Systems and methods for improved optical wireless communications based on mobility patterns

Abstract

Techniques are described herein for to improving optical wireless communications based on mobility patterns. In various embodiments, one or more mobility patterns observed in an area over time may be determined (302). The area may be illuminated by one or more lighting units (102) configured to transmit information using optical wireless communications (OWC). An applicable mobility pattern may be selected (308) from the one or more mobility patterns. Based on the selected mobility pattern, usage in the area of a plurality of OWC-based mobile apps (230) may be predicted (310). One or more OWC resources of at least one of the one or more lighting units may be allocated (312) for transmission of data to one or more of the plurality of OWC-based mobile apps operating on one or more mobile devices operated within the area. In various embodiments, the allocating may be based at least in part on the predicted usage.

Claims

1. A method for improving optical wireless communications based on mobility patterns, comprising: determining, by one or more processors, one or more mobility patterns observed in an area over time, wherein the area is illuminated by one or more lighting units, and the one or more lighting units are configured to transmit information using optical wireless communications; selecting, by one or more processors, an applicable mobility pattern from the one or more mobility patterns; predicting, by one or more processors, based on the selected mobility pattern, usage in the area of a plurality of optical wireless communications (OWC)-based mobile apps; and allocating one or more OWC resources of at least one of the one or more lighting units for transmission of data to one or more of the plurality of OWC-based mobile apps operating on one or more mobile devices operated within the area, wherein the allocating is based at least in part on the predicted usage.

2. The method of claim 1, further comprising estimating demographic data about a plurality of individuals in the area based on census data associated with the area.

3. The method of claim 2, wherein the predicted usage in the area of the plurality of OWC-based mobile apps is further predicted based on the demographic data.

4. The method of claim 1, further comprising predicting resource allocation needs of the plurality of OWC-based mobile apps based at least in part on the predicted usage.

5. The method of claim 1, wherein the allocating comprises enforcing a network protocol that allocates OWC physical layer resources based at least in part on the predicted usage.

6. The method of claim 1, wherein the one or more OWC resources include a plurality of light bands.

7. The method of claim 6, wherein the allocating further comprises allocating a first subset of the plurality of light bands for transmission of data to a first of the plurality of OWC-based mobile apps, and allocating a second subset of the plurality of light bands for transmission of data to a second of the plurality of OWC-based mobile apps.

8. The method of claim 7, wherein the first and second subsets are disjoint.

9. The method of claim 1, wherein the one or more OWC resources comprise one or more visible light communications resources.

10. The method of claim 1, wherein the allocating is further based on a quality of service requirement associated with at least one of the plurality of OWC-based mobile apps.

11. The method of claim 1, wherein the mobility patterns are determined based on accumulated call data records generated by mobile devices operated within the area.

12. The method of claim 1, wherein the mobility patterns are determined based on position coordinate data generated by mobile devices operated within the area.

13. A system comprising: one or more lighting units configured to illuminate an area, wherein the one or more lighting units are configured to transmit information using optical wireless communication; one or more processors operably coupled with the one or more lighting units; and memory operably coupled with the one or more processors, wherein the memory stores instructions that, in response to execution of the instructions by the one or more processors, cause the one or more processors to: determine one or more mobility patterns observed in the area over time; select an applicable mobility pattern from the one or more mobility patterns; predict usage in the area of a plurality of mobile apps; and allocate one or more optical wireless communications (OWC) resources of at least one of the one or more lighting units for transmission of data to one or more of the plurality of OWC-based mobile apps operating on one or more mobile devices operated within the area, wherein the allocating is based at least in part on the predicted usage and one or more mobility patterns.

14. The system of claim 13, further comprising instructions to estimate demographic data about a plurality of individuals in the area at a given time.

15. At least one non-transitory computer-readable medium stores instructions that, in response to execution of the instructions by one or more processors, cause the one or more processors to perform the following operations: determining one or more mobility patterns observed in an area over time, wherein the area is illuminated by one or more lighting units, and the one or more lighting units are configured to transmit information using optical wireless communications; selecting an applicable mobility pattern from the one or more mobility patterns; predicting, based on the selected mobility pattern, usage in the area of a plurality of optical wireless communications (OWC)-based mobile apps; and allocating one or more OWC resources of at least one of the one or more lighting units for transmission of data to one or more of the plurality of OWC-based mobile apps operating on one or more mobile devices operated within the area, wherein the allocating is based at least in part on the predicted usage.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

(1) In the drawings, like reference characters generally refer to the same parts throughout the different views. Also, the drawings are not necessarily to scale, emphasis instead generally being placed upon illustrating the principles of the invention.

