Dynamic Blockchain Scheduling for Trustworthy Industrial Wireless Tasks
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Solution Overview
Problem
Industrial wireless networks face challenges with low trustworthiness between devices and edge servers, leading to high failure rates in edge computing due to the introduction of blockchain, which exacerbates overhead and reduces computing efficiency.
Innovation Solution
A dynamic blockchain-based trustworthy scheduling method and device that constructs an industrial wireless network using a dynamic blockchain mechanism, optimizing task and resource joint scheduling through a multi-agent Markov decision process model and rotating multi-agent deep reinforcement learning algorithm to adjust leader edge servers and blockchain waiting times based on network status and task requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If blockchain technology is introduced to enhance security and trustworthiness, then trustworthiness between industrial devices and edge servers is improved, but system overhead increases and computing efficiency deteriorates
Solution Approach 1:
The patent segments the blockchain system into a dynamic leader-node architecture where one edge server is selected as the leader to perform consensus operations, while other nodes perform lighter validation tasks. This segmentation reduces the computational overhead for each individual node compared to a traditional distributed blockchain where all nodes perform full consensus operations.
Solution Approach 2:
The patent implements dynamic blockchain mechanisms including dynamic leader election and dynamic block generation intervals. The leader edge server is dynamically selected based on network conditions and task requirements, and block generation intervals are adjusted in real-time. This dynamic approach optimizes the balance between trustworthiness and overhead by adapting blockchain operations to current system states.
2Reliability
If blockchain technology is introduced to enhance security and trustworthiness, then trustworthiness between industrial devices and edge servers is improved, but computing efficiency deteriorates
Solution Approach 1:
By segmenting consensus operations to be performed primarily by the leader edge server rather than all nodes, the patent reduces the computational burden on individual devices. Task execution can proceed in parallel with blockchain consensus operations, improving overall computing efficiency while maintaining trustworthiness through the segmented architecture.
Solution Approach 2:
The dynamic block generation mechanism adjusts blockchain operation intensity based on system conditions. During high-computation periods, block generation intervals can be extended or batched, reducing the impact on computing efficiency. The system dynamically balances trustworthiness requirements with computing efficiency demands.
3Device complexity
If traditional scheduling methods are used in industrial wireless networks, then system complexity is low, but task trustworthy processing efficiency deteriorates
Solution Approach 1:
The patent introduces an intermediary optimization module that acts as a mediator between the blockchain trust layer and the task scheduling layer. This intermediary translates blockchain trustworthiness metrics into scheduling decisions, enabling efficient task allocation based on trust scores without requiring complex integration of blockchain protocols into every scheduling decision. The intermediary maintains simplicity while improving trustworthy processing efficiency.
Solution Approach 2:
The edge servers in the patent perform multiple functions including task execution, blockchain consensus participation, and trust score maintenance. This multi-functionality allows the system to achieve high trustworthy processing efficiency without adding separate dedicated components, thereby limiting the increase in system complexity.
Data Source
AI summary
A dynamic blockchain-based trustworthy scheduling method and device for industrial wireless networks are provided. The method comprises: Construct an optimization model for scheduling the industrial wireless network with the goal of maximizing the trustworthy processing efficiency of tasks. performing model reconstruction on the optimization model based on a preset multi-agent Markov decision process model to obtain a target optimization model; and optimizing the target optimization model using a preset rotating multi-agent deep reinforcement learning algorithm model on the basis of observation information of industrial devices in the industrial wireless network collected in real time, to obtain target parameters corresponding to the parameters to be optimized for scheduling the industrial wireless network. The method of the present application improves the trustworthy processing efficiency of tasks in the industrial wireless network.