Gateway Controller for Distributed Data Analysis
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Solution Overview
Problem
Current distributed ecosystems rely on a centralized approach for analysis and decision-making, which is inefficient due to the need for data transmission to a central computing resource, leading to time delays and business continuity challenges, especially in dynamic environments where local analysis may be more timely.
Innovation Solution
A distributed ecosystem with localized computing resources, sensors, and gateway controllers that monitor data transmission and determine if local resources have the necessary capabilities for processing, allowing data to be analyzed locally or transmitted to an ideal network location for processing, thereby optimizing resource utilization and decision-making efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If data is transmitted to a centralized computing resource for analysis, then centralized control and resource management are improved, but analysis time and system responsiveness deteriorate
Solution Approach 1:
The patent segments the centralized analysis function into distributed localized computing resources. Each sensor node or edge device is equipped with local computing capabilities to perform data analysis independently, eliminating the need to transmit all data to a central server. This segmentation resolves the contradiction by maintaining control functionality while reducing transmission time.
Solution Approach 2:
The patent introduces a spatial dimension to the analysis process by enabling multi-location analysis. Instead of a single centralized analysis point, the system allows data to be analyzed at multiple distributed locations (sensor nodes, edge devices, cloud). This dimensional expansion allows the system to simultaneously maintain centralized coordination while enabling fast local analysis, thus resolving the time-delay contradiction.
2Loss of information
If data is transmitted to a centralized computing resource, then comprehensive analysis capability is improved, but business continuity and system reliability deteriorate
Solution Approach 1:
The patent applies local quality by equipping different localized computing resources with specific analysis capabilities appropriate to their context. Edge devices perform real-time critical analysis, while cloud resources handle comprehensive historical analysis. This distribution of quality across different locations ensures that critical functions remain operational even if centralized resources fail, resolving the reliability contradiction.
Solution Approach 2:
The patent implements prior cushioning by pre-positioning computing resources and analysis capabilities at multiple distributed locations before failures occur. This redundancy ensures that if the centralized system fails, local resources can continue operating independently, maintaining business continuity while preserving comprehensive analysis capability across the distributed network.
3Productivity
If localized computing resources are used for data analysis, then analysis speed and responsiveness are improved, but resource capability requirements and system complexity increase
Solution Approach 1:
The patent applies universality by designing standardized computing resource interfaces and communication protocols that allow different types of devices (sensors, edge devices, cloud services) to perform similar analysis functions. This standardization enables fast local analysis while simplifying the coordination complexity through universal interaction patterns, resolving the contradiction between productivity and device complexity.
4Loss of information
If all data is transmitted to centralized resources, then complete data availability for analysis is improved, but transmission costs and energy consumption increase
Solution Approach 1:
The patent applies partial action by having localized computing resources perform only the necessary portion of data analysis locally, transmitting only essential data or processed results to centralized resources. This selective approach ensures complete data availability for critical local decisions while reducing unnecessary transmission energy consumption, resolving the contradiction between data availability and energy loss.
Data Source
AI summary
A distributed ecosystem including an analysis computing resource and a network is disclosed. The distributed ecosystem also includes at least one sensor for monitoring a characteristic of the distributed ecosystem, at least one localized computing resource in communication with the at least one sensor, and a gateway controller. The gateway controller is in communication with the at least one sensor, the at least one localized computing resource, and the analysis computing resource. The gateway control includes control logic for monitoring the at least one sensor for a data transmit function. The gateway controller also includes control logic for determining if the at least one localized computing resource has requisite resource capabilities for processing data generated by the at least one sensor in response to the data transmit function being transmitted by the at least one sensor.