考虑链路重传的工业无线网络确定性调度算法

王恒; 刘清华; 李敏; 谭帅

doi:10.16383/j.aas.c190368

考虑链路重传的工业无线网络确定性调度算法

doi: 10.16383/j.aas.c190368

王恒^1,,
刘清华^1,,
李敏^1,,
谭帅^1,

1.
重庆邮电大学工业物联网与网络化控制教育部重点实验室重庆 400065

基金项目: 国家自然科学基金(61972061), 重庆市自然科学基金杰出青年基金(cstc2019jcyjjqX0012)资助

详细信息

作者简介:
王恒：重庆邮电大学自动化学院教授. 2010年获得重庆大学博士学位. 主要研究方向为工业物联网, 无线传感器网络, 协作通信. 本文通信作者. E-mail: wangheng@cqupt.edu.cn

刘清华：重庆邮电大学自动化学院硕士研究生. 主要研究方向为工业无线网络. E-mail: liuqinghua318@163.com

李敏：重庆邮电大学自动化学院副教授. 2014年获得重庆大学博士学位. 主要研究方向为协作通信, 无线传感器网络, 机器人技术. E-mail: limin@cqupt.edu.cn

谭帅：重庆邮电大学自动化学院硕士研究生. 主要研究方向为工业物联网. E-mail: tanshuai4253@163.com

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出版历程
- 收稿日期: 2019-05-14
- 录用日期: 2020-01-17
- 刊出日期: 2021-11-18

Orchestration Methods With Determinacy in Wireless Industrial Network by Considering Repeat Transmissions

WANG Heng^1
,,
LIU Qing-Hua^1
,,
LI Min^1
,,
TAN Shuai^1
,

1.
Key Laboratory of Industrial Internet of Things & Networked Control, Ministry of Education, Chongqing University of Posts and Telecommunications, Chongqing 400065

Funds: Supported by National Natural Science Foundation of China (61972061), and Natural Science Foundation of Chongqing, China, for Distinguished Young Scholars (cstc2019jcyjjqX0012)

More Information

Author Bio:
WANG Heng　Professor at the College of Automation, Chongqing University of Posts and Telecommunications. He received his Ph. D. degree from Chongqing University in 2010. His research interest covers industrial internet of things, wireless sensor networks, and cooperative communications. Corresponding author of this paper

LIU Qing-Hua　Master student at the College of Automation, Chongqing University of Posts and Telecommunications. His research interest covers industrial wireless networks

LI Min　Associate professor at the College of Automation, Chongqing University of Posts and Telecommunications. She received her Ph. D. degree from Chongqing University in 2014. Her research interest covers cooperative communications, wireless sensor networks, and robotics

TAN Shuai　Master student at the College of Automation, Chongqing University of Posts and Telecommunications. His main research interest is industrial internet of things

摘要

摘要: 作为工业网络的关键技术, 确定性调度通过合理安排网络传输资源, 满足工业数据在规定时间内到达目标设备的实时性要求. 工业网络往往部署在环境恶劣、电磁情况复杂的工业现场, 与有线网络相比, 工业无线网络还面临着严重的丢包问题. 考虑到重传是克服链路丢包的简便高效方法, 本文提出了支持持续重传和区间重传两种策略的确定性调度算法. 基于链路时槽松弛度和动态优先级, 调度算法在每个时槽按照调度规则为重传链路配置通信资源, 缓解丢包对数据传输的影响, 并围绕对应重传策略进行相应的时槽、频点优化分配, 保障数据端到端按时到达. 仿真结果表明, 所提调度算法在满足传输确定性的前提下, 有效提升了数据传输的可靠性.
- 工业无线网络 /
- 确定性调度 /
- 截止时间约束 /
- 链路重传
Abstract: As a key technology of industrial networks, orchestration with determinacy meets the real-time requirement of the industrial data arriving at target device within a specified time by arranging the network transmission resources reasonably. In most cases, the filed environment and electromagnetic situation of industrial networks are harsh and complex. Moreover, compared with the wired networks, the packet loss is a significant problem in industrial wireless networks. Considering that repeat transmission is a simple and efficient method to overcome link packet loss, this paper optimizes link retransmission in the process of orchestration and proposes orchestration methods with determinacy that support consequent retries and interval-working retries respectively. Based on time slot laxities and dynamic priorities of links, the two orchestration algorithms allocate communication resources for the retransmission link in accordance with the orchestration rules at each time slot to alleviate the impact of packet loss on data transmission, and optimize the allocation of time slot and channel resources according to the two retransmission strategies to ensure the timely arrival of end-to-end data. Extensive simulations demonstrate that our orchestration methods enhance communication reliability while the deterministic characteristic is maintained.
- Industrial wireless networks /
- deterministic scheduling /
- deadline constraint /
- link retransmission

