Abstract: Determining a device's location in a space in real time is computing intensive. To offload some of the workload in conducting this hyperlocation, the access points in the network conduct some of process in determining the location of a device. The cloud determines a restricted AoA search area based on previous client locations. After this determination, a three-dimensional (3D) AoA search is conducted by each AP in the restricted area (restricted by a range of azimuth directions) for a device. Finally, each AP reports a location(s) for the device, which comprises weights for selected angular sectors. The cloud can then construct a probability heat map for location computation from the weights provided from each AP for the device.

Abstract: Offloading of location computation from a location server to an access point through the use of projections on base phase vectors may be provided. First, an Access Point (AP) may receive a set of two or more base phase vectors from a location server. Next, the AP may measure a measured phase vector for a first signal from a user device. Then, the AP can determine projection values based on a comparison of the measured phase vector to each base phase vector. From these comparisons, the AP can determine a subset of base phase vectors with the highest projection values. The AP can then send the projection values and the subset of base phase vectors to the location server, wherein the location server determines the device location from these projection values and subset of base phase vectors.

Abstract: In one embodiment, a network assurance service associates a target key performance indicator (tKPI) measured from a network with a plurality of causation key performance indicators (cKPIs) measured from the network that may indicate a root cause of a tKPI anomaly. The network assurance service applies a machine learning-based anomaly detector to the tKPI over time, to generate tKPI anomaly scores. The network assurance service calculates, for each of cKPIs, a mean and standard deviation of that cKPI using a plurality of different time windows associated with the tKPI anomaly scores. The network assurance service uses the calculated means and standard deviations of the cKPIs in the different time windows to calculate cross-correlation scores between the tKPI anomaly scores and the cKPIs. The network assurance service selects one or more of the cKPIs as the root cause of the tKPI anomaly based on their calculated cross-correlation scores.

Abstract: Offloading of location computation from a location server to an access point through the use of projections on base phase vectors may be provided. First, an Access Point (AP) may receive a set of two or more base phase vectors from a location server. Next, the AP may measure a measured phase vector for a first signal from a user device. Then, the AP can determine projection values based on a comparison of the measured phase vector to each base phase vector. From these comparisons, the AP can determine a subset of base phase vectors with the highest projection values. The AP can then send the projection values and the subset of base phase vectors to the location server, wherein the location server determines the device location from these projection values and subset of base phase vectors.

Abstract: In one embodiment, a network assurance service that monitors one or more networks receives data indicative of networking device configuration changes in the one or more networks. The service also receives one or more performance indicators for the one or more networks. The service trains a machine learning model based on the received data indicative of the networking device configuration changes and on the received one or more performance indicators for the one or more networks. The service predicts, using the machine learning model, a change in the one or more performance indicators that would result from a particular networking device configuration change. The service causes the particular networking device configuration change to be made in the network based on the predicted one or more performance indicators.

Abstract: In one embodiment, a network assurance service that monitors a plurality of networks obtains characteristic data regarding network entities deployed in the plurality of networks. The network assurance service assigns the network entities to entity clusters by applying a clustering mechanism to the characteristic data regarding the network entities. The network assurance service generates, for each of the entity clusters, a training dataset using the characteristic data for the network entities assigned to that cluster. The network assurance service uses, for each of the entity clusters, the training datasets for an entity cluster to train a machine learning-based model that models the behavior of that entity cluster.

Abstract: A plurality of access points located within a building structure may provide various location-based services, such as navigation, to mobile devices within the building structure. To more accurately determine the location of the plurality of access points, a floorplan of the interior of the building structure may be obtained. A plurality of images of the interior of the building structure is then obtained. At least one of the images includes an image of at least one of the plurality of access points. A plurality of reference points may also be located within the building structure and obtained. Based on the plurality of images, reference points, and the floorplan, a composite image of the interior of the structure is generated. Based on the composite image, the location of each of the plurality of access points is determined.

Abstract: An enterprise system configures access point devices at an enterprise location to communicate with a location determination system. The location determination system receives wireless signal attributes of user computing devices broadcasting Wi-Fi signal data at the enterprise location from one or more access point devices. For a particular time window, the location determination system determines aggregated features of received wireless signal data across all access point devices, and classifies each of the user computing devices as moving or stationary by applying the wireless signal data to a model. For each of the user computing devices determined to be moving, the location determination system calculates a respective position of the user computing device based on the wireless signal data. For each of the user computing devices determined to be stationary, the location determination system does not calculate a respective position of the respective user computing device.

Abstract: In one embodiment, a network assurance service that monitors a network detects, using a machine learning-based anomaly detector, network anomalies associated with source nodes in the monitored network. The network assurance service identifies, for each of the detected anomalies, a set of network paths between the source nodes associated with the anomaly and one or more potential destinations of traffic for that source node. The network assurance service correlates networking devices along the network paths in the identified sets of network paths with the detected network anomalies. The network assurance service adjusts the machine learning-based anomaly detector to use a performance measurement for a particular one of the networking devices as an input feature, based on the correlation between the particular networking device and the detected network anomalies.

