Patent Publication Number: US-2022239393-A1

Title: Systems and methods for premises monitoring

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation of U.S. patent application Ser. No. 16/820,708, filed Mar. 17, 2020, which is hereby incorporated by reference in its entirety. 
    
    
     BACKGROUND 
     Presence detection or in-home behavior monitoring systems may be used to detect an intruder or identify normal and abnormal user behavior in a home. Existing presence detection or in-home behavior monitoring systems suffer from several drawbacks. For example, the systems often intrude on user privacy. As another example, the systems may cause user inconvenience by requiring a user to routinely and actively engage with components of the system. Improvements in presence detection or in-home behavior monitoring systems are needed. 
     SUMMARY 
     Devices, such as user devices, entertainment devices, and/or home automation devices (for example, Internet of Things (IOT) devices), may be located at a premises. Based on strengths of signals output by the devices over time, such as based on signal strength fluctuations, it may be determined that one or more of the devices comprise a spatially static device. The spatially static device may comprise a device that is not frequently moved by users. 
     The strength of signals output by the spatially static device may be affected by the presence and/or movement of a user, and such effect on the output signals can be measured by receiving devices. For example, the body of the user may absorb and/or interfere with the signals. Therefore, a user activity pattern may be determined based on strengths of the signals over time (e.g., times of day), such as based on signal strength fluctuations over time. Patterns of the strengths of the signals may be determined. The patterns of signal strength may be associated with normal user behavior, such as associated with absorption of interference of the signals occurring when a user follows a normal daily routine. Patterns of strengths of received signals may be compared to the patterns determined to be associated with normal user behavior in order to determine that captured signals represent normal or abnormal behavior. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The following drawings show generally, by way of example, but not by way of limitation, various examples discussed in the present disclosure. In the drawings: 
         FIG. 1  shows an example operating environment. 
         FIG. 2  shows an example operating environment. 
         FIG. 3  shows an example method. 
         FIG. 4  shows example pattern data. 
         FIG. 5  shows an example pattern. 
         FIG. 6  shows an example method. 
         FIG. 7  shows an example method. 
         FIG. 8  shows an example method. 
         FIG. 9  shows an example computing device. 
     
    
    
     DETAILED DESCRIPTION 
     One or more spatially static devices may be located at a premises. The spatially static devices may comprise user devices, such as desktop computers or televisions, as examples. The spatially static devices may comprise Internet of Things (IoT) devices. The spatially static devices may comprise premises management devices. The premises management devices may comprise security system devices, such as sensors, alarms, cameras, and/or control panels. The premises management devices may comprise home automation devices, lighting devices, thermostats, garage door openers, locks, digital assistants, and/or smart appliances. The spatially static devices may comprise entertainment devices, such as set-top boxes, audio devices, gaming systems, and/or media players. 
     Spatially static devices may comprise devices that are not frequently moved by users; their position in a space may be stationary, and they may remain substantially static over time. For example, a spatially static device may comprise a device that is not portable, such as a large device and/or a heavy device. For example, a user may not move a large television or a large speaker on a daily or weekly basis. The spatially static device may comprise a device that is fixed, secured, and/or installed at a premise. For example, a user may not move premises management system devices, such as a thermostat device, a motion sensor, an alarm, and/or a lighting device mounted on a wall of a house. The spatially static device may comprise a device that is configured to be connected to one or more peripheries or external devices, such as an external power source or an output device. Cords and other peripheries connected to the device may make it inconvenient for a user to move the device. For example, a home computer that is connected to a monitor, a keyboard, a mouse, and/or a power source may not be moved by a user on a daily or weekly basis. The spatially static device may comprise a device that is not frequently moved by a user during normal use of the device, such as a lighting device and/or a premises management device (e.g., a home automation device and/or a security system device). 
     A gateway device may be located at the premises and may configured to communicate with the spatially static devices. Signals received by the gateway device from the spatially static devices may have associated received signal strength indicators (RSSIs). The RSSI may comprise a measurement of the power level of the signal. The RSSI associated with a signal received from a spatially static device may be determined by the gateway device. 
     Movement and/or presence of user at the premises may cause absorption and/or interference of the signals from the spatially static devices. The absorption and/or interference of the signals may affect the RSSIs of the signals, such as by causing the RSSIs to fluctuate. For example, the RSSI associated with a signal from a spatially static device may be weaker as a result of a user being near the spatially static device compared to an RSSI associated with a signal when no one is around the spatially static device. Machine learning models may be used to understand how the spatially static devices are arranged in a premises without a user&#39;s input regarding such arrangement of spatially static devices at a premises. The machine learning models may use RSSI data associated with the spatially static devices and/or patterns of the RSSI values. 
     RSSIs of signals output by the spatially static devices may be used to determine the spatially static devices. For example, the spatially static devices may be identified from a group of devices that may include non-spatially static devices, such as wearable devices and/or mobile devices, as examples. Since spatially static devices are not constantly moved, RSSI values associated with signals received from the spatially static devices may comprise distinct features compared to RSSI values of non-spatially static devices. For example, an RSSI associated with a signal from a spatially static device may exhibit fewer fluctuations than an RSSI associated with a signal received from a non-spatially static device. 
     RSSIs of signals output by the spatially static devices may be used to determine clusters of spatially static devices. The clusters of devices may comprise groups of devices that are spatially-related, such as within a particular room of area. RSSIs associated with signals output by a cluster of spatially static devices may exhibit disruptions at the same time or at similar times. Based on the RSSIs exhibiting disruptions, the group of spatially static devices may be determined to be in a same area and clustered together. For example, an RSSI associated with signals output by a home assistant device in a kitchen and an RSSI associated with signals output by a smart refrigerator in the kitchen may experience a disruption caused by a user walking through the kitchen. The home assistant device and the smart refrigerator may be determined to comprise a cluster, such as a cluster associated with the kitchen. 
     RSSIs patterns associated with normal user behavior may be determined. The patterns may be determined based on RSSI fluctuations across clustered areas. As an example, a user may arrive at a premises most weekdays at 5:00 p.m. The user may enter the premises through the kitchen and generally spend about five minutes in the kitchen making a snack. The user may next enter the living room. As the user moves through the kitchen and living room, that movement may cause fluctuations in the RSSI of signals received from the spatially static devices in those locations. An RSSI pattern associated with normal behavior for the user may be determined based on the determined fluctuations in the RSSIs of received signals. 
