Patent Publication Number: US-2023142829-A1

Title: Autonomous room boundary detection and classification with low resolution sensors

Description:
BACKGROUND 
     Buildings typically have rooms which may be used for varying purposes. For example, some rooms may be used as a general meeting room where several individuals may congregate to facilitate communication, such as for a meeting. As another example, some rooms may be used as a private office which may be assigned to one individual at a time, where the individual may have privacy to improve concentration. Other types of rooms may include break rooms, lunch rooms, washrooms, libraries, mechanical rooms, etc. Accordingly, rooms may have a variety of sizes and shapes and are typically separated by a boundary, such as a wall or partition. The boundaries generate a floorplan or an internal map of the building. In addition, the boundaries may be changed and rooms may be altered, such as during a renovation. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Reference will now be made, by way of example only, to the accompanying drawings in which: 
         FIG.  1    is a schematic representation of the components of an apparatus to locate and classify a room boundary; 
         FIG.  2    is a schematic representation of the components of a lighting controller to identify and control a plurality of lighting devices; 
         FIG.  3    is a schematic representation of a room where a system of a plurality of lighting devices and a lighting controller are deployed; 
         FIG.  4    is a flowchart of an example of a method of locating and classifying a wall; 
         FIG.  5    is a schematic representation of a floor plan with deployed lighting devices and lighting controllers; 
         FIG.  6    is a schematic representation of the components of another apparatus to locate and classify a room boundary; and 
         FIG.  7    is a schematic representation of the components of another lighting controller to identify and control a plurality of lighting devices. 
     
    
    
     DETAILED DESCRIPTION 
     Smart lighting technology for commercial buildings offers a myriad of energy conservation, facility management and personalization capabilities. For example, smart lighting may allow lights to be grouped in a flexible manner and for the light level of each group to be automatically adjusted based on input from various sources such as motion sensors, daylight sensors, and a variety of user devices. Although automatic adjustment of lighting levels may be suitable for most of the time, lighting levels may be adjusted by users with a controller, such as wall-mounted switch or interface, to personalize light level within a room in some instances. The controller may have one or more buttons, each of which is assigned to a particular group of lights. In other examples, the controller may have a programmable graphical user interface with virtual buttons on a touch screen. 
     In some examples, a smart lighting system topology may include one or more sensors mounted on each unit and a controller. Each sensor may be assigned to a group, which may be associated to a button or control interface of the controller. The setup of the units and controller is typically done manually by mapping each unit for the controller. Accordingly, the deployment and configuration of a smart lighting system in a commercial building may be an arduous process that presents challenges. For example, a building may contain thousands of sensors and controllers that are to be networked together and configured to operate in a manner based on user preferences and local lighting codes. This process may be highly prescriptive and involve a design phase, a programming and verification phase and a maintenance phase. Each phase may be performed by different parties and involve several iterations that may take months to complete for large installations. The design phase may be to consider constraints such as the maximum communication range between devices and the maximum number of devices per communication channel. The design phase may also produce an illustration of the group configuration on a lighting plan that shows various groupings of units to be controlled by a controller. The programming and verification phase may be performed by trained technical personnel typically at the location of the installation and may involve implementing the group configuration by installing wiring and switches to the communication channel or by manually assigning the units to a common network address. Operating parameters for each unit, wall switch and additional associated control system hardware and software are set during this phase. The building manager is responsible for maintaining the integrity of the control system topology and all settings as units may be added, removed or relocated post deployment. 
     A system including a network of apparatus and a lighting controller that self-organize into logical group configurations is provided. It is to be appreciates by a person of skill in the art that the apparatus, method, and system describe may reduce or eliminate the design process, the programming and verification process, and/or the maintenance process involved with smart lighting systems. In particular, the system is autonomous such that upon “power-up”, the system may self-organize without any user intervention. In the present example, the system may also be decentralized and autonomous, such that there is no host controller, external software agent or mobile device to start, monitor or end the process. Accordingly, the deployment and configuration process may be based exclusively on contextual awareness between the apparatus and the lighting controller via the detection of room boundaries, the physical arrangement of the apparatus and the lighting controller and sensory data collected, such as motion patterns and daylight distributions. Furthermore, the system may automatically detects and adapts to changes to room boundaries, such as the position of a movable wall, objects being added, removed or relocated, and reconfiguration of room boundaries, such as from a renovation of the space. The apparatus may classify room boundaries as one of opaque walls, interior transparent or translucent walls, exterior windows and doorways. 
     In some examples, each apparatus and lighting controller may divide themselves into groups. The groups are not particularly limited and may be based on room boundaries that may be dynamically updated when a space is re-configured. Furthermore, since the system may be decentralized in some examples, each device in the system may not be in direct communication with all other devices during operation. Instead, each apparatus or lighting controller may be in communication with proximate apparatus or lighting controllers. Therefore, the system may be scaled to a large number of apparatus and a lighting controllers with reduced latency and increased reliability. 
     Referring to  FIG.  1   , a schematic representation of an apparatus to locate and classify a room boundary, such as a wall, is generally shown at  50 . The apparatus  50  may include additional components, such as various additional interfaces and/or input/output devices such as indicators to interact with a user of the apparatus  50 . The interactions may include viewing the operational status, updating parameters, or resetting the apparatus  50 . In the present example, the apparatus  50  is to collect data based on actively generated signals to locate a room boundary and to classify the room boundary. In the present example, the apparatus  50  includes a light source  55 , a light source controller  60 , a low resolution sensor  65 , a memory storage unit  70 , and an image processing engine  75 . 
