Cisco Technology, Inc.

Vinay S Raghuram

Abhishek Mukherji

Santosh Ghanshyam Pandey

Abhishek Bhattacharyya

Rong Peng

Prasad Walawalkar

Balaji Gurumurthy

Matthew Aaron Silverman

Santosh Ramrao Patil

Xu Zhang

Huy Phuong Tran

Liu Huang

Systems and methods provide for improving the accuracy of a location system. The location system can capture partial phase vector data from one or more access points (APs). The location system can capture associated data associated with the partial phase vector data across multiple dimensions, such as identity data of the APs and client devices generating the partial phase vector data and frequency band data, location data, a time and date, and other data associated with the partial phase vector data. The location system can determine correlation data across the multiple dimensions using the first partial phase vector data and the associated data. The location system can a cause of the partial phase vector data based on the correlation data. The location system can perform one or more remediation actions based on the cause of the partial phase vector data.

Partial phase vectors as network sensors

Location-based, context-aware challenge-response authentication may be provided. First, a challenge may be provided to a user. The challenge may be based on a context corresponding to the user. The context corresponding to the user may comprise a location of a device associated with the user within an environment. Next, in response to providing the challenge, a response to the challenge may be received from the user. Then, in response to receiving the response to the challenge, it may be determined that the response is a correct answer to the challenge. In response to determining the response is the correct answer, a privilege may be provided to the user.

Location-based, context-aware challenge-response authentication

Techniques for generating a prediction for a location of a mobile device includes a network controller obtaining signal strength information and angle-of-arrival information for mobile devices from a plurality of access points disposed at a location that includes two or more zones. The network controller extracts features from the obtained information. The features indicate whether the mobile devices are more likely in a first zone than in one or more of second zones of the two or more zones. The network controller generates a software model for classifying a mobile device into one of the two or more zones by applying a computer learning process to the extracted features. The network controller uses the software model to generate a prediction indicating that a first mobile device is in one of the two or more zones, and performs an action at the location based on the prediction.

Device location classification using data collected by wireless access points

In one embodiment, a device determines that location accuracy performance of an indoor positioning system deployment is below a predefined threshold. The device obtains characteristic data for the indoor positioning system deployment. The device identifies, by using the characteristic data as input to a machine learning model, one or more contributing factors from the characteristic data for the location accuracy performance of the indoor positioning system deployment being below the predefined threshold. The device initiates a remediation action based on the identified one or more contributing factors for the location accuracy performance of the indoor positioning system deployment being below the predefined threshold.

Location accuracy assessment and remediation for indoor positioning system deployments

In one embodiment, a device receives location estimates for a wireless node in a network, each location estimate having an associated timestamp. The device applies hierarchical clustering to the received location estimates and their associated timestamps, to identify locations and points in time in which the wireless node was stationary. The device performs sequence modeling on the identified locations and points in time in which the wireless node was stationary, to form a sequence of locations and associated time periods in which the wireless node was stationary. The device associates the wireless node with a behavioral profile based on the sequence of locations and associated time periods in which the wireless node. The device generates, based in part on the behavioral profile for the wireless node, a predictive model that predicts a location of the wireless node at a particular point in time.

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Cupertino, CA, United States of America

Vinay S Raghuram