(2) FIG. 1 illustrates schematically various components of the present disclosure, in accordance with various embodiments.

(3) FIG. 2 schematically depicts an example flow of data, in accordance with various embodiments.

(4) FIG. 3 depicts an example method for practicing selected aspects of the present disclosure, in accordance with various embodiments.

DETAILED DESCRIPTION

(5) Optical wireless communication (OWC) facilitates wireless transmission of information using visible and invisible light. Equipping lighting units for OWC may enable the lighting units to support networking-based applications, such as online retail, coupon distribution, airline/hotel/train reservations/check-ins, online review services, and so forth. As OWC-based applications gain popularity in indoor and outdoor environments, the Quality of Service (QoS) provided by the employed OWC technology to the various applications takes on increasing importance. QoS may be impacted by varying demand generated by end users that carry or otherwise operate various mobile devices (e.g., smart phones, tablets, smart watches, other wearable devices, vehicle computing systems, etc.). For example, in outdoor environments, mobility patterns of individuals may cause significant variability in traffic patterns. Accordingly, there is a need in the art for awareness about mobility patterns and/or demographics of end users for delivering optimal QoS and differentiated services to OWC-based applications. More generally, in view of the foregoing, various embodiments and implementations of the present invention are directed to allocating OWC-related resources based on mobility patterns and/or other information to improve OWC QoS to various mobile apps. Allocating OWC resources as described herein may lead to lower packet drop rates and/or may increase channel utilization. Rather than every mobile app using all available frequency bands (which may result in each frequency band having periods of significant downtime intermixed with occasional periods of congestion), allocating frequency bands amongst multiple apps as described herein may separate traffic from different mobile apps into different frequency bands, reducing the periods of overall congestion.

(6) One type of OWC that is commonly used is visible light communication (VLC). VLC may be used to modulate visible light waves to transmit and receive information, thereby facilitating wireless communication. One non-limiting VLC standard uses the visible portion of the spectrum of the light emitted by commonly available light sources such as LEDs for communication. Table 1, below, shows one non-limiting example of different bands that may be allocated as carrier waves or channels for communication:

(7) TABLE-US-00001 TABLE 1 Color Band Wavelength (nm) Spectral width Band 1 380-450 70 Band 2 450-510 60 Band 3 510-560 50 Band 4 560-600 40 Band 5 600-650 50 Band 6 650-710 60 Band 7 710-780 70
As will be described in further detail below, in various embodiments, these bands may constitute OWC resources (and in this instance, VLC resources) that are available to an OWC-equipped lighting unit for communication, e.g. with one or more mobile apps operating on one or more mobile devices within a line of sight of the lighting unit. By dynamically allocating these bands (or more generally, any OWC-related resource) based on mobility patterns and/or other information, QoS associated with OWC transmissions may be improved, particularly with mobile apps deemed likely to be operated in the area.

(8) Referring to FIG. 1, in one embodiment, a geographic area 100 may include a plurality of lighting units 102.sub.1-12 that are configured to illuminate various portions of geographic area 100. Lighting units 102 may take various forms, such as the outdoor street lamps depicted in FIG. 1, indoor lighting units (e.g., in an office building, mall, airport, stadium, etc.), architectural lights, and so forth. In various embodiments, one or more of lighting units 102.sub.1-12 may be configured to transmit information to remote computing devices and/or other lighting units 102 using OWC, such as VLC. Lighting units 102.sub.1-8 illuminate a north/south street, whereas lighting units 102.sub.9-11 illuminate a pedestrian walkway. Geographic area 100 also includes one or more buildings 124.sub.A and 124.sub.B. Geographic area 100 is just one example of an area in which disclosure techniques may be employed. Disclosed techniques may also be used in other types of areas and/or environments, such as indoor environments, mixed indoor/outdoor environments, in-vehicle environments (e.g., on trains, planes, in automobiles, etc.) and so forth.

(9) Individuals (not depicted) may travel through and/or otherwise occupy geographic area 100 at particular times in manners that may form mobility patterns. For example, if building 124.sub.A is a train station and building 124.sub.B is an office building, then people may travel from building 124.sub.A to building 124.sub.B during the morning rush hour, and in the reverse direction in the evening rush hour. Individuals travelling through or otherwise occupying geographic area 100 may operate mobile devices while in geographic area 100. Mobile devices may come in various forms, such as smart phones, tablet computers, wearable devices (e.g., smart watches, smart glasses), vehicular computing systems (e.g., navigation systems), and so forth.

