Patent Publication Number: US-10778516-B2

Title: Determination of a next state of multiple IoT devices within an environment

Description:
BACKGROUND 
     In today&#39;s ever connected world, the use of Internet of Things (IoT) is set to explode over the next decade. In using IoT, physical devices are embedded with technology that provide network connectivity to enable the devices to collect and exchange data. However, there are several anticipated challenges with the explosion of IoT. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       In the accompanying drawings, like numerals refer to like components or blocks. The following detailed description references the drawings, wherein: 
         FIG. 1  is an example environment system including multiple IoT devices and a state machine to transition the multiple IoT devices to a next state in accordance with the present disclosure; 
         FIGS. 2A-2C  illustrate example states of multiple IoT devices within an environment in accordance with the present disclosure; 
         FIG. 3  is an example system to receive crowdsourcing information to transition multiple IoT devices within an environment into a next state in accordance with the present disclosure; 
         FIG. 4  illustrates an example flow diagram to determine a next state of multiple IoT devices in which to transition the multiple IoT devices in accordance with the present disclosure; 
         FIG. 5  illustrates an example flow diagram to determine a next state of multiple IoT devices within an environment by using crowdsourcing information in accordance with the present disclosure; 
         FIG. 6  is a block diagram of an example computing device with a processing resource to execute instructions in a machine-readable storage medium for identifying a next state and automating multiple IoT devices to attain the next state in accordance with the present disclosure; and 
         FIG. 7  is a block diagram of an example computing device with a processing resource to execute instructions in a machine-readable storage medium for automating multiple IoT devices to transition to a next state by receiving crowdsourcing information to attain the next state in accordance with the present disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     There are several anticipated challenges with the explosion of IoT. One such challenge is streamlining and automating repetitive tasks with the IoT devices. In particular, IoT devices designed for home environments use smart applications of IoT and/or user(s) to manually initiate actions for the IoT devices. For example, the IoT devices may be installed in an environment with network connections, which allows the IOT devices&#39; manufacturers to update the device behavior and users to control remotely through an application. However, this does not the IoT devices to be fully automated nor adapt to different scenarios without user or manufacturer input. Rather, in this example, users manually instruct the IoT devices to initiate an action or pre-programs the IoT device behavior through third party applications. Using third party applications, the IoT device behavior is designed based on correlations between the IoT actions (e.g., when the motion sensor senses, turn lights on). Alternatively, these third party applications allow users to specify the correlations at a high semantic level. As such, both of these examples use input from either a manufacturer or user and is not intuitively intelligent to fully automate the IoT devices for transitions. 
     Accordingly, the present disclosure provides a state machine engine to model behavior or states of multiple IoT devices within an environment such as a home and/or building. Based on the states of the multiple IoT devices, the state machine receives contextual information to indicate transitions to the next states for the multiple IoT devices. Contextual information may include: a temporal attribute to infer a time characteristic; a spatial attribute to infer a user&#39;s location related to the environment; and/or notification to describe events that trigger an IoT device&#39;s status. Using contextual information to transition to the next state, provides an adaptive context aware framework that automates the various states of IoT devices within the environment. 
     Additionally, using a state machine to model a state of multiple IoT devices within the environment. The state machine incorporates both contextual information such as time attributes and a location of the user relative to the environment and crowdsourcing information. Using these different sources of information, the state transitions are calculated to predict which state in which to transition the multiple IoT devices. This provides an adaptive feature that allows the state transitions to change based on the information. 
     Further, the present disclosure also considers crowdsourcing information to determine the next state in which to transition the multiple IoT devices. Using crowdsourcing information from other participating environments fine tunes the transitional states based on correlations shared between these environments. This allows an additional intelligence layer to adjust the IoT devices to attain the next state. 
     The following detailed description refers to the accompanying drawings. Wherever possible, the same reference numbers are used in the drawings and the following description to refer to the same or similar parts. It is to be expressly understood, however, that the drawings are for the purpose of illustration and description only. While several examples are described in this document, modifications, adaptations, and other implementations are possible. Accordingly, the following detailed description does not limit the disclosed examples. Instead, the proper scope of the disclosed examples may be defined by the appended claims. 
