Patent Publication Number: US-10334707-B1

Title: Heuristic occupancy and non-occupancy detection in a lighting system with a single transmitter and multiple receivers

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     This application is related to U.S. patent application Ser. No. 15/840,616, filed Dec. 13, 2017, entitled “Heuristic Occupancy and Non-Occupancy Detection in a Lighting System.” This application is also related to U.S. patent application Ser. No. 15/840,827, filed Dec. 13, 2017, entitled “Heuristic Occupancy and Non-Occupancy Detection a Lighting System with Multiple Transmitters and a Single Receiver.” 
     BACKGROUND 
     In recent years, a number of systems and methods have been proposed for occupancy detection within a particular area utilizing radio frequency (RF) based technologies. Examples of such systems include video sensor monitoring systems, radio frequency identification (RFID) systems, global positioning systems (GPS), and wireless communication systems among others. However, many of these systems have several disadvantages. For example, the video sensor monitoring system requires a considerable number of dedicated sensors that are expensive and the system requires a large amount of memory for storing data. The RFID systems rely on occupants carrying an RFID tag/card that can be sensed by the RFID system to monitor the occupants. The GPS system uses orbiting satellites to communicate with the terrestrial transceiver to determine a location of the occupant in the area. However, such systems are generally less effective indoors or in other environments where satellite signals may be blocked, reducing accuracy of detecting the occupant in the area. 
     Electrically powered artificial lighting has become ubiquitous in modern society. Since the advent of electronic light emitters, such as lighting emitting diodes (LEDs), for general lighting type illumination application, lighting equipment has become increasingly intelligent with incorporation of sensors, programmed controller and network communication capabilities. Automated control, particularly for enterprise installations, may respond to a variety of sensed conditions, such a daylight or ambient light level or occupancy. Commercial grade lighting systems today utilize special purpose sensors and related communications. 
     There also have been proposals to detect or count the number of occupants in an area based on effects on an RF signal received from a transmitter due to the presence of the occupant(s) in the area. These RF wireless communication systems generally detect an occupant in the area based on change in signal characteristics of a data packet transmitted over the wireless network. However, an inaccurate detection of the occupant in a region or a sub-area in the area can occur when multiple transmitters are transmitting the RF signals from multiple different regions/sub-areas of the area. 
     SUMMARY 
     The examples disclosed herein improve over RF-based sensing technologies by heuristically detecting one or more occupants in a space. In such examples, occupancy is sensed based on measurements of RF perturbations in an area or space. An example machine learning algorithm involves determining optimized heuristic algorithm coefficients associated with the RF perturbations to provide occupancy sensing in the area at a time. The optimized heuristic algorithm coefficients are utilized in the example machine learning algorithm to provide the occupancy sensing in the area at real time. In one example, prior to the real time detection, learning occurs to optimize the coefficients, for example, prior to shipping of a product or as part of commissioning. In another example, learning occurs in real time operation, thus resulting in an on-going learning process to further optimize the coefficients. 
     An example lighting system includes a light source and a wireless communication transmitter for wireless radio frequency (RF) spectrum transmission in an area, including RF spectrum transmission of at a plurality of times. The lighting system also includes a plurality of wireless communication receivers configured to receive signals of transmissions from the transmitter through the area at the plurality of times. Each of the plurality of the receivers is configured to generate an indicator data of a signal characteristic of received RF spectrum signal at each of the plurality of times. The lighting system also includes a control module coupled to the light source and coupled to obtain the indicator data of RF spectrum signal generated at each of the plurality of times from each of the plurality of receivers. At each respective one of the plurality of times, the control module is configured to apply one of a plurality of heurist algorithm coefficients to each indicator data from each of the plurality of receivers for the respective time, based on results of the applications of the coefficients to indicator data, generate an indicator data metric value for each of the indicator data from each of the plurality of receivers for the respective time and process the indicator data metric values to compute an output value. The control module is also configured to compare the output value at each of the plurality of times with a threshold to detect one of an occupancy condition or a non-occupancy condition in the area. 
     An example method includes obtaining, in a lighting system, an indicator data generated at each of a plurality of times from each of a plurality of receivers configured to receive radio frequency (RF) spectrum signals from a RF transmitter in an area. At each respective one of the plurality of times in the lighting system, the method also includes applying a plurality of heurist algorithm coefficients (coefficients) to each indicator data from each of the plurality of receivers for the respective time, based on results of the applications of the coefficients to indicator data, generating an indicator data metric value for each of the indicator data from each of the plurality of receivers for the respective lime, and processing each of the indicator data metric value for each of the indictor data to compute an output value for the respective time. The method further includes comparing the output value for the respective time with a threshold to detect one of a one of an occupancy condition or a non-occupancy condition in the area. 
     Additional objects, advantages and novel features of the examples will be set forth in part in the description which follows, and in part will become apparent to those skilled in the art upon examination of the following and the accompanying drawings or may be learned by production or operation of the examples. The objects and advantages of the present subject matter may be realized and attained by means of the methodologies, instrumentalities and combinations particularly pointed out in the appended claims. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The drawing figures depict one or more implementations in accordance with the present teachings, by way of example only, not by way of limitation. In the figures, like reference numerals refer to the same or similar elements. 
         FIG. 1A  illustrates an example of a wireless topology of a lighting system with a single transmitter and multiple receivers. 
         FIG. 1B  illustrates an example of a wireless topology of a lighting system with a single receiver and multiple transmitters. 
         FIG. 2A  is a functional block diagram illustrating an example of a heuristic occupancy sensing system based on the wireless topology of  FIG. 1A  in accordance with an implementation of a local control of a light source in a lighting system. 
         FIG. 2B  is a functional block diagram illustrating an example of a heuristic occupancy sensing system based on the wireless topology of  FIG. 1A  in accordance with an implementation of a local control of a light source in a lighting system. 
         FIG. 3  illustrates an example of a wireless topology of a lighting system with multiple transmitters and multiple receivers. 
         FIG. 4  is a functional block diagram depicting an example of a heuristic occupancy sensing system based on the wireless topology of  FIG. 3  in accordance with an implementation of a local control of a light source in a lighting system. 
         FIG. 5  illustrates an example of a neural network for heuristically determining an occupancy or non-occupancy condition in a lighting system. 
         FIG. 6  is a high-level flow chart illustration of an example of a method for heuristically determining an occupancy or non-occupancy condition. 
         FIG. 7  is a functional block diagram illustrating an example relating to a lighting system of networked devices that provide a variety of lighting capabilities and may implement RF-based occupancy sensing. 
         FIG. 8  is a block diagram of an example of a lighting device that operates in and communicates via the lighting system of  FIG. 7 . 
         FIG. 9  is a block diagram of an example of a wall switch type user interface element that operates in and communicates via the lighting system of  FIG. 7 . 
         FIG. 10  is a block diagram of an example of a sensor type element that operates in and communicates via the lighting system of  FIG. 7 . 
         FIG. 11  is a block diagram of an example of a plug load controller type element that operates in and communicates via the lighting system of  FIG. 7 . 
     
    
    