     Based on the RSSI pattern, a normal behavior profile for the user may be generated. The normal behavior profile for the user may indicate that the user is expected to arrive at an area associated with the kitchen at or around 5:00 p.m. on weekdays. The normal behavior profile for the user may indicate that the user is expected to stay in the area associated with the kitchen for about five minutes. The normal behavior profile for the user may indicate that the user is expected to arrive at an area associated with the living room after leaving the kitchen. The normal behavior profile may be determined by the gateway device and/or a computing device located external to the premises. The gateway device may send an indication of the normal behavior profile and/or the RSSI pattern associated with normal behavior of the user to a computing device located external to the premises. 
     An intruder to the premises may break into the premises at 2:00 p.m. on a weekday. The intruder may enter the premises via a window in a living room. The intruder&#39;s movement and/or presence in the living room may cause RSSIs associated with signals output by spatially static devices in the living room to fluctuate. An RSSI pattern may indicate normal RSSI of signals of the living room devices at 2:00 p.m. on a weekday. The RSSI values when the intruder enters the living room may at 2:00 p.m. may differ from the RSSI values of the pattern associated with normal behavior of a user of the premises. Based on the difference between the RSSI values of the signals received and the RSSI values indicated by the pattern, abnormal user behavior may be determined. The abnormal user behavior may be determined by the gateway device and/or a computing device located external to the premises. The gateway device may send an indication of the abnormal user behavior to a computing device located external to the premises. 
     Based on determining abnormal user behavior, an alert may be generated. The alert may be sent to a device associated with the premises. The alert may be sent to a device associated with a user associated with the premises. The alert may be sent to one or more emergency contacts, such as a 911 dispatch, a hospital, a police office, and/or a fire station. The gateway device may generate the alert or cause the alert to be generated. The gateway device may send the alert to a computing device located external to the premises. 
     Determining abnormal user behavior may aid in care for the elderly. Elderly users may tend to have regular routines. Based on determining abnormal user behavior in an elderly user, an alarm or alert may be generated and sent to one or more relatives of the elderly user, an elderly care center management, a medical services provider, and/or an emergency services provider, as examples. 
       FIG. 1  shows an example operating environment. The operating environment may comprise a premises  100 . The premises  100  may comprise a residential structure or unit, such as a house or an apartment unit. The premises  100  may comprise a commercial structure or unit, such as a retail store. The premises  100  may comprise any premises wherein activity of users may form a behavioral pattern and wherein monitoring of user behavior is desired. The premises  100  may comprise one or more areas, such as a first area  110 , a second area  120 , and a third area  130 . 
     A gateway device  140  may be located at the premises  100 . The gateway device  140  may comprise a home automation device. The gateway device  140  may be configured to control and communicate with a plurality of devices, such as devices located at a premises and devices external to the premises. One or more spatially static devices may be located at the premises  100 . The spatially static devices may comprise a first spatially static device  112 , a second spatially static device  114 , a third spatially static device  116 , a fourth spatially static device  122 , a fifth spatially static device  124 , a sixth spatially static device  126 , a seventh spatially static device  132 , an eighth spatially static device  134 , and a ninth spatially static device  136 . The spatially static devices may be configured to communicate with the gateway device  140 . For example, the spatially static devices may be configured to communicate with the gateway device  140  via Wi-Fi communication. A Wi-Fi signal or other communication signal output by one or more of the spatially static devices may have a received signal strength indicator (RSSI). 
     One or more of the spatially static devices may be located in an area of the premises  100 . For example, the first spatially static device  112 , the second spatially static device  114 , and the third spatially static device  116  may be located at a first area  110  of the premises  100 . The first area  110  may be associated with a first room of the premises  100 . The fourth spatially static device  122 , the fifth spatially static device  124 , and the sixth spatially static device  126  may be located at a second area  120  of the premises  100 . The second area  120  may be associated with a second room of the premises  100 . The seventh spatially static device  132 , the eighth spatially static device  134 , and the ninth spatially static device  136  may be located at a third area  130  of the premises  100 . The third area  130  may be associated with a third room of the premises  100 . 
     The gateway device  140  may be configured to receive signals from the devices located at the premises. The gateway device  140  may be configured to determine RSSI values associated with the signals. The gateway device  140  may be configured to send an indication of the RSSI values, such as to a computing device  160  located external to the premises  100 . The gateway device  140  may be configured to send the indication of the RSSI values via a network  150 . The network  150  may comprise a private network. The network  150  may comprise a public network, such as the Internet. The network  150  may use a communication protocol, such as an Internet Protocol (IP). 
     The gateway device  140  and/or the computing device  160  may be configured to classify the devices as spatially static devices or as non-spatially static devices. For example, the devices may be classified based on RSSIs associated with signals output by the devices over a period of time (e.g., a week, ten days, as examples), such as a training period. The gateway device  140  and/or the computing device  160  may be configured to determine a magnitude of fluctuations of the RSSI values associated with the devices. The gateway device  140  and/or the computing device  160  may be configured to classify the devices based on the determined magnitude of fluctuations of the RSSI values. The gateway device  140  and/or the computing device  160  may be configured to classify devices that output signals having RSSIs having low magnitude fluctuations as spatially static devices. The gateway device  140  and/or the computing device  160  may be configured to classify devices that output signals having RSSIs having high magnitude fluctuations as non-spatially static devices. For example, the gateway device  140  and/or the computing device  160  may classify the first spatially static device  112 , the second spatially static device  114 , the third spatially static device  116 , the fourth spatially static device  122 , the fifth spatially static device  124 , the sixth spatially static device  126 , the seventh spatially static device  132 , the eighth spatially static device  134 , and the ninth spatially static device  136  as spatially static devices based on low magnitude fluctuations of RSSIs of signals output by the spatially static devices. 
     The gateway device  140  and/or the computing device  160  may be configured to classify the devices as spatially static devices or non-spatially static devices based on connectivity of the devices. Connectivity of the devices may be determined based on a percent of a period of time (e.g., an hour, a day, a week, etc.) or a frequency at which the devices are connected to (e.g., in communication with) another device at the premises, such as the gateway device  140 ). The percent of time or frequency at which the devices are connected to another device may be determined based on reception of signals from the devices by the other device. 