(10) Under various circumstances, mobile devices may generate various data such as CDRs (e.g., when the operating individuals make a call or send a text), position coordinate data, network application transactions (e.g., individuals operating mobile apps while in geographic area 100), and so forth. As noted above, this data may be used to detect mobility patterns of the individuals over time. For example, individuals conducting telecommunications transactions (e.g., calls, texts, etc.) may cause one or more cellular towers 104 that serve geographic area 100 to generate corresponding CDRs. Additionally or alternatively, the mobile devices may generate (with or without human intervention) position coordinate data (e.g., GPS coordinates) that may indicate the individuals' location(s) within geographic area 100 at various times. In some cases, a telecommunication provider may make at least some of this data available to a mobility pattern analysis and prediction engine (MPAPE) 106.

(11) MPAPE 106 may include one or more computing systems (which may be connected via one or more networks, not depicted) configured (e.g., with software) to analyze data received from one or more cellular towers 104 or other resources controlled by telecommunications providers to determine mobility patterns of individuals within geographic area 100. In some embodiments, MPAPE 106 may include standard computing components such as one or more processors 110 that may be operably coupled with memory 112. Generally, MPAPE 106 may analyze the data received from the telecommunication provider(s) to determine, for instance, how long individuals stay in a particular location or building, how often individuals change locations within geographic area 100, and/or any other patterns relevant to mobility of individuals within geographic area 100. In some embodiments, the data analyzed by MPAPE 106 may include anonymized bulk data from one or more sources such as CDRs, outdoor cameras, indoor cameras, spectrometers, vehicle tracking logs, surveys, position coordinate logs, and/or any other source of data suitable for indicating a pattern of mobility of individuals in geographic area 100. In the context of FIG. 1, for instance, MPAPE 106 may detect a first mobility pattern that suggests increased traffic from building 124.sub.A to building 124.sub.B during weekday mornings, and a second mobility pattern that suggests increased traffic from building 124.sub.B to building 124.sub.A during weekday afternoons.

(12) In some embodiments, MPAPE 106 may obtain other information as well, from telecommunication providers or elsewhere. For example, in some embodiments, MPAPE 106 may be provided with information about web traffic data sent and/or received by individuals operating mobile devices within geographic area 100. This web traffic data may include various levels of information exchanged by mobile apps, such as hypertext transfer protocol (HTTP) data, hypertext markup language (HTML) data, extensible markup language (XML) data, JavaScript Object Notation (JSON) data, simple object access protocol (SOAP) data, web services description language (WSDL) data, and so forth. In various embodiments, MPAPE 106 may use this web traffic data to determine one or more mobile app usage patterns, preferences (e.g., determined from likes and dislikes on social media), and/or demographics of individuals operating mobile devices within geographic area 100.

(13) For example, MPAPE 106 may detect increased usage of weather- and/or traffic-related mobile apps during the morning and evening commutes. During lunch hours, MPAPE 106 may detect increased usage of mobile apps related to restaurants (e.g., review apps, navigational apps, reservation apps, etc.). In some embodiments, MPAPE 106 may infer one or more demographics about individuals in geographic area 100 based on web traffic data as well. For example, if a large fraction of individuals operating mobile devices in geographic area 100 at a given time tend to operate a particular mobile app, then user demographics known to be associated with that mobile app may be inferred with respect to the population of individuals within geographic area 100 at the given time.

(14) MPAPE 106 may infer preferences and/or demographics of individuals within geographic area 100 based on other data as well. For example, in some embodiments, MPAPE 106 may utilize census data (not depicted) to determine demographics of individuals (e.g., ages, genders, socioeconomic status, etc.) that live within geographic area 100, such as age distributions, gender distributions, income distributions, and so forth. In various embodiments, MPAPE 106 may project these demographics onto the mobility patterns, e.g., as an assumption that the individuals conforming to a mobility pattern are similar demographically to individuals indicated by census data to be located within geographic area 100. Preferences of individuals may be determined, for instance, using web traffic to determine what individuals operating mobile devices within geographic area like or dislike on social media, as well as using click through rates and/or other similar metrics on what links individuals select while within geographic area 100.