     The light source  55  is to emit light. In the present example, the light source  55  is to emit light that is in the infrared spectrum. The light may be monochromatic, or emit a band of light with a peak wavelength in the infrared spectrum. For example, the light source  55  may emit light having a peak wavelength greater than about 780 nm to be beyond the typical visual range of a human eye. In some examples, the peak wavelength may be about 850 nm. The light source  55  is not particularly limited and may be any device capable of generating light that may be reflected off a surface, such as a room boundary, and detected by the low resolution sensor  65 . For example, the light source  55  may be an incandescent light bulb, a fluorescent light bulb, a laser, or a light emitting diode. The area onto which the light source  55  projects is not particularly limited. In the present example, the light source  55  may project a uniform intensity across the field of view of the low resolution sensor  65 . In other examples, the light source  55  may direct wider or narrow light, or the illumination may not be uniform across substantially all of the field of view. 
     In the present example, the light source controller  60  is to control the light source  55 . In particular, the light source controller  60  may provide power to the light source  55  or turn off the light source  55  by cutting off power. Furthermore, the light source controller  60  further controls the intensity of the light source  55 . For example, the light source controller  60  may vary the intensity of the light source  55  to adjust the illumination level to achieve different effects in the reflected light that may be subsequently processed. 
     The low resolution sensor  65  is to measure light data from a reflection off a room boundary, such as a wall. In particular, the low resolution sensor  65  may be used to specifically measure the reflected light from the light source  55 . In the present example, the low resolution sensor  65  may be a two-dimensional image sensor is capable of capturing images in the infrared or near infrared spectrum. For example, the low resolution sensor  65  may also be capable of capturing images in part of or all of the visible spectrum. In other examples, the low resolution sensor  65  may be used to detect light having a wavelength of about 850 nm with pixels having a high quantum efficiency in the 850 nm spectrum. In other examples, the low resolution sensor  65  may also be capable of capturing images in part of or all of the visible spectrum. In some examples, a lens may be used to provide a wide coverage area to increase a field of view to detect motion patterns and objects. The low resolution sensor  65  has a resolution sufficiently low such that the light data captured is cannot be used to distinguish or identify people. However, the low resolution sensor  65  may be able to detect the presence of walls, windows, and doorways. In addition, movement patterns of objects and people within the field of view may also be measured. The number of pixels in each low resolution sensor  65  is not particularly limited. For example, each low resolution sensor  65  may have about 4 pixels to cover a field of view of about 20 m. In other examples, the low resolution sensor  65  may have more or fewer pixels to improve detection of objects, but not to provide capability to distinguish facial features of a person. 
     The memory storage unit  70  is to store the light data measured by the low resolution sensor  65 . In addition, the memory storage unit  70  is to store the corresponding control data provided by the light source controller  60  as the low resolution sensor  65  measures the light data. For example, the memory storage unit  70  may store the light data and the control data together in a single database as a function of time. Accordingly, as the intensity of the light source  55  is varied by the light source controller  60 , the low resolution sensor  65  is used to detect a change in the light data due to the reflected light. In the present example, the memory storage unit  70  may be in communication with the light source controller  60  and the low resolution sensor  65  where they each may include processing capabilities to read and write to the memory storage unit  70  directly. In other examples, a separate processor (not shown) may be used to control the light source controller  60  and the low resolution sensor  65  and act as in intermediary for communications between each of the light source controller  60  and the low resolution sensor  65  and the memory storage unit  70 . 
     The memory storage unit  70  may be also used to store addition data to be used by the apparatus  50 . For example, the memory storage unit  70  may store motion data as well as ambient light data as discussed in greater detail below. Furthermore, the memory storage unit  70  may be used to store mapping data as well as information from adjacent or proximate devices. 
     In the present example, the memory storage unit  70  may include a non-transitory machine-readable storage medium that may be any electronic, magnetic, optical, or other physical storage device. In other examples, the memory storage unit  70  may be an external unit such as an external hard drive, or a cloud service providing content. The memory storage unit  70  may also be used to store instructions for general operation of the apparatus  50 . In particular, the memory storage unit  70  may store an operating system that is executable by a processor to provide general functionality to the apparatus  50 , for example, functionality to support various applications. The memory storage unit  70  may additionally store instructions to operate the image processing engine  75 . Furthermore, the memory storage unit  70  may also store control instructions to operate other components and peripheral devices of the apparatus  50 , such additional sensors, cameras, user interfaces, and light sources. 
     The image processing engine  75  is to locate and classify a room boundary, such as a wall, based on the light data and the control data stored in the memory storage unit  70 . In contrast to a high resolution image sensor which may be used to easily locate room boundaries, such as walls, and to classify the wall type into various types such as opaque walls, transparent walls, translucent walls, exterior windows, and doorways with image processing algorithms, the low resolution sensor  65  is not capable of making such determinations based solely on the light data measured by the low resolution sensor  65 . In the present example, the light data is combined with the control data which records changes in the illumination level from the light source  55 . The image processing engine  75  may use the combined data to locate and classify room boundaries based on the reflections, intensity distributions and other features. In some examples, the intensity distribution may be dependent on the intensity of the light emitted by the light source  55  such that the dependence is uniquely associated with a specific type of wall or room boundary. Therefore, the image processing engine  75  may use machine learning techniques, such as a trained classification model to perform accurate locating of a room boundary as well as classify the room boundary as a type of wall, such as an opaque wall, a transparent wall, a translucent wall, an exterior wall, a windowed wall or a wall with a doorway. It is to be appreciated that these types of walls are not particularly limited and may be defined such that the types of walls are mutually exclusive. 
