# US 11,451,945 B2 · Transparent roaming in virtual access point (VAP) enabled networks > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,451,945 B2 - Document ID: US11451945B2 - Status: Granted - Kind: B2 - Application number: 16/820,843 - Filed: 2020-03-17 - Published: 2020-07-09 - Granted: 2022-09-20 - Earliest claimed: 2016-10-31 - Assignees: Cisco Technology, Inc. - Applicants: Cisco Technology, Inc. - Inventors: Pascal Thubert; Jean-Philippe Vasseur; Patrick Wetterwald; Eric Levy-Abegnoli - Web page: https://patentgrep.com/patent/US11451945B2 ## CPC - H04W 4/70: Services for machine-to-machine communication [M2M] or machine type communication [MTC] - H04W 24/02: Arrangements for optimising operational condition - H04W 48/18: Selecting a network or a communication service - H04W 88/08: Access point devices - H04W 88/12: Access point controller devices - H04W: WIRELESS COMMUNICATION NETWORKS (broadcast communication H04H; communication systems using wireless links for non-selective communication, e.g. wireless extensions H04M1/72) ## Abstract In one embodiment, a supervisory device in a network forms a virtual access point (VAP) for a node in the network. A set of access points (APs) in the network are mapped to the VAP as part of a VAP mapping and the node treats the APs in the VAP mapping as a single AP for purposes of communicating with the network. The supervisory device receives measurements from the APs in the VAP mapping regarding communications associated with the node. The supervisory device identifies a movement of the node based on the received measurements from the APs in the VAP mapping. The supervisory device adjusts the set of APs in the VAP mapping based on the identified movement of the node. ## Claims ### Claim 1 A method, comprising: forming, by a supervisory device in a network, a virtual access point (VAP) for a node in the network, wherein a set of access points (APs) in the network are mapped to the VAP during a VAP mapping, wherein the VAP mapping allows the APs to be treated by the node as a single AP to which the node connects, and wherein the node communicates with the network through the VAP; receiving, at the supervisory device and from the set of APs in the VAP mapping, measurements gathered by the APs regarding communications associated with the node; identifying, by the supervisory device, a movement of the node and a predicted future location of the node based on the received measurements from the set of APs in the VAP mapping; and adjusting, by the supervisory device, the set of APs in the VAP mapping using a machine learning model that predicts an optimal set of changes to the set of APs in the VAP mapping based on the predicted future location of the node. ### Claim 2 (depends on 1) The method as in claim 1, wherein the measurements from the set of APs in the VAP mapping comprise at least one of: a signal strength or signal quality of the communications associated with the node. ### Claim 3 (depends on 1) The method as in claim 1, wherein identifying the movement of the node based on the received measurements from the set of APs in the VAP mapping comprises: computing, by the supervisory device, a time difference of arrival of communications from the node received by the set of APs in the VAP mapping. ### Claim 4 (depends on 1) The method as in claim 1, wherein identifying the movement of the node based on the received measurements from the set of APs in the VAP mapping comprises: matching, by the supervisory device, the node to a particular user; and receiving, at the supervisory device, scheduling information regarding the user, wherein the identified movement of the node is based in part on the scheduling information regarding the user. ### Claim 5 (depends on 1) The method as in claim 1, wherein the optimal set of changes includes an addition or a removal of an AP from the set of APs in the VAP mapping. ### Claim 6 (depends on 5) The method as in claim 5, further comprising: receiving, at the supervisory device, a notification that the predicted optimal set of changes to the set of APs in the VAP mapping caused the node to roam; and retraining, by the supervisory device the machine learning model used to predict the optimal set of changes to the set of APs in the VAP mapping, based on the received notification that the changes to the set of APs in the VAP mapping caused the node to roam. ### Claim 7 (depends on 5) The method as in claim 5, wherein the machine learning model to predict an optimal set of changes to the set of APs in the VAP mapping further bases the prediction on roaming criteria used by the node to determine when to roam. ### Claim 8 (depends on 1) The method as in claim 1, further comprising: determining, by the supervisory device, that the measurements from the set of APs in the VAP mapping indicate reduced signal performance of the communications associated with the node; and varying, by the supervisory device, the set of APs in the VAP mapping to obtain test measurements regarding the communications associated with the node. ### Claim 9 An apparatus, comprising: one or more network interfaces to communicate with a network; a processor coupled to the one or more network interfaces and configured to execute a process comprising program instructions stored in a memory; and the memory configured to store the process