Online learning based smart steering system for wireless mesh networks
AssigneeSabanci Universitesi
InventorsHuseyin Ozkan, Ozgur Gurbuz, Bulut Kuskonmaz
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Abstract
An online learning-based smart steering system and method comprises an online kernel perceptron classifier. The smart steering approach/method performs learning sequentially at the cloud from the entire data of multiple mesh networks, and, operates at APs for steering; both of which are executed in real-time. The system and method uses network features such as current RSSI (received signal strength indicator), current cost, target RSSI and target cost for every steering action resulting in either success of failure. The system and method uses a classifier based on this data for obtaining a model in the feature space which predicts whether a steering action can succeed or not before it is issued.
Claims9
A method comprising:
- observing steering actions employed in a cloud-based network traffic steering system for clients in a large-scale wireless mesh network;
- data collection;
- where data pertaining to instances of steering actions are collected in the cloud,
- feature selection;
- where a plurality of descriptive operational network features are determined at a time of each respective steering action instance,
- model training;
- where a classification problem with successful steering actions and unsuccessful steering actions is formulated based on the descriptive operational network features, and at least one classifier model is trained to learn nonlinear critical regions of successful steering actions in the collected data;
- and
- issuing a steering action request to a client device in the large-scale wireless mesh network at an instance where a steering action is taken by the client device, the steering action request issued based on identification, with the trained at least one classifier model, of a non-linear critical region of successful steering actions being present in the large-scale wireless mesh network during the instance.
A system comprising:
- a processor configured to execute instructions stored in non-transitory memory to observe steering actions employed in a cloud-based network traffic steering system for clients in a large-scale wireless mesh network;
- the processor further configured to execute instructions stored in memory to collect data;
- where data pertaining to instances of steering actions are collected in the cloud;
- the processor further configured to execute instructions stored in memory to select features;
- where a plurality of descriptive operational network features are determined at a time of each respective steering action instance;
- the processor further configured to execute instructions stored in memory to train a model;
- where a classification problem with successful steering actions and unsuccessful steering actions is formulated based on the descriptive operational network features, and at least one classifier model is trained to learn nonlinear critical regions of successful steering actions in the collected data;
- and
- the processor further configured to execute instructions stored in memory to issue a steering action request to a client device in the large-scale wireless mesh network at an instance where a steering action is taken by the client device, the steering action request issued based on identification, with the trained at least one classifier model, of a non-linear critical region of successful steering actions being present in the large-scale wireless mesh network during the instance.
Dependencies are inferred from the claim text.