Edge Sensor Graph Integration for Low-Bandwidth Data Querying
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Solution Overview
Problem
Manually gathering and parsing sensor data from different locations is difficult, time-consuming, and creates a bandwidth communication burden.
Innovation Solution
Utilizing edge devices to locally generate and manage sensor nodes, forming a structured graph through graph queries, which automatically populate and update based on sensor observations, enabling efficient data integration and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual gathering and parsing of sensor data is performed, then data integration is achieved, but it is difficult and time-consuming
Solution Approach 1:
The system enables self-service through automated data collection where sensor nodes automatically publish their data to the graph database without manual intervention. The graph queries automatically traverse the structured graph to retrieve integrated sensor data, eliminating the need for manual data gathering and parsing operations.
2Productivity
If manual gathering and parsing of sensor data is performed, then data integration is achieved, but it creates a bandwidth communication burden
Solution Approach 1:
The system segments the data architecture into distributed sensor nodes that independently publish their data locally to the graph database. This segmentation allows each sensor to operate autonomously and publish only its own data updates, reducing the overall communication burden compared to centralized data collection where all sensors would need to communicate with a central system simultaneously.
3Adaptability or versatility
If structured graph with sensor nodes is created, then querying capabilities are enhanced, but device complexity increases
Solution Approach 1:
The graph database serves multiple functions: it stores sensor data, maintains the structured relationships between sensors and their data, and enables flexible querying through graph traversal. This multi-functionality reduces the need for separate systems for data storage and data relationship management, thereby managing complexity while enhancing querying capabilities.
4Productivity
If edge devices locally manage sensor nodes, then data processing is improved, but device complexity increases
Solution Approach 1:
The graph database acts as an intermediary between the sensor nodes and the querying system. Edge devices publish sensor observations to the graph database, which then handles the complex task of data integration and relationship management. This intermediary approach allows edge devices to focus on simple data publication while the graph database manages the complexity of data integration and querying.
Data Source
AI summary
Different edge devices are connected to different sensors. Individual edge devices locally generate and manage sensor nodes that represent sensors connected to the individual edge devices. Sensor observations from the sensors are received by the edge devices, and the edge devices populate the locally managed sensor nodes based on the received sensor observations. The sensor nodes locally managed by different edge devices form a structured graph, and sensor queries are answered using graph queries on the structured graph. Connections between the nodes of the structured graph are dynamically created based on the graph queries.