Modular Edge Computing Container for IoT Latency Reduction
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Solution Overview
Problem
Current edge computing infrastructure is limited in its ability to provide real-time data processing and analysis for IoT devices, especially in time-sensitive applications like autonomous driving, due to the need for data to be sent to distant cloud centers, which results in delays and high costs for data transfer.
Innovation Solution
A modular, mobile edge computing system that includes a container unit with plug-and-play modules for IT devices, cooling, energy, and power systems, allowing for deployment closer to IoT devices, enabling on-site processing and analysis without relying solely on cloud resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data from IoT devices is sent to cloud data centers for processing, then centralized computing power and storage resources can be utilized, but latency increases and response time deteriorates
Solution Approach 1:
The patent segments the centralized cloud computing system into distributed edge computing nodes deployed at multiple locations closer to IoT devices. Each edge node handles local data processing while connecting to the broader cloud network, thereby reducing latency for time-sensitive applications while maintaining access to centralized resources when needed.
Solution Approach 2:
The patent introduces edge computing infrastructure as an intermediary layer between IoT devices and cloud data centers. This intermediary performs real-time data processing and filtering locally, reducing the volume of data transmitted to the cloud and enabling faster response times for critical operations.
2Speed
If edge computing infrastructure is deployed closer to IoT devices, then response time and computation performance improve, but infrastructure complexity and deployment difficulty increase
Solution Approach 1:
The patent designs edge computing nodes with multi-functional capabilities that can handle various computing, storage, and networking tasks within a single standardized platform. This universal approach reduces overall infrastructure complexity while maintaining high processing speeds for diverse IoT applications.
Solution Approach 2:
The patent implements dynamic resource allocation and scalable deployment strategies for edge computing nodes, allowing the system to adapt to varying workload demands and be deployed incrementally based on specific application needs, thereby managing complexity while delivering high performance.
3Loss of information
If all IoT data is transmitted to cloud centers, then comprehensive data analysis can be performed, but data transfer costs and network bandwidth consumption increase significantly
Solution Approach 1:
The patent extracts and processes critical data elements at the edge computing nodes before transmission to the cloud. This extraction approach performs preliminary filtering, aggregation, and analysis locally, sending only essential processed data to cloud centers, thereby reducing network bandwidth consumption and transfer costs while maintaining comprehensive analysis capabilities.
Solution Approach 2:
The patent implements partial data transmission strategies where only a subset of processed and prioritized data is sent to cloud centers, rather than transmitting all raw IoT data. This partial action approach reduces network costs while ensuring that the most important information is available for comprehensive analysis.
Data Source
AI summary
Distributed infrastructure and mobile architecture for edge computing are disclosed. For one example, an edge computing container includes a plurality of modules. Each module has plug and play connectivity, and the modules are assembled to provide information technology (IT) space to house IT devices, cooling system, energy source and storage, and power system. The modules can be pre-fabricated and assembled as a single container unit. A source distribution unit (SDU) can assembled on an IT rack and connected to the modules. The single container unit can be loaded in a vehicle or transportation system for transportation in the process of deployment. The modules in the single container unit can also be operational during transportation.