     The gateway device  140  and/or the computing device  160  may be configured to cluster the spatially static devices. The gateway device  140  and/or the computing device  160  may be configured to cluster the spatially static devices into different groups and/or areas of activity. For example, based on one or more users being in one area of activity, RSSIs associated with spatially static devices in the area may experience a similar disturbance. The gateway device  140  and/or the computing device  160  may be configured to cluster the spatially static devices into areas based on time of disturbances and/or magnitude of disturbances experienced during a period of time (e.g., a week, ten days, as examples), such as a training period. For example, the first spatially static device  112 , the second spatially static device  114 , and the third spatially static device  116  may experience similar disruptions. The gateway device  140  and/or the computing device  160  may cluster the first spatially static device  112 , the second spatially static device  114 , and the third spatially static device  116 . The fourth spatially static device  122 , the fifth spatially static device  124 , and the sixth spatially static device  126  may experience similar disruptions. The gateway device  140  and/or the computing device  160  may cluster the fourth spatially static device  122 , the fifth spatially static device  124 , and the sixth spatially static device  126 . The seventh spatially static device  132 , the eighth spatially static device  134 , and the ninth spatially static device  136  may experience similar disruptions. The gateway device  140  and/or the computing device  160  may cluster the seventh spatially static device  132 , the eighth spatially static device  134 , and the ninth spatially static device  136 . 
     The gateway device  140  and/or the computing device  160  may be configured to determine patterns of user behavior at the premises  100 . The patterns of user behavior may comprise a probability that there will be user activity (e.g., user movement and/or presence) in an area  110 ,  120 ,  130  of the premises  100  as a function of time and/or day. The gateway device  140  and/or the computing device  160  may be configured to determine the probability based on determining a duration of user activity within an area  110 ,  120 ,  130  of the premises  100 . The patterns of user behavior may comprise patterns of transition of user activity between areas  110 ,  120 ,  130  of the premises  100 . The gateway device  140  and/or the computing device  160  may be configured to determine the patterns of user behavior by determining time, duration, and/or transition of user activity associated with areas  110 ,  120 ,  130  of the premises  100 . The gateway device  140  and/or the computing device  160  may be configured to determine the time, duration, and/or transition of user activity associated with areas  110 ,  120 ,  130  of the premises  100  for a period of time (e.g., a week, ten days, etc.), such as a training period. 
     For example, a determined pattern of behavior may indicate that a user at the premises  100  may have a high probability (e.g., greater than or equal to 50%, 60%, 70%, 80%, 90%) of entering the first area  110  at or around 5:00 p.m. on a weekday. The determined pattern of behavior may indicate that five minutes after entering the first area  110 , the user may have a high probability of transitioning to the second area  120 . The determined pattern of behavior may indicate that thirty minutes after entering the second area  120 , the user may have a high probability of transitioning to the third area  130 . 
     The gateway device  140  and/or the computing device  160  may be configured to determine abnormal user activity. The gateway device  140  and/or the computing device  160  may be configured to determine abnormal user activity by determining that the probability of disruptions in RSSIs of signals from spatially static devices associated with an area  110 ,  120 ,  130  at a time of day is low (e.g., equal than or less than 50%, 40%, 30%, etc.). For example, disruptions in RSSI of signals from spatially static devices associated with an area that are improbable may be caused by the presence of an intruder. Disruptions in RSSI of signals from spatially static devices associated with an area that are improbable may be caused by absence of a user, such as due to injury, illness, or incapacitation. Based on the determining abnormal user activity, the gateway device and/or the computing device  160  may be configured to send an alert, such as to a device of another user, a healthcare provider, or an emergency responder. 
       FIG. 2  shows an example operating environment. The operating environment may comprise a premises (e.g., the premises  100  in  FIG. 1 ). A gateway device  240  (e.g., the gateway device  140  in  FIG. 1 ) may be located at the premises  200 . One or more spatially static devices  220  (e.g., the spatially static devices  112 - 136  in  FIG. 1 ) may be located at the premises  200 . The spatially static devices  220  may be configured to communicate with the gateway device  240 . The gateway device  240  may be configured to receive signals from the devices located at the premises. The gateway device  240  may be configured to determine RSSI values associated with the signals. 
     The RSSI values of signals received from the spatially static device  220  may be affected by the presence of a user  280 , such as in a direction of propagation of a signal from the spatially static device  220 . The body of the user  280  may cause absorption and/or interference of signals from the spatially static device  220 . 
     For example, the spatially static device  220  may send a signal  270 . The user  280  may be present (e.g., standing, moving, etc.) through a path of propagation of the signal  270 . The signal  270  may enter the user&#39;s  280  body. At least a portion of the signal  270  may be absorbed by the user&#39;s  280  body. 
     The signal  290 , the signal  270  having passed through and/or emerged from the user&#39;s body  280 , may be received by the gateway device  240 . The signal  290  received from the gateway device  240  may have a different RSSI than the signal  270  that the spatially static device  220  initially transmitted. For example, the RSSI of the signal  290  may be weaker than the RSSI of the signal  270 . 
     The signal  290  may have a different RSSI than a signal received from the spatially static device  220  (e.g., by the gateway device  240 ) at a different time. For example, the signal  290  may have a different RSSI than signals that are received from the spatially static device  220  when a user is not in the path of propagation, in an area proximate the spatially static device, and/or at the premises  200 . The difference in RSSI of signals received from the spatially static device  220  at different times may comprise a “fluctuation” in RSSI. 
       FIG. 3  shows example clusters of spatially static devices. The spatially static devices  312 ,  314 ,  316 ,  322 ,  324 ,  326 ,  332 ,  334  (e.g., spatially static devices  112 ,  114 ,  116 ,  122 ,  124 ,  126 ,  132 ,  134 ,  136  in  FIG. 1 ) may be located at a premises (e.g., premises  100  in  FIG. 1 ) The spatially static devices may be located in a first area  310 , a second area  320 , or a third area  330  of a premises (e.g., the premises  100  in  FIG. 1 ). The spatially static devices may be in communication with a gateway device  240  (e.g., the gateway device  140  in  FIG. 1 ). The gateway device  340  may be located at the premises. The gateway device  340  may be in communication with a computing device located external to the premises. 
     The gateway  340  and/or in the computing device may cluster the devices into areas and/or groups. The gateway  340  and/or in the computing device may cluster the devices into areas and/or groups based on RSSIs of signals output by the spatially static devices. The clusters of devices may comprise groups of devices that are spatially-related, such as within a particular room of area. RSSIs associated with signals output by a cluster of spatially static devices may exhibit disruptions at the same time or at similar times. Based on the RSSIs exhibiting similar disruptions, the gateway  340  and/or in the computing device may cluster the group of spatially static devices in the area. 