(15) In various embodiments, MPAPE 106 may provide mobility and/or mobile app usage patterns, and/or any demographics it can infer, to dynamic OWC resource allocation engine (DOAE) 108. In some embodiments, DOAE 108 may include one or more computing systems configured to control light emitted by one or more lighting units 102 within geographic area 100. In various embodiments, DOAE 108 may include standard computing components such as one or more processors 110 that may be operably coupled with memory 112. Based on data received from MPAPE 106, DOAE 108 may be configured to optimize one or more layers of OWC, such as the physical and/or medium layers, to improve QoS. For example, in some embodiments, DOAE 108 may enforce a dynamic network protocol at the physical layer to allocate one or more OWC resources of at least one of the one or more lighting units 102 for transmission of data to one or more mobile apps operating on one or more mobile devices operated within geographic area 100. For example, suppose mobility and mobile app usage patterns provided by MPAPE 106 indicate increased usage of a particular mobile app during the lunch hour on weekdays. DOAE 108 may increase OWC resources (e.g., the number of bands) used by one or more lighting units 102 within geographic area 100 to exchange data with the particular mobile app. By increasing or otherwise selectively allocating OWC resources used to communicate with a particular mobile app, QoS for the mobile app may be improved.

(16) FIG. 2 schematically depicts an example process flow that may be implemented by various components of FIG. 1. In this example, raw mobility data such as CDRs, position coordinate data, or other similar data described above may be obtained from a database 228. As noted above, in some embodiments, database 228 may be populated with data that is provided by one or more telecommunications providers, e.g., using data generated at cellular towers and/or other components of a telecommunications system. In other embodiments, data may be proved by telecommunications providers in real time or using other data transfer means, such as batch downloads, etc. In yet other embodiment, data provided by telecommunications providers or other similar entities may already be analyzed to identify mobility patterns (e.g., the data may include annotations and/or may be summarized to indicate mobility patterns).

(17) MPAPE 106 may obtain this data and as described above (assuming mobility patterns are not already pre-identified and/or pre-annotated) may identify one or more mobility patterns from the data. In some embodiments, data in database 228 may be periodically and/or continuously updated. In some such embodiments, MPAPE 106 may obtain data from database 228 periodically (e.g., hourly, daily, weekly, monthly, etc.) and/or continuously, so that MPAPE 106 can continue to detect new mobility patterns and/or modify existing mobility patterns based on new data. As noted above, in some embodiments, MPAPE 106 may additionally or alternatively identify mobile app usage patterns of individuals in geographic area 100, e.g., based on web traffic data or other data that may or may not be provided by the same telecommunications provider(s). In some instances, mobile app usage patterns may be determined by MPAPE 106 based at least in part on mobility patterns from database 228.

(18) The mobility patterns identified by MPAPE 106 may be provided to DOAE 108 on a periodic or continuous basis (e.g., as new mobility patterns are detected by MPAPE 106), and/or on demand. As described above, DOAE 108 may allocate one or more OWC resources of at least one of the one or more lighting units (102) for transmission of data to one or more of a plurality of mobile apps operating on one or more mobile devices operated within geographic area 100. For example, DOAE 108 may transmit one or more allocation commands to one or more lighting units 102, e.g., using various network communication protocols (e.g., cellular, Wi-Fi, ZigBee, OWC, Z-Wave, etc.). As noted above, in some embodiments, this allocation of OWC resources may be based at least in part on the determined mobile app usage patterns described above. In FIG. 2, for instance, the OWC resources includes light channels 232.sub.1-M, which may be allocated among various mobile apps 230.sub.1-N, using techniques such as frequency-division multiplexing (FDM). In some embodiments, more complex schemes such as orthogonal frequency-division multiplexing (OFDM) may be utilized across one or more channels, or within a single channel.

(19) Suppose APP 1 230.sub.1 relates to local restaurants (e.g., review app, reservation app, discount coupon app, etc.) and that APP 2 230.sub.2 relates to local establishments pertaining to children (e.g., toy stores, playgrounds, etc.). Suppose further that in the geographic location under consideration, at the present time, MPAPE 106 determines that an applicable mobility pattern and/or mobile app usage pattern indicates that the instantaneous population (e.g., a population snapshot) is likely to include mostly adults looking for meals (e.g., dinnertime). APP 1 230.sub.1 is more likely to be useful to the projected current population of the geographic area than APP 2 230.sub.2. Consequently, DOAE 108 may allocate two channels (Channel 1 232.sub.1 and Channel 2 232.sub.2) available to local light sources to APP 1 230.sub.1. By contrast, because the instantaneous local population is less likely to include many children, APP 2 230.sub.2 may be less useful to the projected current population of the geographic area. Accordingly, APP 2 230.sub.2 may only be allocated a single channel, Channel 3 232.sub.3.

