IoT Service Layer Semantic Data Conversion
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Solution Overview
Problem
Conventional M2M service layers face challenges in efficiently managing and processing sensor data from diverse sources, as they lack a standardized method for describing data formats, leading to complexity and overhead in data representation and conversion across different verticals and devices.
Innovation Solution
The implementation of a service layer that uses semantic annotations and meta-data to dynamically specify and convert data formats, allowing entities to retrieve and process data in a consistent representation, reducing complexity and overhead by separating content descriptors from actual data and using RDF triples and QUDT ontology for unit conversions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional M2M service layers process sensor data from diverse sources without standardized format descriptions, then data compatibility across different devices is improved, but data processing complexity and conversion overhead increase significantly
Solution Approach 1:
The patent introduces a service layer with semantic annotation capabilities that acts as an intermediary between diverse sensor data sources and applications. This service layer automatically annotates data with format descriptors, units, and semantic meanings, enabling data compatibility without requiring applications to handle complex conversion logic. The intermediary service layer absorbs the complexity of format standardization and conversion, while presenting unified, compatible data interfaces to applications.
Solution Approach 2:
The patent implements preliminary annotation of sensor data with format descriptors, units, and semantic information at the data source or service layer level, before data is consumed by applications. This preliminary action ensures that data is pre-processed and standardized with metadata, eliminating the need for complex runtime conversion and reducing processing complexity when applications access the data.
2Stability of the object's composition
If semantic annotations and meta-data are used to dynamically specify data formats, then data representation consistency is improved, but data processing overhead increases
Solution Approach 1:
The patent creates lightweight semantic annotations and metadata copies that describe data formats, units, and meanings without duplicating the actual sensor data. These annotations are stored separately and referenced by applications, providing consistent data representation information without the overhead of processing multiple full data copies or complex format conversion logic.
Solution Approach 2:
The patent segments data representation information into separate semantic annotations and metadata components that can be independently processed and stored. This segmentation allows the system to maintain consistent data representation through structured annotations while reducing processing overhead by only handling the lightweight metadata rather than entire data structures.
3Ease of operation
If data formats are dynamically specified and converted at the service layer, then ease of data retrieval for applications is improved, but service layer complexity increases
Solution Approach 1:
The patent implements a universal service layer with multi-functional capabilities that handles diverse data formats, semantic annotations, unit conversions, and format transformations through a unified architecture. This universal service layer provides ease of data retrieval for applications by managing all format conversion and semantic interpretation complexities internally, while presenting a consistent, simplified interface to applications regardless of the underlying data source diversity.
Data Source
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AI summary
An M2M entity may retrieve data such that the representation of the data may consistently be returned in a form that can be dynamically specified in order to reduce complexity and overhead required by a requestor or consumer of the data. The semantic descriptions of the data that exist in the service layer may be used in order to provide desired results to the requestor or consumer of the data.