     The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting. As used herein, the singular forms “a,” “an,” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. The term “multiple,” as used herein, is defined as two, or more than two. The term “another,” as used herein, is defined as at least a second or more. The term “coupled,” as used herein, is defined as connected, whether directly without any intervening elements or indirectly with at least one intervening elements, unless otherwise indicated. Two elements can be coupled mechanically, electrically, or communicatively linked through a communication channel, pathway, network, or system. The term “and/or” as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items. It will also be understood that, although the terms first, second, third, etc. may be used herein to describe various elements, these elements should not be limited by these terms, as these terms are only used to distinguish one element from another unless stated otherwise or the context indicates otherwise. As used herein, the term “includes” means includes but not limited to, the term “including” means including but not limited to. The term “based on” means based at least in part on. As such, these terms may be used interchangeably throughout. Additionally, the term “IoT device” is understood to be a physical device that includes a machine type of communication device and machine-to-machine communication type devices capable of network connectivity. 
     The foregoing disclosure describes a number of example implementations for automating multiple IoT devices within an environment. The disclosed examples may include systems, devices, computer-readable storage media, and methods for transitioning multiple IoT devices to a next state. For purposes of explanation, certain examples are described with reference to the components illustrated in  FIGS. 1-7 . The functionality of the illustrated components may overlap, however, and may be present in a fewer or greater number of elements and components. Further, all or part of the functionality of illustrated elements may co-exist or be distributed among several geographically dispersed locations. Moreover, the disclosed examples may be implemented in various environments and are not limited to the illustrated examples. For example,  FIGS. 1-7  may illustrate a wired connection; however, this was done for illustration purposes as it may be understood the wired connection may comprise a wireless connection or combination thereof. 
       FIG. 1  is an example environment  102  including state machine  108  to determine current state  104  of multiple IoT device  106   a - 106   d  and transition to next state  114 . State machine  108 , coupled to controller  112 , receives contextual information  110  to transitions multiple IoT devices  106   a - 106   d  from current state  104  to next state  114 . Environment  102  represents a surrounding area that collects status information of multiple IoT devices  106   a - 106   d  and transitions the status of the multiple IoT devices  106   a - 106   d . As such, implementations of environment  102  may include a home, office, location building, campus, stadium, theater, or other type of surrounding area that includes multiple IoT devices  106   a - 106   d . Although environment  102  includes components  106   a - 106   d ,  108 , and  112 , implementations should not be limited as environment  102  may also include an access point for multiple IoT devices  106   a - 106   d  to gain access to a network. 
     Current state  104  represents a particular condition of multiple IoT devices  106   a - 106   d  within environment  102  at a specific time. As such, current state  104  further includes a status of each of multiple IoT devices  106   a - 106   d . In this manner, current state  104  comprises various statuses of multiple IoT devices  106   a - 106   d  at the present moment. The status of each of multiple IoT devices  106   a - 106   d  is considered the position of the each device at a particular time. The status may include a standby, non-operational, low powered, off, normal operation, operational, on, etc. Each of these statuses represents the condition of each device. For example in  FIG. 1 , current state  104  includes the status of a light bulb  106   a , coffee maker  106   b , computing device  106   c , and mobile device  106   d . As such, in current state  104  the status of light bulb  106   a  is off, the status of coffee maker  106   b  is not brewing, computing device  106   c  is off, and mobile device  106   d  may be in standby mode. Over time, the statuses of multiple IoT devices  106   a - 106   d  may change to create next state  114 . To anticipate which statuses change, state machine  108  uses contextual information to accurately predict which IoT device status will change. In a further implementation state machine  108  receives crowdsourcing information to more accurately predict which status should change and automate the transition appropriately. 
     Multiple IoT devices  106   a - 106   d  are physical devices that are embedded with electronics and software that provide network capability. Providing network capability allows multiple IoT devices  106   a - 106   d  to collect and exchange data over the network.  FIG. 1  illustrates multiple IoT devices  106   a - 106   d  as including a light bulb  106   a , coffee maker  106   b , computing device  106   c , and mobile device  106   d ; however, implementations should not be so limiting as IoT devices may include any physical device that is capable of machine type of communication and network connectivity. For example this may include a household appliance, office appliance, vending machines, printer, retail point of sale device, etc. 
