M2M Semantic Resource Tree for Context-Aware Services
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Solution Overview
Problem
Current machine-to-machine (M2M) communication systems require extensive software and hardware efforts to support services at the application service layer (ASL), lacking efficient data interpretation and context-aware functionalities to generate meaningful insights from sensor data.
Innovation Solution
A device and method that store data with associated semantic information, using content functions to generate semantic descriptions, and context-aware functions to process these descriptions, thereby deducing higher-level contextual cues and situational context, supporting services at the M2M services capability layer (SCL).
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional M2M communication systems are used without semantic interpretation layers, then hardware and software complexity is reduced, but the ability to generate meaningful insights from sensor data is insufficient
Solution Approach 1:
The patent introduces an Application Service Layer (ASL) as an intermediary between the sensor data collection layer and the application layer. This ASL contains content functions that interpret raw sensor data and generate semantic descriptions, thereby preventing loss of meaningful information without requiring complex processing at the sensor or application endpoints. The intermediary layer handles the complexity of semantic interpretation centrally, allowing simple sensors and applications to benefit from rich contextual understanding.
2Reliability
If context-aware functions are added to process semantic descriptions, then contextual understanding is improved, but system complexity increases
Solution Approach 1:
The patent segments the context-aware processing functionality into distinct modular components within the ASL: content functions for semantic interpretation, context-aware functions for processing semantic descriptions, and context-aware reasoning functions for deducing higher-level context. This segmentation allows each component to be developed, maintained, and optimized independently, improving contextual understanding accuracy while managing system complexity through modular architecture.
Solution Approach 2:
The ASL is designed as a universal service layer that can be applied across multiple M2M applications and domains. By creating multi-functional context-aware processing capabilities at this layer, the system achieves improved contextual understanding for diverse applications without requiring separate complex processing systems for each application, thereby managing overall system complexity.
3Ease of operation
If semantic information is stored with data in a resource tree structure, then data interpretability is improved, but storage and processing overhead increases
Solution Approach 1:
The patent implements a nested resource tree structure where semantic information is stored hierarchically alongside the raw sensor data. The resource tree organizes data at multiple levels: raw data nodes, semantic description nodes, contextual cue nodes, and higher-level context nodes. This nesting allows data interpretability to be improved at each level without duplicating the entire data set, as each level references and builds upon the previous level rather than storing complete copies.
Data Source
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AI summary
A method of providing context-aware services using Machine-to-Machine (M2M) communication, the method comprising obtaining data from an M2M device; creating an M2M resource; storing the data as content in the M2M resource; storing, in the M2M resource, a semantic description attribute of the content stored in the M2M resource and a reference attribute to a semantic description of the M2M resource; and providing the M2M resource for use by at least one of a service capability layer (SCL) and an application to perform content-based semantic functionalities based on the semantic description included in the M2M resource.