     For example, as a result of one or more users being present in the first area  310  of the premises, the first spatially static device  312 , the second spatially static device  314 , and the third spatially static device  316  may output signals having RSSIs with similar disruptions. The spatially static devices  322 ,  324 ,  326 ,  332 ,  334 ,  336  that are not in the first area  310  may output signals having RSSIs with dissimilar or no disruptions. Based on the similarity of the disruptions in the RSSIs of the signals output by the spatially static devices in the first area  310 , the gateway device  340  and/or the computing device may cluster the first spatially static device  312 , the second spatially static device  314 , and the third spatially static device  316  into a group. The gateway device  340  and/or the computing device may determine that the group is associated with the first area  310 . The gateway device  340  and/or the computing device may perform similar steps to cluster the fourth spatially static device  322 , the fifth spatially static device  324 , and the sixth spatially static device  326  in a group and/or determine that the group is associated with the second area  320 . The gateway device  340  and/or the computing device may perform similar steps to cluster the seventh spatially static device  332 , the eighth spatially static device  334 , and the ninth spatially static device  336  in a group and/or determine that the group is associated with the third area  330 . 
       FIG. 4  shows example pattern data. Graph  400  may show a probability distribution associated with user activity. The graph  400  may be associated with the first area  310  of the premises shown in  FIG. 3 . A horizontal axis of the graph  400  may represent a time of day. A vertical axis (not shown) of the graph  400  may represent a probability associated with user activity in the first area  310 . The graph  400  may show a probability distribution associated with user activity in the first area  310  at a particular time. A time  402  on the horizontal axis of the graph  400  may correspond to a time when user activity in the first area  310  is most likely. Data represented by the graph  400  may be generated, maintained, and/or analyzed by the gateway device  140  in  FIG. 1  and/or the gateway device  340  in  FIG. 3  and/or an associated computing device. The data in graph  400  may be generated, maintained and analyzed by the computing device  160  in  FIG. 1  and/or an associated computing device. 
     Graph  410  may show a probability distribution associated with user activity. The graph  410  may be associated with the second area  320  of the premises shown in  FIG. 3 . A horizontal axis of the graph  410  may represent a time of day. A vertical axis (not shown) of the graph  410  may represent a probability associated with user activity in the second area  310 . The graph  410  may show a probability distribution associated with user activity in the second area  320  at a particular time. A time  412  on the horizontal axis of the graph  410  may correspond to a time when user activity in the second area  320  is most likely. Data represented by the graph  410  may be generated, maintained, and/or analyzed by the gateway device  140  in  FIG. 1 , the gateway device gateway device  440  in  FIG. 4 , and/or an associated computing device. The data represented by the graph  410  may be generated, maintained, and/or analyzed by the computing device  160  in  FIG. 1  and/or an associated computing device. 
     Graph  420  may show a probability distribution associated with user activity. The graph  420  may be associated with the third area  330  shown in  FIG. 3 . A horizontal axis of the graph  420  may represent a time of day. A vertical axis (not shown) of the graph  420  may represent a probability of a determined user activity in the third area  330 . The graph  420  may show a probability distribution associated with user activity in the third area  330  at a particular time. A time  422  on the horizontal axis of the graph  420  may correspond to a time when user activity in the third area  330  is most likely. Data represented by the graph  420  may be generated, maintained, and/or analyzed by the gateway device  340 , the computing device, and/or an associated computing device. 
       FIG. 5  shows an example user activity pattern. Data represented by the profile  500  of normal behavior may be generated, maintained, analyzed, and/or retrieved. The data represented by profile  500  may be generated, maintained, analyzed, and/or retrieved by the gateway device  140  in  FIG. 1 , the gateway device  240  in  FIG. 2 , the gateway device  340  in  FIG. 3 , and/or an associated computing device. The data represented by the profile  500  may be generated, maintained, analyzed, and/or retrieved by the computing device  160  in  FIG. 1  and/or an associated computing device. The profile  500  may indicate a transition probability across areas of activity  520 . A graph associated with the profile  500  may represent the transition probability across the areas of activity  520 .  FIG. 5  shows a representation of a first area  522 , a second area  524 , and a third area  526 . The representation of the first area  522  may correspond to the first area  310  in  FIG. 3 . The representation of the second area  524  may correspond to the second area  320  in  FIG. 3 . The representation of the third area  526  may correspond to the third area  330  in  FIG. 3 . The transition probability may indicate a likelihood that a user would move across the areas  522 ,  524 ,  526 . The transition probability may indicate an expected order of user activity associated with areas  522 ,  524 ,  526 . The expected order of user activity may comprise a sequence of spaces and/or areas that a user moves through. The expected order of user activity may comprise a speed of movement of the user. The expected order of user activity may comprise a time that a user spends in a space and/or area. 
     The profile  500  may comprise a probability of user activity with respect to time (e.g., time of day). The profile  500  may comprise and/or be based on a determined user activity pattern and/or transition probability across the areas of activity  520 . The profile  500  may comprise a probability distribution of user activity (e.g., in the areas of activity  520 ) over time, as represented by the graph  510 . For example, the graph  510  may show that activity (e.g., in an area of activity  520 - 522 ,  524 ,  526 ) is more likely to occur in the morning than in the afternoon or the night. Based on a determination of user activity at a time that the profile  500  indicates that user activity is unlikely, abnormal user behavior may be determined. It may be determined that the profile  500  indicates that user activity (e.g., in an area of activity  520 - 522 ,  524 ,  526 ) is unlikely at the time based on the probability distribution associated with normal behavior. For example, based on determined activity and night and the normal behavior pattern indicating that the probability of activity at night is less than 5%, abnormal behavior may be determined. Based on user activity (e.g., in an area of activity  520 - 522 ,  524 ,  526 ) not being determined at a time when the profile  500  indicates that user activity is likely, abnormal user behavior may be determined. Based on user activity being different from the expected order of user activity and/or out of sequence of the transition probability across areas of activity  520  of the profile  500 , abnormal user behavior may be determined. 