     State machine  108  is a component that determines next state  114  of multiple IoT devices  106   a - 106   b  for transition. Initially, state machine  108  determines current state  104  of multiple IoT devices  106   a - 106   b . Based on receiving contextual information  110  from other components  102  in environment, such as sensors, trackers, records, etc., state machine  108  identifies next state  114  of multiple IoT devices  106   a - 106   d . In another implementation, state machine  108  also receives crowdsourcing information to determine next state  114 . The crowdsourcing information is collected data from other environments representing transitions for those environments&#39; IoT devices. This allows other information to be input for anticipating next state  114 . Additionally, state machine  108  may calculate and model current state  104 . In this implementation, state machine  108  models one of a finite number of states at a given time. In response to input such as contextual information  110  and crowdsourcing information, state machine  108  can change from one state (e.g., current state  104 ) to another state (e.g., next state  114 ). Implementations of state machine  108  include, electronic circuitry (i.e., hardware) that implements the functionality of the state machine  108 , such as an integrated circuit, programmable circuit, application integrated circuit (ASIC), controller, processor, semiconductor, processing resource, chipset, or other type of hardware component capable of the functionality of state machine  108 . Alternatively, state machine  108  may include instructions (e.g., stored on a machine-readable medium) that, when executed by a hardware component (e.g., controller  112 ), implements the functionality of state machine  108 . 
     Contextual information  110  is information used to infer time and/or user&#39;s location relative to environment  102 . In one implementation, contextual information  110  includes at least one temporal attribute or spatial attribute or combination thereof. The temporal attribute is a property that may be used in infer time, such as a time of day, season of year, etc. The spatial attribute is a property used to infer the user&#39;s location relative to environment  102 , such as user is within environment  102  or outside of environment  102 . Contextual information  110  may be provided to state machine  108  through controller  112  or other components (not illustrated) such as trackers, sensors, and/or recorders that can be used to infer information such as the time and user&#39;s location. In further implementations, contextual information  110  includes a notification that triggers modification of a status of the multiple IoT devices  106   a - 106   d . As seen in  FIG. 1 , contextual information  110  may include a time (e.g., alarm clock and sun) and user&#39;s location relative to environment  102  (e.g., user in bed). In this example, in the morning, the user may have awoken and as such, multiple IoT devices  106   a - 106   d  which may have been in a powered down or off status, may power on as indicated by next state  114 . 
     Controller  112 , as coupled to state machine  108 , operates in conjunction with state machine  108  to transition multiple IoT devices  106   a - 106   d  from current state  104  to next state  114 . In one implementation, controller  112  executes instructs that provides the functionality of state machine  108 .  FIG. 1  illustrates controller  112  and state machine  108  as separate components; however, controller  112  and state machine  108  may be an integral component. Controller  112  manages state machine  108  and may include, by way of example, an integrated circuit, semiconductor, memory module, central processing unit (CPU), processing resource, application-specific integrated circuit (ASIC), controller, processor, chipset, virtualized component or other type of management component capable of managing environment  102  and/or state machine  108 . 
     Next state  114  represents a modification of status among multiple IoT devices  106   a - 106   d  to adapt to different scenarios based on contextual information  110 . As illustrated by next state  114 , based on a user awakening and/or daybreak, light bulb  106   a  modifies status from off to on, coffee maker  106   b  may remain in a power off mode, computer  106   c  turns from off to on, and mobile device  106   d  may remain in standby mode. In another implementation, next state  114  may also be attained through available crowdsourcing information. This implementation is discussed in later figures. 
       FIGS. 2A-2C  illustrate various example states of multiple IoT devices within an environment based on contextual information to adapt to different scenarios. Further, a probability for each of the various states is determined to identify a likelihood a specific environment should adapt to that state. In these figures, the various states are modeled using a finite state machine model (FSM) and using the contextual information to transition between the various IoT devices status. 
     For example, in  FIG. 2A , the time of day and user&#39;s location can be used as contextual information to switch a status of a light bulb from off to on and a coffee maker from off to on to transition from a current state to next state. Although states  204  and  214  include a single IoT device and transition example, this was done for illustration purposes. For example, states  204  and  214  may include addition IoT devices. In each state  204  and  214 , a current state and next state is represented for transitioning the respective IoT devices. Trivially assuming each device status can be modeled as a different vertex on a FSM graph would result in may disjointed graphs where a variance in the contextual information could affect transitions in one model or all models. As such, the state machine would use an edge unique to each state. 
