Edge Platform Interface for Prioritized Sensor Data Uploads
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Solution Overview
Problem
Current edge computing systems face challenges in seamlessly interfacing with a wide range of sensors, managing software deployment, and supporting communication between cloud services and edge devices, especially in resource-sensitive settings with limited bandwidth, leading to high costs and network resource intensity.
Innovation Solution
An edge platform interface (EPI) framework that includes edge-side components for data and software management, integrated linking components for communication across various channels, and remote-side components for device and service management, enabling efficient data prioritization and communication based on available bandwidth, and automated software updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If data is uploaded to cloud servers for processing, then centralized data processing capability is improved, but network resource consumption and strain on centralized datacenters increases
Solution Approach 1:
The patent segments the centralized cloud computing architecture into distributed edge computing nodes deployed at network perimeters and data centers. This segmentation allows data processing to occur locally at edge devices rather than requiring all data to be transmitted to centralized cloud servers, thereby reducing network resource consumption while maintaining processing capability.
Solution Approach 2:
The patent introduces a new dimensional approach by deploying edge computing infrastructure at multiple strategic locations (network perimeters, data centers, and end devices) rather than relying solely on centralized cloud data centers. This spatial distribution across different dimensions of the network architecture reduces the burden on centralized infrastructure.
2Loss of energy
If edge computing systems are deployed in distributed environments, then network resource usage is reduced, but deployment complexity and hurdles increase
Solution Approach 1:
The patent employs a universal container-based software deployment architecture that can run across diverse edge computing hardware platforms. This multi-functional approach allows the same software stack to be deployed uniformly across different device types and environments, significantly reducing deployment complexity while enabling widespread distributed edge computing adoption.
Solution Approach 2:
The patent introduces an intermediary containerization layer that abstracts the complexity of edge device deployment. This intermediary architecture provides standardized interfaces and management capabilities, making it easier to deploy and manage edge computing systems across heterogeneous distributed environments without directly dealing with underlying hardware complexities.
3Productivity
If more sensors and edge devices are integrated, then data generation and processing capability improve, but cost and resource requirements increase
Solution Approach 1:
The patent implements selective data processing at the edge by prioritizing and processing only the most critical data locally, while less critical data can be transmitted to centralized systems. This partial action approach allows the system to handle large volumes of sensor data from multiple devices without requiring proportional increases in centralized processing resources, as edge devices perform initial filtering and processing.
Data Source
AI summary
Systems and methods for operably coupling one or more edge devices to one or more remote components are disclosed herein. In some implementations, the system comprises an edge platform interface (EPI) framework that includes an edge component, a linking component, and a remote component. The edge component is communicatively coupled to the remote component through the edge and remote-portions of the linking component. The edge component can be configured to receive data from one or more onboard sensors, store the sensor data in corresponding data structures, and append metadata to each of the corresponding data structures. The metadata can include prioritization elements, each of which includes an assigned value that is specific to the sensor data in the corresponding data structures. The edge component can then identify communication constraints, prioritize the sensor data, pick candidate data, and upload the candidate data through the edge-portion of the linking component.