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: 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: In one embodiment, a service obtains spatial information regarding a physical area. The service estimates locations of a device within the physical area over time, based on wireless signals sent by the device. The service generates a set of images based on the spatial information regarding the physical area and on the estimated locations of the device within the physical area over time. The service updates an estimated location of the device by inputting the generated set of images to a machine learning model trained to minimize a location estimation error.

Abstract: A system may be configured to identify from among a plurality of access points in an area in communication with a network, a first access point associated with the area. In addition the system may also be configured to determine first location-related data for the first access point. Determining second location-related data for a second access point of the plurality of access points, the second access point being interior to the first access point within the network includes exchanging ranging data indicative of a first relative distance between the first access point and the second access point, the ranging data based at least in part on ranging message exchange measurements, and communicating the first location-related data from the first access point to the second access point.

Abstract: In one embodiment, a network assurance system that monitors a network labels time periods with positive labels, based on the network assurance system detecting problems in the network during the time periods. The network assurance system assigns tags to discrete portions of a feature space of measurements from the monitored network, based on whether a particular range of values in the feature space has a threshold probability of occurring during a positively-labeled time period. The network assurance system determines a set of the assigned tags that frequently co-occur with the positively-labeled time periods in which problems are detected in the network. The network assurance system causes performance of a mitigation action in the network based on the set of assigned tags that frequently co-occur with the positively-labeled time periods.

Abstract: In one embodiment, a service obtains spatial information regarding a physical area. The service estimates locations of a device within the physical area over time, based on wireless signals sent by the device. The service generates a set of images based on the spatial information regarding the physical area and on the estimated locations of the device within the physical area over time. The service updates an estimated location of the device by inputting the generated set of images to a machine learning model trained to minimize a location estimation error.

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 maps time series of values of key performance indicator (KPIs) measured from the network to lists of unique values from the time series. The service sets a target alarm rate for anomaly detection alarms raised by the network assurance service. The service uses an optimization function to identify a set of thresholds for the KPIs. The optimization function is based on: a comparison between the target alarm rate and a fraction of network issues flagged by the service as outliers, KPI thresholds selected based on the lists of unique values from the time series, and a number of thresholds that the KPIs must cross for the service to raise an alarm. The service raises an anomaly detection alarm for the monitored network based on the identified set of thresholds for the KPIs.

Abstract: Access Point (AP) placement using Fine Time Measurement (FTM) may be provided. First, a plurality of Time-of-Flight (ToF) values between a first service end point and a second service end point may be determined. Each one of the plurality of ToF values may be derived from packets transmitted via different beamforming vector patterns at the first service end point and the second service end point. Then a minimum ToF value of the plurality of ToF values may be determined. Next, a distance between the first service end point and the second service end point may be determined based on the minimum ToF value.

Abstract: In one embodiment, a network assurance service that monitors a wireless network identifies a set of wireless network anomalies detected in the wireless network that are associated with a set of one or more network measurements. The network assurance service classifies the set of wireless anomalies as radio-related or backend-related. The network assurance service, when the set of wireless anomalies are classified as radio-related, determines that the wireless anomalies are recurring for a particular wireless access point in the network. The network assurance service initiates a change to the wireless network in part to move clients in the wireless network from the particular wireless access point to another wireless access point in the network.

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: 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: Client analytics-driven dynamic channel assignment may be provided. At a client, Radio Frequency (RF) channels may be scanned to detect access points of a network. A subset of the access points belonging to a same Extended Set Service (ESS) of the network may be determined based on data collected from the access points during the scan. For each access point in the subset, a Channel Quality Index (CQI) may be measured for an RF channel assigned to the access point, and a non-preferred channel report may be generated based on the CQI. The non-preferred channel report may be transmitted from the client to a network management system for use in dynamic channel assignment.

Abstract: A system may be configured to identify, from among a plurality of access points in an area in communication with a network, a plurality of edge access points associated with an edge of the area. In addition, the system may also be configured to determine first edge location-related data for a first edge access point and first interior location-related data for a first interior access point. Determining the first interior location-related data for the first interior access point includes exchanging ranging data indicative of a first relative distance between the first edge access point and the first interior access point. The ranging data may be based at least in part on fine timing measurements and/or time-of-flight-based measurements. Determining the first interior location-related data for the first interior access point may also include communicating the first edge location-related data from the first edge access point to the first interior access point.

Abstract: In one embodiment, a network assurance service that monitors a network maps time series of values of key performance indicator (KPIs) measured from the network to lists of unique values from the time series. The service sets a target alarm rate for anomaly detection alarms raised by the network assurance service. The service uses an optimization function to identify a set of thresholds for the KPIs. The optimization function is based on: a comparison between the target alarm rate and a fraction of network issues flagged by the service as outliers, KPI thresholds selected based on the lists of unique values from the time series, and a number of thresholds that the KPIs must cross for the service to raise an alarm. The service raises an anomaly detection alarm for the monitored network based on the identified set of thresholds for the KPIs.

Abstract: In one embodiment, a network assurance service that monitors a wireless network identifies a set of wireless network anomalies detected in the wireless network that are associated with a set of one or more network measurements. The network assurance service classifies the set of wireless anomalies as radio-related or backend-related. The network assurance service, when the set of wireless anomalies are classified as radio-related, determines that the wireless anomalies are recurring for a particular wireless access point in the network. The network assurance service initiates a change to the wireless network in part to move clients in the wireless network from the particular wireless access point to another wireless access point in the network.

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.

Abstract: In one embodiment, a network assurance service that monitors a wireless network receives data regarding connection failures of a wireless client of the wireless network. The network assurance service forms a behavioral profile for the wireless client based on the received data regarding the connection failures of the wireless client. The network assurance service uses machine learning to determine whether the behavioral profile of the wireless client is an outlier in relation to behavioral profiles of other wireless clients of the wireless network. The network assurance service causes performance of a mitigation action with respect to the wireless client, when the wireless client is determined to be an outlier.

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.