     In  FIG. 2A , an example smart home  202  is used as the state machine where a vertex represents various home states  204  and  214  to include the set of multiple IoT devices&#39; statuses. The various equations below reference how the state machine model can be leveraged to automate an environment (e.g., house). Although the house environment is used, other environments could include an office, stadium, etc. As such, in a more formalized fashion, D below may represent the set of IoT devices that can be found in home  202 . Additionally, let be the status i of device j and the house state (HS)  202 .
 
 D={d   1   ,d   2   , . . . ,d   d }, where | D|=d  
 
 HS={S   i   1   ,S   j   2   , . . . ,S   k   n }
 
     The transition between states, or a change in the house state occurs based on modifications or changes in the contextual information. A state transition function δ is a function that given a set of inputs applied on a set of states, outputs a new set of state. In this example, the set of states is the house state HS and thus δ becomes, where Σ is the input (e.g., contextual information):
 
δ:  S×Σ−&gt;S  
 
     Let T denote the set of temporal attributes, L the set of spatial attributes and N the notifications that may trigger an adaption to another state, then:
 
Σ={ T,L,N} 
 
     Turning now to  FIG. 2B , a state transition Σ between states  204  and  214  is illustrated. Each model  216  and  218  represent the various statuses of the IoT devices within home state  202 . Given a specific user, the state machine can learn over time the state transition that may apply to that specific user. If there are multiple users in given environment, then spatial attributes to infer user location and other information would be more specific to avoid conflicting behaviors. 
     Turning now to  FIG. 2C , a modeling approach uses crowdsourcing information to predict state transitions  214  and  220  based on associated probabilities with each state transition. As discussed above in connection with  FIGS. 2A-2B , a given context and current state is used to predict the next state to transition. However, for different environments, the output of the transition function can be different. As such, patterns such as human habits can be modeled as a Markov chain for the global environment, such as a global household. That is a global household state can be modeled as a random process. Further, state transitions are associated with transition probabilities. For example, the contextual information C i  and initial global state X, there is a probability β of transitioning to state Y  214  and a probability γ of transition to state Z  220 . The approach is scaled toward common behaviors, meaning the most probably states given some contextual information is similar for most people. In this regard, the approach can take a probabilistic and recommendation in the network. Global states have possible useful attributes (individual device statuses) and own contextual information. in this examples, learning techniques can be applied to the global state to learn which transition is more likely for the given environment. The probabilities associated with each state  214  and  220  can be calculated through weighting parameters or other mechanism that may take into account the given environment. 
       FIG. 3  illustrates an overall example system architecture that includes different environments  302  and  304 . IoT device types, statuses, spatial attribute(s), temporal attribute(s), and other information is communicated from participating environments  302  and  304  through an overlay protocol to a hosted and managed cloud  300 . This cloud  300 , includes a crowdsourcing handler  306  that collects the crowdsourcing information to use as input to a home state to global home state  308  to identify the patterns that are common among environments  302  and  304 . Feature extraction  310  extracts features from the collected information and uses the information to train the machine learning models seen in  FIGS. 2A-2C  in training module  312  and testing module  314 . Testing module  314  reports information to probabilistic model  316  to predict a next state based on crowdsourcing information. Probabilistic model  316  associates a probability for each of the various states for a given environment. Status selection module  318  identifies which IoT devices&#39; statuses should transition to achieve the next state. Based on identifying the individual IoT devices in which to transition to attain the next state, request handle  322  transmits a communication to those impacted IoT devices to transition their states. 
     In the example architecture, a participant may query for state transition. Thus, in one of the environments  302  and  304  given a new context (e.g., new contextual information), the environment  302  and  304  can request a new calculation to predict the next state in which to transition the multiple IoT devices. In this example, the global home state module  308  extracts the requestor&#39;s current state and contextual information. At module  308 , the current state extracts the statuses of the multiple IoT devices and feeds them as testing attributes to the machine learning model  316 . The model  316  ranks the edges to the various states. As such, cloud  300  finds the most appropriate state in which to transition and responds to requestor to predict the next state and transition the multiple IoT devices to attain the next state. 
     Referring now to  FIGS. 4 and 5 , flow diagrams are illustrated in accordance with various examples of the present disclosure. The flow diagrams represent processes that may be utilized in conjunction with various systems and devices as discussed with reference to the preceding figures. While illustrated in a particular order, the flow diagrams are not intended to be so limited. Rather, it is expressly contemplated that various processes may occur in different orders and/or simultaneously with other processes than those illustrated. As such, the sequence of operations described in connection with  FIGS. 4-5  are examples and are not intended to be limiting. Additional or fewer operations or combinations of operations may be used or may vary without departing from the scope of the disclosed examples. Thus, the present disclosure merely sets forth possible examples of implementations, and many variations and modifications may be made to the described examples. 
