Distributed Network Prediction via Node-to-Node Data Requests
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In distributed communication networks, predicting future operational conditions for network optimization is challenging due to the lack of centralized control, especially when complex predictions require data from different nodes, which is not efficiently addressed by current peer-to-peer architectures.
Innovation Solution
Network nodes are configured to request and receive predicted data from other nodes using machine learning models, specifying triggers, confidence levels, and operational parameters, enabling coordinated predictions across the network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a centralized control mode is used to coordinate predictions across network nodes, then prediction accuracy and network optimization performance improve, but system complexity and single point of failure risks increase
Solution Approach 1:
The patent introduces a standardized prediction data interface and message exchange protocol as an intermediary layer between distributed network nodes. This allows nodes to share prediction data and coordinates without requiring complex centralized control logic, resolving the contradiction by enabling coordinated predictions through simple, standardized communication rather than complex centralized management.
2Device complexity
If a distributed peer-to-peer architecture is used for network nodes, then system complexity and single point of failure risks are reduced, but coordination of predictions across nodes becomes inefficient
Solution Approach 1:
The patent implements a universal prediction data interface that enables multiple network nodes to perform prediction coordination functions through standardized message exchange. This universal interface allows distributed nodes to efficiently coordinate predictions without requiring complex node-specific protocols, resolving the contradiction by making the coordination mechanism simple yet effective across all nodes.
3Reliability
If prediction data is collected from multiple network nodes, then prediction accuracy and reliability improve, but data collection complexity and communication overhead increase
Solution Approach 1:
The patent changes the parameter of data collection from active querying to event-triggered reporting. Network nodes automatically generate and share prediction data when specific events occur or time thresholds are met, eliminating the need for complex continuous data collection protocols while maintaining high prediction reliability through timely data sharing from multiple nodes.
4Speed
If real-time prediction data is shared across all network nodes, then network optimization responsiveness improves, but communication bandwidth consumption and network traffic increase
Solution Approach 1:
The patent implements local quality by allowing each network node to selectively receive and process prediction data relevant to its specific function and location. Instead of broadcasting all prediction data to all nodes, the system enables targeted data sharing based on node roles and requirements, improving optimization responsiveness while reducing unnecessary communication overhead and bandwidth consumption.
Data Source
AI summary
A communications network comprising network nodes (20), one network node being configured to generate a requests (200) requesting predicted data from at least one other network node. An other network node being configured in response to receiving a request for predicted data, to generate the predicted data and to transmit (220) the predicted data to the requesting network node.