Edge Environmental Data Hub for Multi-Sensor Real-Time Parsing
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Solution Overview
Problem
Conventional environmental monitoring systems are rigid, time-consuming, and costly to integrate new sensor types, and rely heavily on centralized data processing that introduces latency and high bandwidth usage, making real-time analysis and visualization challenging, especially in resource-constrained environments.
Innovation Solution
A system that operates as a middleware at the edge, automatically detecting and configuring various sensors, parsing data in real-time, and performing local analysis and visualization, reducing reliance on cloud services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If centralized data processing is used, then data processing capability is improved, but latency increases and bandwidth usage increases
Solution Approach 1:
The patent divides the centralized data processing architecture into distributed edge computing nodes deployed at monitoring sites. Each edge node independently processes sensor data locally, segmenting the monolithic centralized processing into multiple autonomous processing units that operate in parallel, thereby reducing latency and bandwidth requirements while maintaining processing capability.
Solution Approach 2:
The patent introduces a new architectural dimension by deploying computing resources at the edge of the network rather than solely at the center. This spatial redistribution of processing power creates a hybrid architecture that combines local real-time processing with centralized aggregate analysis, solving the latency-bandwidth tradeoff through dimensional expansion of the system architecture.
2Adaptability or versatility
If new sensor types are integrated, then measurement capability is improved, but integration time increases and cost increases
Solution Approach 1:
The patent implements a universal edge computing platform with standardized interfaces and protocols that can accommodate multiple sensor types. The edge nodes are designed with multi-functional capabilities to ingest, process, and analyze data from diverse sensor sources including air quality sensors, noise sensors, and environmental monitors through a common architecture, eliminating the need for type-specific integration procedures.
Solution Approach 2:
The edge computing nodes incorporate automatic sensor detection and configuration capabilities that enable self-service integration. When new sensors are deployed, the system automatically detects their presence, identifies their type through metadata or identification protocols, and configures appropriate processing parameters without requiring manual intervention, thereby dramatically reducing integration time and cost.
3Speed
If real-time data processing is implemented, then responsiveness is improved, but bandwidth usage increases and cloud resource consumption increases
Solution Approach 1:
The patent extracts real-time data processing functions from the centralized cloud infrastructure and relocates them to distributed edge computing nodes at monitoring sites. This extraction of processing functions to the network edge enables real-time local analysis of sensor data without transmitting all raw data to the cloud, thereby achieving responsive processing while significantly reducing bandwidth consumption and cloud resource utilization.
Data Source
AI summary
A system, device, and method for collecting and processing sensor data at an edge system is disclosed. The system includes (i) a bridge configured to receive time series sensor data sets from a plurality of environmental sensors and having a plurality of formats, and (ii) a data hub, coupled with the bridge, configured to parse each of the time series data sets, determine a sensor type and data format for each of the plurality of environmental sensors, and convert the data format to a predefined format.