       FIG. 4  is a flow diagram illustrating a method executable by a network controller to determine a next state. The determined next state is a state in which multiple IoT devices are transitioned from a current state in accordance with the present disclosure. In discussing  FIG. 4 , references may be made to the components in  FIGS. 1-2  to provide contextual examples. In one implementation, network controller  112  as in  FIG. 1  executes operations  402 - 406  to transition the multiple IoT devices to the next state. In another implementation, state machine  108  may operate in conjunction with network controller  112  to execute operations  402 - 406 . Although  FIG. 4  is described as implemented by network controller  112 , it may be executable on other suitable hardware components. For example,  FIG. 4  may be implemented in the form of executable instructions on a machine-readable storage medium  604  and  704  as in  FIGS. 6-7 . 
     At operation  402  by operation of the state machine, the current state of the multiple IoT devices within the environment is determined. The current state includes a status of each of the multiple IoT devices to comprise the current state. As such, having different IoT devices within an environment, such as a light switch, coffee maker, soda machine, etc. each may have a different status. The various statuses for the different IoT devices forms the current state. Depending on a user&#39;s location and/or time of day, these IoT devices may modify their particular statuses. In this case, the contextual information, e.g., user&#39;s location and/or time, may modify the statuses of the IoT devices to comprise the next state. For example, the presence of the user within the environment may indicate to turn on a light while the absence of the user and/or nighttime may indicate to turn off the light. In another implementation, the state machine models the current state of the multiple IoT devices by modeling the state of each multiple IoT devices. In this implementation, a state machine uses the contextual information to transition the statuses of the multiple IoT devices to attain the different states of the environment. These statuses that comprise the state between the states of an environment may change over time and/or user&#39;s presence to encompass the next state as determined at operation  406 . 
     At operation  404 , the state machine receives contextual information that includes at least one of either a spatial attribute and/or temporal attribute. As described in connection with earlier figures, the spatial attribute is a spatial feature used to infer a user&#39;s location relative to the environment. The spatial attribute may be as coarse as a user being present or absent from a particular environment, while finer location may include a user&#39;s GPS coordinates. The spatial attribute may be collected through a sensor and/or tracker to record the user&#39;s location. The temporal attribute is a feature used to infer the time. The time can be as fine-grained as a specific timestamp on a specific day of a specific month or as coarse grained as a season and/or year. In this implementation, the temporal attribute may be defined as time information including a collection of a minute, hour, day, week, season, and/or year, etc. In another implementation, the contextual information may also include a notification feature that describes events that may trigger an IoT devices status, such as a button pressed, sensor readings as motion sensed, and/or external events such as cloud notifications (e.g., weather alerts). The contextual information may be reported by sensors and/or tracking devices as input to the state machine. Using the contextual information, the state machine can anticipate the transition to the next state of the multiple IoT devices within the environment. In a further implementation, crowdsourcing information is received in addition to the contextual information to predict the next state. This implementation is explained in detail in a later figure. 
     At operation  406 , based on the current state of the multiple IoT devices within the environment as determined at operation  402  and the contextual information received at operation  404 , the state machine determines the next state. As such, the state machine transitions the multiple IoT devices within the environment to the next status to attain the next state. In this implementation, communication(s) are transmitted to the individual IoT devices to transition each status for attaining the next state. In a further implementation, the probabilities of the different states for the environment is calculated. Using these probabilities determines the likelihood for the environment to transition to that next state. In this manner, the state machine can predict the likelihood of which state to transition the multiple IoT devices to attain the next state. 
       FIG. 5  is a flow diagram illustrating a method executable by a networking device to determine a next state of multiple IoT devices within an environment. Based on the determination of the next state, each of the multiple IoT devices is transitioned to attain the next state. The networking device initially determines a current state by modeling a status of each of the multiple IoT devices. The networking device may proceed to receive contextual information that includes at least one of either a spatial attribute and/or temporal attribute. Based on the current state and this contextual information, the networking device proceeds to determine the next state in which to transition the multiple IoT devices. In discussing  FIG. 5 , references may be made to the components in  FIGS. 1-2  to provide contextual examples. In one implementation, network controller  112  as in  FIG. 1  executes operations  502 - 522  to transition the multiple IoT devices to the next state. Although  FIG. 5  is described as implemented by network controller  112 , it may be executable on other suitable hardware components. For example,  FIG. 5  may be implemented in the form of executable instructions on a machine-readable storage medium  604  and  704  as in  FIGS. 6-7 . 
