Metadata-Based IoT Data Distribution for Lower Cloud Latency
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Solution Overview
Problem
The increasing amount of data generated by IoT devices and complex AI models has outpaced network and infrastructure capabilities, leading to bandwidth and latency issues in conventional cloud computing systems due to the need to transfer all data to a centralized location.
Innovation Solution
Implementing edge computing environments to classify and generate labeled metadata for IoT data, identifying specific portions to transmit to the cloud while evaluating data at the edge for storage and deletion, thereby reducing network strain and memory usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If all IoT data is transferred to a centralized cloud location, then data processing capability is improved, but network bandwidth and latency deteriorate
Solution Approach 1:
The patent segments the centralized cloud computing system into distributed edge computing nodes. Data processing is divided between edge devices that perform local processing and cloud centers that handle aggregated results. This segmentation allows processing to occur closer to data sources, reducing network bandwidth consumption while maintaining processing capability.
Solution Approach 2:
The patent introduces a spatial dimension to data processing by deploying edge computing nodes at multiple geographic locations near IoT devices. Instead of all data traveling to a single centralized cloud, processing occurs across multiple distributed nodes, adding a spatial distribution dimension that reduces network strain.
2Power
If all IoT data is transferred to a centralized cloud location, then data processing capability is improved, but system latency increases
Solution Approach 1:
The patent segments the centralized cloud computing system into distributed edge computing nodes. Data processing is divided between edge devices that perform local processing and cloud centers that handle aggregated results. This segmentation allows processing to occur closer to data sources, reducing network bandwidth consumption while maintaining processing capability.
Solution Approach 2:
The patent introduces edge computing nodes as intermediary systems between IoT devices and centralized cloud centers. These intermediaries perform preliminary data processing, filtering, and aggregation locally, reducing the amount of data that needs to be transmitted to the cloud and thereby reducing system latency.
3Loss of energy
If edge computing is implemented to filter and classify data, then network bandwidth is reduced, but device complexity increases
Solution Approach 1:
The patent implements self-service mechanisms where edge computing nodes autonomously classify, filter, and prioritize IoT data using local intelligence. The system automatically determines which data requires cloud processing and which can be handled locally, reducing the need for complex centralized management while minimizing network bandwidth usage.
Data Source
AI summary
A computer-implemented method, according to one approach, includes: receiving, at an edge computing environment, internet of things (IoT) data generated by IoT devices in communication with the edge computing environment. One or more inference models are used at the edge computing environment to: classify the IoT data, and generate labeled metadata for the classified IoT data. The labeled metadata is further used at the edge computing environment to identify portions of the IoT data to transmit to a cloud computing environment. Copies of the identified portions of the IoT data are also sent to the cloud computing environment.