     DETAILED DESCRIPTION 
     In the following detailed description, numerous specific details are set forth by way of examples in order to provide a thorough understanding of the relevant teachings. However, it should be apparent that the present teachings may be practiced without such details. In other instances, well known methods, procedures, components, and/or circuitry have been described at a relatively high-level, without detail, in order to avoid unnecessarily obscuring aspects of the present teachings. 
     Although there have been suggestions to control lighting based on RF wireless detection results, prior RF-based detection systems have not themselves been integrated as part of a machine learning (ML) in a lighting system of which the lighting operation are controlled as a function of the detection. 
     There is also room for improvement in the RF wireless detection algorithms for lighting system control. For example, a ML algorithm in the lighting system may enable a more rapid and real time response so that an occupant entering a previously empty area perceives that the system instantly turns ON the light(s) in the area. As another example, the ML algorithm may offer improved detection accuracy, e.g. to reduce false positives in detecting an occupant in the area. 
     Further, there is room for improvement for accurate detection of the occupant in a sub-area among multiple different sub-areas of the area. False positives may occur when detecting an occupant in a specific sub-area when multiple transmitters are transmitting the RF signals from multiple different sub-areas of the area. For example, a ML algorithm may offer improved occupancy detection accuracy, e.g. to reduce false positives in detecting the occupant in the actual sub-area of interest in the facility. 
     The examples described below and shown in the drawings integrate RF wireless based ML occupancy/non-occupancy detection capabilities in one or more lighting devices or into lighting devices and/or other elements forming a lighting system. Examples of a detection system address some or all of the concerns noted above regarding rapid real time detection of changes in occupancy/non-occupancy status and/or improved detection performance, such as reduction or even elimination of false positive occupancy detections. These advantages and possibly other advantages may be more readily apparent from the detailed description below and illustration of aspects of the examples in the drawings. 
     Referring to  FIG. 1A , an example of a wireless topology  101  of a lighting system includes a single wireless communication transmitter (Tx) and a number of wireless communication receivers (Rx) in physical space/area  105 . In one implementation, an indoor environment is described, but it should be readily apparent that the systems and methods described herein are operable in external environments as well. Specifically, in this example, the area  105  is a room. In one implementation, although, not shown, the area  105  may also include corridors, additional rooms, hallways etc. 
     As illustrated in the example in  FIG. 1A , the area  105  includes three intelligent system nodes  132 ,  134 ,  136 . Each such system node has an intelligence capability to transmit a signal or receive a signal and process data. In one example, at least one system node includes a light source and is configured as a lighting device. In another example, a system node includes a user interlace component and is configured as a lighting controller. In another example a system node includes a switchable power connector and is configured as a plug load controller. In a further example, a system node includes sensor detector and is configured as a lighting related sensor. 
     System node  132  includes a transmitter T 1  and system nodes  134  and  136  includes receivers R 1  and R 2  respectively. In one implementation, one of the occupancy condition and the non-occupancy condition in the area  105  is detected according to a heuristic occupancy sensing procedure as will be described below with respect to  FIG. 2A . 
     In the wireless topology  101 , the T 1  in the area  105  transmits a RF spectrum (RF) signal for some number (plurality &gt;1) of times. The transmission may be specifically for the occupancy detection. Each of the receivers R 1 -R 2  receives the transmissions of the RF signal through the area  105  for each of the plurality of times from T 1 . Accordingly, each of the R 1  and R 2  is configured to detect a metric of the received RF, which the system (e.g. at one or more of the nodes) uses to detect one of an occupancy condition and a non-occupancy condition based on the RF spectrum signals received from the T 1  in the area  105 . 
     Referring to  FIG. 2A , there is shown a functional block diagram of an example of a heuristic occupancy sensing system  200  configured to function on a radio frequency (RF) wireless communication network in accordance with an implementation of a local control of a light source in a lighting system. As illustrated, the heuristic occupancy sensing system  200  includes a lighting system (system)  202  disposed within the physical space/area  105  such as a room, corridor, etc. as described above with respect to  FIG. 1A . In one implementation, an indoor environment is described, but it should be readily apparent that the systems and methods described herein are operable in external environments as well. 
     In one implementation, the system  202  includes the three intelligent system nodes  132 ,  134  and  136  as described with respect to  FIG. 1A  above. As discussed above, each such system node has an intelligence capability to transmit and receive data and process the data. Each system node, for example, may include a receiver (R) and/or a transmitter (T) along with another component used in lighting operations. In one example, a system node includes a light source and is configured as a lighting device. In another example, a system node includes a user interlace component and is configured as a lighting controller. In another example the system node includes a switchable power connector and is configured as a plug load controller. In a further example, a system node includes sensor detector and is configured as a lighting related sensor. The system node  132  includes a T 1 , and each of the system nodes  134  and  136  includes a R 1  or R 2  respectively. 
     As described above, the Tx is configured to transmit RF signals and each of the Rx is configured to receive signals from the Tx. In one implementation, the system  202  includes a light source  206 , and a system node containing the source  206  or coupled to and operating together with the source  206  is configured as lighting device. The lighting device, for example, may take the form of a lamp, light fixture, or other luminaire that incorporates the light source, where the light source by itself contains no intelligence or communication capability, such as one or more LEDs or the like, or a lamp (e.g. “regular light bulbs”) of any suitable type. The light source  206  is configured to illuminate some or all of the area  105 . In one example, each of some number of individual light sources  206  to illuminate portion(s) or sub-area(s) of the area  105 . Typically, a lighting system will include one or more other system nodes, such as a wall switch, a plug load controller, or a sensor. 
     In one implementation, the lighting system includes a control module  216  coupled to the receivers R 1  and R 2 . In one implementation, the control module is coupled to the light source  206 . In an alternate implementation, the control module  216  is coupled to the light source  206  via a network (not shown). In another alternate implementation, the control module  216  is coupled to the lighting system  202  via a network (not shown). In one implementation, the control module  216  is implemented in firmware of a processor configured to determine one of an occupancy condition or a non-occupancy condition in the area  105 , although other circuitry or processor-based implementations may be used. In one implementation, the control module  216  is implemented in firmware of the processor in the R 1  and/or R 2 . 
     In one implementation, the system  202  includes a controller  218  coupled to the control module  216 . In one implementation the controller  218  may be the same or an additional processor configured to control operations of elements in the system  202  in response to determination of one of the occupancy condition or the non-occupancy condition in the area  105 . For example, in an alternate implementation, when the system  202  includes a light source  206 , the controller  218  is configured to process a signal to control operation of the light source  206 . In one alternate implementation, the controller  218  is configured to turn ON the light source  206  upon an occupancy condition detected by the control module  216 . In one implementation, die controller  218  is configured to turn OFF the light source  206  upon a non-occupancy condition detected by the control module  216 . In another implementation, upon the detection of the occupancy or non-occupancy condition in the area  105 , the controller  218  may be configured to provide other control and management junctions in the area such as heating, ventilation and air conditioning (HVAC), heat mapping, smoke control, equipment control, security control, etc. instead of or in addition to control of the light source(s). In yet another implementation, the controller  218  communicates the occupancy condition or non-occupancy condition to the lighting network via a data packet. The data packet is received by one or more luminaries in the lighting network, which are configured to turn ON or OFF the light source(s)  206  based on the occupancy or the non-occupancy condition respectively provided in the data packet. The luminaire or another node on the lighting network may receive the packet and respond to provide automation of other energy control, equipment control, operational control and management systems (e.g. HVAC, heat mapping, smoke control, equipment control and security control) in the area. Accordingly, the occupancy sensing system  200  communicates the occupancy/non-occupancy condition with other networks. In another alternate implementation, the controller  218  is coupled to the lighting system  202  via a network (not shown). Accordingly, the heuristic occupancy sensing system  200  is configured to function on the RF wireless communication network in accordance with an implementation of a global control of a light source as well as other automation control of energy, equipment, operational and management, as discussed above, of the area in a lighting system. 
     In one implementation, the system nodes typically include a processor, memory and programming (executable instructions in the form of software and/or firmware). Although the processor may be a separate circuity (e.g. a microprocessor), in many cases, it is feasible to utilize the central processing unit (CPU) and associated memory of a micro-control unit (MCU) integrated together with a transceiver in the form of a system on a chip (SOC). Such an SOC can implement the wireless communication functions as well as the intelligence (e.g. including any detector or controller capabilities) of the system node. 
     In examples discussed in more detail later, system nodes often may include both a transmitter and a receiver (sometimes referenced together as a transceiver), for various purposes. At times, such a transceiver-equipped node may use its transmitter as part of a heuristic occupancy sensing operation; and at other times such a transceiver-equipped node may use its receiver as part of a heuristic occupancy sensing operation. Such nodes also typically include a processor, memory and programming (executable instructions in the form of software and/or firmware). Although the processor may be a separate circuity (e.g. a microprocessor), in many cases, it is feasible to utilize the central processing unit (CPU) and associated memory of a micro-control unit (MCU) integrated together with physical circuitry of a transceiver in the form of a system on a chip (SOC). Such an SOC can implement the wireless communication functions as well as the intelligence (e.g. including any detector or controller capabilities) of the system node. 
     Although the system nodes  132 ,  134  and  136  of  FIG. 2A  illustrate an implementation of a single Tx and a single Rx in each of the nodes, the system  202  may include other implementations such as multiple Txs in one or more nodes (see e.g.  FIG. 2B ). Also,  FIG. 2A  illustrates the implementation of a single Rx in each of the nodes, the system  202  may include other implementations such as multiple Rx in one or more nodes. Further, the system  202  may include one or more Tx and one or more Rx in each of the nodes. In the illustrated implementation, the system  202  includes a single lighting device with one source  206 , however, the system  202  may include multiple lighting devices  206   a - 206   n  (see e.g.  FIG. 7 ) including one or more Tx and one or more Rx. 
     For discussion of an initial example of a heuristic RF-based occupancy sensing operation, assume that the system  202  includes just the elements shown in  FIG. 1A . In one example, each of the system nodes  132 ,  134  and  136  includes the capabilities to communicate over two different RF bands, although the concepts discussed herein are applicable to devices that communicate with luminaires and other system elements via a single RF band. Hence, in the dual band example, the Tx/Rx may be configured for sending and receiving various types of data signals over one band, e.g. for the RF detection leading to occupancy detection. The other band may be used or for pairing and commissioning messages over another band and/or for communications related to detection of RF or higher level occupancy sensing functions, e.g. between receivers R 1  and R 2  and the controller  220  or the control module  216 . For example, the Tx and Rx are configured as a 900 MHz transmitter and receiver for communication of a variety of system or user data, including lighting control data, for example, commands to turn lights on/off, dim up/down, set scene (e.g., a predetermined light setting), and sensor trip events. Alternatively, foe Tx and Rx may be configured as a 2.4 GHz transmitter and receiver for Bluetooth low energy (BLE) communication of various messages related to commissioning and maintenance of a wireless lighting system. 
     In one implementation, benefits of the system include the ability to take advantage of Tx and the Rx (e.g. RF Tx and RF Rx) already installed in a location in the area  105 , and because the system passively monitors signal broadcasts in the area  105  at a plurality of times, foe heuristic occupancy detection functionality docs not require (docs not rely on) the occupants to carry any device. 
     At a high level, the T 1  transmits a RF signal at a plurality of times. The transmission may be specifically for the occupancy detection. In some cases, however, where the transmitter is in another lighting device or other lighting system element (e.g. a sensor or a wall switch), the transmissions maybe regular lighting related communications, such as reporting status, sending commands, reporting sensed events, etc. Each of the R 1 -R 2  receives the transmissions of the RF signal from the T 1  through the area  105  during each of the plurality of times. Each of the R 1 -R 2  generates an indicator data of one or more characteristics of the received RF signal at the plurality of times. Some of examples of the characteristics include but are not limited to received signal strength indicator (RSSI) data, bit error rate, packet error rate, phase change etc. or a combination of two or more thereof. The RSSI data represents measurements of signal strength of the received RF. The bit error rate is rate of incorrect bits in received RF signals versus total number of bits in the transmitted RF signals. The packet error rate is rate of incorrect packets in received RF signals versus total number of packets the transmitted RF signals. Phase change is a change of phase of a received RF signal compared to previous reception of the RF signal (typically measured between the antennas spaced apart from each other). For the purpose of the present description, we use RSSI data as the characteristics of the RF signal for processing by each of the R 1 -R 2  to generate as the indicator data. Each of the R 1 -R 2  measures the signal strength of the received RF signal and generates the RSSI data based on the signal strength. The signal strength of each of the RF signal is based whether an occupant exists in a path between each of the  11  and R 1 -R 2  in the area  105 . 
     For each time, each of the receivers R 1 -R 2  supplied the generated indicator data of one or more characteristics of the received RF signal to the control module. In one implementation using RSSI as the characteristic of interest, the control module  216  obtains the generated RSSI data at each of the plurality of times from the various receivers R 1 -R 2  and utilizes a heuristic algorithm to determine one of an occupancy condition or a non-occupancy condition in the area  105  as described in greater detail herein below. 
     In one implementation that takes advantage of the machine learning (ML) capability of the heurist algorithm, the system  202  includes a trusted detector  230 , which provides a known value (similar to the “known answer” as discussed above). Input from the trusted detector  230  trusted detector  230  to “learn” so as to improve performance. The trusted detector  230  in the example may be a standard occupancy sensor, such as passive infrared occupancy detector, a camera based occupancy sensing system, BLE signal sensor (i.e. detecting presence of a phone), manual operation of lighting control (i.e. someone walking into a dark room turning on lights), microphone signal, voice command (a la Alexa), and any other signal or sensor data that can establish the presence of a person in the room. Specifically, the trusted detector  230  provides a known occupancy value for an accurate occupancy condition in the area  105  and a known non-occupancy value for an accurate non-occupancy condition in the area  105 . In one implementation, the known occupancy value and the known non-occupancy value are pre-determined prior to heuristically determining one of an occupancy or non-occupancy detection in the area  105 . 
     In one implementation, the control module  216  obtains the indicator data of the RF signal generated for multiple times (ta-tn) from each of the R 1  and R 2 . The control module  216  applies one of a heuristic algorithm coefficient (coefficient) among a set of heuristic algorithm coefficients to each of the indicator data from each of the R 1  and R 2  to generate an indicator data metric value for each of the indicator data from each of the R 1  and R 2  for the times ta-tn. Each coefficient among the set of coefficients may be randomly selected at an initial stage of training. In one implementation, a coefficient is a variable, in one implementation, a value of the coefficient applied to an indicator data from R 1  is the same as the value of the coefficient applied to another indicator data that is from R 2 . In another implementation, a value of a first coefficient applied to an indicator data from the R 1  is different from value of another (second) coefficient applied to another indicator data from R 2 . In one implementation, the control module  216  processes the indicator data metric values to compute an output value at each of the times ta-tn. In one implementation, the control module  216  determines a relationship of the output value (detected one of an occupancy or non-occupancy condition in the area) with the known value (one of an occupancy value or a non-occupancy value) generated by the trusted detector for each of the ta-tn. Specifically, the control module  216  compares the output value at each of the ta-tn with a threshold of a known value, for example, an output of the trusted detector  230 , to detect one of a one of an occupancy condition or a non-occupancy condition in the area as described in greater detail below. In one implementation, the system  202  includes a learning module  220  coupled to the control module  216  to determine whether the set of coefficients are optimized coefficients based on the relationship determined by the control module  216  at the times ta-tn to detect an accurate detection of the occupancy or the non-occupancy condition in the area. In one implementation, upon determination, that the set of coefficients are optimized coefficients, the control module  216  instructs the control module  216  to utilize the optimized coefficients in real time. In one implementation, upon determination, that the set of coefficients are optimized coefficients, the control module  216  instructs the control module  216  to update one or more coefficients among the set of coefficients and utilize the updated one or more coefficients in a next time. The above implementations are described in greater detail below. 
     In one example, the known value is a known occupancy value at a time t 1  among the times ta-tn. In one implementation, the control module  216  determines that the output value falls within the threshold of the known occupancy value. In one implementation, the learning module  220  determines, that the set of coefficients are determined to be optimized coefficients to be applied to the indicator data for the time t 1  to determine the accurate detection for occupancy condition. In one implementation, the learning module  220  instructs the control module  216  to utilize the optimized coefficients to apply to each indicator data among the plurality of indicator data from each of the plurality of receivers for the time  11  to detect the occupancy condition in real time. Accordingly, the control module  216  applies the optimized coefficients to determine the occupancy condition in real time. In another implementation, the control module  216  determines that the output value does not fall within the known occupancy value. The learning module  220  determines that the set of coefficients are not optimized coefficients and thus updates the one or more coefficients among the set of the coefficients to generate updated set of coefficients The learning module  220  instructs the control module  216  to utilize the updated set of coefficients in a next time. The control module  216  applies the updated coefficients to corresponding indicator data from each of the R 1  and R 2  to generate an updated indicator data metric value for each of the indicator data from each of the R 1  and R 2  at the time t 1 . In one implementation, the control module  216  processes each of the updated indicator data metric values to compute an updated output value at t 1 . In one implementation, the control module  216  determines that the updated output value at the time t 1  falls within the threshold of the known occupancy value. As such, the learning module  220  determines that the updated set of coefficients are optimized coefficients to be applied to the indicator data for the time t 1  to determine the accurate detection for occupancy condition in real time. In another implementation, the control module  216  determines that the updated output value does not fell within the known occupancy value. The control module  216  and the learning module  220  repeats the above process for t 1  until the output value falls within the threshold of the known occupancy value to determine that the set of coefficients corresponding to the indicator data from each of the R 1  and R 2  are the optimized coefficients for the t 1  among the ta-tn to accurately detect the occupancy condition at real time. Accordingly, the control module  216  applies the optimized coefficients to determine the occupancy condition in real time. 
     In another example, the known value is a known non-occupancy value at the time t 1 . In one implementation, the control module  216  determines that the output value falls within the threshold of the known non-occupancy value. In one implementation, the learning module  220  determines, that the set of coefficients are determined to be optimized coefficients to be applied to the indicator data for the time t 1  to determine the accurate detection for non-occupancy condition. In one implementation, the learning module  220  instructs the control module  216  to utilize the optimized coefficients to apply to each indicator data among the plurality of indicator data from each of the plurality of receivers for the time t 1  to detect the non-occupancy condition in real time. Accordingly, the control module  216  applies the optimized coefficients to determine the non-occupancy condition in real time. In another implementation, the control module  216  determines that the output value does not fell within the known non-occupancy value. The learning module  220  determines that the set of coefficients are not optimized coefficients and thus updates the one or more coefficients among the set of the coefficients to generate updated set of coefficients The learning module  220  instructs the control module  216  to utilize the updated set of coefficients in a next time. The control module  216  applies the updated coefficients to corresponding indicator data from each of the R 1  and R 2  to generate an updated indicator data metric value for each of the indicator data from each of the R 1  and R 2  at the time t 1 . In one implementation, the control module  216  processes each of the updated indicator data metric values to compute an updated output value at t 1 . In one implementation, the control module  216  determines that the updated output value at the time t 1  fells within the threshold of the known non-occupancy value. As such, the learning module  220  determines that the updated set of coefficients are optimized coefficients to be applied to the indicator data for the time t 1  to determine the accurate detection for non-occupancy condition in real time. In another implementation, the control module  216  determines that the updated output value does not fell within the known non-occupancy value. The control module  216  and the learning module  220  repeats the above process for t 1  until the output value falls within the threshold of the known non-occupancy value to determine that the set of coefficients corresponding to the indicator data from each of the R 1  and R 2  are the optimized coefficients for the t 1  among the ta-tn to accurately detect the non-occupancy condition at real time. Accordingly, the control module  216  applies the optimized coefficients to determine the occupancy condition in real time. 
     In one implementation, the output value is computed for each of the indicator data at each of the ta-tn and compared with the one of a known occupancy value or the known non-occupancy value to determine the optimized coefficients for each of the ta-tn to detect an accurate occupancy or non-occupancy condition in the area  105  of  FIG. 1A  at each of the ta-tn. In one implementation, the optimized set of coefficients for each of the ta-tn are utilized by the control module  216  to detect one of an accurate occupancy and non-occupancy condition in the area  105  of  FIG. 1A  at real time. 
     Referring to  FIG. 1B , an example of a wireless topology  103  of a lighting system includes a single wireless communication receiver (Rx) and a number of wireless communication transmitters (Txs) in physical space/area  105 . In one implementation, an indoor environment is described, but it should be readily apparent that the systems and methods described herein are operable in external environments as well. Specifically, in this example, the area  105  is a room. In one implementation, although, not shown, the area  105  may also include corridors, additional rooms, hallways etc. As illustrated in the example in  FIG. 1B , the area  105  includes three intelligent system nodes, out of which two are Tx  132 , and Tx  133 , and one is the Rx  136 . As discussed above, each such system node has an intelligence capability to transmit a signal or receive a signal and process data. In one example, at least one system node includes a light source and is configured as a lighting device. In another example, a system node includes a user interface component and is configured as a lighting controller. In another example a system node includes a switchable power connector and is configured as a plug load controller. In a further example, a system node includes sensor detector and is configured as a lighting related sensor. In one implementation, one of the occupancy condition and the non-occupancy condition in die area  105  is detected according to a heuristic occupancy sensing procedure as will be described below with respect to  FIG. 2B . 