     In the present example, the image processing engine  75  may assign a confidence value to the classification. The confidence value may be associated with the accuracy of the classification and may be calculated using metrics such as an F-score. 
     The manner by which the image processing engine  75  carries out the locating and classification functions is not limited. In the present example, the light data measured by the low resolution sensor  65  may be stored in the memory storage unit as a primary dataset. The primary dataset may be combined with a supplementary data set containing a different type of data than the primary dataset to improve the accuracy of classification when analysed in combination with the primary dataset. The supplementary data type is not limited and may be spatial, temporal or both. In some examples, the supplementary data may include current or historic ambient light readings as a function of time. In other examples, the supplementary data may include current or historic motion patterns, such as a detected motion detected from a specific direction. 
     The supplementary dataset may be collected by the low resolution sensor  65 . In other examples, the supplementary dataset may be collected by other sensors, such as a separate daylight sensor or motion sensor. The supplementary data may be combined with the primary dataset using various fusion techniques that involve various weighting factors to increase the accuracy of the combined dataset. 
     Referring to  FIG.  2   , a schematic representation of a lighting controller to identify and control a plurality of lighting devices is generally shown at  100 . The lighting controller  100  may include additional components, such as various additional interfaces and/or input/output devices such as indicators to interact with a user of the lighting controller  100 . The interactions may include viewing the operational status on a touchscreen device (not shown). In the present example, the lighting controller  100  is to collect data based on actively generated signals to locate a room boundary and to group a plurality of lighting devices. In the present example, the lighting controller  100  includes a light source  105 , a light source controller  110 , a low resolution sensor  115 , a memory storage unit  120 , an image processing engine  125 , and a communications interface  130 . 
     In the present example, the lighting controller  100  may locate and classify a room boundary in a similar manner as the apparatus  50 . For example, the lighting controller  100  may use the light source  105 , light source controller  110 , low resolution sensor  115 , memory storage unit  120 , and image processing engine  125  in a similar manner to the light source  55 , light source controller  60 , low resolution sensor  65 , memory storage unit  70 , and image processing engine  75 . In some examples, the light source  105  and low resolution sensor  115  may be capable of locating and classifying walls at a greater range than the corresponding components in the apparatus  50 . 
     The communications interface  130  is to transmit a control signal to a plurality of lighting devices, which may each include an apparatus  50 . In the present example, each lighting device of the plurality of lighting devices is to be bounded by a room boundary, such as a wall. The determination of which lighting device is to be included in the plurality of lighting devices is not particularly limited. For example, the memory storage unit  120  may include a mapping of the room boundaries as determined by the image processing engine  125 . 
     In the present example, the communications interface  130  may communicate with lighting devices over a network, which may be a public network shared with a large number of connected devices, such as a WiFi network or cellular network. The connection with external devices may involve sending and receiving electrical signals via a wired connection with other external devices or a central server. Since the lighting controller and lighting devices are typically mounted at a stationary location on a wall, using a wired connection between the lighting controller and the external device may provide a robust connection. In other examples, the communications interface  130  may connect to external devices wirelessly to simply the setup procedure since the process may not involve placing wires in the walls. For example, the communications interface  130  may be a wireless interface to transmit and receive wireless signals directly to each external device via a Bluetooth connection, radio signals or infrared signals and subsequently relayed to additional devices. 
     In other examples, the mapping of the room boundaries may be received from an external device, such as a lighting device with an apparatus  50  to locate and classify room boundaries via the communications interface  130 . The mapping data may also include an identifier to indicate from which lighting device the mapping data is received. Accordingly, the lighting controller  100  may receive data from multiple lighting devices within the room boundary, or wall. In some examples, the lighting controller  100  may receive identifiers to indicate which lighting device with an apparatus  50  has identified itself to be within the same room as the lighting controller  100  such that the lighting devices may be grouped together. In further examples, mapping data received via the communications interface  130  may be compared with internally generated mapping data to validate the mapping data to determine which lighting devices are within a room boundary. 
     The control signals transmitted from via the communications interface  130  is not particularly limited. For example, the control signals may control all of the lighting devices within a room to adjust light level and to operate under various rules, user inputs, and energy conservation settings. In other examples, the lighting controller  100  may control a subset of the lighting devices within a room such that groups of lights may be controlled in unison. The manner by which the lighting devices are divided into subsets of lighting device is not limited. In some examples, the lighting devices may autonomously divide among themselves and assign generated an identifier to be received by the lighting controller  100 . In other examples, the lighting controller  100  may divided the lighting devices based on type, which may be identified with an identifier. 
