Edge-Based Microcontroller Control via Cloud Intermediary
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Solution Overview
Problem
Current radio access networks (RANs) with Multi-Access/Mobile Edge Computing (MEC) devices face challenges in providing efficient, low-latency processing and control for microcontroller devices, such as those in self-driving vehicles, due to limitations in processing capabilities and the need for complex computing infrastructure.
Innovation Solution
The implementation of edge-controlled systems that leverage edge-based resources, including processing, memory, and storage, to create logical devices and systems, utilizing machine learning techniques for real-time data processing and instruction generation, allowing for the aggregation of data from multiple controllers and devices without requiring extensive onboard processing hardware.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If complex computing infrastructure is deployed onboard devices, then processing capabilities are improved, but device complexity and cost increase
Solution Approach 1:
A cloud-based platform serves as an intermediary between edge devices and centralized processing. The platform receives data from multiple edge devices, performs complex computing operations, and returns instructions to the devices. This eliminates the need for complex onboard processing hardware while maintaining advanced computing capabilities through remote cloud resources.
2Adaptability or versatility
If more edge devices are deployed, then system coverage and functionality are improved, but data aggregation and processing load increase
Solution Approach 1:
The system segments processing tasks between edge devices and the cloud platform. Edge devices perform simple local functions (sensing, basic data collection), while the cloud platform handles complex data aggregation, analysis, and instruction generation. This segmentation allows unlimited device deployment without proportionally increasing processing burden on individual devices.
Solution Approach 2:
The cloud platform merges data from multiple edge devices into a unified processing stream. By aggregating data centrally, the system can process information from numerous devices simultaneously using shared computational resources, improving overall processing efficiency despite increased system coverage.
3Speed
If real-time processing is implemented, then response time is improved, but computational resource consumption increases
Solution Approach 1:
The cloud platform acts as an intermediary that receives data from edge devices, performs computationally intensive real-time processing, and returns instructions. This intermediary approach enables real-time response without requiring high computational resources at the edge devices themselves, as the heavy lifting is performed remotely using shared cloud infrastructure.
Data Source
AI summary
Embodiments described herein provide for the leveraging of edge-based resources (e.g., processing, memory, storage, and/or other resources of one or more Multi-Access/Mobile Edge Computing devices (“MECs”), such as MECs associated with a radio access network (“RAN”) of a wireless network) to process data from and/or provide instructions to microcontroller devices, System on Chip (“SoC”) devices, configurable logic boards, Internet of Things (“IoT”) devices, and/or other types of devices or systems. For example, embodiments described herein may provide for the creation and configuration of logical devices or systems based on one or more microcontrollers, SoC devices, etc., edge-based processing of sensor data and/or other types of data received or generated by the microcontrollers, SoC devices, etc., and the generation of instructions to control physical devices communicatively coupled to the microcontrollers, SoC devices, etc.