Edge Nodes for Forensic Feedback Architecture
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Solution Overview
Problem
Digital networks face challenges in isolating and responding to meaningful situational information due to massive data accumulation and heterogeneous data from multiple sources, making real-time situational awareness and responsiveness difficult.
Innovation Solution
The implementation of edge-computing systems with edge-nodes that generate and process situational data locally, using machine-learning engines to create event profiles and execute responses when consensus is reached among digital devices, allowing for real-time situational awareness and responsiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is accumulated on a very large scale in the network, then a rich and inclusive dataset is obtained, but it becomes difficult to isolate and pinpoint meaningful situational information within the data
Solution Approach 1:
The system segments the large-scale data accumulation problem by distributing data collection and processing across multiple edge nodes rather than centralizing it. Each edge node processes local data independently, dividing the massive dataset into manageable segments that can be analyzed for meaningful information without overwhelming a single processing point.
Solution Approach 2:
The patent introduces a spatial dimension to data processing by deploying edge nodes at different physical locations throughout the network. This dimensional approach allows the system to process data in parallel across multiple spatial points, transforming a single-point analysis problem into a distributed multi-point analysis system that can isolate meaningful information more effectively.
2Adaptability or versatility
If data is culled from a number of disparate devices, then comprehensive situational coverage is achieved, but the heterogeneous nature of data creates additional challenge in identifying meaningful situational information
Solution Approach 1:
The patent implements a universal data processing framework that can handle heterogeneous data from disparate devices. The edge nodes are designed with multi-functional capabilities to process various data types and formats from different sources, applying consistent processing logic across diverse inputs to identify meaningful situational information regardless of source heterogeneity.
Solution Approach 2:
The system transforms heterogeneous data by standardizing parameters at the edge nodes. Each edge node adjusts and normalizes data parameters from different devices to a common format, changing the parameter representation to enable consistent analysis across diverse data sources while preserving the essential situational information.
3Extent of automation
If data is processed centrally rather than at the edge, then centralized control is maintained, but response time increases and network bandwidth is consumed
Solution Approach 1:
The patent segments the centralized processing function by distributing computational capabilities to edge nodes throughout the network. Each edge node performs local data processing and analysis independently, eliminating the need to transmit all raw data to a central processor and enabling faster local responses while maintaining centralized oversight through selective data reporting.
Solution Approach 2:
The system performs preliminary data processing and analysis at the edge nodes before data needs to be transmitted or acted upon centrally. Edge nodes pre-process data, identify meaningful patterns, and prepare processed results in advance, reducing the time required for centralized processing and enabling faster overall system response.
4Loss of information
If all digital devices transmit their data streams to a central location, then comprehensive data analysis is enabled, but communication bandwidth is consumed and network congestion occurs
Solution Approach 1:
The patent extracts and processes data locally at edge nodes rather than transmitting all raw data streams to a central location. Each edge node extracts meaningful information from its local data sources and transmits only the processed results or relevant summaries, significantly reducing communication bandwidth consumption while maintaining the ability to perform comprehensive data analysis through distributed processing.
Data Source
AI summary
Aspects of the disclosure relate to systems and methods for maintaining situational stability at a target location. The systems may include a database of machine-learning (“ML”)-derived event profiles. The systems may include a plurality of edge-nodes that are proximal to the target location. Each edge-node may generate a data stream of situational data pertaining to the target location. Each edge-node may transmit its data stream to the other edge-nodes. Each edge-node may conglomerate its own data stream with the data streams received from the other edge-nodes to create a conglomerated data stream. Each edge-node may monitor its conglomerated data stream for data that matches one of the event profiles. When a consensus is determined among the edge-nodes that a match occurred, the systems may execute a pre-determined response.