     In the wireless topology  101 , the T 1  and T 2  in the area  105  transmits a RF spectrum (RF) signal for some number (plurality &gt;1) of times. The transmission may be specifically for the occupancy detection. The R 1  receives the transmissions of the RF signals through the area  105  for each of the plurality of times from T 1  and T 2 . Accordingly, the R 1  is configured to detect a metric of the received RF, which the system (e.g. at one or more of the nodes) uses to detect one of an occupancy condition and a non-occupancy condition based on the RF signals received from the T 1  and the T 2  in the area  105 . 
     Referring to  FIG. 2B , there is shown a functional block diagram of an example of a heuristic occupancy sensing system  201  configured to function on a radio frequency (RF) wireless communication network in accordance with an implementation of a local control of a tight source in a lighting system. As illustrated, the heuristic occupancy sensing system  201  includes a lighting system (system)  203  disposed within the physical space/area  105  such as a room, corridor, etc. as described above with respect to  FIG. 1B . In one implementation, an indoor environment is described, but it should be readily apparent that the systems and methods described herein are operable in external environments as well. 
     In one implementation, the system  203  includes the three intelligent system nodes, as described with respect to  FIG. 1B  above. As discussed above, each such system node has an intelligence capability to transmit and receive data and process the data. Each system node, for example, may include a receiver (R) and/or a transmitter (T) along with another component used in lighting operations. In one example, a system node includes a tight source and is configured as a lighting device. In another example, a system node includes a user interface component and is configured as a lighting controller. In another example the system node includes a switchable power connector and is configured as a plug load controller. In a further example, a system node includes sensor detector and is configured as a lighting related sensor. The system node  134  includes a R 1 , and each of the system nodes  132  and  133  includes a T 1  or T 2  respectively. 
     As described above, each of the Tx is configured to transmit RF signals and the Rx is configured to receive signals from each of the Tx. Similar to the system  202  in  FIG. 2A , in one implementation, the system  203  includes a light source  206 , and a system node containing the source  206  or coupled to and operating together with the source  206  is configured as lighting device. The lighting device, for example, may take the form of a lamp, light fixture, or other luminaire that incorporates the light source, where the light source by itself contains no intelligence or communication capability, such as one or more LEDs or the like, or a lamp (e.g. “regular light bulbs”) of any suitable type. The light source  206  is configured to illuminate some or all of the area  105 . In one example, each of some number of individual light sources  206  to illuminate portion(s) or a sub-area(s) of the area  105 . Typically, a lighting system will include one or more other system nodes, such as a wall switch, a plug load controller, or a sensor. 
     Similar to the system  202  in  FIG. 2A , in one implementation, the lighting system  203  also includes a control module  216 . The control module  216  is coupled to the R 2   134 . In one implementation, the control module is coupled to the light source  206 . In an alternate implementation, the control module  216  is coupled to the light source  206  via a network (not shown). In another alternate implementation, the control module  216  is coupled to the lighting system  203  via a network (not shown). In one implementation, the control module  216  is implemented in firmware of a processor configured to determine one of an occupancy condition or a non-occupancy condition in the area  105 , although other circuitry or processor-based implementations may be used. In one implementation, the control module  216  is implemented in firmware of the processor in the R 1 . 
     Similar to the system  202  in  FIG. 2A , in one implementation, the system  203  includes a controller  218  coupled to the control module  216 . In one implementation the controller  218  may be the same or an additional processor configured to control operations of elements in the system  203  in response to determination of one of the occupancy condition or the non-occupancy condition in the area  105 . For example, in an alternate implementation, when the system  203  includes a light source  206 , the controller  218  is configured to process a signal to control operation of the light source  206 . In one alternate implementation, the controller  218  is configured to turn ON the light source  206  upon an occupancy condition detected by the control module  216 . In one implementation, the controller  218  is configured to turn OFF the light source  206  upon a non-occupancy condition detected by the control module  216 . In another implementation, upon the detection of the occupancy or non-occupancy condition in the area  105 , the controller  218  is configured to provide other control and management functions in the area such as heating, ventilation and air conditioning (HVAC), heat mapping, smoke control, equipment control, security control, etc. In another implementation, the controller  218  communicates the occupancy condition or non-occupancy condition to the lighting network via a data packet. The data packet is received by one or more luminaires in the lighting network, which are configured to turn ON or OFF the light source(s)  206  and/or in the luminaire or another network node to provide automation of other energy control, equipment control, operational control and management systems (e.g. HVAC, heat mapping, smoke control, equipment control, security control) in the area  105  based on the occupancy or the non-occupancy condition respectively provided in the data packet. Accordingly, the occupancy sensing system  201  communicates the occupancy/non-occupancy condition with other networks. In another alternate implementation, the controller  218  is coupled to the lighting system  203  via a network (not shown). Accordingly, the heuristic occupancy sensing system  201  is configured to function on the RF wireless communication network in accordance with an implementation of a global control of a light source as well as other automation control of energy, equipment, operational and management, as discussed above, of the area in a lighting system. 
     Although,  FIG. 2B  illustrates the implementation of a single Rx and a single Tx in each of the nodes, the system  203  may include other implementations such as multiple Rx in one or more nodes and multiple Tx in one or more nodes. Further, the system  203  may include one or more Tx and one or more Rx in each of the nodes. In the illustrated implementation, the system  203  includes a single lighting device with one source  206 , however, the system  203  may include multiple lighting devices  206   a - 206   n  (see e.g.  FIG. 7 ) including one or more Tx and one or more Rx. 
     For discussion of an initial example of a heuristic RF-based occupancy sensing operation, assume that the system  203  includes just the elements shown in  FIG. 1B . In one example, each of the system nodes  132 ,  133  and  134  includes the capabilities to communicate over two different RF bands, although the concepts discussed herein are applicable to devices that communicate with luminaires and other system elements via a single RF band. Hence, in die dual band example, the Tx/Rx may be configured for sending and receiving various types of data signals over one band, e.g. for the RF detection leading to occupancy detection. The other band may be used or for pairing and commissioning messages over another band and/or for communications related to detection of RF or higher level occupancy sensing functions, e.g. between receiver R 1  and the controller  220  or the control module  216 . For example, the Tx and Rx are configured as a 900 MHz transmitter and receiver for communication of a variety of system or user data, including lighting control data, for example, commands to turn lights on/off, dim up/down, set scene (e.g., a predetermined light setting), and sensor trip events. Alternatively, the Tx and Rx may be configured as a 2.4 GHz transmitter and receiver for Bluetooth low energy (BLE) communication of various messages related to commissioning and maintenance of a wireless lighting system. 
     In one implementation, benefits of the system include the ability to take advantage of Tx and the Rx (e.g. RF Tx and RF Rx) already installed in a location in the area  105 , and because the system passively monitors signal broadcasts in the area  105  at a plurality of times, the heuristic occupancy detection functionality does not require (does not rely on) the occupants to carry any device. 
     At a high level, each of the T 1  and T 2  transmits a RF signal at a plurality of times. The transmission may be specifically for the occupancy detection. In some cases, however, where the transmitter is in another lighting device or other lighting system element (e.g. a sensor or a wall switch), the transmissions maybe regular lighting related communications, such as reporting status, sending commands, reporting sensed events, etc. The R 1  receives the transmissions of the RF signals from the T 1  and the T 2  through the area  105  during each of the plurality of times. The R 1  generates an indicator data of one or more characteristics of the received RF signal at the plurality of times. As discussed above, some of examples of the characteristics include but are not limited to received signal strength indicator (RSSI) data, bit error rate, packet error rate, phase change etc. or a combination of two or more thereof. For the purpose of the present description, we use RSSI data as the characteristics of the RF signal for processing by the R 1  to generate as the indicator data. The R 1  measures the signal strength of the received RF signals transmitted by T 1  and T 2  and generates the RSSI data based on the signal strength of the RF signals transmitted by T 1  and T 2 . The signal strength of each of the RF signal is based whether an occupant exists in a path between each of the T 1  and R 1  and/or T 2  and R 1  in the area  105 . 
     For each time, the R 1  supplied the generated indicator data of one or more characteristics of the received RF signal transmitted by the T 1  and T 2  to the control module. In one implementation using RSSI as the characteristic of interest, the control module  216  obtains the generated RSSI data at each of the plurality of times from the R 1  and utilizes a heuristic algorithm to determine one of an occupancy condition or a non-occupancy condition in the area  105  as described in greater detail herein below. 
     As discussed above, in one implementation that takes advantage of the machine learning (ML) capability of the heurist algorithm, the system  203  also includes a trusted detector  230 , which provides a known value (similar to the “known answer” as discussed above). Input from the trusted detector  230  trusted detector  230  to “learn” so as to improve performance. The misted detector  230  in the example may be a standard occupancy sensor, such as passive infrared occupancy detector, a camera based occupancy sensing system, BLE signal sensor (i.e. detecting presence of a phone), manual operation of lighting control (i.e. someone walking into a dark room turning on lights), microphone signal, voice command (a la Alexa), and any other signal or sensor data that can establish the presence of a person in the room. Specifically, the trusted detector  230  provides a known occupancy value for an accurate occupancy condition in the area  105  and a known non-occupancy value for an accurate non-occupancy condition in the area  105 . In one implementation, the known occupancy value and the known non-occupancy value are pre-determined prior to heuristically determining one of an occupancy or non-occupancy detection in the area  105 . 
     In one implementation, the control module  216  obtains the indicator data of the RF signals (transmitted by T 1  and T 2 ) generated for multiple times (ta-tn) from the R 1 . The control module  216  applies one of a heuristic algorithm coefficient (coefficient) among a set of heuristic algorithm coefficients to each of the indicator data from the R 1  to generate an indicator data metric value for each of the indicator data from R 1  for the times ta-tn. Each coefficient among the set of coefficients may be randomly selected at an initial stage of training. In one implementation, coefficient is a variable. In one implementation, the control module  216  processes the indicator data metric values to compute an output value at each of the times ta-tn. In one implementation, the control module  216  determines a relationship of the output value (detected one of an occupancy or non-occupancy condition in the area) with the known value (one of an occupancy value or a non-occupancy value) generated by the trusted detector for each of the ta-tn. Specifically, the control module  216  compares the output value at each of the ta-tn with a threshold of a known value, for example, an output of the trusted detector  230 , to detect one of a one of an occupancy condition or a non-occupancy condition in the area as described in greater detail below. In one implementation, the system  203  includes a learning module  220  coupled to the control module  216  to determine whether the set of coefficients are optimized coefficients based on the relationship determined by the control module  216  at the times ta-tn to detect an accurate detection of the occupancy or the non-occupancy condition in the area. In one implementation, upon determination, that the set of coefficients are optimized coefficients, the control module  216  instructs the control module  216  to utilize the optimized coefficients in real time. In one implementation, upon determination, that the set of coefficients are optimized coefficients, the control module  216  instructs the control module  216  to update one or more coefficients among the set of coefficients and utilize the updated one or more coefficients in a next time. The above implementations are described in greater detail below. 
     In one example, the known value is a known occupancy value at a time t 1  among the times ta-tn. In one implementation, the control module  216  determines that the output value fells within the threshold of the known occupancy value. In one implementation, the learning module  220  determines, that the set of coefficients are determined to be optimized coefficients to be applied to the indicator data for the time t 1  to determine the accurate detection for occupancy condition. In one implementation, the learning module  220  instructs the control module  216  to utilize the optimized coefficients to apply to each indicator data among the plurality of indicator data from each of the plurality of receivers for the time t 1  to detect the occupancy condition in real time. Accordingly, the control module  216  applies the optimized coefficients to determine the occupancy condition in real lime. In another implementation, the control module  216  determines that the output value does not fall within the known occupancy value. The learning module  220  determines that the set of coefficients are not optimized coefficients and thus updates the one or more coefficients among the set of the coefficients to generate updated set of coefficients The learning module  220  instructs the control module  216  to utilize the updated set of coefficients in a next time. The control module  216  applies the updated coefficients to corresponding indicator data from the R 1  to generate an updated indicator data metric value for each of the indicator data from the R 1  at the time t 1 . In one implementation, the control module  216  processes each of the updated indicator data metric values to compute an updated output value at t 1 . In one implementation, the control module  216  determines that the updated output value at the time t 1  falls within the threshold of the known occupancy value. As such, the learning module  220  determines that the updated set of coefficients are optimized coefficients to be applied to the indicator data for the time t 1  to determine the accurate detection for occupancy condition in real time. In another implementation, the control module  216  determines that the updated output value does not fell within the known occupancy value. The control module  216  and the learning module  220  repeats the above process for t 1  until the output value falls within the threshold of the known occupancy value to determine that the set of coefficients corresponding to the indicator data from the R 1  are the optimized coefficients for the t 1  among the ta-tn to accurately detect the occupancy condition at real time. Accordingly, the control module  216  applies the optimized coefficients to determine the occupancy condition in real time. 
     In another example, the known value is a known non-occupancy value at the time t 1 . In one implementation, the control module  216  determines that the output value fells within the threshold of the known non-occupancy value. In one implementation, the learning module  220  determines, that the set of coefficients are determined to be optimized coefficients to be applied to the indicator data for the time t 1  to determine the accurate detection for non-occupancy condition. In one implementation, the learning module  220  instructs the control module  216  to utilize the optimized coefficients to apply to each indicator data from R 1  for the time t 1  to detect the non-occupancy condition in real time. Accordingly, the control module  216  applies the optimized coefficients to determine the non-occupancy condition in real time. In another implementation, the control module  216  determines that the output value does not fell within the known non-occupancy value. The learning module  220  determines that the set of coefficients are not optimized coefficients and thus updates the one or more coefficients among the set of the coefficients to generate updated set of coefficients The learning module  220  instructs the control module  216  to utilize the updated set of coefficients in a next time. The control module  216  applies the updated coefficients to corresponding indicator data from the R 1  to generate an updated indicator data metric value for each of the indicator data from the R 1  at the time t 1 . In one implementation, the control module  216  processes each of the updated indicator data metric values to compute an updated output value at t 1 . In one implementation, the control module  216  determines that the updated output value at the time t 1  falls within the threshold of the known non-occupancy value. As such, the learning module  220  determines that the updated set of coefficients are optimized coefficients to be applied to the indicator data for the time t 1  to determine the accurate detection for non-occupancy condition in real time. In another implementation, the control module  216  determines that the updated output value does not fell within the known non-occupancy value. The control module  216  and the learning module  220  repeats the above process for t 1  until the output value falls within the threshold of the known non-occupancy value to determine that the set of coefficients corresponding to the indicator data from R 1  are the optimized coefficients for the t 1  among the ta-tn to accurately detect the non-occupancy condition at real time. Accordingly, the control module  216  applies the optimized coefficients to determine the occupancy condition in real time. 
     In one implementation, the output value is computed for each of the indicator data at each of the ta-tn and compared with the one of a known occupancy value or the known non-occupancy value to determine the optimized coefficients for each of the ta-tn to detect an accurate occupancy or non-occupancy condition in the area  105  of  FIG. 1B  at each of the ta-tn. In one implementation, the optimized set of coefficients for each of the ta-tn are utilized by the control module  216  to detect one of an accurate occupancy and non-occupancy condition in the area  105  of  FIG. 1B  at real time. 
     Referring to  FIG. 3 , an example of a wireless topology  301  of a lighting system includes a number of wireless communication transmitters (Tx) and a number of wireless communication receiver (Rx) in physical space/area  305 . In one implementation, indoor environment is described, but it should be readily apparent that the systems and methods described herein are operable in external environments as well. Specifically, in this example, the area  305  includes a combination of a room  360 , and a hallway  380 . In one implementation, although, not shown, the area  305  may also include corridors, additional rooms, additional hallways etc. In one implementation, although, not shown, the area  305  may also include corridors, additional rooms, hallways etc. 
     A wall  390  separates the room  360  from the hallway  380  with an opening  392 . As illustrated in the example in  FIG. 1 , the area  305  includes six intelligent system nodes  332 ,  334 ,  336 ,  338 ,  340 , and  342 . Each such system node has an intelligence capability to transmit a signal or receive a signal and process data. In one example, at least one system node includes a light source and is configured as a lighting device. In another example, a system node includes a user interface component and is configured as a lighting controller. In another example a system node includes a switchable power connector and is configured as a plug load controller. In a further example a system node includes sensor detector and is configured as a lighting related sensor. 
     In general, a heuristic algorithm with prior or ongoing training for machine learning, “learns” how to manipulate various inputs, possibly including previously generated outputs, in order to generate current new outputs. As part of this learning process, the algorithm receives feedback on prior outputs and possibly some other inputs. Then, the machine learning algorithm calculates parameters to be associated with the various inputs (e.g. the previous outputs, feedback, etc.). The parameters are then utilized by the machine learning to manipulate the inputs and generate the current outputs intended to improve some aspect of system performance in a desired manner. During the machine learning phase, the training data is the discrepancy between the outputs of a present system and the outputs of a trusted system. 
     In a lighting system with occupancy detection, for example, the training data may be the discrepancy between the outputs of an RF based detection system operating in a user/consumer installation and a trusted occupancy detection system such as a standard occupancy sensor (e.g. such as a sensor using passive infrared (PIR), a camera based system, BLE signal sensor (i.e. detecting presence of a phone), manual operation of lighting control (i.e. someone walking into a dark room turning on lights), microphone signal, voice command (a la Alexa), and any other signal or sensor data that can establish the presence of a person in the room). Machine learning techniques such as logical regression and artificial neural networks are applied to reduce the discrepancy, or example, by optimizing one or more coefficients used in the real time occupancy/non-occupancy decision. Training can take place ahead of the time (before product shipment/commissioning) or in the field as an on-going optimization to reduce false positives in detecting an occupant. 
     An example may apply a “supervised learning” approach in which the system will be provided a “known answer” from a “trusted detector” and machine learning is used to optimize the occupancy/non-occupancy detect algorithm to minimize the difference between the system output and the “known answer.” A trusted detector may be a passive infrared occupancy detector, a camera, BLE signal sensor (i.e. detecting presence of a phone), manual operation of lighting control (i.e. someone walking into a dark room turning on lights), microphone signal, voice command (a la Alexa), and any other signal or sensor data that can establish the presence of a person in the room. The particular machine learning approach can be one of decision tree or artificial neural net. 
     Learning can take place prior to shipping product or as part of commissioning after installation. In either such case, the system may normally operate in the field without using the trusted detector in real time. 
     Alternatively, a trusted detector can be installed with the system in the field and utilized in real time, in which case, there may be on-going machine learning. For an ongoing learning implementation, the data can be routed to a cloud, learning can take place on another system, and then the improved algorithm (e.g. in the form of new node parameters in the case of a neural network) can be downloaded to the installed lighting system. 
     In one implementation, details of the heuristic algorithm and machine learning are provided in more detail with respect to the example of  FIG. 3 . 
     System nodes  332 ,  334 ,  136  and  138  are located in the room  360  and the system nodes  340  and  342  are located in the hallway  380 . Each of the system nodes  332 ,  334  and  336  include transmitters T 1 , T 2  and T 3  respectively and each of the system nodes  338 ,  340  and  342  include receivers R 1 , R 2  and R 3  respectively. In one implementation, one of the occupancy condition and the non-occupancy condition in the entire area  305  and or a sub-area (for example, room  360 ) in the area  305  is detected according to a ML occupancy sensing procedure as will be described below with respect to  FIG. 4 . 
     In the wireless topology  301  each of the transmitters T 1 -T 3  in the area  305  transmits a RF spectrum (RF) signal for some number (plurality &gt;1) of limes. The transmissions from T 1 -T 3  may be specifically for the occupancy detection or for other lighting system communications. Each of the receivers R 1 -R 3  in the area  305  receives the transmissions of the RF signal through the area  305  for each of the plurality of times from each of the multiple T 1 -T 3 . Logically, such a three transmitter-three receiver arrangement provides nine T-R pairings for the analysis (each of the three transmitters T 1 -T 3  each paired logically with each of the three receivers R 1 -R 3 ). Accordingly, each of the R 1 -R 3  is configured to detect a metric of the received RF, which the system (e.g. at one or more nodes) uses to detect one of an occupancy/non-occupancy analysis condition in its own sub-area (room  360  or the hallway  380 ) based on receipt of the multiple RF signals received globally from the multiple T 1 -T 3  in the area  305 . 
     In one example, it is desired to determine an occupancy or non-occupancy condition in a sub-area such as room  360  of the area  305 . Thus, for example, a RF perturbation caused by a person in room  380  is detected by the T 1 /R 1  and T 2 /R 2  (in the room  360 ), each of which generates a signal indicator data for the heuristic analysis. A person in the room  360  can also trigger a response in the hallway  390  detected by the T 1 /R 3  and/or T 2 /R 3  in the hallway  380 , but at a lower signal level. A signal level threshold may be used to reject the false positive in the hallway  390 . A similar threshold approach may be implemented to reject the false positives at the nodes, i.e. T 3 /R 1  and T 3 /R 2  in the room  360  when a person in the hallway  390  causes T 3  to transmit RF signals detected by R 1  and R 2  in the room  360 . A similar threshold approach may be implemented to prevent false positives at the nodes i.e. T 3 /R 3  in the hallway  390 . 
