Dynamic Network Slicing for Real-Time Robotic Repair Latency
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Solution Overview
Problem
Current robotic repair technologies using wireless communication lack efficiency, leading to increased latencies that are too slow for real-time industrial protocols, particularly in robotic repairing processes such as milling and monitoring.
Innovation Solution
Implementing dynamic network slicing technology with M/M/1 queuing theory to efficiently allocate network resources, reduce queuing and propagation latency, and balance load among different network slices, using a directed acyclic graph (DAG) to prioritize tasks and allocate user plane functions (UPFs) based on priority and load thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If dynamic network slicing is implemented to reduce latency, then real-time performance is improved, but network complexity increases
Solution Approach 1:
The network is divided into multiple network slices, each dedicated to specific robotic repair tasks with different latency requirements. This segmentation allows critical real-time operations to have dedicated network resources while non-critical operations use shared resources, reducing overall latency without requiring complete network redesign.
Solution Approach 2:
The network slicing configuration is dynamically adjusted based on real-time task priorities and network conditions. The system can create, modify, or terminate network slices on-demand to match varying robotic repair operation requirements, optimizing performance while adapting to changing workloads.
2Productivity
If network resources are allocated to multiple robotic tasks simultaneously, then productivity is improved, but resource contention increases latency
Solution Approach 1:
Different network slices are assigned different quality of service parameters tailored to their specific robotic tasks. Critical tasks receive slices with guaranteed low latency and high reliability, while non-critical tasks use slices with relaxed parameters, allowing high overall throughput without compromising real-time performance for essential operations.
Solution Approach 2:
The system changes network parameters such as bandwidth allocation, packet prioritization, and routing policies dynamically based on task requirements. By adjusting these parameters per network slice, the system maintains high productivity across multiple tasks while ensuring critical tasks meet real-time latency constraints.
Data Source
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AI summary
Dynamic slicing technology can be performed to assign network resources for various robotic repairing subtasks having different priorities, while satisfying various real-time and high throughput requirements. In some cases, M/M/1 queuing theory is applied so as to efficiently improve the utilization of network resources, reduce queuing latency and propagation latency, and balance the load among different network slicing, so as to perform robotic repairing operations.