Successive Approximation Joint Scheduling for TSN and Industrial Wireless Networks
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Solution Overview
Problem
Current industrial wireless network scheduling methods fail to effectively integrate time sensitive networks with industrial wireless networks, especially in large areas with diverse communication requirements, leading to issues with collision and congestion.
Innovation Solution
A successive approximation-based joint scheduling method is introduced, utilizing a heterogeneous network architecture with a user plane, control plane, and data plane, including an industrial software defined controller and industrial network system managers, to perform collaborative scheduling and prioritize data streams, ensuring both flexibility and determinacy across the network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If industrial wireless networks are integrated with time sensitive network to cover large areas, then network coverage area is improved, but network complexity increases
Solution Approach 1:
The network is segmented into multiple industrial wireless networks (first industrial wireless network and second industrial wireless network) that are connected through the time sensitive network. Each wireless network can be independently managed and scheduled, reducing the complexity of managing a single large network while expanding overall coverage area.
Solution Approach 2:
The time sensitive network acts as an intermediary backbone connecting multiple industrial wireless networks. The industrial software defined controller serves as a mediator that performs joint scheduling across different network types, coordinating resource allocation and managing complexity centrally while allowing distributed operation.
2Reliability
If joint scheduling is implemented on heterogeneous network information flows, then transmission reliability is improved, but scheduling complexity increases
Solution Approach 1:
The industrial software defined controller implements a universal joint scheduling mechanism that handles multiple types of information flows (periodic, aperiodic, high-priority, low-priority) through a unified scheduling framework. This multi-functional approach ensures reliable transmission across different traffic types while avoiding the need for separate scheduling systems for each flow type.
Solution Approach 2:
The scheduling system dynamically adjusts scheduling parameters such as time slot allocations, priority levels, and resource distribution based on network conditions and traffic requirements. By changing these parameters adaptively, the system maintains high transmission reliability for diverse traffic types without requiring complex fixed scheduling rules for each scenario.
3Productivity
If successive approximation-based joint scheduling is used to optimize time slot assignments, then transmission efficiency is improved, but computational complexity increases
Solution Approach 1:
The system performs preliminary classification of information flows into periodic and aperiodic categories, and further into high-priority and low-priority groups, before executing the successive approximation optimization. This pre-processing step organizes the scheduling problem into manageable segments, allowing the optimization algorithm to focus on specific sub-problems rather than handling all traffic simultaneously, thus improving transmission efficiency while controlling computational complexity.
Data Source
AI summary
A successive approximation-based joint scheduling method for a time sensitive network and an industrial wireless network includes: in an offline stage: S1, performing customization processing on a superframe structure in an industrial wireless network, determining a superframe duration and a slot length, and calculating the number of slots; S2, acquiring data packet information sent by a node, wherein the data packet information remains consecutive temporally; S3, constructing a training set and a test set for a slot requirement forecasting model; and S4, training the slot requirement prediction model; and in an online stage: S5, configuring a heterogeneous network, which involves configuring configuration information, which is determined in the off-line stage, for a cooperative scheduling subsystem, an industrial wireless gateway, a wireless routing device and an industrial wireless node; S6, performing slot prediction and assignment, and broadcasting beacons; and S7, performing data transmission according to an allocated path.