Abstract: In some implementations, a system may identify a plurality of sensor devices that are associated with a physical environment. The system may receive an indication that a set of sensor devices (e.g., that includes two or more sensor devices) are to be associated with a virtual sensor that is associated with the physical environment. The system may generate, based on receiving the indication, the virtual sensor. The system may obtain sensor data that was captured by the set of sensor devices. The system may process the sensor data to determine a set of parameters associated the virtual sensor. The system may determine, based on the set of parameters associated with the virtual sensor, that a physical environment condition is satisfied. The system may cause, based on determining that the physical environment condition is satisfied, one or more actions to be performed.

Abstract: In some implementations, a system may identify a plurality of sensor devices that are associated with a physical environment. The system may receive an indication that a set of sensor devices (e.g., that includes two or more sensor devices) are to be associated with a virtual sensor that is associated with the physical environment. The system may generate, based on receiving the indication, the virtual sensor. The system may obtain sensor data that was captured by the set of sensor devices. The system may process the sensor data to determine a set of parameters associated the virtual sensor. The system may determine, based on the set of parameters associated with the virtual sensor, that a physical environment condition is satisfied. The system may cause, based on determining that the physical environment condition is satisfied, one or more actions to be performed.

Abstract: A system described herein may generate visual representations of physical locations, such as floors of office buildings, outdoor areas, or other types of places. The visual representations may be based on a node map, which may be generated based on data received from user devices and/or by owners or proprietors of business or other types of locations. The node map, for a given place, may indicate features of the place, such as architectural features (e.g., doors, walls, or rooms), visible features (e.g., wall hangings, signs, or other landmarks), Radio Frequency (“RF”) footprint features, or other features. The node map may indicate the likelihood of a given user device moving from one node to another node, and these likelihoods may be used to generate navigation instructions.

Abstract: A system described herein may generate visual representations of physical locations, such as floors of office buildings, outdoor areas, or other types of places. The visual representations may be based on a node map, which may be generated based on data received from user devices and/or by owners or proprietors of business or other types of locations. The node map, for a given place, may indicate features of the place, such as architectural features (e.g., doors, walls, or rooms), visible features (e.g., wall hangings, signs, or other landmarks), Radio Frequency (“RF”) footprint features, or other features. The node map may indicate the likelihood of a given user device moving from one node to another node, and these likelihoods may be used to generate navigation instructions.

Abstract: A system described herein may generate visual representations of physical locations, such as floors of office buildings, outdoor areas, or other types of places. The visual representations may be based on a node map, which may be generated based on data received from user devices and/or by owners or proprietors of business or other types of locations. The node map, for a given place, may indicate features of the place, such as architectural features (e.g., doors, walls, or rooms), visible features (e.g., wall hangings, signs, or other landmarks), Radio Frequency (“RF”) footprint features, or other features. The node map may indicate the likelihood of a given user device moving from one node to another node, and these likelihoods may be used to generate navigation instructions.