(20) These techniques may be applied to allocate OWC resources to other mobile apps 230.sub.3-230.sub.N. For example, suppose that mobility and/or usage patterns reveal that APP 3 230.sub.3 is also not likely to be useful to a substantial portion of the current projected local population. APP 3 230.sub.3 may also be allocated a single channel, Channel 3 232.sub.3. As depicted in FIG. 2, in some embodiments, multiple mobile apps may share the same channel, as is the case with APP 2 230.sub.2 and APP 3 230.sub.3. In such embodiments, the shared channel may be allocated in a manner such that data is modulated differently for different applications. For example, in some embodiments, within a single frequency channel, time-division multiplexing (TDM) or other similar techniques may be employed to distinguish traffic to/from one associated mobile app from traffic to/from another mobile app sharing the frequency channel. In other embodiments, frequency channels 232 may not be shared among mobile apps (i.e. different subsets of bands allocated to different mobile apps may be disjoint).

(21) DOAE 108 may select how it allocates OWC resources in various ways. In some embodiments, DOAE may employ a rules based approach to allocate frequency channels 232.sub.1-M amongst a plurality of mobile apps 230.sub.1-N. For example, if the majority of people in a geographic area are projected by MPAPE 106 to be in their twenties, then more bandwidth (e.g., more frequency channels 232) may be allocated to mobile apps that benefit or otherwise are predominantly used by such a demographic.

(22) In some embodiments, allocation of OWC resources may include enforcement of a network protocol that allocates OWC physical layer resources based at least in part on predicted usage of OWC-based mobile apps. Table 2, below, demonstrates one example of how OWC physical layer resourcesnamely, multiple OWC frequency bandsmay be allocated using a rule-based technique. In Table 2, a seven bit code is used to denote to mobile apps how the seven available bands (similar to Table 1) should be used.

(23) TABLE-US-00002 TABLE 2 Bit Band Band Pattern Band 1 Band 2 Band 3 Band 4 Band 5 6 7 0000001 X X X X X X 0000010 X X X X X X . . . 0100110 X X X X 1000000 X X X X X X . . . 1111111
Suppose a user is travelling through an area illuminated by lighting units configured with selected aspects of the present disclosure. Suppose further that the user is carrying or otherwise operates a mobile device that executes a particular mobile app that receives push notifications (e.g., happy hour specials nearby) from establishments that have previously arranged for such notifications to be pushed to users of the particular application. Traditionally, a cellular connection of the user's mobile device may be used to push this notification to the user's mobile device, e.g., as a pop-up message on the user's screen, in response to a determination based on the user's GPS coordinates that the user is near a participating establishment. However, using techniques described herein, the user's mobile device may instead receive such a push notification (and may send responsive data) using OWC from one or more lighting units near a participating establishment. Accordingly, mobile apps may receive such notifications in areas in which cellular and/or GPS signals may not be available, such as indoors, on trains or planes, etc.

(24) In some embodiments, an operating system or other component of the user's mobile device (e.g., a separate OWC interface controller) may be notified, e.g., via one or more OWC transmissions from nearby lighting units or by other out-of-band means such as cellular communication, etc., that a first mobile app has been allocated particular OWC band(s) for communication. Using the example of Table 2, the bit pattern 0000001 may be associated with the first mobile app, and that association may be communicated to the operating system of the user's mobile device. The operating system (or separate OWC interface controller) may use that bit pattern to determine that Band 7 is allocated for the first mobile app to exchange data via one or more nearby lighting units using OWC. Thereafter, the operating system may only provide inbound data (e.g., a serial stream of bits) received via Band 7 to the first mobile app. Inbound data received via other OWC bands may be ignored, or at least not provided to the first mobile app. Likewise, outbound data from the first mobile app may be transmitted using Band 7.

(25) A second mobile app that is deemed (based on mobility and/or usage patterns, demographics, etc.) more likely than the first mobile app to be used by a relatively large portion of an instantaneous population of an area may be allocated a greater amount of OWC resources. For example, the bit pattern 0100110 may allocate Bands 2, 5, and 6 for transmission of traffic to/from the second mobile app. Thereafter, the operating system may provide inbound data (e.g., a serial stream of bits) received via Bands 2, 5, and 6 to the second mobile app. Inbound data received via other OWC bands may not be provided to the second mobile app. Likewise, outbound data from the second mobile app may be transmitted using one or more of Bands 2, 5, and 6.

