Abstract:
Various methods, apparatuses and/or articles of manufacture are provided for use in one or more electronic devices to perform and/or otherwise support certain positioning capabilities with regard to a mobile device. For example, certain positioning capabilities may make use of one or more portal transition parameters that may be based, at least in part, on a determined likelihood that a mobile device, if located in a first region of a specific environment and within a threshold area of a portal connecting the first region to a second region of the specific environment, may or may not make use of the portal to transition from the first region to the second region, e.g., through the portal.
Abstract:
The various aspects configure a mobile computing device to efficiently identify, classify, model, prevent, and/or correct the conditions and/or behaviors occurring on the mobile computing device that are related to one or more peripheral devices connected to the mobile computing device and that often degrade the performance and/or power utilization levels of the mobile computing device over time. In the various aspects, the mobile computing device may obtain a classifier model that includes, tests, and/or evaluates various conditions, features, behaviors and corrective actions on the mobile computing device that are related to one or more peripheral devices connected to the mobile computing device. The mobile computing device may utilize the classifier model to quickly identify and correct undesirable behaviors occurring on the mobile computing device that are related to the one or more connected peripheral devices.
Abstract:
During access point discovery, an initial list of access points available for connection is generated. The received signal strength indication (RSSI) and round trip time (RTT) delay for the access points in the initial list are measured, e.g., during discovery and by a plurality of active measurements. The initial list is pruned based on an initial and subsequent RSSI and RTT measurements to produce a master list of access points. The pruning of the initial list of access points may be based on the differentials in the RSSI measurements and the RTT measurements as well as a determination of access points that the mobile device is moving away from. As the mobile device moves to different locations, access points from the master list may be selected and connected with based on the expected duration of availability as determined by RSSI and RTT measurements.
Abstract:
Methods, systems, computer-readable media, and apparatuses selecting access points and generating assistance data for access points is provided. In one embodiment a plurality of access points in a first area are identified, a location assistance quality value with each access point of the plurality of access points is associated with each access point, and a subset of the plurality of access points is selected based on the location assistance quality value of each access point of the plurality of access points. Assistance data is then generated for the selected access points.
Abstract:
Techniques for determining a position of a mobile device in an indoor environment are provided. An example method includes receiving a request for the position of the mobile device within the indoor environment from an application running on the mobile device, estimating the position of the mobile device within the indoor environment based on signals received from a plurality of wireless access points responsive to receiving the request for the position of the mobile device, identifying an ambiguity in estimating the position of the mobile device, identifying disambiguation information for resolving the ambiguity in the position, requesting disambiguation information for resolving the ambiguity associated with the position, receiving the disambiguation information for resolving the ambiguity associated with the position; resolving the ambiguity in estimating the position using the disambiguation information; and determining the position of the mobile device in the indoor environment.
Abstract:
The disclosure generally relates to behavioral analysis to automate monitoring Internet of Things (IoT) device health in a direct and/or indirect manner. In particular, normal behavior associated with an IoT device in a local IoT network may be modeled such that behaviors observed at the IoT device may be compared to the modeled normal behavior to determine whether the behaviors observed at the IoT device are normal or anomalous. Accordingly, in a distributed IoT environment, more powerful “analyzer” devices can collect behaviors locally observed at other (e.g., simpler) “observer” devices and conduct behavioral analysis across the distributed IoT environment to detect anomalies potentially indicating malicious attacks, malfunctions, or other issues that require customer service and/or further attention. Furthermore, devices with sufficient capabilities may conduct (local) on-device behavioral analysis to detect anomalous conditions without sending locally observed behaviors to another aggregator device and/or analyzer device.
Abstract:
Systems and methods are disclosed for automating customer service for a monitored device (MD). A method for an Internet of Everything management device to automate customer service for a monitored device comprises collecting sensor data from a plurality of sensors, wherein the plurality of sensors comprises a first sensor that is not included in the MD, determining whether the MD is exhibiting abnormal behavior based on an analysis of the collected sensor data, and transmitting a report to a customer service entity associated with the MD in response to a determination that the MD is exhibiting abnormal behavior.
Abstract:
Techniques described herein enable a mobile device in selecting an access point (AP) from a plurality of APs for determining the position of the mobile device. In one aspect, the mobile device receives assistance data including traffic load information associated with one or more APs. The mobile device may determine which AP to communicate with based on the traffic load information associated with a plurality of APs. The assistance data including the traffic load information associated with the plurality of APs may be compiled using information from a controller connected to the plurality of APs, crowdsourcing (using information from a plurality of mobile devices over time), or information regarding the traffic conditions received from the plurality of APs. In one implementation, the mobile device may receive the assistance data information including the traffic load information from an assistance data (AD) Server.
Abstract:
The various aspects provide a system and methods implemented on the system for generating a behavior model on a server that includes features specific to a mobile computing device and the device's current state/configuration. In the various aspects, the mobile computing device may send information identifying itself, its features, and its current state to the server. In response, the server may generate a device-specific lean classifier model for the mobile computing device based on the device's information and state and may send the device-specific lean classifier model to the device for use in detecting malicious behavior. The various aspects may enhance overall security and performance on the mobile computing device by leveraging the superior computing power and resources of the server to generate a device-specific lean classifier model that enables the device to monitor features that are actually present on the device for malicious behavior.
Abstract:
A method of obtaining and using access point signal information includes: receiving signals at a mobile device from a first set of access points during a passive measurement; and performing a first active measurement at the mobile device for the first set of the access points, including: sending at least one first communication each sent toward a respective one of the access points of the first set; and receiving at least one second communication each corresponding to, and responsive to, one of the at least one first communication and received from a corresponding one of the access points of the first set; where the passive measurement and the first active measurement is each performed repeatedly with the first set of the access points being reestablished at each repeat performance of the passive measurement, and with the passive measurement being performed less often than the first active measurement.