Abstract:
Techniques of range free proximity determination are described. A mobile device can determine an entry into or exit from a proximity fence upon determining that the mobile device is sufficiently close to a signal source. The proximity fence can be a virtual fence defined by the signal source and associated with a service. The mobile device can detect signals from multiple signal sources. The mobile device can determine that, among the signal sources, one or more signal sources are located closest to the mobile device based on a ranking of the signal sources using signal strength. The mobile device can determine a probability indicating a confident level of the ranking. The mobile device can determine that the mobile device entered or exited a proximity fence associated with a highest ranked signal source satisfying a confidence threshold.
Abstract:
A mobile device can be in multiple states of location determination. In each state, the mobile device can use a distinct subsystem to determine a location. A state machine of the mobile device can manage the states, including determining which state the mobile device is in and whether a transition between the states has occurred. A transition can be triggered by a sensor of the mobile device and confirmed by another sensor of the mobile device. When the state machine detects a transition, the mobile device can switch location determination from one subsystem to another subsystem, and change a map user interface to one that is best suited for the new subsystem.
Abstract:
Methods, program products, and systems for using a location fingerprint database to determine a location of a mobile device are described. A mobile device can use a location fingerprint database to determine the location where GPS signals are unavailable. A server can generate location fingerprint data for the database. The server can generate the location fingerprint data using crowd sourcing, using known locations of signal sources, or both. The server can receive, from a sampling device, measurements of environment variables, e.g., signals from a signal source at one or more sampling points. The server can extrapolate, from the received measurements, estimated measurements at one or more locations in a venue. The server can store the extrapolated measurements as location fingerprint data. The server can send the location fingerprint data to a mobile device for determining a location of the mobile device when the mobile device is at the venue.
Abstract:
Techniques of delivering location data are described. A location server can receive, from a mobile device, a request for location data for determining a location of the mobile device at a venue. The request can include an estimated location of the mobile device. The location server can provide to the mobile device coarse location data for each venue that is located within a threshold distance to the estimated location of the mobile device. The coarse location data can include a list of coarse tiles at each venue, and parameters of a probability distribution function for determining in which tile of the venue the mobile device is located based on signals detected by the mobile device. The location server can the provide location fingerprint data associated with the tile and neighboring tiles to the mobile device. The mobile can use the location fingerprint data to determine a more detailed location.
Abstract:
A location of a mobile device in a venue can be estimated by using a state space estimator to determine candidate locations of the mobile device at a first time point based on previous candidate positions conditioned upon an observation of one or more environmental variables. A second observation is received at a second time point, and the state space estimator performs a propagation step to determine the candidate locations at the second time point based on the candidate locations at the first time point and the second observation. The propagation step includes a plurality of sub-propagation steps in which a time length between the sub-propagation steps is a fraction of the time length between the first and second time points, and at each sub-propagation step each candidate location is propagated according to a stochastic process. The location of the mobile device at the second time point is determined based on the candidate locations at the second time point.
Abstract:
A processor-based personal electronic device (such as a smartphone) is programmed to automatically respond to data sent by various sensors from which the user's activity may be inferred. One or more alarms on the device may be temporarily disabled when sensor data indicates that the user is asleep. One or more of the sensors may be worn by the user and remote from the device. A wireless communication link may be used by the device to obtain remote sensor data. Data from on-board sensors in the device—such as motion sensors, location sensors, ambient light sensors, and the like—may also be used to deduce the user's current activity. User data (such as calendar entries) may also be used to determine likely user activity and set alarms accordingly. Biometric data from a second, nearby person may also be used to automatically select certain alarm modes on a first person's device.
Abstract:
A processor-based personal electronic device (such as a smartphone) is programmed to automatically respond to data sent by various sensors from which the user's activity may be inferred. One or more alarms on the device may be temporarily disabled when sensor data indicates that the user is asleep. One or more of the sensors may be worn by the user and remote from the device. A wireless communication link may be used by the device to obtain remote sensor data. Data from on-board sensors in the device—such as motion sensors, location sensors, ambient light sensors, and the like—may also be used to deduce the user's current activity. User data (such as calendar entries) may also be used to determine likely user activity and set alarms accordingly. Biometric data from a second, nearby person may also be used to automatically select certain alarm modes on a first person's device.
Abstract:
Techniques of range free proximity determination are described. A mobile device can determine an entry into or exit from a proximity fence upon determining that the mobile device is sufficiently close to a signal source. The proximity fence can be a virtual fence defined by the signal source and associated with a service. The mobile device can detect signals from multiple signal sources. The mobile device can determine that, among the signal sources, one or more signal sources are located closest to the mobile device based on a ranking of the signal sources using signal strength. The mobile device can determine a probability indicating a confident level of the ranking. The mobile device can determine that the mobile device entered or exited a proximity fence associated with a highest ranked signal source satisfying a confidence threshold.
Abstract:
Techniques of category-based fence are described. A category-based fence can correspond to a group of signal sources instead of a point location fixed to latitude and longitude coordinates. The group of signal sources can represent a category of entities, e.g., a particular business chain. The signal sources can be distributed to multiple discrete locations. A category-based fence associated with the group, accordingly, can correspond to multiple locations instead of a single point location and a radius. Each signal source in the group can be associated with a category identifier unique to the group and uniform among signal sources in the group. The category identifier can be programmed into each signal source. A mobile device can enter the category-based fence by entering any of the discrete locations when the mobile device detects the signal identifier. The mobile device can then execute an application program associated with the category-based fence.
Abstract:
A proximity fence can be a location-agnostic fence defined by signal sources having no geographic location information. The proximity fence can correspond to a group of signal sources instead of a point location fixed to latitude and longitude coordinates. A signal source can be a radio frequency (RF) transmitter broadcasting a beacon signal. The beacon signal can include a payload that includes an identifier indicating a category to which the signal source belongs, and one or more labels indicating one or more subcategories to which the signal source belongs. The proximity fence defined by the group of signal sources can trigger different functions of application programs associated with the proximity fence on a mobile device, when the mobile device moves within the proximity fence and enters and exits different parts of the proximity fence corresponding to the different subcategories.