     For example, the area spanned by a plurality of the lighting devices controlled by the lighting controller may have an upper limit due to hardware limitations, or by design which may be to meet building codes or satisfy installation specifications. Accordingly, some lighting devices co-located in the same room may be divided into a separate group based on this area limitation. In this example, the division of lighting devices into subsets of a plurality may represent a logical choice of lighting devices based on the mapping data as determined by each apparatus  50  or lighting controller  100 . For example, the lighting devices may be divided such that the lighting devices form a regular shaped area or two or more contiguous regular shape areas. In other examples, the total power consumed by the lighting devices within an area may be calculated to determine a lighting power density of the area. The lighting power density may then be used as an additional or alternative metric to limit the number of lighting devices controlled by a lighting controller  100 . 
     Accordingly, the lighting devices co-located in a room and that do not exceed an area limit may be organized into a plurality of lighting devices. The lighting devices that belong to a given group may form a continuous and uniform arrangement to capture the intent of an architectural design. In some examples, the relative distance between lighting devices may be used in whole or in part to determine the groupings. For example, a room with lighting devices that are located at a distance of about one meter or about four meters apart may group lighting devices separated by about one meter into group. In other examples, this grouping may be further subdivided such that lighting devices in a row are grouped together. In further examples, a concentric arrangements of lighting devices may be grouped. 
     Referring to  FIG.  3   , a room with a plurality of lighting devices  150 - 1  and  150 - 2  (generically, these lighting devices are referred to herein as “lighting device  150 ” and collectively they are referred to as “lighting devices  150 ”, this nomenclature is used elsewhere in this description) deployed in operation is shown. In the present example, each of the lighting devices  150  are substantially identical units and operate together with the lighting controller  100  as a system that may be autonomously grouped or associated with each other upon placing each of the lighting devices  150  and the lighting controller  100  without wiring or additional configuration by an installer. In the present example, each of the lighting devices  150  includes an apparatus  50  to locate and classify room boundaries such as the opaque wall  200 , doorway wall  205 , transparent wall  210  and exterior window wall  215 . 
     In the present example, the lighting devices  150  are to locate the positions at which they are disposed within the room. The manner by which the lighting devices locate their respective positions is not particularly limited. For example, each lighting device  150  may have an apparatus  50  to locate and classify room boundaries. The located and classified room boundaries, such as walls, may then be used to generate a floor plan using a mapping engine. In addition, the lighting devices  150  may be detect stationary objects within the room. It is to be appreciated that the range of the apparatus  50  on each lighting device  150  may not be able to locate and classify all the room boundaries of the room in some examples. For example, the lighting device  150 - 1  may be able to locate a portion of the opaque wall  200 , the exterior window wall  215 , and a portion of the transparent wall  210  and the lighting device  150 - 2  may be able to locate another portion of the opaque wall  200 , the doorway wall  205 , and another portion of the transparent wall  210 . 
     In some examples, each lighting device may have multiple defined regions of interest within its field of view. For example, the lighting device  150 - 1  may have nine defined regions of interest arranged in a 3×3 grid  152  as shown in  FIG.  3   . The number of regions of interest is not limited and may be selected based consideration of factors such as the coverage area, processing power, classification accuracy, and data privacy. In this example, the lighting device  150 - 1  may assign a classification of the room boundary to each region in the grid  152 . The classification assigned to a given region of interest may not match the category assigned to another region of the grid  152 . For example, some regions in the grid  152  corresponding to the opaque wall  200  may classify to room boundary as such. Similarly, regions in the grid  152  corresponding to the exterior window wall  215  and a portion of the transparent wall  210  may be classified. 
     In further examples, it is to be appreciated that the lighting devices  150  may use supplementary data such as directional motion patterns and/or ambient light measurements as a function of time. In particular, the supplementary may be used to locate and/or classify a room boundary, such as the opaque wall  200 , doorway wall  205 , transparent wall  210 , or exterior window wall  215 . 
     Furthermore, the lighting devices  150 - 1  and  150 - 2  may communicate the room boundaries and combine data to identify their positions within the room. In other examples, the lighting device  150  may also include a mapping engine to generate a floor plan of the room that may be stored locally on a memory storage unit within each lighting device  150  or shared with other lighting devices  150  for verification or appending to a floor map limited by the range of the sensors in the lighting devices  150 . In further examples, the floor plan may be used to group the lighting devices  150  by identifying the lighting devices within the same room. The process by which the lighting devices  150  determine whether other devices are in the same room may communicate partial floor plans to other lighting devices and a voting process may be used. In some examples, the voting process may involve taking a confidence value into consideration to weigh the data from each lighting device  150 . 
     In the present example, the lighting devices  150 - 1  and  150 - 2  are autonomously grouped together. The manner by which the lighting devices  150 - 1  and  150 - 2  are grouped is not limited. For example, it may be grouped based on the being in the same room as each other. Furthermore, each of the lighting devices  150 - 1  and  150 - 2  are in communication with the lighting controller  100  and also grouped the lighting controller  100  autonomously. The lighting controller  100  is to transmit control signals to the lighting devices  150 - 1  and  150 - 2 . 
     During the operation of the lighting devices  150 , it is to be appreciated by a person of skill with the benefit of this description that the lighting devices  150  may interfere with each other as their respective apparatus  50  emits light to locate and classify a room boundary. In a specific example, the lighting device  150 - 1  may emit light via the apparatus  50  at any time to generate light data to locate and classify a room boundary. Similarly, the lighting device  150 - 2  may do the same and detect the light emitted by the lighting device  150 - 1  which may interfere with the measurement of light data by the lighting device  150 - 2 . To address this interference, the lighting device  150 - 2  may check whether the lighting device  150 - 1  is in the process making a measurement prior to beginning the measurement process carried out by the lighting device  150 - 2  to avoid interference with the lighting device  150 - 1 . In some examples, the lighting devices  150 - 2  may not be aware of the lighting device  150 - 1  and may not be able to obtain the status of the lighting device  150 - 1 . In particular, the lighting devices  150  is such systems may not be able to obtain the status of other lighting devices  150 . Although the present example illustrates two lighting devices  150 , it is to be appreciated that the system may be scaled to many more lighting devices such that it is impractical to implement coordination across all lighting devices in a system due to large propagation delays in a large decentralized system. 
