Client Wi-Fi Traffic Classification for Latency-Power Save Selection
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Solution Overview
Problem
Existing wireless communication systems face challenges in accurately detecting traffic types and identifying quality of service requirements, particularly with applications using security protocols like HTTPS, and high-volume traffic, leading to high complexity and hardware costs in real-time inspection and classification.
Innovation Solution
A traffic classifier using a two-stage random forest model and time domain autoencoder is trained with both offline and online data to detect real-time and streaming traffic, enabling configuration of service level agreements and power saving operations based on traffic types, activities, and loads, with reinforcement learning for dynamic adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep packet inspection is used for traffic classification, then traffic type detection accuracy is improved, but device complexity and hardware costs increase
Solution Approach 1:
The patent extracts only the necessary features from packet data (packet size, inter-arrival time, protocol type) rather than performing full deep packet inspection. This selective feature extraction maintains traffic classification accuracy while significantly reducing computational complexity and hardware requirements.
Solution Approach 2:
The patent employs lightweight machine learning models (random forest, autoencoders) that can be trained offline and deployed with minimal computational resources. These models provide accurate traffic classification without requiring complex real-time inspection hardware, effectively replacing expensive DPI infrastructure.
2Use of energy by moving object
If power save parameters are extended for power consumption reduction, then energy efficiency is improved, but communication latency increases
Solution Approach 1:
The patent dynamically adjusts power save parameters based on traffic flow characteristics and environmental conditions. The system learns optimal inactivity timeouts and poll periods through reinforcement learning, adapting to changing network conditions to balance power savings with latency requirements for different traffic types.
Solution Approach 2:
The patent changes power save parameters (inactivity timeout, poll period) based on detected traffic patterns and QoS requirements. For high-priority traffic, shorter timeouts maintain low latency, while for low-priority traffic, extended timeouts maximize power savings, effectively resolving the latency-power tradeoff.
3Speed
If power save parameters are reduced for latency reduction, then communication responsiveness is improved, but power consumption increases
Solution Approach 1:
The system continuously monitors traffic patterns and dynamically adjusts power save parameters in real-time. When high-latency traffic is detected, the system reduces inactivity timeouts to improve responsiveness, accepting increased power consumption only when necessary for maintaining QoS.
Solution Approach 2:
The patent implements parameter changes that adapt power save settings to traffic requirements. Shorter poll periods and inactivity timeouts are applied selectively to time-sensitive traffic flows, while power-saving settings are maintained for non-critical traffic, optimizing the responsiveness-power consumption tradeoff.
Data Source
AI summary
This disclosure provides methods, components, devices and systems for traffic classifier for service aware Wi-Fi operation. The techniques described herein support the creation of a common traffic detection model on an access point (AP) or client. In some examples, the model may reclassify traffic over time to avoid detection failure during an atypical traffic pattern period of an interested flow. The traffic classifier may provide traffic detection results and traffic activity such that a power saving operation may be enabled. Some aspects more specifically relate to mechanisms according to which the AP or client may select power save parameters associated with a classified traffic flow to the AP or client.