     Based on determining abnormal user behavior, a notification of abnormal user behavior may be generated. The notification may be generated by the gateway device  140  in  FIG. 1  or the gateway device  340  in  FIG. 3 . The notification may be generated by the computing device  360  in  FIG. 3 . The notification may be sent to a device  530 . The device  530  may be associated with an emergency dispatch, a family member of a user, an emergency contact, a hospital, and/or healthcare personnel, as examples. 
       FIG. 6  shows an example method. At step  610 , signals may be received. The signals may be received from a plurality of devices. The gateway device  140  in  FIG. 1  may receive the signals from the plurality of devices. The computing device  160  in  FIG. 1  may receive signals from a plurality of devices. The signals may comprise Wi-Fi signals. Receiving the signals may comprise receiving the signals within a period of time. 
     At step  620 , one or more spatially static devices of the plurality of devices may be determined. Determining the one or more spatially static devices may comprise distinguishing spatially static devices from non-spatially static (e.g., mobile) devices of the plurality of devices. The one or more spatially static devices may be determined based on strengths of the received signals. The one or more spatially static devices may be determined based on fluctuations in strengths of the received signals. For example, spatially static devices may exhibit fewer or smaller fluctuations in signal strength than non-spatially static devices. The gateway device  140  in  FIG. 1  may determine one or more spatially static devices of the plurality of devices based on strengths of the received signal. The gateway device  140  in  FIG. 1  may determine one or more spatially static devices of the plurality of devices based on fluctuations in strengths of the received signals. The computing device  160  in  FIG. 1  may determine one or more spatially static devices of the plurality of devices based on strengths of the received signals. The computing device  160  in  FIG. 1  may determine one or more spatially static devices of the plurality of devices based on fluctuations in strengths of the received signals. The received signals may have received signal strength indicators (RSSIs). 
     At step  630 , a user activity pattern may be determined. The user activity pattern may be determined based on changes in strengths of signals received from the spatially static devices. The activity pattern may be determined based on changes in strengths of signals received from the spatially static devices within a training time period. The gateway device  140  in  FIG. 1  may determine a user activity pattern based on changes in strengths of signals received from the spatially static devices within a training time period. The computing device  160  in  FIG. 1  may determine a user activity pattern based on changes in strengths of signals received from the spatially static devices within a training time period. The user activity pattern may be based on changes in the strengths of the signals caused by absorption or interference of the signals by one or more users. The user activity pattern may associate user activity at a time of day with a probability. 
     At step  640 , abnormal user activity may be determined. The abnormal user activity may be determined based on the user activity pattern. The abnormal user activity may be determined based on a strength of at least one signal received from at least one of the spatially static devices. The abnormal user activity may be determined based on a strength of at least one signal received from at least one of the spatially static devices after the training time period. The gateway device  140  in  FIG. 1  may determine abnormal user activity based on the user activity pattern and a strength of at least one signal received from at least one of the spatially static devices. The computing device  160  in  FIG. 1  may determine abnormal user activity based on the user activity pattern and a strength of at least one signal received from at least one of the spatially static devices. Determining abnormal user activity may comprise determining that a change in the strength of at least one signal deviates from a portion of a signal strength pattern associated with the training time period. The abnormal user activity may be associated with at least one of abnormally low user activity or abnormally high user activity. 
     The determining the abnormal user activity may comprise determining the abnormal user activity in an area of a premises. Determining the abnormal user activity in the area of the premises may be based on RSSIs associated with signals received from a group of devices located in another area of the premises. The determining the abnormal user activity may comprise determining a movement of a user between the area of the premises and the other area of the premises. The determining the abnormal user activity may comprise determining that a probability of the user moving between the area of the premises and the other area of the premises may be equal to or less than a threshold probability. The area of the premises may be associated with a room of the premises. The abnormal user activity may be associated with an intruder at the premises. 
     An alert may be sent based on the abnormal user activity. The gateway device  140  in  FIG. 1  may send an alert based on the abnormal user activity. The computing device  160  in  FIG. 1  may send an alert based on the abnormal user activity. 
     An alarm may be triggered based on the abnormal user activity. The gateway device  140  in  FIG. 1  may trigger an alarm based on the abnormal user activity. The computing  160  in  FIG. 1  may trigger an alarm based on the abnormal user activity. An alarm may be caused to be triggered based on the abnormal user activity. The gateway device  140  in  FIG. 1  may cause an alarm to be triggered based on the abnormal user activity. The computing  160  in  FIG. 1  may cause an alarm to be triggered based on the abnormal user activity. 
     For example, a premises may comprise areas or rooms, such as a kitchen and a living room. A gateway device may be located at the premises. A smart refrigerator and a smart dish washer may be located in the kitchen. A smart speaker and a smart television may be located in the living room. A smart phone may be located at the premises. The refrigerator, the dish washer, the smart speaker, the smart television, and the smart phone may be in communication with the gateway device via Wi-Fi. Wi-Fi signals received by the gateway device from the devices may have associated signal strength indicators (RSSIs). The gateway device may classify the refrigerator, the dish washer, the smart speaker, and the smart television as spatially static devices based on a low variation in RSSI values of signals received by the gateway device over a training period. The gateway device may classify the smart phone as a mobile device based on a high variation in RSSIs of signals received by the gateway device. 
     Based on user activity occurring in the kitchen, signals having RSSI values lower than a baseline value may be received from the refrigerator. The signals may be received from a gateway device. The baseline value may be determined based on RSSI values when there is no user activity in an area, such as the kitchen. For example, it may be determined that a baseline value may comprise an RSSI of −60. As an example, the baseline value may be determined for the kitchen at midnight, when there is no user activity. Signals having RSSI values lower than a baseline value may be received from the IoT device associated with the dish washer. RSSIs of signals received from the smart speaker and the smart television may be equal to an expected baseline value. Based on the RSSIs and/or the expected baseline values, the refrigerator and the dish washer may be clustered into a first area of activity. 
     Based on user activity occurring in the living room, the gateway device may receive signals having RSSIs lower than an expected baseline value from the smart speaker. The gateway device may receive signals having RSSIs lower than an expected baseline value from the smart television. RSSIs of signals received from the IoT device associated with the refrigerator and the IoT device associated with the dish washer may remain equal to an expected baseline. The gateway device may cluster the smart speaker and the smart television into a second area of activity. 
     The gateway device may generate a profile of normal user activity based on RSSIs of signals received from the spatially static devices over a training period. After generating the profile, the gateway device may receive an indication of user activity in the second area of activity at 2 p.m. on a Monday. Based on the profile, the gateway device may determine that user activity in the second area of activity at 2 p.m. on a Monday is abnormal user activity. The gateway device may cause a notification indicative of the of abnormal user activity to be sent to a device of a user associated with the premises. 