     Accordingly, each lighting device  150  may coordinate the emission of light from an apparatus  50  locally with the activation sequence of proximate lighting devices  150 . For example, an activation sequence may involve one or more successive on/off cycle of an infrared light source. The activation sequence is not limited to a specific number of on/off cycles, the on level, the off level, and the duration of time between levels or successive cycles. The coordination of the activation sequence may involve a pattern that results in one lighting device  150  being in a state of activation sequence at a given time relative to proximate lighting devices. 
     In some examples, the lighting devices  150  may communicate with each other to determine and/or confirm a room boundary. For example, each lighting device  150  may execute a process involving the measurement of light data in a manner that does not cause interference. The exchange of light data from each lighting device  150  to the other lighting devices  150  that may be detected by a prescribed number of heartbeat messages. Accordingly, each lighting device  150  may then combine lighting data into a database to locate and classify room boundaries as described above. 
     The manner by which the lighting devices  150  in a large decentralized system may coordinate autonomously is not particularly limited. For example, the lighting devices  150  may not have knowledge of all other lighting devices  150  in the system or even the number of lighting devices in the system. In some examples, this coordination process may involve the construction of a spanning tree with one or more unique initiators and may also involve the use of traversal protocols whereby special messages or tokens are used to visit each lighting device  150  sequentially. Execution of some or all of these processes may assume that each lighting device  150  to be in the same state. It is to be appreciated by those with skill in the art and the benefit of this description that a variety of protocols may be used to implement suitable processes. The unique initiator may be selected and contentions may be resolved among multiple candidate initiators. 
     Referring to  FIG.  4   , a flowchart of an example method of locating and classifying a room boundary is generally shown at  500 . In order to assist in the explanation of method  500 , it will be assumed that method  500  may be performed with the apparatus  50 . Indeed, the method  500  may be one way in which the apparatus  50  may be configured. Furthermore, the following discussion of method  500  may lead to a further understanding of the apparatus  50  and its components. In addition, it is to be emphasized, that method  500  may not be performed in the exact sequence as shown, and various blocks may be performed in parallel rather than in sequence, or in a different sequence altogether. 
     Beginning at block  510 , light is emitted onto to wall. The manner by which the light is emitted is not particularly limited. In the present example, the apparatus  50  may include a light source  55  from which light may be emitted. The light may be monochromatic, or emit a band of light with a peak wavelength in the infrared spectrum. For example, the light source  55  may emit light having a peak wavelength greater than about 780 nm to be beyond the typical visual range of a human eye. In some examples, the peak wavelength may be about 850 nm. 
     Block  520  comprises changing the intensity of the light emitted at block  510 . By changing the intensity of the light emitted, it is to be appreciated that the illumination level of light generated at block  510  may be adjusted. The light generated at block  510  is generally not visible to the human eye so that varying the illumination level does not generate undesired effects and may not be noticeable to occupants in the room. Furthermore, in examples where an apparatus  50  is part of a lighting device  150 , the light generated at block  510  is separate from the light generated to illuminate the room in which the lighting device  150  is disposed. In particular, the light intensity may be varied in a manner to adjust the illumination level to achieve different effects in the reflected light that may be subsequently processed to determine a location and classification of the wall. The manner by which the intensity of the light is varied may be recorded in as control data. 
     Next, block  530  comprises measuring, with a low resolution sensor  65 , the light generated at block  510  as it is reflected off the wall. The measured light may then be stored as light data along with the control data generated by the light source controller on a memory storage unit  70  at block  540 . 
     Blocks  550  and  560  use the light data and the control data to locate the position of the wall relative to the apparatus  50  and to classify the wall, respectively. An image processing engine  75  may be used to locate the wall and classify the wall. In the present example, the intensity distribution measured at block  530  may be dependent on the intensity of the light emitted by the light source  55  such that the dependence is uniquely associated with a specific type of wall or room boundary. Therefore, the image processing engine  75  may use machine learning techniques, such as a trained classification model, to perform accurate locating of a room boundary as well as classify the room boundary as a type of wall, such as an opaque wall, a transparent wall, a translucent wall, an exterior wall, a windowed wall or a wall with a doorway. It is to be appreciated that these types of walls are not particularly limited and may be defined such that the types of walls are mutually exclusive. 
     Referring to  FIG.  5   , a building space  300  with a plurality of rooms  310 ,  320 ,  330  and hallway  340  is shown. The building space  300  also includes a plurality of lighting controllers  100 - 1 ,  100 - 2 ,  100 - 3 ,  100 - 4  (generically, these lighting controllers are referred to herein as “lighting controller  100 ” and collectively they are referred to as “lighting controllers  100 ”), a plurality of lighting devices  150 - 1 ,  150 - 2 , . . . ,  150 - 25  (generically, these lighting devices are referred to herein as “lighting device  150 ” and collectively they are referred to as “lighting devices  150 ”) deployed throughout the building space  300 . The building space  300  may be an office unit, a warehouse, a residential home, or any other interior space. It is to be appreciated that in the present example, the lighting devices may be pre-installed in the building space prior to the placement of the walls to form the rooms  310 ,  320 , and  330 . 
