Hierarchical Graph Map Generation for IoT Sensor Data
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Solution Overview
Problem
Managing and efficiently accessing vast amounts of data generated by IoT devices in physical spaces is challenging due to difficulties in intuitively grouping devices, controlling user access, and visualizing live sensor data, especially as the number of devices and data types increases, leading to inefficient data management and access.
Innovation Solution
A multi-database environment is implemented, using one or more graphs to store static information about the physical environment and IoT devices, along with a separate data structure for dynamic sensor data, allowing for quick queries and efficient access to data, enabling the generation of maps that display real-time sensor data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single database stores all IoT device data, then data completeness is maintained, but data access efficiency deteriorates
Solution Approach 1:
The patent divides the database into multiple specialized databases: a topology database storing static spatial relationships and device locations, and a sensor data database storing dynamic sensor readings. This segmentation allows efficient querying of specific data types while maintaining complete data storage, resolving the contradiction between data completeness and access efficiency.
2Measurement precision
If all device data is stored in detail, then data accuracy is improved, but data management complexity increases
Solution Approach 1:
The patent applies local quality by storing different types of data with different levels of detail in appropriate databases. The topology database stores precise spatial relationships and device metadata, while the sensor data database stores time-series measurements. This allows accurate data storage without uniformly high complexity across all data management operations.
Solution Approach 2:
The patent introduces a data access layer with query interfaces that mediate between users and the underlying databases. This intermediary layer simplifies data management by providing standardized access methods, handling data retrieval and processing, while the databases themselves maintain detailed accurate data.
3Reliability
If static topology data and dynamic sensor data are stored together, then data integration is improved, but query performance deteriorates
Solution Approach 1:
The patent segments integrated data into two separate databases: topology database for static spatial data and sensor data database for dynamic measurements. This segmentation maintains data integration through defined relationships while enabling optimized queries for each data type, improving query performance without sacrificing integration.
Solution Approach 2:
The patent adds a temporal dimension to data storage by organizing sensor data as time-series measurements separate from static topology data. This dimensional separation allows efficient time-based queries for sensor data while maintaining spatial relationship queries for topology data, improving overall query performance.
Data Source
AI summary
Generating a map based on nodes of a hierarchical graph that defines a topology of a physical space and is configured to provide access to sensor data from devices within the physical space includes accessing the hierarchical graph. The hierarchical graph comprises a plurality of nodes, including a top node for the physical space and a plurality of other nodes coupled to the top node. One of the plurality of nodes comprises an area node that represents an area within the physical space. One of the plurality of nodes also comprises a device node representing a device located within the physical space and a user node. Map data corresponding to a particular node is generated. The generated map data is associated with generating at least a portion of a map corresponding to the particular node. The generated map data corresponding to the particular node is stored within the hierarchical graph.