(26) In other embodiments, DOAE 108 may use more complex techniques to allocate OWC resources. For example, in some embodiments, supervised and/or unsupervised machine learning techniques may be used to train one or more models (e.g., neural networks) to provide output that dictates how one or more OWC resources should be allocated in a given scenario. For example, a neural network or other machine learning model could be trained using, as input, training data that includes various aspects of mobility and/or usage patterns in a particular scenario, and in some cases corresponding demographics, as well as various features relating to how OWC resources were allocated in that particular scenario. Negative training examples may include feature vectors that represent scenarios in which a particular allocation of OWC resources yielded a negative outcome, such as an unacceptable packet loss rate. Positive training examples may include feature vectors that represent scenarios in which a particular allocation of OWC resources yielded a positive outcome, such as relatively low packet loss. In some embodiments, the outcome (or label) of the particular training example may be determined automatically, e.g., by lighting units in the area detecting packet loss rate, jitter, etc. In some such embodiments, the model may be continuously trained using additional training examples generated over time when the lighting units use OWC to exchange data with mobile apps. For example, the lighting units may detect packet loss rate in a particular scenario, label new training data with that packet loss rate, and provide that new training data to the model as labeled input.

(27) FIG. 3 depicts an example method 300 for implementing various aspects of the present disclosure. While operations of method 300 are depicted in a particular order, this is not meant to be limiting. In various embodiments, one or more operations of method 300 may be omitted, reordered, and/or one or more operations may be added.

(28) At block 302, one or more mobility patterns observed in a given area over time may be determined. The area may include a geographic area such as all or a portion of a city (e.g., 100), and/or may include other indoor and/or outdoor areas illuminated by lighting units configured with selected aspects of the present disclosure, such as campuses, compounds, camps, airports, train stations, etc. As noted above, the mobility patterns may be determined on a variety of different data, including but not limited to CDRs, position coordinate data (e.g., GPS logs), and so forth. In some embodiments, the data in which mobility patterns or observed may be provided by telecommunications providers, although this is not required.

(29) At optional block 304, mobile app usage patterns in the area may be determined over time. As noted above, these mobile app usage patterns may relate to usage of mobile apps within the area, and may be detected, for instance, using data such as the above-described web traffic data, as well as other data such as application logs, etc. In some embodiments, one or more telecommunication providers may also provide at least some of the web traffic data that is used to identify mobile app usage patterns. However, this is not required. In various embodiments, mobile app usage patterns may or may not be based on, e.g., correlated with, mobility patterns. At block 306, demographics of the area may be determined, e.g., based on census data, surveys, mobile app usage patterns, etc. As noted above, these demographics may effectively be projected onto a mobility pattern to estimate the demographics of individuals forming part of a given mobility pattern.

(30) At block 308, an applicable mobility pattern may be selected. As a simple example, a mobility may be applicable to a current time (or just after a current time). For example, if rush hour is about to begin, then a mobility pattern associated with rush hour may be selected as being instantaneously (or momentarily) applicable. Then, the remaining operations of method 300 may be implemented to allocate OWC resources in real time. However, it is not necessary that an applicable mobility pattern be selected in real time. In some embodiments, an applicable mobility pattern may be selected for some point in the future. At least some of remaining operations of method 300, such as 310, may be performed immediately or later, and other remaining operations method 300 may be performed at or just before the point in the future.

(31) At block 310, usage of mobile apps in the area may be predicted. This prediction may be based at least in part on the mobility data, but also may be predicted based on mobile app usage patterns determined at block 304 and/or demographics determined at block 306. For example, it may be predicted that for given mobility pattern expected to occur at a particular time, the individuals predicted to conform to the mobility pattern will have certain demographic characteristics (e.g., age, gender). Additionally or alternatively, and in some embodiments based on the demographics, it may be predicted that the individuals will use some mobile apps more than others.

(32) Based on the predicted usage of mobile apps, at block 312, one or more OWC resources, such as the bands/channels described above, may be selectively allocated for use by one or more lighting units in the area to exchange information with particular mobile apps. An example of such allocation was demonstrated in FIG. 2. At block 314, one or more lighting units may transmit data to, and/or receive data from, one or more mobile apps executing on one or more mobile devices within the area using the OWC resources as allocated at block 312.

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

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

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

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

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

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

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

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