     Each of the lighting devices  150  may be substantially identical units and unaware of the manner by which the building space  300  is divided. Similarly, each of the lighting controllers  100  may be substantially identical units and unaware of the manner by which the building space  300  is divided or which of the lighting devices  150  are within the same room. The lighting controllers  100  and the lighting devices  150  may include a light emitter and a low resolution sensor to locate and classify the room boundary. The classification of the room boundary is not limited and may include different wall types, such as an opaque wall  220 ,  235 ,  240 ,  250 ,  255 ,  260 ,  265 ,  275 , a doorway wall  225 ,  270 ,  280 ,  285 , an exterior window wall  230 ,  245 , and an interior translucent wall  290 . 
     In the present example, the lighting controllers  100  and the lighting devices  150  may not have prior knowledge of the physical environment, including the building size or type, room size, room layout, room boundary or the physical arrangement within the building or any given room. The lighting controllers  100  and the lighting devices  150  are not provided with any information that describes the physical environment, such as via a connection to a server or to another external device. Without knowledge of the number of devices (the lighting controllers  100  and the lighting devices  150  in aggregate or by type), the devices may not be able to maintain an internal list of all devices connected to the system due to limitations of each device, such as the size of a local memory storage unit. In some examples, the lighting controllers  100  and the lighting devices  150  may keep a list of about 50 other devices that may be added to the system with over 500 devices. 
     Continuing with the present example, a collection of the lighting controllers  100  and the lighting devices  150  may self-organize, cooperate together and operate in a spontaneous manner to solve the common goal of determining a group having a plurality of lighting devices  150  each that may be controlled by a lighting controller  100  without human involvement or an external software agent to manage, process, compute or instruct the lighting devices  150  at any time. 
     In some examples, the process of forming the group of devices with a plurality of lighting devices  150  may involve application of a set of rules or conditions. First, the devices to be grouped may be located within the same room. Second, the area spanned by the lighting devices  150  and controlled by the lighting controller  100  may be limited to a predefined amount. Third, the lighting controllers  100  and the lighting devices  150  that belong to a given group may form a continuous and uniform arrangement. In some examples, the groups of lighting devices may be irregularly shaped on a floor plan. Fourth, the lighting controllers  100  and the lighting devices  150  within the same room may be arranged into a logical number of groups. Defining the lighting controller  100  groupings in a given room may depends on the number of lighting devices  150  in the room, the arrangement of the lighting devices  150  in the room as well as other factors. 
     In some examples, the lighting devices  150  within the same room may self-assign an identifier that is common to the lighting devices  150  within the same room and unique from identifiers used by other the lighting devices  150  in the same system. In other examples, the lighting controllers  100  and the lighting devices  150  may be used to determine an area covered by all lighting controllers  100  and lighting devices  150  in the system and limit the area spanned by a given group or collection of groups such that no group spans an area greater than a prescribed amount. For example, the electrical building code in some jurisdictions limit the maximum area of a group controlled by a single wall controller to be no more than 2,500 sq. ft. if the total building area is less than 10,000 sq. ft. 
     In some examples, the lighting controller  100  may be used to control more than one group of lighting devices  150 . The number of groups of lighting devices  150  that are controlled by a lighting controller  100  may be determined dynamically based on a discovered arrangement of lighting devices  150  within a room. 
     Referring to  FIG.  6   , another schematic representation of an apparatus to locate and classify a room boundary, such as a wall, is generally shown at  50   a . Like components of the apparatus  50   a  bear like reference to their counterparts in the apparatus  50 , except followed by the suffix “a”. In the present example, the apparatus  50   a  is to collect data based on actively generated signals to locate a room boundary and to classify the room boundary group other apparatus autonomously. Furthermore, the apparatus  50   a  is to communicate the groupings to external devices. In the present example, the apparatus  50   a  includes a light source  55   a , a low resolution sensor  65   a , a memory storage unit  70   a , a processor  80   a  and a communications interface  85   a . In the present example, the processor  80   a  includes components to operate a light source controller  60   a , an image processing engine  75   a , and a grouping engine  77   a.    
     In the present example, the light source  55   a  and the low resolution sensor  65   a  are substantially similar to the light source  55  and the low resolution sensor  65 , respectively. In particular, the light source  55   a  is to emit light that is not visible to the human for use in locating and classifying room boundaries. The low resolution sensor  65   a  is to measure light data based on the reflected non-visible light as it is varied in intensity. Accordingly, the light source  55   a  and the low resolution sensor  65   a  may operate without changing the room lighting levels that may be visible to a human eye. 