     In one example, it is desired to determine an occupancy or non-occupancy condition in the room  180 . The heuristic algorithm is configured to processing indicator data from R 1  and R 2  to detect one of an inaccurate occupancy or inaccurate non-occupancy condition in the room  180  since each of R 1  and R 2  receives RF signals not only from the T 1  and the T 2  in the room  180  but also receives RF signals from the T 3  in the hallway  180 . Accordingly, the heuristic algorithm is applied to allow processing of indicator data from the R 1  and R 2  in the room  160  to ignore/eliminate the RF signals received from the R 3 , which are generated by the T 3  due to the presence of the occupants in the hallway  180  and/or multipath returns of signals generated by the T 1  and T 2  in the room  180  but received due to or modified by the presence of occupants in the hallway  180 . 
     Referring to  FIG. 4 , there is shown a functional block diagram of another example of a heuristic occupancy sensing system  400  configured to function on a radio frequency (RF) wireless communication network in accordance with an implementation of a local control of a light source in a lighting system. As illustrated, the ML occupancy sensing system  400  includes a lighting system (system)  402  disposed within the physical space/area  305  such as a room and a hallway etc. as described above with respect to  FIG. 3 . In one implementation, an indoor environment is described, but it should be readily apparent that the systems and methods described herein are operable in external environments as well. 
     In one implementation, the system  402  includes the six intelligent system nodes  332 ,  334 ,  336 ,  338 ,  340 , and  342  as described with respect to  FIG. 3  above. As discussed above, each such system node has an intelligence capability to transmit and receive data and process the data. Each system node, for example, may include a receiver (R) and/or a transmitter (T) along with another component used in lighting operations. In one example, a system node includes a light source and is configured as a lighting device. In another example, a system node includes a user interface component and is configured as a lighting controller. In another example the system node includes a switchable power connector and is configured as a plug load controller. In a further example, a system node includes sensor detector and is configured as a lighting related sensor. In the example of the wireless topology of  FIG. 3 , the system nodes  332 ,  334 , and  336  include T 1 , T 2  and T 3  respectively; and the system nodes  338 ,  340  and  342  include R 1 , R 2  and R 3  respectively. 
     As described above, the Tx is configured to transmit RF signals and each of the Rx is configured to receive signals from each Tx. In one implementation, the system  402  includes a light source  406 , and a system node containing the source  206  or coupled to and operation together with the source  406  and is configured as lighting device. The lighting device, for example, may take the form of a lamp, light fixture, or other luminaire that incorporates the light source, where the light source by itself contains no intelligence or communication capability, such as one or more LEDs or the like, or a lamp (e.g. “regular light bulbs”) of any suitable type. The light source  406  is configured to illuminate some or all of the area  405 . In one example, each of some number of individual the light sources  406  to illuminate portion(s) or sub-area(s) of the area  405 . Typically, a lighting system will include one or more other system nodes, such as a wall switch, a plug load controller, or a sensor. 
     In one implementation, the lighting system includes a control module  416  coupled to the receivers R 1 , R 2  and R 3 . In one example, the control module  416  is coupled to each of the R 1 , R 2  and R 3  via  530  (not shown). In one implementation, the control module  416  is coupled to the light source  406 . In one alternate implementation, the control module  416  is coupled to the light source  406  via a network (not shown). In another alternate implementation, the control module  416  is coupled to the lighting system  402  via a network (not shown). In one implementation, the control module  416  is implemented in firmware of a processor configured to determine one of an occupancy condition or a non-occupancy condition in the area  305  or the sub-area (for example, room  360 ) in the area  305 , although other circuitry or processor based implementations may be used. In one implementation, the control module  216  is implemented in firmware of the processor in or more of the R 1 , R 2  or R 3 . 
     In one implementation, the system  402  includes a controller  418  coupled to the control module  416 . In one implementation the controller  418  may be the same or an additional processor configured to control operations of elements in the system  402  in response to determination of one of the occupancy condition or the non-occupancy condition in the area  305  or a sub-area (for example, room  360 ) in the area  305 . For example, in an alternate implementation, when the system  402  includes a light source  406 , the controller  418  is configured to process a signal to control operation of the light source  406 . In one alternate implementation, the controller  418  is configured to turn ON the light source  406  upon an occupancy condition detected by the control module  416 . In one alternate implementation, the controller  418  is coupled to the control module  416  via a network (not shown). In one implementation, the controller  418  is configured to turn OFF the light source  406  upon a non-occupancy condition detected by the control module  416 . In another implementation, upon the detection of the occupancy or non-occupancy condition in the area  105 , the controller  218  is configured to provide other control and management functions in the area such as heating, ventilation and air conditioning (HVAC), heat mapping, smoke control, equipment control, security control, etc. In another implementation, the controller  418  communicates the occupancy condition or non-occupancy condition to the lighting network via a data packet. The data packet is received by one or more luminaires in the lighting network, which are configured to turn ON or OFF the light source(s)  406  and/or in the luminaire or another network node to provide automation of other energy control, equipment control, operational control and management systems (e.g. HVAC, heat mapping, smoke control, equipment control, security control) in the area  105  based on the occupancy or the non-occupancy condition respectively provided in the data packet. Accordingly, the heuristic occupancy sensing system  400  communicates the occupancy/non-occupancy condition with other networks. In another alternate implementation, the controller  418  is coupled to the lighting system  402  via a network (not shown). Accordingly, the heuristic occupancy sensing system  400  is configured to function on the RF wireless communication network in accordance with an implementation of a global control of a light source, as well as other automation control of energy, equipment, operational and management, as discussed above, of the area in a lighting system. 
     In one implementation, the system nodes typically include a processor, memory and programming (executable instructions in the form of software and/or firmware). Although the processor may be a separate circuity (e.g. a microprocessor), in many cases, it is feasible to utilize the central processing unit (CPU) and associated memory of a micro-control unit (MCU) integrated together with a transceiver in the form of a system on a chip (SOC). Such an SOC can implement the wireless communication functions as well as the intelligence (e.g. including any detector or controller capabilities) of the system node. 
     In examples discussed in more detail later, system nodes often may include both a transmitter and a receiver (sometimes referenced together as a transceiver), for various purposes. At times, such a transceiver-equipped node may use its transmitter as part of a heuristic occupancy sensing operation; and at other times such a transceiver-equipped node may use its receiver as part of a heuristic occupancy sensing operation. Such nodes also typically include a processor, memory and programming (executable instructions in the form of software and/or firmware). Although the processor may be a separate circuity (e.g. a microprocessor), in many cases, it is feasible to utilize the central processing unit (CPU) and associated memory of a micro-control unit (MCU) integrated together with physical circuitry of a transceiver in the form of a system on a chip (SOC). Such an SOC can implement the wireless communication functions as well as the intelligence (e.g. including any detector or controller capabilities) of the system node. 
     Although the system nodes  332 ,  334 ,  336 ,  338 ,  340  and  342  of  FIG. 3  illustrates an implementation of a single Tx and a single Rx in each of the nodes, the system  402  may include other implementations such as multiple Txs (see e.g.  FIGS. 2A to 2C ) in one or more nodes. Also,  FIG. 3  illustrates the implementation of a single Rx in each of the nodes, the system  202  may include other implementations such as multiple Rx (see e.g.  FIGS. 2B to 2C ) in one or more nodes. Further, in the illustrated implementation the system  402  includes a single lighting device with one source  406 , however, the system  402  may include multiple lighting devices  406   a - 406   n  (see e.g.  FIG. 7 ) including one or more Tx and one or more Rx. 
     For discussion of an initial example of a heuristic RF-based occupancy sensing operation, assume that the system  402  includes just the elements shown in  FIG. 3 . In one example, each of the system nodes  332 ,  334 ,  336  and  338  includes the capabilities to communicate over two different RF bands, although the concepts discussed herein are applicable to devices that communicate with luminaires and other system elements via a single RF band. Hence, in the dual band example, the Tx/Rx may be configured for sending and receiving various types of data signals over one band, e.g. for the RF detection leading to occupancy detection. The other band may be used or for pairing and commissioning messages over another band and/or for communications related to detection of RF or higher level occupancy sensing functions, e.g. between receivers R 1  and R 2  and the controller  420  or the control module  416 . For example, the Tx and Rx are configured as a 900 MHz transmitter and receiver for communication of a variety of system or user data, including lighting control data, for example, commands to turn lights on/off, dim up/down, set scene (e.g., a predetermined light setting), and sensor trip events. Alternatively, the Tx and Rx may be configured as a 2.4 GHz transmitter and receiver for Bluetooth low energy (BLE) communication of various messages related to commissioning and maintenance of a wireless lighting system. 
     In one implementation, benefits of the system include the ability to take advantage of Tx and the Rx (e.g. RF Tx and RF Rx) already installed in a location in the area  305 , and because the system passively monitors signal broadcasts in the area  305  at a plurality of times, the heuristic occupancy detection functionality does not require (docs not rely on) the occupants to carry any device. 
     At a high level, the T 1  transmits a RF spectrum (RF) signal at a plurality of times. The transmission may be specifically for the occupancy detection, in some cases, however, where the transmitter is in another lighting device or other lighting system element (e.g. a sensor or a wall switch), the transmissions maybe regular lighting related communications, such as reporting status, sending commands, reporting sensed events, etc. Each of the R 1 -R 3  receives the transmissions of the RF signal from each of the T 1 -T 3  through the area  305  for each of the plurality of times. Each of the R 1 -R 3  generates an indicator data of one or more characteristics of the received RF signal at the plurality of times. Some examples of the characteristics include but are not limited to received signal strength indicator (RSSI) data, bit error rate, packet error rate, phase change etc. or a combination of two or more thereof. The RSSI data represents measurements of signal strength of the received RF. The bit error rate is rate of incorrect bits in received RF signals versus total number of bits in the transmitted RF signals. Ille packet error rate is rate of incorrect packets in received RF signals versus total number of packets the transmitted RF signals. Phase change is a change of phase of a received RF signal compared to previous reception of the RF signal (typically measured between the antennas spaced apart from each other). For the purpose of the present description, we use RSSI data as the characteristics of the RF signal for processing by each of the R 1 -R 3  to generate as the indicator data. Each of the R 1 -R 3  measures the signal strength of the received RF signal and generates the RSSI data based on the signal strength. The signal strength of each of the RF signal is based whether an occupant exists in a path between each of the T 1 -T 3  and R 1 -R 3  in the area  305 . 
     For each time, each of the receivers R 1 -R 3  supplied the generated indicator data of one or more characteristics of the received RF signal to the control module. In one implementation using RSSI as the characteristic of interest, the control module  416  obtains the generated RSSI data at each of the plurality of times from the various receivers R 1 -R 3  and utilizes a heuristic algorithm to determine one of an occupancy condition or a non-occupancy condition in the area  305  or the sub-area (for example, room  360 ) in the area  305  as described in greater detail herein below as a training method to determine one of an occupancy or non-occupancy detection of the area  305  or the sub-area (for example, room  360 ) in the area  305  as described in greater detail herein below. 
     The control module  416  applies one of a heuristic algorithm coefficient (coefficient) among a set of heuristic algorithm coefficients to each of the indicator data from each of the R 1 -R 3  to generate an indicator data metric value for each of the indicator data from each of the R 1 -R 3  for the times ta-tn. Each coefficient among the set of coefficients may be randomly selected at an initial stage of training. In one implementation, a set of coefficients are utilized to detect occupancy and non-occupancy condition in the entire area  305 . In one implementation, a different set of coefficients are utilized to detect the occupancy condition and the non-occupancy condition in the region (example, room  360 ) in the area  305 . As such, the different set of coefficients are selected to reject false positives such as transmission signals from T 3  and received signals from R 3  that are not part of the room  360 . In one implementation, the heuristic algorithm is trained using the appropriate set of coefficients to detect the occupancy and non-occupancy condition in the entire area  305  or the region of the area, for example, the room  360 . As discussed above, training can take place ahead of the time (before product shipment/commissioning) or in the field as an on-going optimization to reduce false positives in detecting an occupant. Also as discussed above, the training is executed by the trusted detector. Accordingly, the occupancy and non-occupancy detection as discussed below with respect to the area may include the entire area  305  or a region (example room  360 ) of the entire area  305 . 
     In one implementation, a coefficient is a variable. In one implementation, a value of a coefficient applied to an indicator data from one of the R 1 -R 3  is the same as a value of a coefficient applied to another indicator data that is from another one of the R 1 -R 3 . In another implementation, a value of a first coefficient applied to an indicator data from one of the R 1 -R 3  is different from value of another (second) coefficient applied to another indicator data from another one of the R 1 -R 3 . In one implementation, the control module  416  processes the indicator data metric values to compute an output value at each of the times ta-tn. In one implementation, the control module  416  determines a relationship of the output value (detected one of an occupancy or non-occupancy condition in the area) with the known value (one of an occupancy value or a non-occupancy value) generated by the trusted detector for each of the times ta-tn. Specifically, the control module  416  compares the output value at each of the ta-tn with a threshold of a known value, for example, an output of the trusted detector  420 , to detect one of a one of an occupancy condition or a non-occupancy condition in the area as described in greater detail below. In one implementation, the system  402  includes a learning module  420  coupled to the control module  416  to determine whether the set of coefficients are optimized coefficients based on the relationship determined by the control module  416  at times ta-tn to detect an accurate detection of the occupancy or the non-occupancy condition in the area. In one implementation, upon determination, that the set of coefficients are optimized coefficients, the control module  416  instructs the control module  416  to utilize the optimized coefficients in real time. In one implementation, upon determination, that the set of coefficients are optimized coefficients, the control module  416  instructs the control module  416  to update one or more coefficients among the set of coefficients and utilize the updated one or more coefficients in a next time. The above implementations are described in greater detail below. 
     In one example, the known value is a known occupancy value at the time t 1  among the times ta-tn. In one implementation, the control module  416  determines that the output value falls within the threshold of the known occupancy value. In one implementation, the learning module  420  determines, that the set of coefficients are determined to be optimized coefficients to be applied to the indicator data for the time t 1  to determine the accurate detection for occupancy condition. In one implementation, the learning module  420  instructs the control module  416  to utilize the optimized coefficients to apply to each indicator data among the plurality of indicator data from each of the plurality of receivers for the time t 1  to detect the occupancy condition in real time. Accordingly, the control module  416  applies the optimized coefficients to determine the occupancy condition in real time. In another implementation, the control module  416  determines that the output value does not fall within the known occupancy value. The learning module  420  determines that the set of coefficients are not optimized coefficients and thus updates the one or more coefficients among the set of the coefficients to generate updated set of coefficients. The learning module  420  instructs the control module  416  to utilize the updated set of coefficients in a next time. The control module  416  applies the updated coefficients to corresponding indicator data from each of the R 1 -R 3  to generate an updated indicator data metric value for each of the indicator data from each of the R-R 3  at the time t 1 . In one implementation, the control module  416  processes each of the updated indicator data metric values to compute an updated output value at t 1 . In one implementation, the control module  416  determines that the updated output value at the time t 1  falls within the threshold of the known occupancy value. As such, the learning module  420  determines that the updated set of coefficients are optimized coefficients to be applied to the indicator data for the time t 1  to determine the accurate detection for occupancy condition in real time. In another implementation, the learning module  416  determines that the updated output value does not fell within the known occupancy value. The control module  416  and the learning module  420  repeats the above process for t 1  until the output value falls within the threshold of the known occupancy value to determine that the set of coefficients corresponding to the indicator data from each of the R 1 -R 3  are the optimized coefficients for the t 1  among the ta-tn to accurately detect the occupancy condition at real time. Accordingly, the control module  416  applies the optimized coefficients to determine the occupancy condition in real time. 
     In another example, the known value is a known non-occupancy value at the time t 1 . In one implementation, the control module  416  determines that the output value falls within the threshold of the known non-occupancy value. In one implementation, the learning module  420  determines, that the set of coefficients are determined to be optimized coefficients to be applied to the indicator data for the time t 1  to determine the accurate detection for non-occupancy condition. In one implementation, the learning module  420  instructs the control module  416  to utilize the optimized coefficients to apply to each indicator data among the plurality of indicator data from each of the plurality of receivers for the time t 1  to detect the non-occupancy condition in real time. Accordingly, the control module  416  applies the optimized coefficients to determine the non-occupancy condition in real time. In another implementation, the control module  416  determines that the output value does not fall within the known non-occupancy value. The learning module  420  determines that the set of coefficients are not optimized coefficients and thus updates the one or more coefficients among the set of the coefficients to generate updated set of coefficients. The learning module  420  instructs the control module  416  to utilize the updated set of coefficients. The control module  416  applies the updated coefficients to corresponding indicator data from each of the R 1  and R 2  to generate an updated indicator data metric value for each of the indicator data from each of the R 1  and R 2  at the time t 1 . In one implementation, the control module  416  processes each of the updated indicator data metric values to compute an updated output value at t 1 . In one implementation, the control module  416  determines that the updated output value at the time t 1  falls within the threshold of the known non-occupancy value. As such, the learning module  420  determines that the updated set of coefficients are optimized coefficients to be applied to the indicator data for the time t 1  to determine the accurate detection for non-occupancy condition in real time. In another implementation, the control module  416  determines that the updated output value does not fall within the known non-occupancy value. The control module  416  and the learning module  420  repeats the above process for t 1  until the output value falls within the threshold of the known non-occupancy value to determine that the set of coefficients corresponding to the indicator data from each of the R 1 -R 3  are the optimized coefficients for the t 1  among the ta-tn to accurately detect the non-occupancy condition at real time. Accordingly, the control module  416  applies the optimized coefficients to determine the occupancy condition in real time. 
     In one implementation, the output value is computed for each of the indicator data at each of the ta-tn and compared with the one of a known occupancy value or the known non-occupancy value to determine the optimized coefficients for each of the ta-tn to detect an accurate occupancy or non-occupancy condition in the area  305  or the region (for example, room  360 ) in the area  305  of  FIG. 3  at each of the ta-tn. In one implementation, the optimized set of coefficients for each of the ta-tn are utilized by the control module  416  to detect one of an accurate occupancy and non-occupancy condition in the area  305  or the region (for example, room  360 ) in the area  305  of  FIG. 3  at real time. 
     In one implementation, the control module  416  includes a logistic regression technique as a training method to determine one of an occupancy or non-occupancy detection of the area  305  or the sub-area (for example, room  360 ) in the area  305  of  FIG. 3 . A logistic regression is a statistical method for analyzing a dataset in which there are one or more independent variables that determine an outcome. The goal of logistic regression is to find the best fitting model to describe the relationship between the dichotomous characteristic of interest (outcome variable) and a set of independent variables. Logistic regression generates coefficients (and its standard errors and significance levels) of a formula to predict a logit transformation of the probability of presence of the characteristic of interest. In one implementation, the set of independent variables is the indicator data, for example, RSSI data (R 11 , R 12 , R 13 , R 21 , R 22 , R 23 , R 31 , R 32  and R 33 ). In one implementation, heuristic algorithm coefficients (coefficients) are a set of coefficients x 1 -x 9 , the outcome is a binary output value of the formula and the characteristic of interest is one of occupant or non-occupant in the area. Specifically, R 11  is the RSSI data generated by the R 1  based on the RF signal received from T 1 , R 12 , is the RSSI data generated by the R 1  based on the RF signal received from T 2 , R 13  is the RSSI data generated by the R 1  based on the RF signal received from T 3 , R 21  is the RSSI data generated by the R 2  based on the RF signal received from T 1 , R 22  is the RSSI data generated by the R 2  based on the RF signal received from T 2 , R 23  is the RSSI data generated by the R 3  based on the RF signal received from T 3 , R 31  is the RSSI data generated by the R 2  based on the RF signal received from T 1 , R 32 , is the RSSI data generated by the R 3  based on the RF signal received from T 2 , and R 33  is the RSSI data generated by the R 3  based on the RF signal received from T 3 . 
     In this example, the training includes applying a coefficient among a set of the coefficients x 1 -x 9  to each of the RSSI data (R 11 , R 12 , R 13 , R 21 , R 22 , R 23 , R 31 , R 32  and R 33 ) generated at multiple times. In one implementation, during an initial stage of the training, each of the coefficients among the set of coefficient x 1 -x 9  are randomly selected. In one implementation, value of a coefficient in a set of coefficients x 1 -x 9  is different from the value of another coefficient in the set of coefficients. In one implementation, value of a coefficient in a set of coefficients x 1 -x 9  is same as another coefficient in a set of coefficients. In one implementation, one or more of the coefficients among the set of coefficients x 0 -x 9  are updated based on an output value as described in greater detail below. 
     In one implementation, the RSSI data is analyzed by applying one of the coefficients among the set of coefficients to each of the RSSI data at multiple times. Specifically, each of the R 11 , R 12 , R 13 , R 21 , R 22 , R 23 , R 31 , R 32  and R 33  is multiplied by its corresponding coefficient x 1 -x 9  resulting in multiple product values (x 1 R 11 , x 2 R 12 , x 3 , R 13 , x 4 R 21 , x 5 R 22 , x 6 R 23 , x 7 R 3 , x 8 R 32  and x 9 R 33 ). In one implementation, an output value of the logistic regression is generated for the set of coefficients by adding up all the product values and an independent coefficient x 0  to compute a single added value, compute an exponent value of this single added value, adding a value of 1 to the exponent value to compute an added exponent value and dividing a value of 1 with this added exponent value to determine the output value as shown herein below: 
     