Abstract: In one embodiment, a network assurance service that monitors a network calculates network frequency distributions of a performance measurement from the network over a plurality of different time periods. The service calculates entity frequency distributions of the performance measurement for a plurality of different groupings of one or more network entities in the network over the plurality of different time periods. The service determines distance measurements between the network frequency distributions and the entity frequency distributions. The service identifies a particular one of the grouping of one or more networking entities as an outlier, based on a change in distance measurements between the network frequency distributions and the entity frequency distributions for the particular grouping. The service provides an indication of the identified outlier grouping to a user interface.

Abstract: 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.

Abstract: In one embodiment, a network assurance service that monitors a network detects a network anomaly in the network using a machine learning-based anomaly detector. The network assurance service identifies a set of network conditions associated with the detected network anomaly. The network assurance service initiates a network test on one or more clients in the network that exhibit the identified network conditions. The network assurance service retrains the machine learning-based anomaly detector based on a result of the network test.

Abstract: In one embodiment, a network assurance service that monitors a network detects a pattern of network measurements from the network that are associated with a particular network problem. The network assurance service tracks characteristics of the detected pattern over time. The network assurance service uses the tracked characteristics of the detected pattern over time as input to a machine learning-based pattern analyzer. The pattern analyzer is configured to determine whether the detected pattern is a perpetual or transient pattern in the network, and the pattern analyzer is further configured to detect anomalies in the characteristics of the pattern. The network assurance service initiates a change to the network based on an output of the machine learning-based pattern analyzer.

Abstract: 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.

Abstract: 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.

Abstract: In one embodiment, a control device associated with a wireless network of a given location determines a reference quality of location readings between access points and client devices based on using substantially all of an available wireless communication bandwidth. The control device may then determine channel state information (CSI) between the client devices and access points for each orthogonal frequency-division multiple access (OFDMA) resource unit (RU), and selects a subset of RUs for allocation to each respective client device, based on the subset of RUs allocated to each respective client device i) surpassing a determined threshold of certain parameters of the CSI, while also ii) providing a minimum quality of a location reading based on using only the subset of RUs as compared to the reference quality of location readings. The control device may then allocate the selected subset of RUs to each respective client device for location-preserving OFDMA-signaling-based communication.

Abstract: In one embodiment, a network assurance service that monitors a network detects a set of anomalous measurements from the network over time by applying a machine learning-based anomaly detector to the measurements. The service computes, for each of the anomalous measurements, an anomaly severity score based on weighted severity factors used to compute anomaly severity scores. The severity factors include one or more of: a device type associated with the measurements, a duration of the anomalous measurements, a network impact associated with the anomalous measurements, or an aggregate metric based on distances between the measurements and a prediction band of the anomaly detector. The service sends an anomaly alert to a user interface, based on the computed anomaly severity score, and receives feedback from the user interface regarding the anomaly alert. The service adjusts, based on the received feedback, weightings of the severity factors used to compute anomaly severity scores.

Abstract: It may be determined that each of a first plurality of devices was stationary at a location based on received first location information. Next, a first estimated ground truth may be determined for each of the first plurality of devices at the location based on the first location information corresponding to each of the respective first plurality of devices at the location. A first plurality of location estimation errors may then be determined for each of the first plurality of devices at the location respectively based on the first location information corresponding to each of the respective first plurality of devices at the location and the first estimated ground truth for each of the respective first plurality of devices at the location. Next, each of the determined first plurality of location estimation errors for each of the first plurality of devices at the location may be aggregated to provide a first location accuracy measurement for the location.

Abstract: In one embodiment, a network assurance service associates a target key performance indicator (tKPI) measured from a network with a plurality of causation key performance indicators (cKPIs) measured from the network that may indicate a root cause of a tKPI anomaly. The network assurance service applies a machine learning-based anomaly detector to the tKPI over time, to generate tKPI anomaly scores. The network assurance service calculates, for each of cKPIs, a mean and standard deviation of that cKPI using a plurality of different time windows associated with the tKPI anomaly scores. The network assurance service uses the calculated means and standard deviations of the cKPIs in the different time windows to calculate cross-correlation scores between the tKPI anomaly scores and the cKPIs. The network assurance service selects one or more of the cKPIs as the root cause of the tKPI anomaly based on their calculated cross-correlation scores.

Abstract: In one embodiment, a basic service set (BSS) color assignment apparatus includes a processor, and a memory to store data used by the processor, wherein the processor is operative to calculate, for each one BSS color of a plurality of BSS colors, a BSS color assignment metric at least based on use of the one BSS color in at least one neighboring BSS neighboring a BSS of an access point in an infrastructure wireless local area network (WLAN), yielding a plurality of BSS color assignment metrics for the BSS of the access point, select one of the plurality of BSS color assignment metrics associated with an optimal choice BSS color of the plurality of BSS colors for the BSS of the access point and assign the optimal choice BSS color to the BSS of the access point.