     The processor  80   a  may include a central processing unit (CPU), a microcontroller, a microprocessor, a processing core, a field-programmable gate array (FPGA), an application-specific integrated circuit (ASIC), or similar. The processor  80   a  may cooperate with the memory storage unit  70   a  to execute various instructions stored thereon. For example, the memory storage unit  70   a  may store an operating system  430   a  that is executable by the processor  80   a  to provide general functionality to the apparatus  50   a , including functionality to locate and classify a room boundary. Examples of operating systems include Android Things™ Apache Mynewt™ Zephyr™, and Windows 10 IoT™. Further operating systems may also include Windows™, macOS™, iOS™, Android™, Linux™, and Unix™. The processor  80   a  may also control the light source  55   a  via a light source controller  60   a  and process light data measured by the low resolution sensor  65   a  with an image processing engine  75   a . In further examples, the memory storage unit  70   a  may be used to store additional applications that are executable by the processor  80   a  to provide specific functionality to the apparatus  50   a , such as functionality to control various components such as the low resolution sensor  65   a , the communications interface  85   a , and the light source  55   a  at the firmware level. 
     In the present example, the memory storage unit  70   a  may also maintain databases to store various data used by the apparatus  50   a . For example, the memory storage unit  70   a  may include wall data  410   a  and grouping data  420   a . The memory storage unit  70   a  may additionally store instructions to carry out operations at the driver level as well as other hardware drivers to communicate with other components and peripheral devices of the apparatus  50   a , such as various user interfaces to receive input or provide output. 
     In the present example, the database storing wall data  410   a  may store information about room boundaries within the field of view of the low resolution sensor  65   a . In particular, the wall data  410   a  may include information of the location and type of room boundary. For example, the field of view of the sensor  65   a  may be divided into a grid. In this example, each region or cell of the grid may be assigned a position and a description of the contents of the grid. For example, the cell may include no room boundary. As another example, the cell may include a room boundary such as a wall. The wall may be further classified into a type of wall, such as an opaque wall, a transparent wall, a translucent wall, an exterior wall, a windowed wall or a wall with a doorway. It is to be appreciated that these types of walls are not particularly limited and may be defined such that the types of walls are mutually exclusive. Furthermore, it may be appreciated by a person of skill with the benefit of this description that the wall data  410   a  may include a floor plan as detected by the apparatus. In some examples, the wall data  410   a  may include wall data  410   a  from other apparatus  50   a  received via the communications interface  85   a . Accordingly, the wall data  410   a  append additional data to generate a floor plan that extends beyond the field of view of the low resolution sensor  65   a.    
     The database storing the grouping data  420   a  is to store data relating to the group with which the apparatus  50   a  is associated. It is to be appreciated that each apparatus  50   a  may be associated with more than one group. Accordingly, if the apparatus  50   a  is connected to a lighting device, a plurality of lighting devices may be associated with each other to be controlled in unison. For example, all lighting devices in a room may be associated with each other and recorded in the database of the grouping data  420   a  as a list of device identifiers. 
     The processor  80   a  further operates a grouping engine  77   a . The grouping engine  77   a  is not particularly limited and may be operated by a separate processor or even a separate machine in other examples. The grouping engine  77   a  is to associate the apparatus  50   a  with a plurality of lighting devices in an autonomous manner. In the present example, the apparatus  50   a  may be added to a lighting device or integrally built into a lighting device. Accordingly, the grouping engine is to generate a grouping of the lighting devices in a commercial application. By associating the apparatus  50   a  with a plurality of lighting devices, the lighting device to which the apparatus  50   a  is connected may be controlled in unison with the plurality of lighting devices with a single lighting controller. In a specific example, the apparatus  50   a  may be used to determine that a lighting device is in the same room as the plurality of lighting devices and thus associate all lighting devices in room to be controlled with the lighting controller, such as a switch. 
     The manner by which the grouping engine  77   a  operates is not particularly limited. In some examples, a choice of grouping configuration may be verified or detected using supplementary data, such as a directional motion detection by the low resolution sensor  65   a , or an ambient light measurement as a function of time by the low resolution sensor  65   a . In some examples, the grouping engine  77   a  may be used to capture an intention of a designer or architect to improve the design and operation of lighting devices by analysing the lighting arrangement in combination with the supplementary data. The supplementary data is not limited and may include temporal and spatial data. In some examples, the supplementary data may include daylight intensity and motion patterns. The supplementary data may be analysed by the grouping engine  77   a  over a variable period of time that is sufficient in duration to achieve a desired accuracy. The motion pattern is not limited and may include directionality, velocity, frequency of movement and repetition of a given movement pattern. The ambient light pattern measurement is also not limited and may include recording the intensity, rate of change, and repetition of a given daylight reading. The manner in which these features is combined is not limited and the relative importance of each feature may be tunable by the grouping engine. 
     In other examples, the grouping engine  77   a  may determine a grouping based on a floor plan the logical number of groups based on the location of each lighting device, such as the x and y coordinates assigned on a floor plan. The lighting devices may be grouped in rows or columns or as alternating rows and/or columns. 
     The communications interface  85   a  is to communicate with an external device. In the present example, the communications interface  85   a  may communicate with external devices over a network, which may be a public network shared with a large number of connected devices, such as a WiFi network or cellular network. In other examples, the communications interface  85   a  may be to communicate over a private network. In particular, the communications interface  85   a  may communicate with an external device to coordinate the emission of light from the light source  55   a  to reduce potential interference with the external device, such as similar light from a light source of the external device. The communications may check whether the external device is in the process of emitting light to make a measurement prior to emitting light from the light source  55   a.    
