感传算协同工业互联网优化设计

doi:10.11959/j.issn.1000-436x.2023118

通信学报 ›› 2023, Vol. 44 ›› Issue (6): 12-22.doi: 10.11959/j.issn.1000-436x.2023118

感传算协同工业互联网优化设计

李竟博¹, 马礼¹^,², 李阳², 傅颖勋², 马东超²

¹ 北京工业大学信息学部，北京 100124
² 北方工业大学信息学院，北京 100144

修回日期:2023-06-09 出版日期:2023-06-25 发布日期:2023-06-01
作者简介:李竟博（1994- ），男，山东聊城人，北京工业大学博士生，主要研究方向为工业互联网、软件定义网络、一体化融合网络
马礼（1968- ），男，山西朔州人，博士，北京工业大学教授、博士生导师，主要研究方向为分布式系统、物联网、多agent机器人系统
李阳（1992- ），男，河南驻马店人，博士，北方工业大学讲师、硕士生导师，主要研究方向为移动边缘计算
傅颖勋（1986- ），男，湖南永州人，博士，北方工业大学副教授、硕士生导师，主要研究方向为云分布式存储可靠性
马东超（1980- ），男，北京人，博士，北方工业大学教授、硕士生导师，主要研究方向为WSN、下一代互联网
基金资助:
国家自然科学基金资助项目(62272007);国家自然科学基金资助项目(62001007);北京市自然科学基金资助项目(4234083);北京市自然科学基金资助项目(4212018)

Optimized design of sensing transmission and computing collaborative industrial Internet

Jingbo LI¹, Li MA¹^,², Yang LI², Yingxun FU², Dongchao MA²

¹ Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
² School of Information Science and Technology, North China University of Technology, Beijing 100144, China

Revised:2023-06-09 Online:2023-06-25 Published:2023-06-01
Supported by:
The National Natural Science Foundation of China(62272007);The National Natural Science Foundation of China(62001007);The Natural Science Foundation of Beijing(4234083);The Natural Science Foundation of Beijing(4212018)

摘要/Abstract

摘要：

针对工业互联网中感知传输端到计算端的新型场景与时间敏感的需求，解决应用中端到端时延不达标的问题，提出了一种感知传输协同计算组网优化设计方案。首先，提出了基于云边端的感传算协同网络架构，促进多维系统和异构资源的深度融合。其次，对网络拓扑构建方式进行优化，降低网络的平均路径长度，缩短传播距离，抵消使用更高频率导致的信号衰弱变快的问题。最后，改进路由选择方法，并设计拓扑优化和路由策略集成算法，降低网络内排队时延，将端到端时延控制在有界范围内，削弱“长尾现象”。实验结果表明，所提方案通过优化网络拓扑结构和路由策略并集成设计，降低了端到端时延，保证了网络的实时性和可靠性。

关键词: 工业互联网, 感传算一体化, 拓扑优化, 路由策略

Abstract:

In view of the new scenario and time-sensitive requirements of sensing-transmission end to computing end in the industrial Internet, and to solve the problem of end-to-end delay in applications that were not up to standard, a network optimized design scheme for sensing-transmission cooperative computing was proposed.First, the sensing-transmission computing cooperative network architecture based on cloud-edge-end was proposed to promote the deep integration of multidimensional systems and heterogeneous resources.Secondly, the network topology construction mode was optimized to reduce the average path length of the network, shorten the propagation distance, and offset the problem of signal decay due to the use of higher frequencies.Finally, the routing method was improved, and an integrated algorithm of topology optimization and routing strategy was designed to reduce the queuing delay in the network, control the end-to-end delay within a bounded range, and weaken the “long tail phenomenon”.The experimental results show that the scheme reduces the end-to-end delay and ensures the real-time and reliability of the network by optimizing the network topology and routing strategy and integrating the design.

Key words: industrial Internet, integrated sensing transmission and computation, topology optimization, routing strategy

中图分类号:

TP393

李竟博, 马礼, 李阳, 傅颖勋, 马东超. 感传算协同工业互联网优化设计[J]. 通信学报, 2023, 44(6): 12-22.

Jingbo LI, Li MA, Yang LI, Yingxun FU, Dongchao MA. Optimized design of sensing transmission and computing collaborative industrial Internet[J]. Journal on Communications, 2023, 44(6): 12-22.

图/表 9

图1

图2

表1

参数及其含义"

参数	含义
V ,E	网络中节点和边的集合
$f_{n}$	第n个数据流
$s_{n}, d_{n}$	数据流 f_n 的源节点和目的节点
${size}_{n}, {upd}_{n}$	数据流 f_n 的大小和时延上限
$R_{n}^{m}$	数据流 f_n 在网络中的第m条传输路径
$R_{l}, R_{g}$	初始半径和全局半径
c	拓扑扩张速度
apl	平均路径长度
$l_{\min}$	两节点之间的最短路径长度
$k, k_{m}$	节点的度和阈值
p	节点被连接的概率
m ,e	节点和边的个数
P ( )k	度分布函数
$δ$	时延阈值
$α, β$	权重系数
$d_{t}, d_{p}, d_{h}, d_{q}$	发送时延，传播时延，处理时延，排队时延
$D_{n}, D_{n}^{t}$	f _n 有堵塞的总时延和无堵塞的总时延
$B_{n}^{m}$	f _n 在网络中第m条传输路径的可用带宽
F	路由选择函数

