Edge Analytics Platform for Low-Latency Building Condition Detection
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Solution Overview
Problem
Traditional cloud-based computing systems face challenges in handling large amounts of data from industrial machines due to latency, bandwidth limitations, and cost issues, particularly in environments with unreliable connectivity, leading to inefficiencies in real-time decision-making and predictive maintenance.
Innovation Solution
The implementation of an edge computing platform that processes and analyzes data closer to the source using a software-based solution, enabling real-time analytics and automated responses through a FogHorn-like system, which includes a data processing layer with a complex event processing engine and expression language, and a software development kit for developing edge applications, allowing for efficient data management and reduced bandwidth requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all sensor data from industrial machines is sent to cloud storage and processing, then centralized data management and analysis are achieved, but bandwidth requirements increase, latency increases, and real-time decision-making capability deteriorates
Solution Approach 1:
The system segments data processing functions between edge devices (gateway and embedded systems) and cloud infrastructure. Edge devices perform local data processing, filtering, and preliminary analytics, while cloud systems handle aggregate analysis and long-term storage. This segmentation enables real-time responses at the edge while maintaining centralized management capabilities.
Solution Approach 2:
The patent introduces an edge computing layer as an intermediary between industrial sensors and cloud infrastructure. This edge layer includes gateway devices and embedded systems that process data locally before transmitting to the cloud, acting as a mediator that reduces transmission latency and bandwidth requirements while maintaining data flow between sensors and cloud systems.
2Productivity
If sensor data is processed and stored locally at edge devices, then real-time analytics and automated responses are enabled, but device complexity and resource requirements increase
Solution Approach 1:
The edge computing platform is designed as a universal system that can handle multiple functions including data collection, preprocessing, filtering, analytics, and automated responses across different industrial applications. The standardized platform reduces individual device complexity by providing integrated multi-functional capabilities rather than requiring separate specialized systems for each function.
Solution Approach 2:
The edge devices are equipped with autonomous capabilities to perform data processing, filtering, and analytics without constant cloud intervention. The system includes self-managing components that can operate independently when connectivity is unavailable, reducing the complexity burden on individual devices by distributing intelligence across the edge infrastructure.
3Productivity
If continuous monitoring and automated responses are implemented at the edge, then operational efficiency improves, but energy consumption and computational resources increase
Solution Approach 1:
The edge computing system implements selective data processing where only critical data requiring immediate attention is processed continuously at full capacity. Less critical data receives reduced processing intensity or is batched for later analysis. This partial action approach maintains operational efficiency for time-sensitive operations while reducing overall energy consumption compared to continuous full-intensity processing of all data.
Data Source
AI summary
An edge device can include one or more memory devices that store instructions thereon. The instructions can, when executed by one or more processors, cause the one or more processors to receive a first stream data associated with a condition of the building, produce a second stream data based on a virtual device, execute a set of analytic expressions on the first stream data and the second stream data based on a topic associated with the condition of the building, identify a pattern in the first stream data and the second stream data that indicates the condition of the building, and perform one or more actions based at least on the condition of the building.