     Furthermore, the communications interface  85   a  may receive an external data from an external device, such as wall data or grouping data. Similarly, the communications interface  85   a  may transmit the wall data  410   a  and grouping data  420   a  to an external device for verification or to append their databases. The manner by which the communications interface  85   a  transmits and receives the data is not limited and may include receiving an electrical signal via a wired connection with other external devices or via a central server. Since the apparatus  50   a  is may be mounted at a stationary location, using a wired connection between the apparatus  50   a  and the external device may provide a robust connection. In further examples, the communications interface  85   a  may be a wireless interface to transmit and receive wireless signals such as via a WiFi network or directly to the external device. As another example, the communications interface  85   a  may connect to another proximate device via a Bluetooth connection, radio signals or infrared signals and subsequently relayed to additional devices. Although a wireless connection may be more susceptible to interference, the installation process of the apparatus  50   a  and associated external devices is simplified for wireless applications compared with applications that involve running a wire between devices. 
     Referring to  FIG.  7   , another schematic representation of a lighting controller to identify and control a plurality of lighting devices is generally shown at  100   a . Like components of the lighting controller  100   a  bear like reference to their counterparts in the lighting controller  100 , except followed by the suffix “a”. In the present example, the lighting controller  100   a  is to collect data based on actively generated signals to locate a room boundary and to group a plurality of lighting devices. Furthermore, the lighting controller  100   a  is to communicate the groupings to external devices. In the present example, the apparatus  50   a  includes a light source  105   a , a low resolution sensor  115   a , a memory storage unit  120   a , a communications interface  130   a , a processor  135   a , and a user interface  140   a . In the present example, the processor  135   a  includes components to operate a light source controller  110   a , an image processing engine  125   a , and a grouping engine  127   a.    
     In the present example, the light source  105   a  and the low resolution sensor  115   a  are substantially similar to the light source  105  and the low resolution sensor  115 , respectively. In particular, the light source  105   a  is to emit light that is not visible to the human for use in locating and classifying room boundaries. The low resolution sensor  115   a  is to measure light data based on the reflected non-visible light as it is varied in intensity. Accordingly, the light source  105   a  and the low resolution sensor  115   a  may operate without changing the room lighting levels that may be visible to a human eye. 
     The processor  135   a  may include a central processing unit (CPU), a microcontroller, a microprocessor, a processing core, a field-programmable gate array (FPGA), an application-specific integrated circuit (ASIC), or similar. The processor  135   a  may cooperate with the memory storage unit  120   a  to execute various instructions stored thereon and be substantially similar to the processor  80   a  in the apparatus  50   a.    
     In the present example, the memory storage unit  120   a  may maintain databases to store various data used by the lighting controller  100   a . For example, the memory storage unit  120   a  may include wall data  450   a  and grouping data  460   a . The memory storage unit  70   a  may additionally store an operating system  470   a  and additional instructions to carry out operations at the driver level as well as other hardware drivers to communicate with other components and peripheral devices of the lighting controller  100   a , such as various user interfaces to receive input or provide output. 
     The processor  135   a  further operates a grouping engine  127   a . The grouping engine  127   a  is not particularly limited and may be operated by a separate processor or even a separate machine in other examples. The grouping engine  127   a  is to divide the plurality of lighting devices to which the lighting controller  100   a  transmits control signals into subsets of lighting devices where each subset may be controlled using separate control signals. Accordingly, the lighting devices may be controlled by the lighting controller  100   a  as groups. In some examples, the lighting devices may each include an apparatus  50   a  with a grouping engine  77   a  that may operate in a decentralized manner to self-group. The results of the self-grouping procedure may be received by the lighting controller  100   a  and subsequently used to divide the lighting devices. In other examples, the lighting controller  100   a  may impose another grouping scheme to override the grouping data generated by the apparatus  50   a.    
     The lighting controller  100   a  may also include a user interface  140   a  to receive input from a user. For example, the lighting controller  100   a  may be a wall mounted switch for controlling lighting devices in a room. In some examples, the user interface  140   a  may include a mechanical switch for controlling all the lighting devices in a room. The user interface  140   a  may also include additional switches for controlling subsets of lighting devices in the room, such as lighting devices in one end of the room. 
     In other examples, the user interface  140   a  may include a touchscreen device having soft switches or virtual switches. Accordingly, the user interface  140   a  may include a graphical user interface. The graphical user interface is not particularly limited and may be dynamically updated based on the groups of lighting devices generated by the grouping engine  127   a  or based on data received from an apparatus  50   a . In some examples, the grouping of lighting devices may be continually monitored and updated to automatically adjust if the floor plan change, such as if a room boundary is a movable wall or if the walls are changed due to a renovation. 
     In further examples, each apparatus  50   a  in a system may provide additional data to the grouping engine  127   a  to update the grouping configuration. For example, an apparatus  50   a  may analyze a motion pattern detected by the low resolution sensor  65   a  and share the data with other apparatus  50   a  or the lighting controller  100   a  to update groups via the grouping engine  77   a  or the grouping engine  127   a . For example, lighting devices have apparatus  50   a  that detect a similar motion frequency may be grouped together compared to lighting devices with apparatus  50   a  that detect dissimilar motion frequency. The similar motion may be used to infer that the lighting devices are in the same room or area of the room whereas dissimilar motion frequency may suggest a room boundary, such as a wall between the lighting devices. Similarly, the intensity of ambient light measurements may be used by the grouping engine  127   a  to divide the lighting devices. 
     It should be recognized that features and aspects of the various examples provided above may be combined into further examples that also fall within the scope of the present disclosure.