表1

图3

图4

图5

图6

图7

图8

参考文献 25

[1]	DESAI P R , MINI S , TOSH D K . Edge-based optimal routing in SDN-enabled industrial Internet of things[J]. IEEE Internet of Things Journal, 2022,9(19): 18898-18907.
[2]	田辉, 贺硕, 林尚静 ,等. 工业互联网感知通信控制协同融合技术研究综述[J]. 通信学报, 2021,42(10): 211-221.
	TIAN H , HE S , LIN S J ,et al. Survey on cooperative fusion technologies with perception,communication and control coupled in industrial Internet[J. Journal on Communications, 2021,42(10): 211-221.
[3]	QI Q , CHEN X M , ZHONG C J ,et al. Integrated sensing,computation and communication in B5G cellular Internet of things[J]. IEEE Transactions on Wireless Communications, 2021,20(1): 332-344.
[4]	陈旭, 尉志青, 冯志勇 ,等. 面向6G的智能机器通信与网络[J]. 物联网学报, 2020,4(1): 59-71.
	CHEN X , WEI Z Q , FENG Z Y ,et al. Intelligent machine-type communication and network for 6G system[J. Chinese Journal on Internet of Things, 2020,4(1): 59-71.
[5]	CUI Y H , LIU F , JING X J ,et al. Integrating sensing and communications for ubiquitous IoT:applications,trends,and challenges[J]. IEEE Network, 2021,35(5): 158-167.
[6]	WEI M , YANG S J . A network scheduling method for convergence of industrial wireless network and TSN[C]// Proceedings of 2023 17th International Conference on Ubiquitous Information Management and Communication. Piscataway:IEEE Press, 2023: 1-6.
[7]	ROKHAYAH SYAMBAS N R . A heuristic algorithm for ring topology optimization[C]// Proceedings of 2020 6th International Conference on Wireless and Telematics. Piscataway:IEEE Press, 2020: 1-4.
[8]	ZHANG H B , ZHANG H L , DI B Y ,et al. Holographic integrated sensing and communication[J]. IEEE Journal on Selected Areas in Communications, 2022,40(7): 2114-2130.
[9]	HUANG Q Y , LUO Z Q , ZHANG J ,et al. LoRadar:enabling concurrent radar sensing and LoRa communication[J]. IEEE Transactions on Mobile Computing, 2022,21(6): 2045-2057.
[10]	GAO Y J , LIU L , HU B X ,et al. Federated region-learning for environment sensing in edge computing system[J]. IEEE Transactions on Network Science and Engineering, 2020,7(4): 2192-2204.
[11]	HU Q , GAO F F , ZHANG H ,et al. Deep learning for channel estimation:interpretation,performance,and comparison[J]. IEEE Transactions on Wireless Communications, 2020,20(4): 2398-2412.
[12]	LI X , TANG F L , ZHU Y M ,et al. Processing-while-transmitting:cost-minimized transmission in SDN-based STINs[J]. IEEE/ACM Transactions on Networking, 2022,30(1): 243-256.
[13]	王莉, 魏青, 徐连明 ,等. 面向通信-导航-感知一体化的应急无人机网络低能耗部署研究[J]. 通信学报, 2022,43(7): 1-20.
	WANG L , WEI Q , XU L M ,et al. Research on low-energy-consumption deployment of emergency UAV network for integrated communication-navigating-sensing[J. Journal on Communications, 2022,43(7): 1-20.
[14]	XU H S , WU J , LI J H ,et al. Deep-reinforcement-learning-based cybertwin architecture for 6G IIoT:an integrated design of control,communication,and computing[J]. IEEE Internet of Things Journal, 2021,8(22): 16337-16348.
[15]	CHEN J , MENG Z L , GUO Y N ,et al. Hier Topo:towards high-performance and efficient topology optimization for dynamic networks[C]// Proceedings of 2021 IEEE/ACM 29th International Symposium on Quality of Service. Piscataway:IEEE Press, 2021: 1-10.
[16]	CHEN N , QIU T , ZHAO L P ,et al. Edge intelligent networking optimization for Internet of things in smart city[J]. IEEE Wireless Communications, 2021,28(2): 26-31.
[17]	CHANDWANI G , BEHERA S , DAS G . Delay-aware control plane virtual topology design of software defined-elastic optical network[J]. IEEE Transactions on Network and Service Management, 2021,18(2): 1461-1475.
[18]	HE D Q , SUN D L , CHEN Y J ,et al. Topology design and optimization of train communication network based on industrial Ethernet[J]. IEEE Transactions on Vehicular Technology, 2022,71(1): 844-855.
[19]	YI C Y , CAI J , SU Z . A multi-user mobile computation offloading and transmission scheduling mechanism for delay-sensitive applications[J]. IEEE Transactions on Mobile Computing, 2020,19(1): 29-43.
[20]	LI C , ZHANG C Y , ZHENG W ,et al. Joint routing and scheduling for dynamic applications in multicast time-sensitive networks[C]// Proceedings of 2021 IEEE International Conference on Communications Workshops. Piscataway:IEEE Press, 2021: 1-6.
[21]	WU H J , ALAY ? , BRUNSTROM A ,et al. Peekaboo:learning-based multipath scheduling for dynamic heterogeneous environments[J]. IEEE Journal on Selected Areas in Communications, 2020,38(10): 2295-2310.
[22]	WATTS D J , STROGATZ S H . Collective dynamics of ‘small-world’ networks[J]. Nature, 1998,393(6684): 440-442.
[23]	BARABASI A L , ALBERT R . Emergence of scaling in random networks[J]. Science, 1999,286(5439): 509-512.
[24]	YEN J Y . Finding the K shortest loopless paths in a network[J]. Management Science, 1971,17(11): 712-716.
[25]	QIU T , LI B L , QU W Y ,et al. TOSG:a topology optimization scheme with global small world for industrial heterogeneous Internet of things[J]. IEEE Transactions on Industrial Informatics, 2019,15(6): 3174-3184.

感传算协同工业互联网优化设计

Optimized design of sensing transmission and computing collaborative industrial Internet

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