     At operation  502 , the state machine determines the current state of the multiple IoT devices within the environment. In this implementation, the state machine may proceed to model the status of each IoT device that comprises the current state as at operation  504 . Based on the model of the statuses, the controller operates in conjunction with the state machine to identify a pattern for each status as at operation  506 . Operation  502  may be similar in functionality to operation  402  as in  FIG. 4 . 
     At operation  504 , the state machine models the state of each IoT device and in turn the current state. Modeling the current state, the state machine models a state diagram as a way to describe the behavior or statuses of the multiple IoT devices. The state diagrams include a finite number of states, and based on receipt of contextual information received at operation  508 , the state machine determines the current state of the given environment. 
     At operation  506 , based on similar characteristics shared among multiple environments, the state machine can identify patterns with the similar characteristics. For example, most environments may turn on a coffee maker in the morning, thus these similar characteristics may be used to identify patterns with the status of the multiple IoT devices and thus the current state. 
     At operation  508 , the state machine receives the contextual information. In one implementation, the contextual information includes at least one of a spatial attribute, temporal attribute, or combination thereof as at operations  510 - 512 . The spatial attribute as at operation  510  infers a user location relative to the given environment while the temporal attribute as at operation  512  is used to infer time. Operation  508  may be similar in functionality to operation  404  as in  FIG. 4 . 
     At operation  514 , based on the determined current state at operation  502  and received contextual information at operation  508 , the state machine determines the next state in which to transition the multiple IoT devices within the environment. Operation  514  may be similar in functionality to operation  406  as in  FIG. 4 . 
     At operation  516 , the state machine receives crowdsourcing information in addition to the contextual information. The crowdsourcing information are specific state transition in which multiple environments participate so that similar state transitions can be identified. The crowdsourcing information can include the transitions of multiple IoT devices within the given environments to identify which status should be modified based on the contextual information received at operation  508 . 
     At operations  518 - 520 , the likelihood of each state is determined to predict which state in which to transition the multiple IoT devices. At these operations, a probabilistic model may be used to calculate the probability or likelihood of each state. In turn, the state machine can identify which state to transition the multiple IoT devices within the environment. 
     At operation  522 , based on determining the next state in which to transition the multiple IoT devices, the individual status of each of the IoT devices is transitioned to attain the next state. In one implementation, a handler may transmit a communication to each of the impacted IoT devices indicating a status to achieve to transition to the next state. 
     Referring now to  FIGS. 6-7 , example block diagrams of networking devices  600  and  700  with processing resources  602  and  7602  are illustrated to execute machine-readable instructions in accordance with various examples of the present disclosure. The machine-readable instructions represent instructions that may be fetched, decoded, and/or executed by respective processing resources  602  and  702 . While illustrated in a particular order, these instructions are not intended to be so limited. Rather, it is expressly contemplated that various instructions may occur in different orders and/or simultaneously with other instructions than those illustrated in  FIGS. 6-7 . 
       FIG. 6  is a block diagram of networking device  600  with processing resource  602  to execute instructions  606 - 612  within machine-readable storage medium  604 . Although networking device  600  includes processing resource  602  and machine-readable storage medium  604 , it may also include other components that would be suitable to one skilled in the art. For example, networking device  600  may include a controller, memory storage, or other suitable type of component. The networking device  600  is an electronic device with processing resource  602  capable of executing instructions  606 - 612  and as such embodiments of the networking device  600  include a computing device such as a server, switch, router, wireless access point (WAP), or other type of networking device. In other embodiments, the networking device  600  includes an electronic device such as a computing device, or other type of electronic device capable of executing instructions  606 - 612 . The instructions  606 - 612  may be implemented as methods, functions, operations, and other processes implemented as machine-readable instructions stored on the storage medium  604 , which may be non-transitory, such as hardware storage devices (e.g., random access memory (RAM), read only memory (ROM), erasable programmable ROM, electrically erasable ROM, hard drives, and flash memory). 