       FIG. 7  shows an example method. At step  710 , one or more signals may be received. The signals may be received from one or more spatially static devices. The gateway device  140  in  FIG. 1  may receive one or more signals from one or more spatially static devices. The computing device  160  in  FIG. 1  may receive one or more signals from one or more spatially static devices. The receiving of the one or more signals may comprise receiving the one or more signals within a period of time. The one or more signals may comprise Wi-Fi signals. 
     At step  720 , a strength of the one or more signals may be determined. The gateway device  140  in  FIG. 1  may determine a strength of the one or more signals. The computing device  160  in  FIG. 1  may determine a strength of the one or more signals. The strengths of the one or more signals may comprise received signal strength indicators (RSSIs). 
     At step  730 , a determination may be made that the strength of the one or more signals deviates from a signal strength pattern. It may be determined that the strength deviates by at least a threshold value. It may be determined that the signal strength deviates from a signal strength pattern associated with the one or more spatially static devices, such as by the threshold value. The gateway device  140  in  FIG. 1  may determine that the strength of the one or more signals deviates, by at least a threshold value, from a signal strength pattern associated with the one or more spatially static devices. The computing device  160  in  FIG. 1  may determine that the strength of the one or more signals deviates, by at least a threshold value, from a signal strength pattern associated with the one or more spatially static devices. 
     The determining that the strength of the one or more signals deviates from the signal strength pattern by at least the threshold value may comprise determining that a change in the strength of the one or more signals over the period of time may deviate from a portion of the signal strength pattern associated with the period of time. The signal strength pattern may be based on changes in the strengths of the one or more signals caused by absorption or interference of the signals by one or more users. The signal strength pattern may associate a time of day with a probability. The probability may represent a probability of experiencing a disruption from an associated RSSI. 
     At step  740 , abnormal user activity may be determined. The abnormal user activity may be determined based on the strength deviating from the signal strength pattern by at least the threshold value. The gateway device  140  in  FIG. 1  may determine abnormal user activity based on the strength deviating from the signal strength pattern by at least the threshold value. The computing device  160  in  FIG. 1  may determine abnormal user activity based on the strength deviating from the signal strength pattern by at least the threshold value. The abnormal user activity may be associated with at least one of abnormally low user activity or abnormally high user activity. 
     The determining the abnormal user activity may comprise determining the abnormal user activity in an area of a premises. Determining the abnormal user activity in the area of the premises may be based on RSSIs associated with signals received from a group of devices. The group of devices may be located in another area of the premises. The determining the abnormal user activity may comprise determining a movement of a user between the area of the premises and the other area of the premises. The determining the abnormal user activity may comprise determining that a probability of the user moving between the area of the premises and the other area of the premises may be equal to or less than a threshold probability. The area of the premises may be associated with a room of the premises. The abnormal user activity may be caused by an intruder at the premises. 
     The method may comprise sending an alert. The alert may be sent based on the abnormal user activity. The gateway device  140  in  FIG. 1  may send an alert based on the abnormal user activity. The computing device  160  in  FIG. 1  may send an alert based on the abnormal user activity. The method may comprise causing an alert to be sent based on the abnormal user activity. The gateway device  140  in  FIG. 1  may cause an alert to be sent based on the abnormal user activity. The computing  160  in  FIG. 1  may cause an alert to be sent based on the abnormal user activity. 
     The method may comprise triggering an alarm. The alarm may be triggered based on the abnormal user activity. The gateway device  140  in  FIG. 1  may trigger an alarm based on the abnormal user activity. The computing device  160  in  FIG. 1  may trigger an alarm based on the abnormal user activity. The method may comprise causing an alarm to be triggered based on the abnormal user activity. The gateway device  140  in  FIG. 1  may cause an alarm to be triggered based on the abnormal user activity. The computing device  160  in  FIG. 1  may cause an alarm to be triggered based on the abnormal user activity. 
     A premises may comprise rooms and/or areas, such as a kitchen and a living room. The premises may comprise a gateway device. The kitchen may comprise an Internet of Things (IoT) device associated with a refrigerator and an IoT device associated with a dish washer. The living room may comprise a smart speaker and a smart television. A user associated with the premises may comprise a smart phone. 
     The IoT device associated with the refrigerator, the IoT device associated with the dish washer, the smart speaker, the smart television, and the smart phone may be in communication with the gateway device via Wi-Fi. The signals received by the gateway device from the devices at the premises may have received signal strength indicators (RSSIs). The gateway device may classify the IoT device associated with the refrigerator, the IoT device associated with the dish washer, the smart speaker, and the smart television as spatially static devices based on a high percentage of connection with the gateway device and/or a low variation in RSSI values of signals received by the gateway device over a training period. The gateway device may classify the smart phone as a mobile device based on a failure to maintain a high percentage of connection with the gateway device and/or a high variation in RSSIs of signals received by the gateway device. 
     Based on user activity occurring in the kitchen, the gateway device may receive signals having RSSI values lower than an expected baseline value from the IoT device associated with the refrigerator. The gateway device may receive signals having RSSI values lower than an expected baseline value from the IoT device associated with the dish washer. The gateway device may receive signals having RSSI values from the smart speaker and the smart television equal to an expected baseline value. 
     Based on user activity occurring in the living room, the gateway device may receive RSSIs lower than expected baseline value from the smart speaker and the smart television. The gateway device may receive signals from the IoT device associated with the refrigerator and the IoT device associated with the dish washer having RSSIs equal to an expected baseline. The gateway device may cluster the IoT device associated with the refrigerator and the IoT device associated with the dish washer into a first area of activity. The gateway device may cluster the smart speaker and the smart television into a second area of activity. 
     The gateway device may generate a profile of normal user activity. The gateway device may receive an indication of user activity in the second area of activity with no preceding user activity in the first area of activity. The profile may indicate that user activity in the second area of activity with no preceding user activity in the first area of activity comprises abnormal user activity. The gateway device may cause a notification of a possible intruder to be sent to an emergency dispatch system. 