HTML全文

图 1 网络模型图

Fig. 1 Network model

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图 2 通信链接$ {{\mu }_{i}}$的有效调度区间

Fig. 2 Effective scheduling interval of communication link $ {{\mu }_{i}}$

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图 3 调度成功率与设备数量关系图

Fig. 3 Relationship between schedulable ratio and device number

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图 4 调度成功率与重传区间关系图

Fig. 4 Relationship between schedulable ratio and retransmission interval

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图 5 调度成功率与丢包率关系图

Fig. 5 Relationship between schedulable ratio and packet loss rate

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图 6 调度成功率与信道质量关系图

Fig. 6 Relationship between schedulable ratio and channel quality

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图 7 调度成功率与起始设备和最终设备所构成的通信对所占比重关系图

Fig. 7 Relationship between schedulable ratio and proportion of communication pairs from source devices to final destination devices

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图 8 调度成功率与截止时间取值上限比重关系图

Fig. 8 Relationship between schedulable ratio and proportion of maximal deadline

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图 9 调度成功率与周期范围关系图

Fig. 9 Relationship between schedulable ratio and period range

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图 10 调度成功率与频点个数关系图

Fig. 10 Relationship between schedulable ratio and channel number

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表 1 用于仿真比较的各个方法的原理

Table 1 Principle of each method in simulation comparison

优先级	缩写	基本原理
固定不变	DM^[33]	通信流的相对截止时限越小, 越早调度
固定不变	PD^[33]	通信流的相对截止时限除以跳数, 所得结果越小, 越早调度
即时变化	EDF^[33]	通信流的绝对截止时限越小, 越早调度
	LLF^[33]	通信流的松弛度越小, 越早调度
	C-LLF^[9]	通信流的干扰感知松弛度越小, 越早调度

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表 2 各个调度方法的平均计算时间 (ms)

Table 2 Average execution time of each scheduling method (ms)

名称	10 个设备 3 条通信流	20 个设备 6 条通信流	30 个设备 9 条通信流	40 个设备 12 条通信流	50 个设备 15 条通信流	60 个设备 18 条通信流
DS-CR	15.48	40.51	72.49	112.54	162.74	222.19
DS-IWR	18.45	50.49	89.71	135.82	190.92	252.42
C-LLF	13.85	30.78	48.28	66.67	88.89	111.99
PD	18.51	29.13	35.97	42.08	48.07	55.79
EDF	19.54	30.65	37.72	43.45	49.76	55.39
DM	18.31	28.92	35.84	41.78	47.29	53.22
LLF	20	31.09	38.43	44.2	50.85	56.1

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参考文献(34)