       
         
           
             1 
             
               
                 
                   
                     1 
                     + 
                     
                       exp 
                       [ 
                       
                         - 
                         
                           ( 
                           
                             
                               x 
                               0 
                             
                             + 
                             
                               
                                 x 
                                 1 
                               
                               ⁢ 
                               
                                 R 
                                 11 
                               
                             
                             + 
                             
                               
                                 x 
                                 2 
                               
                               ⁢ 
                               
                                 R 
                                 12 
                               
                             
                             + 
                             
                               
                                 x 
                                 3 
                               
                               ⁢ 
                               
                                 R 
                                 13 
                               
                             
                             + 
                             
                               
                                 x 
                                 4 
                               
                               ⁢ 
                               
                                 R 
                                 21 
                               
                             
                             + 
                             
                               
                                 x 
                                 5 
                               
                               ⁢ 
                               
                                 R 
                                 22 
                               
                             
                             + 
                           
                         
                       
                     
                   
                 
               
               
                 
                   
                     
                       
                         
                           
                             x 
                             6 
                           
                           ⁢ 
                           
                             R 
                             23 
                           
                         
                         + 
                         
                           
                             x 
                             7 
                           
                           ⁢ 
                           
                             R 
                             31 
                           
                         
                         + 
                         
                           
                             x 
                             8 
                           
                           ⁢ 
                           
                             R 
                             32 
                           
                         
                         + 
                         
                           
                             x 
                             9 
                           
                           ⁢ 
                           
                             R 
                             33 
                           
                         
                       
                       ) 
                     
                     ] 
                   
                 
               
             
           
         
       
     
     In one implementation, the output value is computed for each of the RSSI data at multiple limes (ta-tn). In one implementation, each output value computed at each time among the multiple times (ta-tn) is compared with a threshold of a true occupancy value and a true non-occupancy value. A true occupancy value or a non-occupancy value is a “known answer” computed from a trusted detector (e.g. a passive infrared occupancy detector, a camera, BLE signal sensor (i.e. detecting presence of a phone), manual operation of lighting control (i.e. someone walking into a dark room turning on lights), microphone signal, voice command (a la Alexa), and any other signal or sensor data that can establish the presence of a person in the room) for each of the times among the multiple times (ta-tn). A threshold for true occupancy value is an occupancy threshold and a threshold for true non-occupancy value is a non-occupancy threshold. For example, the true occupancy value is 1 for a time t 1  among the times ta-tn and the occupancy threshold is any value that is equal to 0.5 or is between 0.5 and 1 or equal to 1. Thus, any output value that fells within the occupancy threshold for the time t 1  is considered to be an accurate detection of the occupancy condition in the area  305  or the sub-area (for example, room  360 ) in the area  305  of  FIG. 3 . In another example, the true non-occupancy value is 0 for a time period t 8  among the times ta-tn and a threshold value is any value that is equal to 0 or is in between 0 and 0.5. Thus, any output value that falls within the non-occupancy threshold for the time t 8  is considered to be an accurate detection of the non-occupancy condition in the area  305  or the sub-area (for example, room  360 ) in the area  305  of  FIG. 3 . 
     In one implementation, when the output value falls within the occupancy threshold at the time t 1 , the x 0  and the coefficients in the x 1 -x 9  are considered to be optimized coefficients and these optimized coefficients are utilized in the logistic regression as described above to detect an occupancy condition in the area  305  or the sub-area (for example, room  360 ) in the area  305  at a real time. In one implementation, when the output value does not fall within the occupancy threshold at the time t 1 , one or more coefficients in the x 1 -x 9  and/or the x 0  are updated using a first gradient function as shown herein below: 
     
       
         
           
             Xn 
             = 
             
               Xn 
               - 
               
                 η 
                 ⁢ 
                 
                   
                     ∂ 
                     C 
                   
                   
                     ∂ 
                     Xn 
                   
                 
               
             
           
         
       
