Abstract: Techniques described herein enable wireless payment reader to transition between lower-power and higher-power states while still performing cryptographic operations for securing payment data without undue latency caused by the transition between sleep and awake power states. For instance, a wireless payment reader may store information associated with cryptographic operations performed by the wireless payment reader in persistent memory of the wireless payment reader so that this information may be retained when the wireless payment reader enters a low-power, sleep state. Thus, when the wireless payment reader awakens from the low-power state, the wireless payment reader may read the cryptographic information from the local persistent memory rather than needing to request this information from a payment service.

Abstract: Techniques described herein are directed to, among other things, wireless payment readers configured to transition between multiple different power states. In some instances, these power states include one or more relatively low-power states such that an overall battery life of the wireless payment readers are lengthened. Further, in some instances, a point-of-sale (POS) application operating on a POS device that wirelessly couples to an example wireless payment reader may cause the wireless payment reader to transition from one state to another, such as from a first, lower-power state to a second, higher-power state in response to the POS application determining that a payment is to be processed using the wireless payment reader.

Abstract: Embodiments of the present disclosure related to methods and systems of determining the floor and location of a device in an indoor space having multiple floors. A map importing module takes a floor map of an indoor space and processes it for further use. A calibration module creates a fingerprint map of the indoor space described by the floor map. A floor localization module searches the fingerprint map of each floor to determine the most likely location for each floor. A floor detection module determines the floor and location based on the most likely locations for each floor and any of map constraints data and device movement data.

Abstract: Embodiments of the present disclosure relate to methods and systems of updating an orientation of a mobile device in a global reference frame. The method includes gathering mobile device gyroscope data, gathering mobile device accelerometer data, gathering mobile device magnetometer data, determining a first orientation of the mobile device based on the gyroscope data, determining a second orientation of the mobile device based on the device accelerometer data and the device magnetometer data, determining a trust factor associated with the second orientation, and updating the orientation of the mobile device based on the first orientation, the second orientation, and the trust factor.

Abstract: Embodiments of the present disclosure related to methods and systems of determining the floor and location of a device in an indoor space having multiple floors. A map importing module takes a floor map of an indoor space and processes it for further use. A calibration module creates a fingerprint map of the indoor space described by the floor map. A floor localization module searches the fingerprint map of each floor to determine the most likely location for each floor. A floor detection module determines the floor and location by comparing the most likely locations for each floor and utilizing device movement history data.

Abstract: Embodiments of the present disclosure related to methods and systems of determining the floor and location of a device in an indoor space having multiple floors. A map importing module takes a floor map of an indoor space and processes it for further use. A calibration module creates a fingerprint map of the indoor space described by the floor map. A floor localization module searches the fingerprint map of each floor to determine the most likely location for each floor. A floor detection module determines the floor and location by comparing the most likely locations for each floor and utilizing device movement history data.