Abstract: Methods and systems for classifying mobile device behavior include generating a full classifier model that includes a finite state machine suitable for conversion into boosted decision stumps and/or which describes all or many of the features relevant to determining whether a mobile device behavior is benign or contributing to the mobile device's degradation over time. A mobile device may receive the full classifier model along with sigmoid parameters and use the model to generate a full set of boosted decision stumps from which a more focused or lean classifier model is generated by culling the full set to a subset suitable for efficiently determining whether mobile device behavior are benign. Results of applying the focused or lean classifier model may be normalized using a sigmoid function, with the resulting normalized result used to determine whether the behavior is benign or non-benign.

Abstract: Methods and apparatus for delayed block acknowledgments in a WLAN. In an aspect, a method is provided for communication with one or more nodes. The method includes transmitting to the one or more nodes a block comprising data and a first bit indicating no acknowledgements for the data are to be transmitted from the one or more nodes. The method also includes determining whether a selected time location within a transmit time duration has been reached, wherein if the selected time location has been reached, transmitting to the one or more nodes a second bit indicating that the one or more nodes are to transmit one or more acknowledgements, respectively, for the data. The method also includes receiving at least one acknowledgment for the data within a remaining portion of the transmit time duration.

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: According to some aspects, a method includes communicating a request from a first device to a second device using near field communication (NFC). The request includes a preferred mode of wireless local area network (Wi-Fi) operation and state information of the first device. The method further includes receiving a reply at the first device, sent from the second device, including acceptance of the preferred mode of Wi-Fi operation. The method further includes communicating wireless information to establish the Wi-Fi communication link from the first device to the second device.

Abstract: The subject matter disclosed herein relates to a method comprising displaying in a camera view of a mobile device a captured image of one or more items listed in a menu of items available for selection at a point of interest identifiable, at least in part, by a location. A method may further include transmitting a message comprising parsed text of the one or more items and information representative of the location, and receiving, in response to the transmission of the message, annotations to be displayed in the camera view.

Abstract: Methods, systems, computer-readable media, and apparatuses for detection of anomalies within indoor map information are presented. In some embodiments, the method includes receiving a digital map. The method may further include identifying one or more anomalies within the digital map using a software-based anomaly detection tool. The method may also include displaying one or more suggested corrections to a user based on the one or more identified anomalies. The method may additionally include correcting the one or more identified anomalies within the digital map.

Abstract: Techniques are provided for adaptively sampling orientation sensors in positioning systems based on location (e.g., map) data. Embodiments can enable a device to use location, direction, and/or location information to anticipate an expected change in motion. The embodiments can then identify and prioritize a number of sampling strategies to alter sampling rates of orientation sensors, and implement at least one strategy, based on priority.

Abstract: Methods, systems and devices compute and use the actual execution states of software applications to implement power saving schemes and to perform behavioral monitoring and analysis operations. A mobile device may be configured to monitor an activity of a software application, generate a shadow feature value that identifies actual execution state of the software application during that activity, generate a behavior vector that associates the monitored activity with the shadow feature value, and determine whether the activity is malicious or benign based on the generated behavior vector, shadow feature value and/or operating system execution states. The mobile device processor may also be configured to intelligently determine whether the execution state of a software application is relevant to determining whether any of the monitored mobile device behaviors are malicious or suspicious, and monitor only the execution states of the software applications for which such determinations are relevant.

Abstract: Methods, systems and devices use operating system execution states while monitoring applications executing on a mobile device to perform comprehensive behavioral monitoring and analysis include configuring a mobile device to monitor an activity of a software application, generate a shadow feature value that identifies an operating system execution state of the software application during that activity, generate a behavior vector that associates the monitored activity with the shadow feature value, and determine whether the activity is malicious or benign based on the generated behavior vector, shadow feature value and/or operating system execution states.

Abstract: A position fix for a mobile platform is determined using RSSI values for wireless signals received from access points (APs), at least one of which has dynamic transmission power control. The transmission power data for the APs is received from an entity separate from the APs, e.g., a central entity or a positioning assistance server. The RSSI values for wireless signals received from the APs are acquired, as is an RSSI heatmap. Using the transmission power data, the RSSI values and the RSSI heatmap, the position fix for the mobile platform is determined. The position fix may be determined by the mobile platform or a positioning assistance server. Additionally, a server may receive transmission power data for APs and may provide to a mobile platform RSSI heatmap information based on the transmission power data. The RSSI heatmap information may be, e.g., the transmission power data or a RSSI heatmap.

