Dynamic Edge Computing for 6G IoT Latency and Power
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Solution Overview
Problem
Current technologies face challenges in efficiently processing and managing the increasing amounts of data from IoT devices in 6G networks, requiring stronger processing power at the edge to reduce latency and power consumption, while existing solutions often rely on offsite processing that is hardware-intensive and inefficient.
Innovation Solution
Implementing a dynamic edge computing system that utilizes microservices and cloud radio access networks to distribute processing power across devices, allowing IoT devices to request and allocate processing resources from nearby nodes, thereby enhancing processing capabilities and reducing power consumption by moving processing closer to data sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If offsite processing is used to handle IoT device data, then centralized processing power is improved, but latency and power consumption increase
Solution Approach 1:
The patent segments the network into multiple edge computing nodes distributed geographically closer to IoT devices. Each node independently processes data locally, eliminating the need for all data to travel to a centralized offsite processing facility. This segmentation reduces transmission latency while maintaining distributed processing power.
Solution Approach 2:
The patent introduces a spatial dimension to processing by distributing compute resources across multiple geographic locations rather than concentrating them in one offsite facility. This creates a multi-dimensional processing architecture where data can be handled at the nearest edge node, reducing the physical distance and time for data traversal.
2Power
If offsite processing is used to handle IoT device data, then centralized processing power is improved, but power consumption increases
Solution Approach 1:
The patent segments processing workloads across multiple edge nodes, allowing data to be processed closer to its source. This reduces the energy required for data transmission over long distances to offsite facilities, while still providing adequate processing power through distributed compute resources at each edge node.
3Loss of time
If processing resources are distributed across edge nodes, then latency is reduced, but device complexity increases
Solution Approach 1:
The patent implements universal edge node architectures that can handle multiple types of processing tasks and support various IoT device protocols. This multi-functionality allows a single edge node design to serve diverse requirements, reducing the complexity that would arise from designing specialized nodes for each device type while still achieving low latency through geographic distribution.
Solution Approach 2:
The patent introduces standardized communication protocols and abstraction layers as intermediaries between diverse IoT devices and the distributed edge nodes. This intermediary layer simplifies the interaction complexity by providing uniform interfaces, allowing devices to communicate with any edge node without requiring device-specific customization at each node.
4Productivity
If microservices architecture is implemented for edge computing, then processing efficiency is improved, but system complexity increases
Solution Approach 1:
The patent applies microservices architecture by segmenting processing functions into independent, modular service units that can be selectively deployed at edge nodes. Each microservice handles a specific processing task, improving efficiency by allowing only necessary services to run at each location while reducing wasted computational resources on unnecessary functions.
Solution Approach 2:
The patent implements dynamic microservice deployment where services can be added, removed, or updated at individual edge nodes based on real-time workload requirements. This dynamic approach improves processing efficiency by adapting the system architecture to actual needs rather than maintaining a static, overly complex configuration for all possible scenarios.
Data Source
AI summary
Peripheral devices such as wearables can link to mobile devices to access network elements. These peripheral devices can comprise a communication part and a processing part. Each of these parts, specially the processing part of the next item in a communication chain, can be multiple times larger and stronger than the previous linked item. For instance, the peripheral device can be connected to the mobile device which can be in line connected to an NodeB device. Because the processing power of mobile device can be several times stronger than the peripheral, a mobile edge computing platform and a multi-access application function facilitate sharing of processing power capabilities between the peripheral devices, mobile devices, and/or the NodeB devices.