       FIG. 8  shows an example method. At step  810 , a group of devices of a plurality of spatially static devices may be determined. The group of devices may be located in an area of a premises. The group of devices may be determined based on one or more received signal strength indicators (RSSI) associated with signals received from the plurality of spatially static devices. The gateway device  140  in  FIG. 1  may determine a group of devices of a plurality of spatially static devices that are located in an area of a premises based on one or more received signal strength indicators (RSSI) associated with signals received from the plurality of spatially static devices located at the premises. The computing device  160  in  FIG. 1  may determine a group of devices of a plurality of spatially static devices that are located in an area of a premises based on one or more received signal strength indicators (RSSI) associated with signals received from the plurality of spatially static devices located at the premises. The area of the premises may be associated with a room of the premises. The one or more RSSI may be associated with one or more Wi-Fi signals. 
     At step  820 , a pattern associated with user activity in the area of the premises may be determined. The pattern may be determined based on changes in one or more RSSI associated with signals received from the group of devices. The pattern may be determined based on changes in one or more RSSI associated with signals received from the group of devices within a first time period. The gateway device  140  in  FIG. 1  may determine a pattern associated with user activity in the area of the premises based on changes in one or more RSSI associated with signals received from the group of devices within a first time period. The computing device  160  in  FIG. 1  may determine a pattern associated with user activity in the area of the premises based on changes in one or more RSSI associated with signals received from the group of devices within a first time period. The first time period may comprise a period from a first time of day to a second time of day. The pattern may comprise a probability of an RSSI of a signal from at least one device of the group of devices at a time of day between the period from the first time of day to the second time of day. The pattern may be based on changes in the one or more RSSIs caused by absorption or interference of the signals by one or more users. 
     At step  830 , abnormal user activity in the area of the premises may be determined. The abnormal user activity may be determined based on the pattern. The abnormal user activity may be determined based on one or more RSSI associated with signals received from the group of devices within a second time period. The gateway device  140  in  FIG. 1  may determine abnormal user activity in the area of the premises based on the pattern and one or more RSSIs associated with signals received from the group of devices within a second time period. The computing device  160  in  FIG. 1  may determine abnormal user activity in the area of the premises based on the pattern and one or more RSSIs associated with signals received from the group of devices within a second time period. The first time period and the second time period may be associated with a time of day. 
     The determining the abnormal user activity in the area of the premises may be based on RSSI associated with signals received from another group of devices. The other group of devices may be located in another area of the premises. The determining the abnormal user activity may comprise determining a movement of a user between the area of the premises and the other area of the premises. The determining the abnormal user activity may comprise determining that a probability of the user moving between the area of the premises and the other area of the premises may be equal to or less than a threshold probability. The abnormal user activity may be associated with an intruder at the premises. The abnormal user activity may be associated with at least one of abnormally low user activity or abnormally high user activity. 
     The method may comprise sending an alert. The alert may be sent based on the abnormal user activity. The gateway device  140  in  FIG. 1  may send an alert based on the abnormal user activity. The computing device  160  in  FIG. 1  may send an alert based on the abnormal user activity. The method may comprise causing an alert to be sent based on the abnormal user activity. The gateway device  140  in  FIG. 1  may cause an alert to be sent based on the abnormal user activity. The computing device  160  in  FIG. 1  may cause an alert to be sent based on the abnormal user activity. The method may comprise triggering an alarm based on the abnormal user activity. The gateway device  140  in  FIG. 1  may trigger an alarm based on the abnormal user activity. The computing device  160  in  FIG. 1  may trigger an alarm based on the abnormal user activity. The method may comprise causing an alarm to be triggered based on the abnormal user activity. The gateway device  140  in  FIG. 1  may cause an alarm to be triggered based on the abnormal user activity. The computing device  160  in  FIG. 1  may cause an alarm to be triggered based on the abnormal user activity. 
     A premises may comprise rooms and/or areas, such as a kitchen and a living room. The premises may comprise a gateway device. The kitchen may comprise an Internet of Things (IoT) device associated with a refrigerator and an IoT device associated with a dish washer. The living room may comprise a smart speaker and a smart television. A user associated with the premises may comprise a smart phone. The IoT device associated with the refrigerator, the IoT device associated with the dish washer, the smart speaker, the smart television, and the smart phone may be in communication with the gateway device via Wi-Fi signals. The Wi-Fi signals may have received signal strength indicators (RSSI). The gateway device may classify the IoT device associated with the refrigerator, the IoT device associated with the dish washer, the smart speaker, and the smart television as spatially static devices based on a high percentage of connection with the gateway device and/or a low variation in RSSIs of signals received by the gateway device over a training period. The gateway device may classify the smart phone as a mobile device based on a failure to maintain a high percentage of connection with the gateway device and/or a high variation in RSSIs of signals received by the gateway device. 
     Based on user activity occurring in the kitchen, the gateway device may receive signals having RSSIs lower than an expected baseline value from the IoT device associated with the refrigerator. The gateway device may receive signals having RSSIs lower than an expected baseline value from the IoT device associated with the dish washer. The gateway device may receive signals having RSSIs from the smart speaker and the smart television equal to an expected baseline. 
     Based on user activity occurring in the living room, the gateway device may receive signals having RSSIs lower than an expected baseline value from the smart speaker. The gateway device may receive signals having RSSIs lower than an expected baseline value from the smart television. The gateway device may receive signals from the IoT device associated with the refrigerator and the IoT device associated with the dish washer having RSSIs equal to an expected baseline. The gateway device may cluster the IoT device associated with the refrigerator and the IoT device associated with the dish washer into a first area of activity. The gateway device may cluster the smart speaker and the smart television into a second area of activity. 
     The gateway device may generate a profile of normal user activity. The profile may indicate that user activity in the first area is probably at 2:00 p.m. on a Saturday. The gateway device may not receive an indication of user activity in the first area at 2:00 p.m. on a Saturday. The gateway device may cause a notification indicative of abnormal user activity to be sent to a device of a family member of a user associated with the premises. 
       FIG. 9  shows an example operating environment  900 . The computing environment  900  is only an example of an operating environment and is not intended to suggest any limitation as to the scope of use or functionality of operating environment architecture. Neither should the operating environment be interpreted as having any dependency or requirement relating to any one or combination of components shown in the example operating environment. 
     The present methods and systems may be operational with numerous other general purpose or special purpose computing system environments or configurations. Examples of well-known computing systems, environments, and/or configurations that may be suitable for use with the systems and methods comprise, but are not limited to, personal computers, server computers, laptop devices, and multiprocessor systems. Additional examples comprise set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that comprise any of the above systems or devices, and the like. 