[1]	王飞跃, 张军, 张俊, 王晓. 工业智联网: 基本概念、关键技术与核心应用. 自动化学报, 2018, 44(9): 1606-1617 Wang Fei-Yue, Zhang Jun, Zhang Jun, Wang Xiao. Industrial internet of minds: concept, technology and application. Acta Automatica Sinica, 2018, 44(9): 1606-1617
[2]	丁进良, 杨翠娥, 陈远东, 柴天佑. 复杂工业过程智能优化决策系统的现状与展望. 自动化学报, 2018, 44(11): 1931-1943 Ding Jin-Liang, Yang Cui-E, Chen Yuan-Dong, Chai Tian-You. Research progress and prospects of intelligent optimization decision making in complex industrial process. Acta Automatica Sinica, 2018, 44(11): 1931-1943
[3]	Raza M, Aslam N, Le-minh H, Hussain S, Cao Y, Khan N. A critical analysis of research potential, challenges and future directives in industrial wireless sensor networks. IEEE Communications Surveys & Tutorials, 2018, 20(1): 39-95
[4]	Pan F, Pang Z, Luvisotto M, Xiao M, Wen H. Physical-layer security for industrial wireless control systems: basics and future directions. IEEE Industrial Electronics Magazine, 2018, 12(4): 18-27 doi: 10.1109/MIE.2018.2874385
[5]	范家璐, 姜艺, 柴天佑. 无线网络环境下工业过程运行反馈控制方法. 自动化学报, 2016, 42(8): 1166-1174 Fan Jia-Lu, Jiang Yi, Chai Tian-You. Operational feedback control of industrial processes in a wireless network environment. Acta Automatica Sinica, 2016, 42(8): 1166-1174
[6]	王恒, 朱元杰, 杨杭, 王平. 基于优先级分类的工业无线网络确定性调度算法. 自动化学报, 2020, 46(2): 373-384 Wang Heng, Zhu Yuan-Jie, Yang Hang, Wang Ping. Deterministic scheduling algorithm with priority classification for industrial wireless networks. Acta Automatica Sinica, 2020, 46(2): 373-384
[7]	Kotsiou V, Papadopoulos G Z, Chatzimisios P, Theoleyre F. Whitelisting without collisions for centralized scheduling in wireless industrial networks. IEEE Internet of Things Journal, 2019, 6(3): 5713-5721 doi: 10.1109/JIOT.2019.2905217
[8]	Pang Z, Luvisotto M, Dzung D. Wireless high-performance communications: the challenges and opportunities of a new target. IEEE Industrial Electronics Magazine, 2017, 11(3): 20-25 doi: 10.1109/MIE.2017.2703603
[9]	Saifullah A, Xu Y, Lu C, Chen Y. Real-time scheduling for WirelessHART networks. In: Proceedings of the 31st Real-Time Systems Symposium. San Diego, USA: IEEE, 2010. 150−159
[10]	Jin X, Wang J, Zeng P. End-to-end delay analysis for mixed-criticality WirelessHART networks. IEEE/CAA Journal of Automatica Sinica, 2015, 2(3): 282-289 doi: 10.1109/JAS.2015.7152662
[11]	Venkata P M, Abusayeed S, Sanjay M. DistributedHART: a distributed real-time scheduling system for wirelessHART networks. In: Proceeding of the 25th IEEE Real-Time and Embedded Technology and Applications Symposium. Montreal, Canada: IEEE, 2019. 216−227
[12]	Chen G, Cao X, Liu L, Sun C, Cheng Y. Joint scheduling and channel allocation for end-to-end delay minimization in industrial wirelessHART networks. IEEE Internet of Things Journal, 2019, 6(2): 2829-2842 doi: 10.1109/JIOT.2018.2875508
[13]	Jin X, Kong F, Kong L, Liu W, Zeng P. Reliability and temporality optimization for multiple coexisting wirelessHART networks in industrial environments. IEEE Transactions on Industrial Electronics, 2017, 64(8): 6591-6602 doi: 10.1109/TIE.2017.2682005
[14]	王恒, 陈鹏飞, 王平. 面向WIA-PA工业无线传感器网络的确定性调度算法. 电子学报, 2018, 46(1): 68-74 doi: 10.3969/j.issn.0372-2112.2018.01.010 Wang Heng, Chen Peng-Fei, Wang Ping. Deterministic scheduling algorithms for WIA-PA industrial wireless sensor networks. Acta Electronica Sinica, 2018, 46(1): 68-74 doi: 10.3969/j.issn.0372-2112.2018.01.010
[15]	Wang H, Shao L, Xia S, Wang P, Li M. An efficient channel utilization scheme for WIA-PA industrial wireless networks based on link skipping. In: Proceedings of the 2016 IEEE International Conference on Internet of Things. Chengdu, China: IEEE, 2016. 406−409
[16]	Dewanta F, Rezha F P, Kim D S. Message scheduling approach on dedicated time slot of ISA100. 11a. In: Proceedings of International Conference on ICT Convergence. Jeju Island, South Korea: IEEE, 2012. 466−471
[17]	Zhang T, Gong T, Yun Z, Han S, Deng Q, Hu X. FD-PaS: a fully distributed packet scheduling framework for handling disturbances in real-time wireless networks. In: Proceeding of the 24th IEEE Real-Time and Embedded Technology and Applications Symposium. Porto, Portugal: IEEE, 2018. 1−12
