IoT Data Model Format Transformation for Version Adaptability
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Solution Overview
Problem
IoT applications face challenges in managing data formats that change with new device versions, requiring continuous updates to meet a single format known by the system, making it infeasible to convert data for specific applications and limiting data accessibility across different versions.
Innovation Solution
The implementation of an IoT data model with transformation rules and data acquisition modules allows for the transformation of IoT data packets from one format to another, enabling storage and access independent of application versions, and using data transformation modules (DTMs) and data acquisition modules (DAMs) to enrich data sets and provide data in various formats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If a single data format is enforced for all IoT devices, then data consistency is improved, but adaptability to different device versions deteriorates
Solution Approach 1:
The patent segments data storage by creating separate data stores for different device versions (version 1 data store, version 2 data store, etc.). Each data store maintains its own format consistency while the system as a whole supports multiple versions, resolving the contradiction between data consistency and version adaptability.
Solution Approach 2:
The patent adds a version dimension to the data storage architecture. Instead of a single flat data store enforcing one format, the system organizes data stores in a multi-dimensional space where each dimension represents a device version, allowing simultaneous support for multiple formats while maintaining consistency within each version.
2Adaptability or versatility
If data format changes with each new device version, then adaptability is improved, but system complexity deteriorates
Solution Approach 1:
The patent implements preliminary action by pre-configuring multiple data stores for different device versions before new devices are deployed. When a new device version is introduced, its corresponding data store is already in place with the appropriate format, eliminating the need for complex real-time format changes and reducing system complexity.
Solution Approach 2:
The patent creates a universal data storage architecture where multiple data stores serve different device versions simultaneously. Each data store is specialized for a particular version format, but the system as a whole provides universal support for all versions, reducing the complexity of managing individual format changes.
3Device complexity
If data format remains unchanged during device lifecycle, then system simplicity is improved, but adaptability to firmware upgrades deteriorates
Solution Approach 1:
The patent implements dynamics by making the data storage architecture flexible and adaptable. Instead of a static single-format data store, the system dynamically provisions appropriate data stores based on the device firmware version, allowing seamless adaptation to firmware upgrades while maintaining operational simplicity through automated format selection.
Data Source
AI summary
An IoT data packet of IoT data in a first data format is received over a distributed system from an Internet of Things (IoT) device. A determination is made that a second different data format of at least some of the IoT data is being used by at least one application by using an IoT data model of an IoT persistency service storing the IoT data in different data formats supporting different applications. A transformation rule in the IoT data model is used to transform the IoT data packet in the first data format to the second different data format. The IoT data packet in the second different data format is stored in the IoT persistency service.