Edge Cloud Architecture for Secure Low-Latency Factory Analytics
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Solution Overview
Problem
Current edge cloud computing for Industrial Internet of Things applications is costly and vulnerable to cyber-attacks, particularly due to high overhead in sensor electronics and network security challenges in resource-limited factories.
Innovation Solution
A hybrid cloud architecture that merges computation layers from Fog and Core protocols into the edge cloud, reducing hardware and cloud subscription costs, and uses proprietary protocols to enhance network security by isolating sensor devices to the edge cloud, thereby reducing latency and improving security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If edge cloud computing is used for Industrial Internet of Things applications, then data processing speed and responsiveness are improved, but computational costs and overhead increase
Solution Approach 1:
The patent segments the cloud computing architecture into multiple edge cloud nodes distributed across the factory network. Each edge cloud node handles local data processing for nearby sensor devices, dividing the computational workload into smaller, localized segments rather than using a single centralized cloud. This reduces the amount of data that needs to be transmitted over the network and enables parallel processing, thereby improving data processing speed while distributing computational overhead across multiple nodes rather than concentrating it in one location.
2Adaptability or versatility
If sensor devices are connected to the cloud, then data accessibility is improved, but network security vulnerabilities increase
Solution Approach 1:
The patent introduces edge cloud nodes as intermediary layers between sensor devices and the centralized cloud. These edge cloud nodes act as local mediators that receive data from sensor devices, perform necessary processing and filtering, and then communicate with the cloud. This intermediary architecture maintains data accessibility by allowing cloud access while enhancing security by isolating sensor devices from direct cloud connections, creating a buffer zone that can filter and validate data before it reaches the cloud network.
3Loss of energy
If computation layers are merged into edge cloud, then hardware costs are reduced, but system complexity increases
Solution Approach 1:
The patent designs edge cloud nodes with multi-functional capabilities that can perform various computation layers (data collection, preprocessing, analytics, and cloud communication) within a single unified platform. Rather than requiring separate hardware components for each function, the edge cloud nodes are configured to handle multiple tasks, reducing the overall hardware footprint and cost. The system manages complexity through standardized protocols and modular architecture, allowing the same edge cloud infrastructure to adapt to different computational requirements without increasing system complexity.
Data Source
AI summary
A method of evaluating factory production machinery up time and down time performance provides a collection of sensors in individual communication with factory production machinery, with each sensor collecting high frequency vector data as respecting a physical parameter associated with the machinery, extracts the data from the sensors to produce a sensor data set, transforms the data set into the frequency domain, extracts statistical and mathematical information from the data set, transfers the data set, to an associated edge cloud, and within the associated edge cloud processes the data set to provide a repair, maintenance and operation board for the machinery to evaluate up time and down time performance for the factory production machinery.