TSN Schedule Generation via Distributed Environmental Adaptation
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Solution Overview
Problem
Current methods for generating time-sensitive network schedules are not responsive to environmental changes, leading to inefficiencies, high overhead, and potential network interruptions due to their reliance on external entities and computational complexity.
Innovation Solution
The integration of a TSN Environmental Condition to Gate Control (EC-GC) Mapper, which directly connects sensors to gate control mechanisms, allowing for dynamic adjustment of data flow based on environmental conditions, thereby eliminating the need for centralized network configuration and re-scheduling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If centralized network configuration and re-scheduling methods are used, then network schedule generation is possible, but the system responds slowly to environmental changes and requires high computational overhead
Solution Approach 1:
The patent segments the network configuration task by introducing distributed agents at each switching node that independently monitor environmental conditions and adjust local gate control lists, eliminating the need for centralized re-scheduling of the entire network. This segmentation enables localized, real-time adaptations without computational overhead of network-wide reconfiguration.
Solution Approach 2:
The patent implements preliminary action by pre-configuring gate control lists at each switching node based on predicted environmental conditions. When environmental changes are detected, pre-established rules and algorithms immediately adjust the gate control lists without requiring time-consuming centralized computation, enabling rapid response to environmental variations.
2Productivity
If centralized network configuration is used, then network scheduling can be generated, but the device complexity and computational overhead increase
Solution Approach 1:
The patent implements self-service by enabling each switching node to autonomously monitor its own environmental conditions, evaluate the impact on data flow, and adjust its gate control lists without external intervention. This distributed self-service approach maintains optimal data flow efficiency while eliminating the complexity of centralized configuration systems.
Solution Approach 2:
The patent establishes feedback loops where environmental sensors continuously monitor conditions, agents evaluate the impact on data flow requirements, and gate control lists are dynamically adjusted based on this feedback. This closed-loop feedback mechanism optimizes productivity while distributing complexity across multiple independent nodes rather than concentrating it in a centralized system.
3Reliability
If current schedule generation methods are used, then network operation is maintained, but overhead is high and network interruptions may occur
Solution Approach 1:
The patent implements dynamics by making gate control lists adaptive and flexible rather than static. Each switching node dynamically adjusts its gate control lists in real-time based on environmental conditions, allowing the network to maintain continuous operation under varying conditions without the high overhead of periodic centralized re-scheduling or risk of interruptions during reconfiguration.
Data Source
AI summary
Systems, methods, and other embodiments described herein relate to generating a time-sensitive network schedule for a time-sensitive network (TSN). In one embodiment, a method includes receiving a measured value of at least one environmental condition from at least one sensor. The method includes determining an impact of the measured value of the at least one environmental condition on at least one component of the TSN. The method includes programming a switching node based on at least the determined impact. The TSN includes a first end node, a second end node, and the switching node. The switching node is communicatively linked between the first end node and the second end node.