Abstract: Systems and methods of limiting wireless discovery range are described. A method may include determining, at a first device, a discovery range threshold that limits a distance from the first device at which a second device is operative to decode a discovery message. The method may include adjusting a transmission attribute at the first device based on the discovery range threshold. The method may include sending the discovery message in accordance with the adjusted transmission attribute.

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: Methods and apparatuses of providing assistance data of a venue to a mobile device are disclosed. According to aspects of the present disclosure, for the same area, multiple or different versions of assistance data may be generated. Restrictions may be applied by access area, such that positioning grid, heat maps, and maps can be restricted to certain sections of the venue; and point of interests (POIs) and search features may be provided based on at least one of user credentials, time-based restrictions, ticket-based restrictions, or loyalty-based restrictions, or any combination thereof.

Abstract: The subject matter disclosed herein relates to a system and method for wirelessly receiving a first communication from a first mobile device. A processing delay may be measured. An acknowledgement communication indicating receipt of the first communication may be generated. A measurement of the processing delay may be broadcast in a broadcast packet, for example, or by transmitting a subsequent communication including the processing delay in response to receiving a second communication having a request for the measurement of the processing delay.

Abstract: Mobile computing devices may be equipped with hardware components configured to monitor key assets of the mobile device at a low level (e.g., firmware level, hardware level, etc.). The hardware component may also be configured to dynamically determine the key assets that are to be monitored in the mobile device, monitor the access or use of these key assets by monitoring data flows, transactions, or operations in a system data bus of the mobile device, and report suspicious activities to a comprehensive behavioral monitoring and analysis system of the mobile device. The comprehensive behavioral monitoring and analysis system may then use this information to quickly identify and respond to malicious or performance degrading activities of the mobile device.

Abstract: In one implementation, a method may comprise: storing a user profile indicative of at least one attribute of a user of a mobile station; determining a measurement value based, at least in part, on a signal from at least one sensor on the mobile station; and estimating a location of the mobile station based, at least in part, on an association of the at least one attribute and the measurement value with a context parameter map database.

Abstract: Systems and methods of network based positioning include a server configured to assign priority levels to mobile devices locatable within the network, and allocate network resources for network based positioning of the locatable mobile devices, based on the corresponding priority levels assigned to the mobile devices. The server may further be configured to admit only a selected subset of the locatable mobile devices into the network for purposes of network based positioning and deny admission to the remaining locatable mobile devices, wherein the selected subset can be determined based on an attribute of the mobile device and/or a characteristic of the user of the mobile device.

Abstract: A position fix for a mobile platform is determined using RSSI values for wireless signals received from access points (APs), at least one of which has dynamic transmission power control. The transmission power data for the APs is received from an entity separate from the APs, e.g., a central entity or a positioning assistance server. The RSSI values for wireless signals received from the APs are acquired, as is an RSSI heatmap. Using the transmission power data, the RSSI values and the RSSI heatmap, the position fix for the mobile platform is determined. The position fix may be determined by the mobile platform or a positioning assistance server. Additionally, a server may receive transmission power data for APs and may provide to a mobile platform RSSI heatmap information based on the transmission power data. The RSSI heatmap information may be, e.g., the transmission power data or a RSSI heatmap.

Abstract: Various methods, apparatuses and articles of manufacture may be implemented for use in one or more computing platforms to adaptively provide and/or obtain indoor navigation assistance data (INAD) update(s) for use by a mobile device. An INAD update may, for example, comprise a subset of INAD available for an indoor environment. Such a subset may be determined based, at least in part, on a bounded region in an electronic map of the indoor environment. Such a bounded region may, for example, have a shape and/or a size based, at least in part, on at least one parameter indicative of a motion state of the mobile device.

Abstract: Methods, and mobile devices implementing the methods, use application-specific and/or application-type specific classifier to improve the efficiency and performance of a comprehensive behavioral monitoring and analysis system predicting whether a software application is causing undesirable or performance depredating behavior. The application-specific and application-type specific classifier models may include a reduced and more focused subset of the decision nodes that are included in a full or more complete classifier model that may be received or generated in the mobile device. The locally generated application-specific and/or application-type specific classifier models may be used to perform real-time behavior monitoring and analysis operations by applying the application-based classifier models to a behavior/feature vector generated by monitoring mobile device behavior.