     
     Xn is the coefficient, n is the learning rate, C is the cost (loss) function. C is the difference between the computed output value and the true occupancy or non-occupancy value. C is minimized by taking the gradient with respect to the coefficients. In one implementation, the updating of one or more of the x 1 -x 9  and/or the x 0 , computing of the output data values are repeated until the output data value falls within the occupancy threshold occupancy at the time t 1 . In one implementation, upon determination of the output value falling within the occupancy threshold at the time t 1 , the corresponding updated x 0  and the updated one or more of x 1 -x 9  are determined to be the optimized coefficients and are utilized in the logistic regression as described above to detect an occupancy condition in the area  305  or the sub-area (for example, room  360 ) in the area  305  at a real time. 
     In one implementation, when the output value falls within the non-occupancy threshold at the lime t 8 , the x 0  and the coefficients in the x 1 -x 9  are considered to be optimized coefficients and these optimized coefficients are utilized in the logistic regression as described above to detect a non-occupancy condition in the area  305  or the sub-area (for example, room  360 ) in the area  305  at a real time. In one implementation, when the output value does not fall within the non-occupancy threshold at the time t 8 , one or more coefficients in the x 1 -x 9  and/or the x 0  are updated using the first gradient function as described above. In one implementation, the updating of the one or more of the x 1 -x 9  and/or x 0 , and computing of the output data values are repeated until the output data value falls within the non-occupancy threshold occupancy at the time t 8 . In one implementation, upon determination of the output value falling within the non-occupancy threshold at the time t 8 , the corresponding updated x 0  and the updated one or more of x 1 -x 9  are determined to be the optimized coefficients and are utilized in the logistic regression as described above to detect a non-occupancy condition in the area  305  or the sub-area (for example, room  360 ) in the area  305  at a real time. 
     In one implementation, the control module  416  includes a neural network as a training method to determine one of an occupancy or non-occupancy detection of the area  305  or the sub-area (for example, room  360 ) in the area  305  of  FIG. 3 . Referring to  FIG. 5 , there is shown an example of a neural network  500 . The neural network  500  includes an input layer  502  of input nodes  502   a - 502   i , al least one middle layer  504  of middle nodes  504   a - 504   j  and an output node  510 . Although, the middle layer  504  includes ten nodes, it is known to one of ordinary skill that the middle layer  504  may include any number of nodes, the number likely to be larger than the number of input nodes. Even though only one middle layer is shown, it known to one of ordinary skill in the art that more than one middle layer of nodes may be implemented in the neural network  500 . As shown, each of the input nodes  504   a - 504   i  is coupled to each of the middle nodes  504   a - 504   j  and each of the middle nodes  504   a - 504   j  is coupled to the output node  510 . In one implementation, each of the middle nodes  504   a - 504   j  in the middle layer  504  includes a corresponding bias constant ba-bi unique to that node. The bias constants ba-bi are initially randomly assigned. In one implementation, each connection from each of the input nodes  502   a - 502   i  to each of the middle nodes  504   a - 504   j  includes a corresponding weight (Wa-Wi) unique to the connection. Ille weights Wa-Wi are initially randomly assigned. In one implementation, the bias constants ba-bi and the weights Wa-Wi are the plurality of coefficients as described above. 
     The input layer of nodes  502   a - 502   i  includes the RSSI data R 11 , R 12 , R 13 , R 21 , R 22 , R 23 , R 31 , R 32  and R 33 . Specifically, input node  502   a  includes R 11 , input node  502   b  includes R 12 , input node  502   c  includes R 13 , input node  502   d  includes R 21 , input node  502   e  includes R 22 , input node  502   f  includes R 23 , input node  502   g  includes R 31 , input node  502   h  includes R 32 , and input node  502   i  includes R 33 . As such, number of input nodes in the input layer of nodes  502   a - 502   i  is equal to number of RSSI data R 11 , R 12 , R 13 , R 21 , R 22 , R 23 , R 31 , R 32  and R 33 . In one implementation, a forward propagation including propagation function and an activation function is executed in the neural network as described herein below. 
     An output of each of the input nodes  502   a - 502   i  is an input to each of the middle nodes  504   a - 504   i  in the middle layer. In one implementation, the forward propagation includes a propagation function executed at each of the middle nodes,  504   a - 504   i  to generate propagation function values. Specifically, the propagation function is determined by multiplying each of the RSSI data, R 11 , R 12 , R 13 , R 21 , R 22 , R 23 , R 31 , R 32  and R 33  with its corresponding weight (W) among the Wa-Wi and added with its corresponding bias constant (b) among the ba-bi of each of the middle nodes  504   a - 504   i  resulting in a propagation value Za-Zi at each of the middle nodes  504   a - 504   i , which is summed together into a single propagation value Zj as shown below: 
     
       
         
           
             
               z 
               j 
               l 
             
             = 
             
               
                 
                   ∑ 
                   k 
                 
                 ⁢ 
                 
                   
                     w 
                     jk 
                     l 
                   
                   ⁢ 
                   
                     R 
                     k 
                     
                       l 
                       - 
                       1 
                     
                   
                 
               
               + 
               
                 b 
                 j 
                 l 
               
             
           
         
       
     
     The single propagation value Zj is fed into the activation function executed in the output node  510  resulting in an output value, a j  as shown herein below:
 
 a   j   l   =f ( z   j   l )
 
     In one implementation, the output value is computed for each of the RSSI data at multiple times (ta-tn). In one implementation, each output value computed at each time among the multiple times (ta-tn) is compared with a threshold of a true occupancy value and a true non-occupancy value. A true occupancy value or a non-occupancy value is a “known answer” computed from a trusted detector (e.g. passive infrared occupancy detector, a camera, BLE signal sensor (i.e. detecting presence of a phone), manual operation of lighting control (i.e. someone walking into a dark room turning on lights), microphone signal, voice command (a la Alexa), and any other signal or sensor data that can establish the presence of a person in the room) for each of the times among the multiple times (ta-tn). A threshold for true occupancy value is an occupancy threshold and a threshold for true non-occupancy value is a non-occupancy threshold. For example, the true occupancy value is 1 for time t 1  among the times ta-tn and an occupancy threshold for the true occupancy value is 0.8 (i.e. 80%). Thus any output value at the time t 1  is equal to or greater than the occupancy threshold is considered to be an accurate detection of the occupancy condition in the area  305  or the sub-area (for example, room  360 ) in the area  305  of  FIG. 3 . In another example, the true non-occupancy value is 0 for time t 8  among the times ta-tn and a non-occupancy threshold of the true non-occupancy value is 0.2 (i.e. 20%). Thus, any output value at the time t 8  is at equal to or greater than the non-occupancy threshold is considered to be an accurate detection of the non-occupancy condition in the area  305  or the sub-area (for example, room  360 ) in the area  305  of  FIG. 3 . 
     In one implementation, the output value al the time t 1  is determined to be 0.9, which is compared with the true occupancy threshold of 0.8. Since the output value at the time  11  is greater than the threshold value of 0.8, then the corresponding weights Wa-Wi and the bias constants ba-bi are considered to be optimized coefficients and these optimized coefficients are utilized in the forward propagation as described above to detect an occupancy condition in the area  305  or the sub-area (for example, room  360 ) in the area  305  at real time. In another implementation, the output value at the time t 1  is determined to be 0.6, which is less than the occupancy threshold of 0.8, accordingly, one or more weights Wa-Wi may be updated using a second gradient descent function as shown below: 
     
       
         
           
             
               w 
               jk 
               ′ 
             
             = 
             
               
                 w 
                 jk 
               
               - 
               
                 η 
                 ⁢ 
                 
                   
                     ∂ 
                     C 
                   
                   
                     ∂ 
                     
                       w 
                       jk 
                     
                   
                 
               
             
           
         
       
     
     Further, one or more bias constants ba-bi may be updated using the third gradient descent function as shown herein below: 
     
       
         
           
             
               b 
               j 
               ′ 
             
             = 
             
               
                 b 
                 j 
               
               - 
               
                 η 
                 ⁢ 
                 
                   
                     ∂ 
                     C 
                   
                   
                     ∂ 
                     
                       b 
                       j 
                     
                   
                 
               
             
           
         
       
     
