Juniper Networks, Inc.

Mist Systems, Inc.

Nagarjun Srinivasan

Neal Dante Castagnoli

Kevin Friday

Shmuel Shaffer

Mohammad Zohoorian

Jie C Jiang

Randall Wayne Frei

Robert J Friday

Sunalini Sankhavaram

Christopher Wesley Wright

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 global 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.

Determining locations of deployed access points

Methods and apparatus for processing and using signals transmitted by a device, e.g., a low cost beacon transmitter device, to facilitate making location determinations with regard to the transmitting device and/or making a decision of when or how to use location information generated based on received signals are described. In accordance with some features the processing performed on the received signal strength measurements is based on whether or not the device from which the signals are received is in motion. The size of a sample period used as a processing window when determining device location is based, in some embodiments, on the rate of motion. When and/or how to use location determinations are performed is also based on motion in some embodiments. Machine learning updates of location determination parameters, based on received signals, are disabled when the signals are from devices determined to be in motion.

Methods and apparatus for using received signal strength information in a wireless system

Techniques are described by which a network management system (NMS) is configured to generate and monitor an RSSI-based proximity zone for a wireless network using a user interface (UI). The NMS may generate a UI comprising UI elements representing access point (AP) devices configured to provide a wireless network at a site; receive, at the user interface, an indication of a user input selecting one or more UI elements representing selected AP devices; establish a proximity zone for each of the selected AP devices based on an RSSI threshold value; receive network data comprising proximity information of a client device relative to the selected AP devices; generate, based on proximity assessments using the proximity information and the RSSI threshold value of the proximity zone, one or more proximity events indicating the client device relation to the proximity zone; and invoke, based on the proximity events, one or more actions.

Wireless access point proximity zones

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.

Determining orientation of deployed access points

Disclosed are methods and systems for estimating a location of a wireless device. In some embodiments, received signal strength indication (RSSI) values of signals from a first wireless device are determined. A rate of motion of the first wireless device is then determined based on a rate at which the RSSI values vary with time. A machine learning model is conditionally updated based on the determined rate of motion, and path loss parameters are then derived from the machine learning model. The path loss parameters are used to estimate the location of the first wireless device.

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Sunnyvale, CA, United States of America

Nagarjun Srinivasan