Abstract: Disclosed are embodiments for estimating risk associated with a user of a wireless device. In some embodiments, the risk relates to a risk of infection by a contagious disease. For example, in some embodiments, the contagious disease is Coronavirus 2019. In some embodiments, locations of multiple wireless devices are estimated based on signal strengths of signals associated with the devices. Neighboring devices are identified based on highest probability regions of the devices that are determined based on associated signals. A measure of proximity to other devices is then determined based on probabilities that each device is located in neighboring regions. The risk is then based on the measure of proximity. In some embodiments, a risk of a first user associated with a first wireless device is based, in part, on a risk of a second user within a proximity of the first user.

Abstract: Techniques are described for orchestration of measurements between a plurality of devices in a wireless network. In an example, a computing system configured to orchestrate round-trip time (RTT) measurements in a network of a plurality of wireless devices comprises one or more processors and a memory comprising instructions that when executed by the one or more processors, cause the one or more processors to: generate a network graph of the plurality of wireless devices in which each wireless device pair of a plurality of wireless device pairs of the network graph is connected by an edge assigned to an identifier, wherein each edge of at least two adjacent edges is assigned a different identifier; and orchestrate the RTT measurements such that RTT measurements between wireless device pairs connected by edges assigned to a same identifier are performed in parallel.

Abstract: A system includes a plurality of access point devices (APs) configured to provide a wireless network at a site, each of the plurality of APs having a known location, and a network management system comprising one or more processors and a memory comprising instructions that when executed by the one or more processors cause the one or more processors to: determine, based on a known location of a first AP of the plurality of APs, a known location of a second AP of the plurality of APs, and received signal strength measurements of wireless signals originating at one or more antennas of the first AP and received by one or more antennas of the second AP, an orientation angle of the second AP; and generate an output indicative of the orientation angle of the second AP.

Abstract: A network management system (NMS) is configured to control roaming in a wireless network using a variable mobility threshold. For a first wireless device associated with a current location, the NMS obtains at least one performance metric of a first wireless signal received by the first wireless device at the current location from a first AP of a plurality of APs, compares the at least one parameter of the first wireless signal to at least one performance metric of a second wireless signal received by at least one other wireless device at the current location from a second AP of the plurality of APs, and triggers a roaming operation of the first wireless device from the first AP to the second AP if the comparison satisfies a mobility threshold that varies based on the at least one performance metric of the first wireless signal.

Abstract: A network management system (NMS) automatically determines locations of deployed access points (APs) in a wireless network. The system identifies clusters of APs, wherein each AP in a cluster is a member of at least one robust quad, and wherein each robust quad in the cluster has three APs in common with another robust quad in the cluster. The NMS identifies one of the plurality of clusters as a gxlobal cluster and the remaining plurality of clusters as local clusters. The NMS determines coordinates of each node in the global cluster based on the coordinates of the anchor APs in the global coordinate system. For each local cluster, the NMS transforms the coordinates of each AP from the respective local coordinate system to the global coordinate system.

Abstract: Techniques are described in which a network management system (NMS) is configured to determine a root cause of degraded network performance based on SLE metrics and the locations associated with network devices providing the SLE metrics. The NMS can determine service level experience (SLE) metrics associated with each client device on a network and location data for each client device of the plurality of client devices. The NMS can generate a time series of parameter vectors, where each parameter vector includes SLE metrics corresponding to each client device of the plurality of client devices. Each parameter vector is associated with the location of the client device corresponding to the SLE metrics. The NMS can determine, based on the time series of parameter vectors and associated locations, a root cause for a degradation in SLE metrics associated with the one or more of the client devices.

Abstract: Methods of deriving location information of a wireless device include deriving, in the continuous domain, a location of a wireless device and at least one time and location varying path loss function parameter. The coordinates and parameter are derived based on signal strength measurements made at the wireless device, with the measured signals originating from a plurality of wireless transmitters, such as access points. The derived path loss function parameter can include one or more of a path loss exponent parameter, an intercept parameter, a receiver antenna gain parameter, transmitter antenna gain parameter, or a transmit power parameter.

Abstract: Disclosed are embodiments for estimating risk associated with a user of a wireless device. In some embodiments, the risk relates to a risk of infection by a contagious disease. For example, in some embodiments, the contagious disease is Coronavirus 2019. In some embodiments, locations of multiple wireless devices are estimated based on signal strengths of signals associated with the devices. Neighboring devices are identified based on highest probability regions of the devices that are determined based on associated signals. A measure of proximity to other devices is then determined based on probabilities that each device is located in neighboring regions. The risk is then based on the measure of proximity. In some embodiments, a risk of a first user associated with a first wireless device is based, in part, on a risk of a second user within a proximity of the first user.

Abstract: Disclosed are embodiments for estimating risk associated with a user of a wireless device. In some embodiments, the risk relates to a risk of infection by a contagious disease. For example, in some embodiments, the contagious disease is Coronavirus 2019. In some embodiments, locations of multiple wireless devices are estimated based on signal strengths of signals associated with the devices. Neighboring devices are identified based on highest probability regions of the devices that are determined based on associated signals. A measure of proximity to other devices is then determined based on probabilities that each device is located in neighboring regions. The risk is then based on the measure of proximity. In some embodiments, a risk of a first user associated with a first wireless device is based, in part, on a risk of a second user within a proximity of the first user.