     W is the weight, b is the bias constant, n is the learning rate, C is the cost (loss) function. C is the difference between the computed output value and the true occupancy or non-occupancy value. C is minimized by taking the gradient with respect to the coefficients. In one implementation, a backward propagation function is applied to the neural network  500  using the one or more updated values of the weights, Wa-Wi and/or the one or more updated bias constants ba-bi. In one implementation, the backward propagation function includes providing the one or more updated values of the weights Wa-Wi and/or one or more updated bias constants ba-bi at the output node  510  and then cascading backwards towards the input node  502  by applying the one or more updated weights Wa-Wi and/or the one or more updated values of the bias constants ba-bi cascade backwards at each of the corresponding middle nodes  504   a - 504   j  in the middle layer  504  (including any additional middle nodes in additional middle layers not shown). 
     In one implementation, an updated output value is generated for the t 1  with the one or more updated weights Wa-Wi and/or the one or more updated bias constants ba-bi using the forward propagation as described above. In one implementation, the updating of Wa-Ai using the second gradient function and/or of the ba-bi using the third gradient function as described above, backward propagation and the forward propagation are repeated until the output data value foils within the occupancy threshold occupancy at the time t 1 . In one implementation, upon determination of the output value falling within the occupancy threshold at the time t 1 , the corresponding updated Wa-Wi and/or the updated ba-bi are utilized in the forward propagation as described above to detect an occupancy condition in the area  305  or the sub-area (for example, room  360 ) in the area  305  at a real time. 
     Referring back to the example, above, in one implementation, when the output value at the time t 8  is determined to be 0.2, which is compared with the true non-occupancy threshold of 0.2. Since, the output value of 0.2 is equal to or less than the true occupancy threshold of 0.2, the corresponding Wa-Wi and the bias constants ba-bi are considered to be optimized coefficients and these optimized coefficients are utilized in the forward propagation as described above to detect a non-occupancy condition in the area  305  or the sub-area (for example, room  360 ) in the area  305  at real time. In another implementation, when the output value at the time t 8 , is determined to be 0.4, which is less than the true non-occupancy threshold of 0.2, than one or more weights Wa-Wi and/or one or more bias constants ba-bi are updated using the above second and third gradient functions respectively as discussed above, in one implementation, the updating of the Wa-Wi and/or the ba-bi, the backward propagation and the forward propagation re repeated until the output data value falls within the non-occupancy threshold at the time, t 8 . In one implementation, upon determination of the output value felling within the non-occupancy threshold at the time t 8 , the corresponding one or more updated Wa-Wi and/or one or more updated ba-bi are utilized in the forward propagation as described above to detect a non-occupancy condition in the area  305  or the sub-area (for example, room  360 ) in the area  305  at a real time. 
       FIG. 6  illustrates an example of a flowchart of a method  600  for heuristic detection of an occupancy and non-occupancy condition for multiple times in area  105  of a lighting system either of  FIGS. 2A, 2B  or the area  305  or the—sub-area (for example, room  360 ) in the area  305  of the lighting system of  FIG. 4 . As discussed above, the lighting system (system) is disposed within a physical space/area such as a room, corridor, hallway, or doorway. In one implementation, indoor environment is described, but it is known to one of ordinary skill that the systems and methods described herein are operable in external environments as well. In one implementation, the method  600  is implemented by the control module  216  and the learning module  220  of  FIG. 2A  or  FIG. 2B . In one implementation, the method  600  is implemented by the control module  416  and the learning module  420  of  FIG. 4 . 
     At block  602 , an indicator data generated at each of the plurality of times from each of the plurality of receivers configured to receive RF spectrum (RF) signals from a RF transmitter in an area is obtained. As discussed above, some of the characteristics include but are not limited to received signal strength indicator (RSSI) data, bit error rate, packet error rate, phase change etc. or a combination of two or more thereof. At block  604 , at each respective one of the plurality of times, a coefficient among a set of coefficients is applied to each of the indicator data from each of the plurality of receivers for the respective time. In one implementation, during the initial stage of the training, each of the coefficients among the set of coefficients are randomly selected. At block  606 , at each respective one of the plurality of times, generate an indicator data metric value for each of the indicator data from each of the plurality of receivers for the respective time based on results of the applications of the coefficients to the indicator data. At block  608 , at each respective one of the plurality of times, each of the indicator data metric value for each of the indicator data is processed to compute an output value for the respective time. At block  610 , at each respective one of the plurality of times, the output value is compared with a threshold to detect one of an occupancy condition or a non-occupancy condition in the area. In one implementation, a threshold is a threshold of the known occupancy value for the occupancy condition. In another implementation, a threshold of the known non-occupancy value for the non-occupancy condition. At block  612 , at each of the respective one of the plurality of times, a relationship is determined of the detected one of the occupancy condition or the non-occupancy condition in the area with a known occupancy value for the occupancy condition or a known non-occupancy value for the non-occupancy condition during the respective one of the plurality of times. At block  614 , at each of the respective one of the plurality of times, it is determined whether the set of coefficients are optimized coefficients based on the determined relationship during the plurality of times. At block  616 , at each of the respective one of the plurality of times, decision is made whether the set of coefficients are the optimized coefficients. When at block  616 , it is determined that the set of coefficients are optimized coefficients, then at step  618 , the optimized coefficients are utilized to apply to each indicator data for the detection of the occupancy condition or the non-occupancy condition in the area at a real time. When at block  616 , it is determined that the set of coefficients are not optimized coefficients, then at step  620 , at each of the respective one of the plurality of times, one or more of the set of coefficients are updated to generate an updated set of coefficients, in one implementation, the method is repeated from block  604  for the updated set of coefficients until it is determined that the updated set of coefficients are optimized coefficients to detect an accurate occupancy or non-occupancy condition at each respective one of the plurality of times. 
       FIG. 7  is a functional block diagram illustrating an example relating to a system of a wireless networked devices that provide a variety of lighting capabilities and may implement RF-based occupancy sensing. The wireless networked devices also provide ng communications in support of lighting functions such as turning lights on/off, dimming, set scene, or sensor trip events and may implement RF-based occupancy sensing. It should be understood that the term “lighting control device” means a device that includes a controller (Control/XCVR module or micro-control unit) that executes a lighting application for communication over a wireless lighting network communication band, of control and systems operations information during control network operation over the lighting network communication band. 
     A lighting system  702  may be designed for indoor commercial spaces, although the system may be used in outdoor or residential settings. As shown, system  702  includes a variety of lighting control devices, such as a set of lighting devices (a.k.a. luminaires)  104   a - 104   n  (lighting fixtures), a set of wall switch type user interface component (a.k.a. wall switches)  720   a - 720   n , a plug load controller type element (a.k.a. plug load controller)  730  and a sensor type element (a.k.a. sensor)  735 . Daylight, ambient light, or audio sensors may embedded in lighting devices, in this case luminaires  704   a - 704   n . RF wireless occupancy sensing as described above is implemented in one or more of the luminaires  704   a - 704   n  to enable occupancy/non-occupancy based control of the light sources. One or more luminaires may exist in a wireless network  750 , for example, a sub-GHz or Bluetooth (e.g. 2.4 GHz) network defined by an RF channel and a luminaire identifier. 
     The wireless network  750  may use any available standard technology, such as WiFi, Bluetooth, ZigBee, etc. An example of a lighting system using a wireless network, such as Bluetooth low energy (BLE), is disclosed in patent application publication US20160248506 A1 entitled “System and Method for Communication with a Mobile Device Via a Positioning System Including RF Communication Devices and Modulated Beacon Light Sources,” the entire contents of which are incorporated herein by reference. Alternatively, the wireless network may use a proprietary protocol and/or operate in an available unregulated frequency band, such as the protocol implemented in nLight® Air products, which transport lighting control messages on the 900 MHz band (an example of which is disclosed in U.S. patent application Ser. No. 15/214,962, filed Jul. 20, 2016, entitled “Protocol for Lighting Control Via a Wireless Network,” the entire contents of which are incorporated herein by reference). The system may support a number of different lighting control protocols, for example, for installations in which consumer selected luminaires of different types are configured for a number different lighting control protocols. 
     The system  702  also includes a gateway  752 , which engages in communication between the lighting system  702  and a server  705  through a network such as wide area network (WAN)  755 . Although  FIG. 7  depicts server  705  as located off premises and accessible via the WAN  755 , any one of the luminaires  704   a - 704   n , for example are configured to communicate one of a occupancy detection or a non-occupancy detection in an area to devices such as the server  705  or even a laptop  706  located off premises. 
     The lighting control  702  can be deployed in standalone or integrated environments. System  702  can be an integrated deployment, or a deployment of standalone groups with no gateway  752 . One or more groups of lighting system  702  may operate independently of one another with no backhaul connections to other networks. 
     Lighting system  702  can leverage existing sensor and fixture control capabilities of Acuity Brands Lighting&#39;s commercially available nLight® wired product through firmware reuse. In general, Acuity Brands Lighting&#39;s nLight® wired product provides the lighting control applications. However, the illustrated lighting system  704  includes a communications backbone and includes model—transport, network, media access control (MAC)/physical layer (PHY) functions. 
     Lighting control  702  may comprise a mix and match of various indoor systems, wired lighting systems (nLight® wired), emergency, and outdoor (dark to light) products that are networked together to form a collaborative and unified lighting solution. Additional control devices and lighting fixtures, gateways)  750  for backhaul connection, time sync control, data collection and management capabilities, and interoperation with the Acuity Brands Lighting&#39;s commercially available SensorView product may also be provided. 
       FIG. 8  is a block diagram of a lighting device (in this example, a luminaire)  804  that operates in and communicates via the lighting system  702  of  FIG. 7 . Luminaire  804  is an integrated light fixture that generally includes a power supply  805  driven by a power source  800 . Power supply  805  receives power from the power source  800 , such as an AC mains, battery, solar panel, or any other AC or DC source. Power supply  805  may include a magnetic transformer, electronic transformer, switching converter, rectifier, or any other similar type of circuit to convert an input power signal into a power signal suitable for luminaire  804 . 
     Luminaire  804  furthers include an intelligent LED driver circuit  810 , control/XCVR module  815 , and a light emitting diode (LED) light source  820 . Intelligent LED driver circuit  810  is coupled to LED light source  820  and drives that LED light source  820  by regulating the power to LED light source  820  by providing a constant quantity or power to LED light source  320  as its electrical properties change with temperature, for example. The intelligent LED driver circuit  810  includes a driver circuit that provides power to LED light source  820  and a pilot LED  817 . The pilot LED  817  may be included as part of the control/XCVR module  315 . Intelligent LED driver circuit  810  may be a constant-voltage driver, constant-current driver, or AC LED driver type circuit that provides dimming through a pulse width modulation circuit and may have many channels for separate control of different LEDs or LED arrays. An example of a commercially available intelligent LED driver circuit  810  is manufactured by EldoLED. 
     LED driver circuit  810  can further include an AC or DC current source or voltage source, a regulator, an amplifier (such as a linear amplifier or switching amplifier), a buck, boost, or buck/boost converter, or any other similar type of circuit or component LED driver circuit  810  outputs a variable voltage or current to the LED light source  820  that may include a DC offset, such that its average value is nonzero, and/or an AC voltage. 
     Control/XCR module  815  includes power distribution circuitry  825  and a micro-control unit (MCU)  830 . As shown, MCU  830  is coupled to LED driver circuit  810  and controls the light source operation of the LED light source  820 . MCU  830  includes a memory  322  (volatile and non-volatile) and a central processing unit (CPU)  823 . The memory  822  includes a lighting application  827  (which can be firmware) for both occupancy detection and lighting control operations. The power distribution circuitry  825  distributes power and ground voltages to the MCU  830 , wireless transmitter  808  and wireless receiver  810 , to provide reliable operation of the various circuitry on the sensor/control module  815  chip. 
     Luminaire  804  also includes a wireless radio communication interface system configured for two way wireless communication on at least one band. Optionally, the wireless radio communication interface system may be a dual-band system. It should be understood that “dual-band” means communications over two separate RF bands. The communication over the two separate RF bands can occur simultaneously (concurrently); however, it should be understood that the communication over the two separate RF bands may not actually occur simultaneously. 
     In our example, luminaire  804  has a radio set that includes radio transmitter  808  as well as a radio receiver  810 , together forming a radio transceiver. The wireless transmitter  808  transmits RF signals on the lighting network. This wireless transmitter  808  wireless communication of control and systems operations information, during luminaire operation and during transmission over the first wireless communication band. The wireless receiver carries out receiving of the RF signals from other system elements on the network and generating RSSI data based on signal strengths of the received RF signals. If provided (optional) another transceiver (Tx and Rx) may be provided, for example, for point-to-point communication, over a second different wireless communication bands, e.g. for communication of information other than the control and systems operations information, concurrently with at least some communications over the first wireless communication band. Optionally, the luminaire  804  may have a radio set forming a second transceiver (shown in dotted lines, transmitter and receiver not separately shown). 
     The included transceiver (solid lines), for example, may be a sub GHz transceiver or a Bluetooth transceiver configured to operate in a standard GHz band. A dual-band implementation might include two transceivers for different bands, e.g. for a sub GHz band and a GHz band for Bluetooth or the like. Additional transceivers may be provided. The particular bands/transceivers are described here by way of non-limiting example, only. 
     If two bands are supported, the two bands may be for different applications, e.g. lighting system operational communications and system element maintenance/commissioning. Alternatively, the two bands may support traffic segregation, e.g. one band may be allocated to communications of the entity owning/operating the system at the premises whereas the other band may be allocated to communications of a different entity such as the system manufacturer or a maintenance service bureau. 
     The RF spectrum or “radio spectrum” is a non-visible part of the electromagnetic spectrum, for example, from around 3 MHz up to approximately 3 THz, which may be used for a variety of communication applications, radar applications, or the like. In the discussions above, the RF transmitted and received for network communication, e.g. Wifi, BLE, Zigbee etc., was also used for occupancy detection functions, in the frequencies bands/bandwidths specified for those standard wireless RF spectrum data communication technologies. In another implementation, the transceiver is an ultra-wide band (also known as UWB, ultra-wide band and ultraband) transceiver. UWB is a radio technology that can use a very low energy level for short-range, high-bandwidth communications over a large portion of the radio spectrum. UWB does not interfere with conventional narrowband and carrier wave transmission in the same frequency band. Ultra-wideband is a technology for transmitting information spread over a large bandwidth (&gt;500 MHz) and under certain circumstances be able to share spectrum with other users. 
     Ultra-wideband characteristics are well-suited to short-distance applications, such as short-range indoor applications. High-data-rate UWB may enable wireless monitors, the efficient transfer of data from digital camcorders, wireless printing of digital pictures from a camera without the need for a personal computer and file transfers between cell-phone handsets and handheld devices such as portable media players. UWB may be used in a radar configuration (emitter and deflection detection at one node) for real-time location systems and occupancy sensing/counting systems; its precision capabilities and low power make it well-suited for radio-frequency-sensitive environments. Another feature of UWB is its short broadcast time. Ultra-wideband is also used in “see-through-the-wall” precision radar-imaging technology, precision detecting and counting occupants (between two radios), precision locating and tracking (using distance measurements between radios), and precision time-of-arrival-based localization approaches. It is efficient, with a spatial capacity of approximately 1013 bit/s/m 2 . In one example, the UWB is used as the active sensor component in an automatic target recognition application, designed to detect humans or objects in any environment. 
     The MCU  830  may be a system on a chip. Alternatively, a system on a chip may include the transmitter  808  and receiver  810  as well as the circuitry of the MCU  830 . 
     As shown, the MCU  830  includes programming in the memory  822 . A portion of the programming configures the CPU (processor)  823  to detect one of an occupancy or non-occupancy condition in an area in the lighting network, including the communications over one or more wireless communication. The programming in the memory  822  includes a real-time operating system (RTOS) and further includes a lighting application  827  which is firmware/software that engages in communications with controlling of the light source based on one of the occupancy or non-occupancy condition detected by the CPU  823 . The lighting application  827  programming in the memory  822  carries out lighting control operations over the lighting network  750  of  FIG. 7 . The programming for the detection of an occupancy or non-occupancy condition in the area may be implemented as part of the RTOS, as part of the lighting application  827 , as a standalone application program, or as other instructions in the memory. 
       FIG. 9  is a block diagram of a wall type user interface element  915  that operates in and communicates via the lighting system  702  of  FIG. 7 . Wall type user interface (UI) element (UI element) is an integrated wall switch that generally includes a power supply  905  driven by a power source  900 . Power supply  905  receives power from the power source  900 , such as an AC mains, battery, solar panel, or any other AC or DC source. Power supply  905  may include a magnetic transformer, electronic transformer, switching converter, rectifier, or any other similar type of circuit to convert an input power signal into a power signal suitable for the UI element  915 . 
     UI element  915  furthers includes an intelligent LED driver circuit  910 , coupled to LED (s)  920  and drives that LED light source (LED)  920  by regulating the power to LED  820  by providing a constant quantity or power to LED  920  as its electrical properties change with temperature, for example. The intelligent LED driver circuit  910  includes a driver circuit that provides power to LED  920  and a pilot LED  917 . Intelligent LED driver circuit  910  may be a constant-voltage driver, constant-current driver, or AC LED driver type circuit that provides dimming through a pulse width modulation circuit and may have many channels for separate control of different LEDs or LED arrays. An example of a commercially available intelligent LED driver circuit  910  is manufactured by EldoLED. 
     LED driver circuit  910  can further include an AC or IX) current source or voltage source, a regulator, an amplifier (such as a linear amplifier or switching amplifier), a buck, boost, or buck/boost converter, or any other similar type of circuit or component. LED driver circuit  910  outputs a variable voltage or current to the LED light source  920  that may include a DC offset, such that its average value is nonzero, and/or an AC voltage. 
     The UI element  915  includes power distribution circuitry  925  and a micro-control unit (MCU)  930 . As shown, MCU  930  is coupled to LED driver circuit  910  and controls the light source operation of the LED  920 . MCU  930  includes a memory  922  (volatile and non-volatile) and a central processing unit (CPU)  923 . The memory  922  includes a lighting application  927  (which can be firmware) for both occupancy detection and lighting control operations. The power distribution circuitry  925  distributes power and ground voltages to the MCU  930 , wireless transmitter  908  and wireless receiver  910 , to provide reliable operation of the various circuitry on the UI element  915  chip. 
     The UI element  915  also includes a wireless radio communication interface system configured for two way wireless communication on at least one band. Optionally, the wireless radio communication interface system may be a dual-band system. It should be understood that “dual-band” means communications over two separate RF bands. The communication over the two separate RF bands can occur simultaneously (concurrently); however, it should be understood that the communication over the two separate RF bands may not actually occur simultaneously. 
