Data Sample Template Management for Fog-Based Data Processing
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Solution Overview
Problem
Current IoT data processing solutions face challenges in efficiently collecting and processing data from multiple sources in local fog areas, particularly in heterogeneous scenarios where users lack knowledge of available IoT devices and edge-to-cloud configuration capabilities, leading to high communication overhead and inefficiencies in data analytics.
Innovation Solution
The Data Sample Collection Service (DSCS) employs a Data Sample Template (DST) to specify data requirements and manages DST creation, update, and activation, enabling collaborative identification and processing of data sources across multiple Local Fog Nodes, facilitating efficient data collection and processing within fog-based Service Layers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users directly configure edge-to-cloud data collection without assistance, then configuration precision may be improved, but device complexity and user operation difficulty increase significantly
Solution Approach 1:
The patent introduces an intermediary service layer (edge computing platform) that mediates between users and IoT devices. This platform automatically discovers available data sources, manages device configurations, and handles data collection workflows, allowing users to specify high-level data requirements without directly configuring individual devices or understanding edge computing infrastructure.
2Loss of information
If comprehensive data source discovery is performed across all local fog nodes, then data source identification completeness is improved, but communication overhead and processing time increase
Solution Approach 1:
The patent implements preliminary action by having edge computing platforms pre-discover and catalog available data sources in their local fog areas before actual data collection requests. This pre-discovery process creates a ready-to-use inventory of data sources that can be quickly matched against user requests, avoiding the need for comprehensive real-time scanning of all devices.
Solution Approach 2:
The patent segments the data source discovery process into localized regions managed by individual edge computing platforms. Each platform independently discovers and manages data sources within its local fog area, allowing parallel processing and reducing the communication overhead associated with centralized discovery of all devices.
3Adaptability or versatility
If multiple edge computing platforms collaborate across different fog areas, then data collection versatility is improved, but system complexity and coordination overhead increase
Solution Approach 1:
The patent implements universality by designing edge computing platforms with standardized interfaces and protocols that enable them to perform multiple functions: local data collection, data processing, cross-platform coordination, and user interaction. This multi-functionality allows diverse data sources across different fog areas to be accessed through a unified platform architecture, reducing the need for specialized components.
Data Source
AI summary
A Data Sample Collection Service (DSCS) may be configured to receive various data sample collection requests from users and to process the one or more requests on behalf of the users. In order to properly describe what kind of Ready-to-Use Data Samples (RDS) a user needs, the DSCS may adopt a Data Sample Template (DST). By using the DST, the user can clearly depict what their desired data samples look like, along with various quality requirements regarding the data samples. The DSCS can further provide a DST Management function to users. In general, DST management involves the processes of DST creation, DST update, DST deletion and DST activation/de-activation. For example, a user may create a DST based on its data sample collection needs. Later, the created DST can also be updated and/or deleted based on dynamic changes to the needs of the user.