comprising the program instructions executable by the processor, the process when executed configured to: form a virtual access point (VAP) for a node in the network, wherein a set of access points (APs) in the network are mapped to the VAP during a VAP mapping that allows the APs to be treated by the node as a single AP to which the node connects, and wherein the node communicates with the network through the VAP; receive, from the set of APs in the VAP mapping, measurements gathered by the APs regarding communications associated with the node; identify a movement of the node and a predicted future location of the node based on the received measurements from the set of APs in the VAP mapping; and adjust the set of APs in the VAP mapping using a machine learning model that predicts an optimal set of changes to the set of APs in the VAP mapping based on the predicted future location of the node. ### Claim 10 (depends on 9) The apparatus as in claim 9, wherein the measurements from the set of APs in the VAP mapping comprise at least one of: a signal strength or signal quality of the communications associated with the node. ### Claim 11 (depends on 9) The apparatus as in claim 9, wherein the apparatus identifies the movement of the node based on the received measurements from the set of APs in the VAP mapping by: computing a time difference of arrival of communications from the node received by the set of APs in the VAP mapping. ### Claim 12 (depends on 9) The apparatus as in claim 9, wherein the apparatus identifies the movement of the node based on the received measurements from the set of APs in the VAP mapping by: matching the node to a particular user; and receiving scheduling information regarding the user, wherein the identified movement of the node is based in part on the scheduling information regarding the user. ### Claim 13 (depends on 9) The apparatus as in claim 9, wherein the optimal set of changes includes an addition or a removal of an AP from the set of APs in the VAP mapping. ### Claim 14 (depends on 13) The apparatus as in claim 13, wherein the process when executed is further configured to: receive a notification that the predicted optimal set of changes to the set of APs in the VAP mapping caused the node to roam; and retrain the machine learning model used to predict the optimal set of changes to the set of APs in the VAP mapping, based on the received notification that the changes to the set of APs in the VAP mapping caused the node to roam. ### Claim 15 (depends on 13) The apparatus as in claim 13, wherein the machine learning model to predict an optimal set of changes to the set of APs in the VAP mapping further bases the prediction on roaming criteria used by the node to determine when to roam. ### Claim 16 (depends on 9) The apparatus as in claim 9, wherein the process when executed is further configured to: determine that the measurements from the set of APs in the VAP mapping indicate reduced signal performance of the communications associated with the node; and vary the set of APs in the VAP mapping to obtain test measurements regarding the communications associated with the node. ### Claim 17 A tangible, non-transitory, computer-readable medium storing program instructions that, when executed by a supervisory device in a network, cause the supervisory device to perform a process comprising: forming, by the supervisory device, a virtual access point (VAP) for a node in the network, wherein a set of access points (APs) in the network are mapped to the VAP during a VAP mapping, wherein the VAP mapping that allows the APs to be treated by the node as a single AP to which the node connects, and wherein the node communicates with the network through the VAP; receiving, at the supervisory device and from the set of APs in the VAP mapping, measurements gathered by the APs regarding communications associated with the node; identifying, by the supervisory device, a movement of the node and a predicted future location of the node based on the received measurements from the set of APs in the VAP mapping; and adjusting, by the supervisory device, the set of APs in the VAP mapping using a machine learning model that predicts an optimal set of changes to the set of APs in the VAP mapping based on the predicted future location of the node. ### Claim 18 (depends on 17) The tangible, non-transitory, computer-readable medium as in claim 17, wherein the optimal set of changes includes an addition or a removal of an AP from the set of APs in the VAP mapping. ### Claim 19 (depends on 18) The tangible, non-transitory, computer-readable medium as in claim 18, further comprising: receiving, at the supervisory device, a notification that the predicted optimal set of changes to the set of APs in the VAP mapping caused the node to roam; and retraining, by the supervisory device the machine learning model used to predict the optimal set of changes to the set of APs in the VAP mapping, based on the received notification that the changes to the set of APs in the VAP mapping caused the node to roam. ### Claim 20 (depends on 18) The tangible, non-transitory, computer-readable medium as in claim 18, wherein the machine learning model predicts an optimal set of changes to the set of APs in the VAP mapping further bases the prediction on roaming criteria used by the node to determine when to roam.