Industrial Radio Resource Allocation for Adaptive Mesh Connectivity
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Solution Overview
Problem
Existing industrial networks struggle to optimally allocate radio communication resources among collaborating units due to varying device requirements and dynamic network conditions, leading to inefficient resource utilization and potential communication bottlenecks.
Innovation Solution
A method for autonomously modeling the topology of a subnetwork as a weighted resource graph, adjusting radio communication resources based on link utilization measures, and using machine learning to optimize resource allocation, allowing for adaptive mesh topologies that can shrink or expand as needed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If maximum possible resource allocation is used for the entire subnetwork, then communication reliability is improved, but resource efficiency deteriorates due to varying individual device requirements
Solution Approach 1:
The patent applies local quality by transitioning from uniform resource allocation across the entire subnetwork to device-specific resource allocation. Each industrial device receives radio communication resources tailored to its individual requirements, capabilities, and current task demands. The network controller dynamically adjusts resource allocation per device based on recorded link utilization measures and evaluated graph weights, ensuring each device gets appropriate resources without over-provisioning the entire network.
2Manufacturing precision
If manual configuration of radio communication resources is performed, then resource allocation precision is improved, but operational complexity deteriorates
Solution Approach 1:
The patent implements self-service by enabling the subnetwork to automatically configure and adjust radio communication resources without manual intervention. The network controller autonomously records link utilization measures, evaluates graph weights, determines optimal resource allocation, and adjusts resources dynamically. This automated self-configuration process eliminates the need for manual resource allocation while maintaining high precision through algorithmic optimization based on actual network conditions.
Solution Approach 2:
The patent applies feedback by continuously monitoring link utilization measures from industrial devices and using this information to dynamically adjust resource allocation. The network controller records utilization data over time, evaluates it against graph weights representing device requirements and network conditions, and automatically refines resource distribution. This closed-loop feedback mechanism enables precise automatic configuration that adapts to changing network conditions without manual intervention.
3Stability of the object's composition
If the subnetwork topology is fixed, then system stability is improved, but adaptability deteriorates when devices join or leave
Solution Approach 1:
The patent applies dynamics by transitioning from a fixed subnetwork topology to a dynamic, adaptable topology that automatically adjusts when devices join or leave. The network controller continuously monitors the network state, updates the resource graph to reflect current device participation, and reconfigures resource allocation accordingly. This dynamic approach maintains system stability through automated topology management while enabling seamless adaptation to changing network composition.
Data Source
Figure 1~3
AI summary
The disclosed embodiments suggest methods for adjusting radio communication resources in a collaboration of industrial units. In one embodiment a method is suggested which is based on a principle of autonomously determining and modeling the topology of the subnetwork in a topological structure, a graph, and weighting this graph with key measures of communication resources, in particular a link utilization measure or a traffic pattern related to a respective local radio communication links of the commonly shared subnetwork. The topology may be set up adaptive to the industrial processing task of collaborating industrial units while communication resources may be optimally assigned or conserved for other priorities. Future repetitions of the same industrial processing task benefit from previously learned topology associated with the preceding industrial processing task.