基于多种群协同进化的工业物联网自适应调度算法AS-MPCA

IF 6 3区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Anying Chai , Lei Wang , Chenyang Guo , Mingshi Li , Wanda Yin , Zhaobo Fang
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引用次数: 0

摘要

工业物联网(IIoT)通过紧密连接物理设备、传感器、控制系统和信息系统,实现实时数据采集、分析和决策。同时,在工业园区的通信环境中,传感器网络、车间以太网、现场总线等构建了多维度的异构信息传感网络。在这种网络中,多源异构数据类型复杂多样,数据规模庞大,各种数据流对传输量和实时性的要求也不尽相同。特别是在网络资源有限的通信环境下,实时业务数据的传输延迟很难满足实际生产的要求。这就导致了感知数据传输实时性低、可靠性不足等问题。针对这些问题,我们提出了一种基于多群协同进化的工业物联网自适应调度算法(AS-MPCA)。该算法将两阶段多蜂群遗传算法与自适应路由机制相结合。首先,两阶段多蜂群遗传算法通过全局搜索和多蜂群策略的结合,扩展了搜索空间,增强了调度方案的多样性,为自适应路由机制提供了多样化的路径选择策略。然后,自适应路由机制根据上述遗传算法的调度结果动态调整最优路径。仿真和实验结果表明,所提出的方法显著提高了系统的可调度性。与传统算法相比,所提出的算法在各种条件下平均提高了 10%的任务接受率,有效降低了时间敏感数据的传输延迟,确保了工业物联网通信系统的服务质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
AS-MPCA: An adaptive scheduling algorithm for industrial Internet of Things based on multi-population co-evolution
The Industrial Internet of Things (IIoT) enables real-time data collection, analysis, and decision-making by tightly connecting physical devices, sensors, control systems, and information systems. Meanwhile, in the communication environment of industrial parks, a multi-dimensional heterogeneous information sensing network is constructed by sensor networks, shop floor Ethernet, field buses, etc. In this kind of network, multi-source heterogeneous data types are complex and diverse, the data scale is huge, and all kinds of data flows have different requirements for transmission volume and real-time performance. Especially in a communication environment with limited network resources, the transmission delay of real-time service data makes it difficult to meet the actual production requirements. These lead to problems such as low real-time and insufficient reliability of sensory data transmission. To address these problems, we propose an Adaptive Scheduling Algorithm for the Industrial Internet of Things Based on Multi-swarm Co-evolution(AS-MPCA). The algorithm combines a two-stage multiple swarm genetic algorithm with an adaptive routing mechanism. Firstly, the two-stage multiple swarm genetic algorithm expands the search space and enhances the diversity of the scheduling scheme through the combination of global search and multiple swarm strategies, which provides diversified path selection strategies for the adaptive routing mechanism. Then, the adaptive routing mechanism dynamically adjusts the optimal path according to the scheduling results of the above genetic algorithm. Simulation and experimental results demonstrate that the proposed method significantly enhances system schedulability. Compared with traditional algorithms, the proposed algorithm improves the task acceptance rate by an average of 10% across various conditions, effectively reduces the transmission delay of time-sensitive data, and ensures the quality of service for industrial IoT communication systems.
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来源期刊
Internet of Things
Internet of Things Multiple-
CiteScore
3.60
自引率
5.10%
发文量
115
审稿时长
37 days
期刊介绍: Internet of Things; Engineering Cyber Physical Human Systems is a comprehensive journal encouraging cross collaboration between researchers, engineers and practitioners in the field of IoT & Cyber Physical Human Systems. The journal offers a unique platform to exchange scientific information on the entire breadth of technology, science, and societal applications of the IoT. The journal will place a high priority on timely publication, and provide a home for high quality. Furthermore, IOT is interested in publishing topical Special Issues on any aspect of IOT.
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