claim 2, further comprising:

causing the client device to temporarily activate the first sensor, determining that a recalibration of the second sensor should be performed, at least based on a comparison between additional first sensor output and additional second sensor output, and

recalibrating the model according to the additional first sensor output from the first sensor and the additional second sensor output from the second sensor.

5. A non-transitory computer readable storage medium configured to store instructions that, when executed by one or more processors, cause the one or more processors perform operations that include:

wherein the client device is a luminaire and the operations further include: determining an operational lifetime estimate (308) for the client device based on at least the second sensor output of the second sensor, wherein the operational-related event corresponds to a sensor operational lifetime of the first sensor reaching or exceeding the operational lifetime estimate for the client device.

6. The non-transitory computer readable storage medium of claim 5, wherein the first data is received by a computing device (116) that is remote from the client device and the model is a parametric model, and the method further comprises:

7. The non-transitory computer readable storage medium of claim 5, wherein the operations further include:

8. The non-transitory computer readable storage medium of claim 6, wherein the operations further include:

causing the client device to temporarily activate the first sensor, determining that a recalibration of the second sensor should be performed, at least based on a comparison between additional first sensor output and additional second sensor output,

9. 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:

10. The system of claim 9, wherein the first data is received by a computing device (116) that is remote from the client device and the model is a parametric model, and the method further comprises:

11. The system of claim 9, wherein the operations further include:

12. The system of claim 10, wherein the operations further include: