Latency-Constrained Abstraction Framework for Sensor Data
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Solution Overview
Problem
Current systems lack an efficient method to collect, process, and combine data from various sources such as sensors in physical environments, virtual environments, and Internet Connected Devices (ICDs) into unified data records, hindering the ability to derive meaningful abstractions and provide personalized services in real-time.
Innovation Solution
A system and method that selectively collects, processes, and combines data from sensors, software sensors, and virtual user identities to produce data records conforming to predetermined formats, using machine learning and human curation to derive abstractions, which are then used to verify events and control devices through a Directory Server and Broker system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data from multiple sources (sensors, virtual environments, ICDs) is collected and processed to produce unified data records, then the ability to derive meaningful abstractions and provide personalized services is improved, but the system complexity and data processing requirements increase
Solution Approach 1:
The system segments data processing into distinct modules: data collection from multiple sources, data normalization to unified formats, abstraction derivation, and service provisioning. Each module handles specific tasks independently, reducing overall system complexity while enabling versatile abstraction derivation from diverse sensor data, virtual environment data, and ICD data.
Solution Approach 2:
The patent introduces intermediary components including a Directory Server that manages service discovery and a Broker system that coordinates data flow between collection, processing, and abstraction layers. These intermediaries simplify the architecture by providing standardized interfaces and mediation mechanisms, allowing the system to handle multiple data sources without proportionally increasing complexity.
2Speed
If real-time data processing is implemented to provide personalized services, then service responsiveness is improved, but computational resources and processing time are consumed
Solution Approach 1:
The system performs preliminary data normalization and formatting during the data collection phase, converting data from various sources into unified formats before processing. This preliminary action reduces the computational burden during real-time service delivery, enabling faster response times without proportionally increasing resource consumption during critical operations.
Solution Approach 2:
The patent implements selective data processing where not all collected data is processed in real-time. Instead, the system identifies and processes only the most relevant data subsets for immediate service delivery, while other data is processed asynchronously or batched. This partial action approach maintains service responsiveness for critical functions while conserving computational resources.
3Measurement precision
If comprehensive data collection from all sources is performed, then data completeness and abstraction accuracy are improved, but data processing time and storage requirements increase
Solution Approach 1:
The system applies local quality by processing and deriving abstractions from different data sources with appropriate methods tailored to each source type. Sensor data, virtual environment data, and ICD data each receive specialized processing routines that optimize accuracy for their specific characteristics while minimizing unnecessary processing steps, thereby maintaining high abstraction accuracy without uniformly increasing processing time across all data types.
Data Source
AI summary
System and methods are described to create successive abstractions of real and virtual environments and objects. A common framework is provided to define abstractions for a large collection of various input data feeds. It is assumed that environments are either instrumented to produce such messages or that they may contain sensors or smart devices that generate such messages. Incoming messages are analyzed preserving latency requirements and without imposing unduly heavy procedures. The analysis yields various abstractions at several scales that can be utilized by application programs. A system architecture using specialized storage mechanisms is proposed that preserves the latency requirements of the incoming data messages and the generated abstractions.