     The processing of the disclosed methods and systems may be performed by software components. The disclosed systems and methods may be described in the general context of computer-executable instructions, such as program modules, being executed by one or more computers or other devices. Generally, program modules comprise computer code, routines, programs, objects, components, data structures, that performs particular tasks or implement particular abstract data types. The disclosed methods may also be practiced in grid-based and distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices. 
     The spatially static devices  112 ,  114 ,  116 ,  122 ,  124 ,  126 ,  132 ,  134 ,  136 , gateway device  140 , and computing device  160  in  FIG. 1  may be implemented in an instance of a computing device  900  in  FIG. 9 . The systems and methods disclosed herein may be implemented via a general-purpose computing device in the form of a computing device  901 . The components of the computing device  901  may comprise, but are not limited to, one or more processors or processing units  903 , a system memory  912 , and a system bus  913  that couples various system components including the processor  903  to the system memory  912 . In the case of multiple processing units  903 , the system may utilize parallel computing. 
     The system bus  913  represents one or more of several possible types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. By way of example, such architectures may comprise an Industry Standard Architecture (ISA) bus, a Micro Channel Architecture (MCA) bus, an Enhanced ISA (EISA) bus, a Video Electronics Standards Association (VESA) local bus, an Accelerated Graphics Port (AGP) bus, and a Peripheral Component Interconnects (PCI), a PCI-Express bus, a Personal Computer Memory Card Industry Association (PCMCIA), Universal Serial Bus (USB) and the like. The bus  913 , and all buses specified in this description may also be implemented over a wired or wireless network connection and each of the subsystems, including the processor  903 , a mass storage device  904 , an operating system  905 , premises monitoring software  906 , premises monitoring data  907 , a network adapter  908 , system memory  912 , an Input/Output Interface  910 , a display adapter  909 , a display device  911 , and a human machine interface  902 , may be contained within one or more remote computing devices  914   a,b,c  at physically separate locations, connected through buses of this form, in effect implementing a fully distributed system. 
     The computing device  901  typically comprises a variety of computer readable media. Example readable media may be any available media that is accessible by the computing device  901  and comprises, for example and not meant to be limiting, both volatile and non-volatile media, removable and non-removable media. The system memory  912  comprises computer readable media in the form of volatile memory, such as random access memory (RAM), and/or non-volatile memory, such as read only memory (ROM). The system memory  912  typically contains data such as premises monitoring data  907  and/or program modules such as operating system  905  and premises monitoring software  906  that are immediately accessible to and/or are presently operated on by the processing unit  903 . 
     The computing device  901  may comprise other removable/non-removable, volatile/non-volatile computer storage media. By way of example,  FIG. 9  shows a mass storage device  904  which may provide non-volatile storage of computer code, computer readable instructions, data structures, program modules, and other data for the computing device  901 . For example and not meant to be limiting, a mass storage device  904  may be a hard disk, a removable magnetic disk, a removable optical disk, magnetic cassettes or other magnetic storage devices, flash memory cards, CD-ROM, digital versatile disks (DVD) or other optical storage, random access memories (RAM), read only memories (ROM), electrically erasable programmable read-only memory (EEPROM), and the like. 
     Optionally, any number of program modules may be stored on the mass storage device  904 , including by way of example, an operating system  905  and premises monitoring software  906 . Each of the operating system  905  and premises monitoring software  906  (or some combination thereof) may comprise elements of the programming and the premises monitoring software  906 . Premises monitoring data  907  may also be stored on the mass storage device  904 . Premises monitoring data  907  may be stored in any of one or more databases known in the art. Examples of such databases comprise, DB2®, Microsoft® Access, Microsoft® SQL Server, Oracle®, mySQL, PostgreSQL, and the like. The databases may be centralized or distributed across multiple systems. 
     The user may enter commands and information into the computing device  901  via an input device (not shown). Examples of such input devices comprise, but are not limited to, a keyboard, pointing device (e.g., a “mouse”), a microphone, a joystick, a scanner, tactile input devices such as gloves, and other body coverings, and the like These and other input devices may be connected to the processing unit  803  via a human machine interface  902  that is coupled to the system bus  913 , but may be connected by other interface and bus structures, such as a parallel port, game port, an IEEE 1394 Port (also known as a Firewire port), a serial port, or a universal serial bus (USB). 
     A display device  911  may also be connected to the system bus  913  via an interface, such as a display adapter  909 . It is contemplated that the computing device  901  may have more than one display adapter  909  and the computing device  901  may have more than one display device  911 . For example, a display device may comprise a monitor, an LCD (Liquid Crystal Display), or a projector. In addition to the display device  911 , other output peripheral devices may comprise components such as speakers (not shown) and a printer (not shown) which may be connected to the computing device  901  via Input/Output Interface  910 . Any step and/or result of the methods may be output in any form to an output device. Such output may comprise any form of visual representation, including, but not limited to, textual, graphical, animation, audio, tactile, and the like. The display  911  and computing device  901  may be part of one device, or separate devices. 
     The computing device  901  may operate in a networked environment using logical connections to one or more remote computing devices  914   a,b,c . By way of example, a remote computing device may comprise a personal computer, portable computer, a smart phone, a server, a router, a network computer, a peer device or other common network node, and so on. Logical connections between the computing device  901  and a remote computing device  914   a,b,c  may be made via a network  915 , such as a local area network (LAN) and a general wide area network (WAN). Such network connections may be through a network adapter  908 . A network adapter  908  may be implemented in both wired and wireless environments. Such networking environments are conventional and commonplace in dwellings, offices, enterprise-wide computer networks, intranets, and the Internet. 
     Application programs and other executable program components such as the operating system  905  are shown herein as discrete blocks, although it is recognized that such programs and components reside at various times in different storage components of the computing device  901 , and are executed by the data processor(s) of the computer. An implementation of premises monitoring software  906  may be stored on or sent across some form of computer readable media. Any of the disclosed methods may be performed by computer readable instructions embodied on computer readable media. Computer readable media may comprise any available media that may be accessed by a computer. By way of example and not meant to be limiting, computer readable media may comprise “computer storage media” and “communications media.” “Computer storage media” comprise volatile and non-volatile, removable and non-removable media implemented in any methods or technology for storage of information such as computer readable instructions, data structures, program modules, or other data. Example computer storage media comprises, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which may be used to store the desired information and which may be accessed by a computer.