     The processing resource  602  may fetch, decode, and execute instructions  606 - 612  to transition multiple IoT devices to a next state within an environment. Specifically, the processing resource  602  executes instructions  606 - 612  to: model a current state of the multiple IoT devices within the environment by modeling each individual status of the multiple IoT devices; receive contextual information over the environment; identify a next state in which to transition the multiple IoT devices within the environment; and automate the multiple IoT devices to attain the next state. 
     The machine-readable storage medium  604  includes instructions  606 - 612  for the processing resource  602  to fetch, decode, and execute. In another embodiment, the machine-readable storage medium  604  may be an electronic, magnetic, optical, memory, storage, flash-drive, or other physical device that contains or stores executable instructions. Thus, machine-readable storage medium  604  may include, for example, Random Access Memory (RAM), an Electrically Erasable Programmable Read-Only Memory (EEPROM), a storage drive, a memory cache, network storage, a Compact Disc Read Only Memory (CDROM) and the like. As such, machine-readable storage medium  604  may include an application and/or firmware which can be utilized independently and/or in conjunction with processing resource  602  to fetch, decode, and/or execute instructions of machine-readable storage medium  604 . The application and/or firmware may be stored on machine-readable storage medium  604  and/or stored on another location of networking device  600 . 
       FIG. 7  is a block diagram of networking device  700  with processing resource  702  to execute instructions  706 - 728  within machine-readable storage medium  704 . Although networking device  700  includes processing resource  702  and machine-readable storage medium  704 , it may also include other components that would be suitable to one skilled in the art. For example, computing device  700  may include a controller, memory storage, or other suitable type of component. The networking device  700  is an electronic device with processing resource  702  capable of executing instructions  706 - 728  and as such embodiments of the networking device  700  include an electronic device such as a server, switch, router, wireless access point (WAP), or other type of computing device. Other embodiments of the networking device  700  include an electronic device such as a computing device, or other type of electronic device capable of executing instructions  706 - 728 . The instructions  706 - 728  may be implemented as methods, functions, operations, and other processes implemented as machine-readable instructions stored on the storage medium  704 , which may be non-transitory, such as hardware storage devices (e.g., random access memory (RAM), read only memory (ROM), erasable programmable ROM, electrically erasable ROM, hard drives, and flash memory). 
     The processing resource  702  may fetch, decode, and execute instructions  706 - 728  to transition multiple IoT devices to attain a next state. Specifically, the processing resource  702  executes instructions  706 - 728  to: model a status of each IoT device to represent the current state of the multiple IoT devices within the environment; receive contextual information including at least one of either a spatial attribute that identifies a user location relative to the environment and/or a temporal attribute; identify a next state of the multiple IoT devices by receiving crowdsourcing information that represents different states of IoT devices in different environments and determine a probability of each of the different states in which to transition the multiple IoT devices within the given environment; and automate multiple IoT devices such that the multiple IoT devices is transitioned from the current state to the next state, the automation occurs upon transmitting a communication to each of the multiple IoT devices that specifies the status of each of the multiple IoT devices. 
     The machine-readable storage medium  704  includes instructions  706 - 728  for the processing resource  702  to fetch, decode, and execute. In another embodiment, the machine-readable storage medium  704  may be an electronic, magnetic, optical, memory, storage, flash-drive, or other physical device that contains or stores executable instructions. Thus, machine-readable storage medium  704  may include, for example, Random Access Memory (RAM), an Electrically Erasable Programmable Read-Only Memory (EEPROM), a storage drive, a memory cache, network storage, a Compact Disc Read Only Memory (CDROM) and the like. As such, machine-readable storage medium  604  may include an application and/or firmware which can be utilized independently and/or in conjunction with processing resource  702  to fetch, decode, and/or execute instructions of machine-readable storage medium  704 . The application and/or firmware may be stored on machine-readable storage medium  704  and/or stored on another location of networking device  700 . 
     Although certain embodiments have been illustrated and described herein, it will be greatly appreciated by those of ordinary skill in the art that a wide variety of alternate and/or equivalent embodiments or implementations calculated to achieve the same purposes may be substituted for the embodiments shown and described without departing from the scope of this disclosure. Those with skill in the art will readily appreciate that embodiments may be implemented in a variety of ways. This application is intended to cover adaptions or variations of the embodiments discussed herein. Therefore, it is manifestly intended that embodiments be limited only by the claims and equivalents thereof.