VoIP Edge Device AI Traffic Classification
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Solution Overview
Problem
VoIP communication systems face inefficiencies due to random quality of service (QoS) allocation, often providing higher QoS to non-media network traffic, which can waste resources that could be used to minimize transmission delays, jitter, and packet loss for media traffic.
Innovation Solution
Incorporating edge devices with signal and media AI models, utilizing logistic regression machines to identify and differentiate between media and non-media network traffic, allowing for precise QoS adjustment by prioritizing bandwidth based on packet type identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If random QoS is provided to network traffic, then implementation is simple, but media traffic suffers from increased transmission delays, jitter, and packet loss
Solution Approach 1:
The patent replaces traditional mechanical/QoS-based traffic differentiation with AI/ML-based classification. The system uses machine learning models to analyze packet characteristics and automatically identify media versus non-media traffic, substituting complex manual QoS configuration with automated intelligent decision-making that adapts to traffic patterns dynamically.
Solution Approach 2:
The patent changes the approach from static QoS parameters to dynamic AI-based classification. Instead of using fixed thresholds or manual policies, the system employs machine learning models that continuously learn from packet data characteristics, enabling adaptive and context-aware traffic differentiation that optimizes QoS based on actual traffic behavior patterns.
2Reliability
If higher QoS is provided to non-media network traffic, then non-media traffic performance improves, but VoIP communication resources are wasted
Solution Approach 1:
The patent replaces traditional mechanical QoS policies with AI-based intelligent classification. The machine learning models analyze packet characteristics such as protocol types, payload patterns, and traffic behavior to accurately distinguish between media and non-media traffic, enabling automated and accurate resource allocation that prevents waste while ensuring quality for critical traffic.
Solution Approach 2:
The system enables self-service traffic classification where the AI models automatically learn traffic patterns and make differentiation decisions without manual intervention. The models continuously adapt to changing network conditions and traffic types, autonomously optimizing resource allocation based on observed packet characteristics and traffic behavior.
3Measurement precision
If AI/ML models are used to identify network traffic, then traffic identification accuracy improves, but device complexity increases
Solution Approach 1:
The patent segments the complex AI-based traffic identification system into modular components. The edge devices perform preliminary packet analysis and feature extraction, while centralized AI models handle the complex classification decisions. This segmentation distributes computational complexity and enables scalable deployment without requiring every edge device to host full AI models.
Solution Approach 2:
The patent introduces an intermediary layer between edge devices and the centralized AI system. This intermediary handles packet data collection, feature extraction, and preliminary processing, reducing the computational burden on edge devices while preparing data for centralized AI model analysis. The intermediary enables accurate traffic identification without requiring complex AI models to run locally on resource-constrained edge devices.
Data Source
AI summary
Devices and systems for voice over Internet protocol (VoIP) for identifying network traffic are described herein. One or more embodiments include a VoIP device for identifying network traffic comprising a signal monitor to identify a signaling protocol from the network traffic and an artificial intelligence engine configured to receive signaling protocol sample data to train a signal artificial intelligence (AI) model and process the signaling protocol identified by the signal monitor in the signal AI model to identify the network traffic.