     In our example, the UI element  915  has a radio set that includes radio transmitter  908  as well as a radio receiver  910  together forming a radio transceiver. The wireless transmitter  908  transmits RF signals on the lighting network. This wireless transmitter  908  wireless communication of control and systems operations information, during luminaire operation and during transmission over the first wireless communication band. The wireless receiver carries out receiving of the RF signals from other system elements on the network and generating RSSI data based on signal strengths of the received RF signals. If provided (optional) another transceiver (Tx and Rx) may be provided, for example, for point-to-point communication, over a second different wireless communication bands, e.g. for communication of information other than the control and systems operations information, concurrently with at least some communications over the first wireless communication band. Optionally, the UI element  915  may have a radio set forming a second transceiver (shown in dotted lines, transmitter and receiver not separately shown). 
     The included transceiver (solid lines), for example, may be a sub GHz transceiver or a Bluetooth transceiver configured to operate in a standard GHz band. A dual-band implementation might include two transceivers for different bands, e.g. for a sub GHz band and a GHz band for Bluetooth or the like. Additional transceivers may be provided. The particular bands/transceivers are described here by way of non-limiting example, only. 
     If two bands are supported, the two bands may be for different applications, e.g. lighting system operational communications and system element maintenance/commissioning. Alternatively, the two bands may support traffic segregation, e.g. one band may be allocated to communications of the entity owning/operating the system at the premises whereas the other band may be allocated to communications of a different entity such as the system manufacturer or a maintenance service bureau. 
     The MCU  930  may be a system on a chip. Alternatively, a system on a chip may include the transmitter  908  and receiver  910  as well as the circuitry of the MCU  930 . 
     As shown, the UI element  915  includes a drive/sense circuitry  935 , such as an application firmware, drives the occupancy, audio, and photo sensor hardware. The drive/sense circuitry  935  detects state changes (such as change of occupancy, audio or daylight sensor or switch to turn lighting on/off, dim up/down or set scene) via switches  965 , such as a dimmer switch, set scene switch. Switches  965  can be or include sensors, such as infrared sensors for occupancy or motion detection, an in-fixture daylight sensor, an audio sensor, a temperature sensor, BLE signal sensor (i.e. detecting presence of a phone), manual operation of lighting control (i.e. someone walking into a dark room turning on lights), microphone signal, voice command (a la Alexa), and any other signal or sensor data that can establish the presence of a person in the room. Switches  965  may be based on Acuity Brands Lighting&#39;s commercially available xPoint® Wireless ES7 product. 
     Also, as shown, the MCU  930  includes programming in the memory  922 . A portion of the programming configures the CPU (processor)  923  to detect one of an occupancy or non-occupancy condition in an area in the lighting network, including the communications over one or more wireless communication bands. The programming in the memory  922  includes a real-time operating system (RTOS) and further includes a lighting application  927  which is firmware/software that engages in communications with controlling of the light source based on one of the occupancy or non-occupancy condition detected by the CPU  923 . As shown, a drive/sense circuitry detects a state change event. Ille lighting application  927  programming in the memory  922  carries out lighting control operations over the lighting system  702  of  FIG. 7 . The programming for the detection of an occupancy or non-occupancy condition in the area may be implemented as part of the RTOS, as part of the lighting application  927 , as a standalone application program, or as other instructions in the memory. 
       FIG. 10  is a block diagram of a sensor type element,  1015  that operates in and communicates via the lighting system  702  of  FIG. 7 . Sensor type element is an integrated sensor detector that generally includes a power supply  1005  driven by a power source  1000 . Power supply  805  receives power from the power source  1000 , such as an AC mains, battery, solar panel, or any other AC or DC source. Power supply  1005  may include a magnetic transformer, electronic transformer, switching converter, rectifier, or any other similar type of circuit to convert an input power signal into a power signal suitable for the sensor type element  1015 . 
     The sensor type element  1015  includes power distribution circuitry  1025  and a micro-control unit (MCU)  1030 . As shown, MCU  1030  includes a memory  1022  (volatile and non-volatile) and a central processing unit (CPU)  1023 . The memory  1022  includes a lighting application  1027  (which can be firmware) for both occupancy detection and lighting control operations. The power distribution circuitry  1925  distributes power and ground voltages to the MCU  1030 , wireless transmitter  1008  and wireless receiver  1010 , to provide reliable operation of the various circuitry on the sensor type element  1015  chip. 
     The sensor type element  1015  also includes a wireless radio communication interface system configured for two way wireless communication on at least one band. Optionally, the wireless radio communication interface system may be a dual-band system. It should be understood that “dual-band” means communications over two separate RF bands. The communication over the two separate RF bands can occur simultaneously (concurrently); however, it should be understood that the communication over the two separate RF bands may not actually occur simultaneously. 
     In our example, the sensor type element  1015  has a radio transmitter  1008  as well as radio receiver  1010  together forming a radio transceiver. The wireless transmitter  1008  transmits RF signals on the lighting network. This wireless transmitter  1008  wireless communication of control and systems operations information, during luminaire operation and during transmission over the first wireless communication band. The wireless receiver carries out receiving of the RF signals from other system elements on the network and generating RSSI data based on signal strengths of the received RF signals. If provided (optional) another transceiver (Tx and Rx) may be provided, for example, for point-to-point communication, over a second different wireless communication bands, e.g. for communication of information other than the control and systems operations information, concurrently with at least some communications over the first wireless communication band. Optionally, the luminaire sensor type element  1015  may have a radio set forming a second transceiver (shown in dotted lines, transmitter and receiver not separately shown). 
     The included transceiver (solid lines), for example, may be a sub GHz transceiver or a Bluetooth transceiver configured to operate in a standard GHz band. A dual-band implementation might include two transceivers for different bands, e.g. for a sub GHz band and a GHz band for Bluetooth or the like. Additional transceivers may be provided. The particular bands/transceivers are described here by way of non-limiting example, only. 
     If two bands are supported, the two bands may be for different applications, e.g. lighting system operational communications and system element maintenance/commissioning. Alternatively, the two bands may support traffic segregation, e.g. one band may be allocated to communications of the entity owning/operating the system at the premises whereas the other band may be allocated to communications of a different entity such as the system manufacturer or a maintenance service bureau. 
     The MCU  1030  may be a system on a chip. Alternatively, a system on a chip may include the transmitter  1008  and the receiver  1010  as well as the circuitry of the MCU  830 . 
     As shown, the sensor type element  1015  includes a drive/sense circuitry  1035 , such as an application firmware, drives the occupancy, daylight, audio, and photo sensor hardware. The drive/sense circuitry  1035  detects state changes (such as change of occupancy, audio or daylight) via sensor detector(s)  1065 , such as occupancy, audio, daylight, temperature or other environment related sensors. Sensors  1065  may be based on Acuity Brands Lighting&#39;s commercially available xPoint® Wireless ES7 product. 
     Also as shown, the MCU  1030  includes programming in the memory  1022 . A portion of the programming configures the CPU (processor)  1023  to detect one of an occupancy or non-occupancy condition in an area in the lighting network, including the communications over one or more different wireless communication bands. The programming in the memory  1022  includes a real-time operating system (RTOS) and further includes a lighting application  1027  which is firmware/software that engages in communications with controlling of the light source based on one of the occupancy or non-occupancy condition detected by the CPU  1023 . The lighting application  1027  programming in the memory  1022  carries out lighting control operations over the lighting system  702  of  FIG. 7 . The programming for the detection of an occupancy or non-occupancy condition in the area may be implemented as part of the RTOS, as part of the lighting application  1027 , as a standalone application program, or as other instructions in the memory. 
       FIG. 11  is a block diagram of a plug load controller type element (plug load element)  1115  that operates in and communicates via the lighting system  702  of  FIG. 7 . In one example, plug load element  1115  is an integrated switchable power connector that generally includes a power supply  1105  driven by a power source  1100 . Power supply  1105  receives power from the power source  1100 , such as an AC mains, battery, solar panel, or any other AC or DC source. Power supply  1105  may include a magnetic transformer, electronic transformer, switching converter, rectifier, or any other similar type of circuit to convert an input power signal into a power signal suitable for the plug load element  1115 . 
     Plug load element  1115  includes an intelligent LED driver circuit  1110 , coupled to LED (s)  1120  and drives that LED light source (LED) by regulating the power to LED  1120  by providing a constant quantity or power to LED  1120  as its electrical properties change with temperature, for example. The intelligent LED driver circuit  1110  includes a driver circuit that provides power to LED  1120  and a pilot LED  1117 . Intelligent LED driver circuit  1110  may be a constant-voltage driver, constant-current driver, or AC LED driver type circuit that provides dimming through a pulse width modulation circuit and may have many channels for separate control of different LEDs or LED arrays. An example of a commercially available intelligent LED driver circuit  1110  is manufactured by EldoLED. 
     LED driver circuit  1110  can further include an AC or DC current source or voltage source, a regulator, an amplifier (such as a linear amplifier or switching amplifier), a buck, boost, or buck/boost converter, or any other similar type of circuit or component. LED driver circuit  1110  outputs a variable voltage or current to the LED light source  1120  that may include a DC offset, such that its average value is nonzero, and/or an AC voltage. 
     The plug load element  1115  includes power distribution circuitry  1125  and a micro-control unit (MCU)  1130 . As shown, MCU  1130  is coupled to LED driver circuit  1110  and controls the light source operation of the LED  1120 . MCU  1130  includes a memory  1122  (volatile and non-volatile) and a central processing unit (CPU)  1123 . The memory  1122  includes a lighting application  1127  (which can be firmware) for both occupancy detection and lighting control operations. The power distribution circuitry  1125  distributes power and ground voltages to the MCU  1130 , wireless transmitter  1108  and wireless receiver  1110 , to provide reliable operation of the various circuitry on the plug load control  1115  chip. 
     The plug load element  1115  also includes a wireless radio communication interface system configured for two way wireless communication on at least one band. Optionally, the wireless radio communication interface system may be a dual-band system. It should be understood that “dual-band” means communications over two separate RF bands. The communication over the two separate RF bands can occur simultaneously (concurrently); however, it should be understood that the communication over the two separate RF bands may not actually occur simultaneously. 
     In our example, the plug load element  1115  has a radio set that includes radio transmitter  1108  as well as a radio receiver  1110  forming a radio transceiver. The wireless transmitter  1108  transmits RF signals on the lighting network. This wireless transmitter  1108  wireless communication of control and systems operations information, during luminaire operation and during transmission over the first wireless communication band. The wireless receiver carries out receiving of the RF signals from other system elements on the network and generating RSSI data based on signal strengths of the received RF signals. If provided (optional) another transceiver (Tx and Rx) may be provided, for example, for point-to-point communication, over a second different wireless communication bands, e.g. for communication of information other than the control and systems operations information, concurrently with at least some communications over the first wireless communication band. Optionally, the plug load element  1115  may have a radio set forming a second transceiver (shown in dotted lines, transmitter and receiver not separately shown). 
     The included transceiver (solid lines), for example, may be a sub GHz transceiver or a Bluetooth transceiver configured to operate in a standard GHz band. A dual-band implementation might include two transceivers for different bands, e.g. for a sub GHz band and a GHz band for Bluetooth or the like. Additional transceivers may be provided. The particular bands/transceivers are described here by way of non-limiting example, only. 
     If two bands are supported, the two bands may be for different applications, e.g. lighting system operational communications and system element maintenance/commissioning. Alternatively, the two bands may support traffic segregation, e.g. one band may be allocated to communications of the entity owning/operating the system at the premises whereas the other band may be allocated to communications of a different entity such as the system manufacturer or a maintenance service bureau. 
     The MCU  1130  may be a system on a chip. Alternatively, a system on a chip may include the transmitter  1108  and the receiver  1110  as well as the circuitry of the MCU  1130 . 
     Plug load element  1115  plugs into existing AC wall outlets, for example, and allows existing wired lighting devices, such as table lamps or floor lamps that plug into a wall outlet, to operate in the lighting system. The plug load element  1115  instantiates the table lamp or floor lamp by allowing for commissioning and maintenance operations and processes wireless lighting controls in order to the allow the lighting device to operate in the lighting system. Plug load element  1115  further comprises an AC power relay  1160  which relays incoming AC power from power source  1100  to other devices that may plug into the receptacle of plug load element  1115  thus providing an AC power outlet  1170 . 
     Also, as shown, the MCU  1130  includes programming in the memory  1122 . A portion of the programming configures the CPU (processor)  1123  to detect one of an occupancy or non-occupancy condition in an area in the lighting network, including the communications over one or more wireless communication bands. The programming in the memory  1122  includes a real-time operating system (RTOS) and further includes a lighting application  1127  which is firmware/software that engages in communications with controlling of the light source based on one of the occupancy or non-occupancy condition detected by the CPU  1123 . As shown, a drive/sense circuitry detects a slate change event. The lighting application  1127  programming in the memory  1122  carries out lighting control operations over the lighting system  702  of  FIG. 7 . The programming for the detection of an occupancy or non-occupancy condition in the area may be implemented as part of the RTOS, as part of the lighting application  1127 , as a standalone application program, or as other instructions in the memory. 
     Aspects of heuristic methods of detecting occupancy and non-occupancy condition in a lighting system as described above may be embodied in programming, e.g. in the form of software, firmware, or microcode executable by a processor of any one or more of the lighting system nodes, or by a processor of a portable handheld device, a user computer system, a server computer or other programmable device in communication with one or more nodes of the lighting system. Program aspects of the technology may be thought of as “products” or “articles of manufacture” typically in the form of executable code and/or associated data that is carried on or embodied in a type of machine readable medium. “Storage” type media include any or all of the tangible memory of the computers, processors or the like, or associated modules thereof, such as various semiconductor memories, tape drives, disk drives and the like, which may provide non-transitory storage at any time for the software programming. All or portions of the software may at times be communicated through the Internet or various other telecommunication networks. Such communications, for example, may enable loading of the software from one computer or processor into another, for example, from a management server or host computer into a platform such as one of the controllers of  FIGS. 2,4, 7-10 . Thus, another type of media that may bear the software elements includes optical, electrical and electromagnetic waves, such as used across physical interfaces between local devices, through wired and optical landline networks and over various air-links. The physical elements that carry such waves, such as wired or wireless links, optical links or the like, also may be considered as media bearing the software. As used herein, unless restricted to one or more of “non-transitory,” “tangible” or “storage” media, terms such as computer or machine “readable medium” refer to any medium that participates in providing instructions to a processor for execution. 
     Hence, a machine readable medium may take many forms, including but not limited to, a tangible or non-transitory storage medium, a carrier wave medium or physical transmission medium. Non-volatile storage media include, for example, optical or magnetic disks, such as any of the storage hardware in any computer(s), portable user devices or the like, such as may be used. Volatile storage media include dynamic memory, such as main memory of such a computer or other hardware platform. Tangible transmission media include coaxial cables; copper wire and fiber optics, including the wires that comprise a bus within a computer system. Carrier-wave transmission media can take the form of electric or electromagnetic signals, or acoustic or light waves such as those generated during radio frequency (RF) and light-based data communications. Common forms of computer-readable media therefore include for example: a floppy disk, a flexible disk, hard disk, magnetic tape, any other magnetic medium, a CD-ROM, DVD or DVD-ROM, any other optical medium, punch cards paper tape, any other physical storage medium with patterns of holes, a RAM, a PROM and EPROM, a FLASH-EEPROM, any other memory chip or cartridge (the preceding computer-readable media being “non-transitory” and “tangible” storage media), a carrier wave transporting data or instructions, cables or links transporting such a carrier wave, or any other medium from which a computer can read programming code and/or data. Many of these forms of computer readable media may be involved in carrying data and/or one or more sequences of one or more instructions to a processor for execution. 
     Program instructions may comprise a software or firmware implementation encoded in any desired language. Programming instructions, when embodied in a machine readable medium accessible to a processor of a computer system or device, render a computer system or a device into a special-purpose machine that is customized to perform the operations specified in the program instructions. 
     Unless otherwise stated, any and all measurements, values, ratings, positions, magnitudes, sizes, and other specifications that are set forth in this specification, including in the claims that follow, are approximate, not exact. They are intended to have a reasonable range that is consistent with the functions to which they relate and with what is customary in the art to which they pertain. For example, unless expressly stated otherwise, a parameter value or the like may vary by as much as ±10% from the stated amount. 
     The scope of protection is limited solely by the claims that now follow. That scope is intended and should be interpreted to be as broad as is consistent with the ordinary meaning of the language that is used in the claims when interpreted in light of this specification and the prosecution history that follows and to encompass all structural and functional equivalents. Notwithstanding, none of the claims are intended to embrace subject matter that fails to satisfy the requirement of Sections 101, 102, or 103 of the Patent Act, nor should they be interpreted in such a way. Any unintended embracement of such subject matter is hereby disclaimed. 
     Except as stated immediately above, nothing that has been stated or illustrated is intended or should be interpreted to cause a dedication of any component, step, feature, object, benefit, advantage, or equivalent to the public, regardless of whether it is or is not recited in the claims. 
     It will be understood that the terms and expressions used herein have the ordinary meaning as is accorded to such terms and expressions with respect to their corresponding respective areas of inquiry and study except where specific meanings have otherwise been set forth herein. Relational terms such as first and second and the like may be used solely to distinguish one entity or action from another without necessarily requiring or implying any actual such relationship or order between such entities or actions. The terms “comprises,” “comprising,” “includes”, “including” or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element preceded by “a” or “an” does not, without further constraints, preclude the existence of additional identical elements in the process, method, article, or apparatus that comprises the element. 
     The Abstract of the Disclosure is provided to allow the reader to quickly ascertain the nature of the technical disclosure. It is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims. In addition, in the foregoing Detailed Description, it can be seen that various features are grouped together in various embodiments for the purpose of streamlining the disclosure. This method of disclosure is not to be interpreted as reflecting an intention that the claimed embodiments require more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive subject matter lies in less than all features of a single disclosed embodiment. Thus the following claims are hereby incorporated into the Detailed Description, with each claim standing on its own as a separately claimed subject matter. 
     While the foregoing has described what are considered to be the best mode and/or other examples, it is understood that various modifications may be made therein and that the subject matter disclosed herein may be implemented in various forms and examples, and that they may be applied in numerous applications, only some of which have been described herein. It is intended by die following claims to claim any and all modifications and variations that fell within the true scope of the present concepts.