[18]	Jin X, Abusayeed S, Lu C, Zeng P. Real-time scheduling for event-triggered and time-triggered flows in industrial wireless sensor-actuator networks. In: Proceeding of the 2019 IEEE Conference on Computer Communications. Paris, France: IEEE, 2019. 1684−1692
[19]	Shi H, Zheng M, Liang W, Zhang J. Convergecast scheduling for industrial wireless sensor networks with local available channel sets. IEEE Sensors Journal, 2019, 19(22): 10764-10772 doi: 10.1109/JSEN.2019.2929672
[20]	Shi H, Zheng M, Liang W, Zhang J. A real-time transmission scheduling algorithm for industrial wireless sensor networks with multiple radio interfaces. In: Proceeding of the 89th Vehicular Technology Conference. Kuala Lumpur, Malaysia: IEEE, 2019. 1−7
[21]	Mohamed K, Nader M. Real-time scheduling for wireless networks with random deadlines. In: Proceeding of the 13th International Workshop on Factory Communication Systems. Trondheim, Norway: IEEE, 2017. 1−9
[22]	张本宏, 王一茗, 俞磊, 吴文生. 基于数据到达率的IWSNs分层调度方法. 电子测量与仪器学报, 2019, 33(6): 76-81 Zhang Ben-hong, Wang Yi-ming, Yu Lei, Wu Wen-Sheng. Hierarchical scheduling method of IWSNs based on data arrival rate. Journal of Electronic Measurement and Instrumentation, 2019, 33(6): 76-81
[23]	恩波, 王晶, 靳其兵, 周靖林. 工业无线网络链路选择与时隙分配的同步优化. 浙江大学学报(工学版), 2016, 50(6): 1203-1213 doi: 10.3785/j.issn.1008-973X.2016.06.027 Si En-Bo, Wang Jing, Jin Qi-bing, Zhou Jing-lin. Synchronous optimization of link-scheduling and timeslot-assignment for industrial wireless network. Journal of Zhejiang University (Engineering Science), 2016, 50(6): 1203-1213 doi: 10.3785/j.issn.1008-973X.2016.06.027
[24]	Osamy W, El-sawy A A, Khedr A M. Effective TDMA scheduling for tree-based data collection using genetic algorithm in wireless sensor networks. Peer-to-Peer Networking and Applications, 2020, 13: 796−815
[25]	Wang T, Wu Z, Mao J. PSO-based hybrid algorithm for multi-objective TDMA scheduling in wireless sensor networks. In: Proceedings of the 2nd International Conference on Communications and Networking in China. Shanghai, China: IEEE, 2007. 1−5
[26]	Osamy W, El-sawy A A, Khedr A M. A simulated annealing based tree construction and scheduling algorithm for minimizing aggregation time in wireless sensor networks. Wireless Personal Communications, 2019, 108(2): 921-938 doi: 10.1007/s11277-019-06440-9
[27]	Wang L, Wang X, Tornatore M, et al. Scheduling with machine-learning-based flow detection for packet-switched optical data center networks. Journal of Optical Communications and Networking, 2018, 10(4): 365-375 doi: 10.1364/JOCN.10.000365
[28]	Girs S, Willig A, Uhlemann E, Bjorkman M. On the role of feedback for industrial wireless networks using relaying and packet aggregation. In: Proceeding of the 2014 IEEE International Conference on Industrial Technology. Busan, South Korea: IEEE, 2014. 1−6
[29]	Serror M, Hu Y, Dombrowski C, Wehrle K, Gross J. Performance analysis of cooperative ARQ systems for wireless industrial networks. In: Proceeding of the 17th International Symposium on a World of Wireless, Mobile and Multimedia Networks. Coimbra, Portugal: IEEE, 2016. 1−4
[30]	Pham T, Kim D. Routing protocol over lossy links for ISA100.11a industrial wireless networks. Wireless Networks, 2014, 20(8): 2359-2370 doi: 10.1007/s11276-014-0747-5
[31]	Sepulcre M, Gozalvez J, Coll-Perales B. Multipath QoS-driven routing protocol for industrial wireless networks. Journal of Network and Computer Applications, 2016, 74: 121-132 doi: 10.1016/j.jnca.2016.08.008
[32]	Lucia S, Gianluca C, Adriano V, Claudio Z. Bandwidth management for soft real-time control applications in industrial wireless networks. IEEE Transactions on Industrial Informatics, 2017, 13(5): 2484-2495 doi: 10.1109/TII.2017.2720638
[33]	Liu J. Real-time systems. Prentice Hall, 2000
[34]	Cheffena M. Propagation channel characteristics of industrial wireless sensor networks. IEEE Antennas and Propagation Magazine, 2016, 58(1): 66-73 doi: 10.1109/MAP.2015.2501227