Decentralized Regional Nodes for Low-Latency Event Processing
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Solution Overview
Problem
Traditional centralized computing networks for data processing across large geographic areas face challenges such as computational expense, network latency, scalability issues, and single points of failure, which hinder real-time data dissemination and system reliability.
Innovation Solution
A decentralized computing network architecture with independent computing nodes processing data for specific geographic regions, enabling efficient data processing, reduced latency, and easy scalability, while maintaining system reliability through geographical hierarchies and node communication protocols.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a centralized network configuration is used to collect and process data across large geographic areas, then data aggregation and analysis can be performed by a single central authority, but computational expense and resource allocation increase significantly
Solution Approach 1:
The patent divides the centralized computing network into multiple decentralized computing nodes, each responsible for processing data from specific geographic regions or channels. This segmentation distributes the computational workload across multiple independent nodes, reducing the energy and resource burden on any single node while maintaining the ability to aggregate and analyze data across the entire network.
2Ease of operation
If a centralized network configuration is used to process aggregated data, then a single central authority manages data storage and processing, but network latency increases due to data transmission and processing time
Solution Approach 1:
The patent enables each computing node to perform local data processing and analysis independently, generating insights and updates within its own geographic region before sharing them with other nodes. This local quality approach reduces the time required for data to travel to and from a central authority, significantly decreasing network latency while still allowing for coordinated analysis across the decentralized network.
3Ease of operation
If a centralized system is used to manage data across geographic areas, then a single central authority performs all analysis and decision-making, but scalability and flexibility are reduced
Solution Approach 1:
The patent creates a dynamic decentralized network where computing nodes can be dynamically added, removed, or scaled independently based on regional data processing needs. Each node operates autonomously with decision-making capabilities, allowing the system to scale flexibly across new geographic areas without requiring proportional increases in central authority resources. This dynamic architecture enables the network to adapt to varying data volumes and processing requirements across different regions.
4Ease of operation
If a centralized authority manages the entire system, then unified data processing is achieved, but the system becomes vulnerable to single points of failure
Solution Approach 1:
The patent segments the centralized system into multiple independent computing nodes distributed across different geographic locations. Each node operates autonomously and can continue processing data even if other nodes fail. This segmentation eliminates the single point of failure vulnerability inherent in centralized systems, as the failure of any single node does not compromise the entire network's availability or functionality.
5Productivity
If additional storage, bandwidth, and processing power are allocated to the central authority to scale the system, then system capacity increases, but infrastructure cost and complexity increase
Solution Approach 1:
The patent designs each computing node to be a universal, multi-functional unit capable of performing data collection, local processing, analysis, and communication functions. This universality allows the system to scale by simply adding more identical or near-identical nodes rather than upgrading complex central infrastructure. Each node can handle multiple tasks independently, reducing the overall infrastructure complexity while increasing system capacity through horizontal scaling.
Data Source
AI summary
This disclosure relates to decentralized computing networks, architectures and techniques for collecting, analyzing, and processing data over multiple channels. A decentralized computing network comprises a plurality of computing nodes, each of which is dedicated to analyzing and processing events for a particular channel corresponding to a geographic region. Each node of the decentralized computing network can operate independently to process channel analysis data for a corresponding channel. The decentralized configuration of the nodes enables efficient processing of data collected over large geographic areas, increases the reliability of the system, and facilitates easy scaling of the system. Other embodiments are disclosed herein as well.