A Survey of Real-time Transmission Scheduling Algorithms for Industrial Wireless Network
-
摘要: 无线网络是工业物联网中的一种具有良好前景的网络互联技术. 它的应用为工业现场设备的部署提供了极大的便利, 使设备摆脱了线缆的束缚从而在空间上的选点更为灵活, 同时能够节省线材和人力等方面的成本. 然而, 无线通信易受环境噪声的影响, 尤其是在复杂电磁干扰的工业环境中, 易导致无线传输的时延增大和数据丢失. 这些问题对于传输实时性要求较高的工业控制系统而言是非常不利的因素. 为了提高无线网络在工业环境中数据传输的实时性, 业界设计了多种传输调度算法以提高无线通信的实时性和可靠性从而满足工业应用的需求. 综述了工业无线网络传输调度算法的研究现状, 对其发展历程、问题定义、评价指标、分类方法和现有标准等方面进行了全面的总结, 详细阐述了具有代表性的调度算法的工作原理, 并指出了未来的研究方向.Abstract: The wireless network provides great convenience for the deployment of industrial devices, gets rid of the shackles of cables, makes the deployment of devices more flexible, and saves the cost of materials and manpower, which is the development trend of the industrial internet of things. However, since wireless communication is susceptible to interference, especially in the industrial environment with complex electromagnetic interference, it is easy to lead to increased delay and data loss in wireless transmission, which is a disadvantage for industrial control systems that require high real-time transmission. In order to retain the convenience and flexibility of wireless network deployment and make it available in industrial environments, the industry has designed a variety of industrial wireless network transmission scheduling algorithms and protocols to improve the real-time and reliability of wireless communication to meet the industrial application requirements. This paper reviews the current researches of transmission scheduling algorithms for industrial wireless network, and comprehensively summarizes the development history, problem definitions, evaluation metrics, classification methods and existing standards, and elaborates the working principles of representative scheduling algorithms in detail, and points out the future research direction.
-
Key words:
- Industrial wireless networks /
- transmission scheduling /
- real-time /
- reliability
-
图 5 网状模型中七个节点的染色过程图
Fig. 5 vertex coloring process diagram of seven nodes in a reticular model
图 6 节点5在传输失败后, 在超帧中的空闲时隙进行重传的示意图
Fig. 6 An example of node 5 retransmit in idle time slots after transmission failure
图 7 节点6失去原有链路后与节点7连接并与空闲节点3占用时隙交换的调度图
Fig. 7 An example of node 6 loses original link, it connects to node 7 and occupies time slots with idle node 3
图 9 节点自治协议算法Orchestra和DiGs的调度示例图
Fig. 9 An example diagram of scheduling for the node autonomy algorithm Orchestra and DiGs
图 10 链路自治协议算法ALICE的调度示例图
Fig. 10 An example of scheduling for the link autonomy algorithm ALICE
图 12 链路自治协议算法OST的调度示例图
Fig. 12 An example of scheduling for the link autonomy algorithm OST
图 13 Wave一个周期进行四次波动的调度示例图
Fig. 13 An example of Wave scheduling with four waves per cycle
表 1 工业无线网络标准和调度机制发展概况
Table 1 Overview of the development standards and scheduling algorithms for industrial wireless network
年份 标准 集中式 分布式 2008 ~ 2010 WirelessHART[28]、WIA-PA[33] TSMP[77]、Bit[61] DRAND[103] 2011 ISA-100.11a[29] C-LLF[81] 文献[104] 2012 ~ 2013 IEEE 802.15.4e[30] TASA[87]、RT-WiFi[38] DeTAS[109]、GCSA[107] 2014 6TiSCH[25]、WIA-FA[33] SAandPSO[83]、MinMax[108] 2015 SSEvent[74]、OLS[73] Orchestra[21] 2016 LDF[63]、SchedEX[54] Wave[110] 2017 ~ 2018 LoRaWAN[115]、5G[44] OBSSA[75]、TDMH[60] 2019 文献[72]、Autobahn[66] Diva[105]、TESLA[98],DiGs[96] 2020 w-SHARP[42] OST[99] 2021 Wi-Fi 7[45] RLSchedule[67] OSCAR[101]、ATRIA[100]、$A^{3}$[102] 2022 ~ 2023 SmartHART[32] EDSF[111] 
下载: 导出CSV
表 2 工业无线标准的对比表
Table 2 Comparison table of industrial wireless standards
标准 物理层 多路径 TDMA 调频 介质访问 IEEE 802.15.4 IEEE 802.15.4控制层 $\times$ $\times$ $\times$ CSMA/CA WirelessHATRT IEEE 802.15.4物理层 $\surd$ 基于TDMA的时隙信道跳频 $\surd$ IEEE 802.15.4控制层 ISA100.11a IEEE 802.15.4物理层 $\surd$ 时隙信道跳频
基于CMSA的慢跳频
混合调频$\surd$ IEEE 802.15.4控制层 WIA-PA/FA IEEE 802.15.4物理层 $\surd$ 时隙跳频
自适应跳频
自适应频率切换$\surd$ IEEE 802.15.4物理层 IEEE 802.15.4e IEEE 802.15.4物理层 $\surd$ 基于TDMA的时隙信道跳频 $\surd$ TSCH DSME LLDN 工业5G 5G NR物理层 $\surd$ 正交频分多址 $\surd$ 5G NR物理层 Wi-Fi 7 IEEE 802.11物理层 $\surd$ 正交频分多址 $\surd$ CSMA/CA
下载: 导出CSV
表 4 经典算法调度方式比较表
Table 4 Comparison table of classical algorithm scheduling modes
调度算法 网络模型 管理模式 支持多跳 数据流 信道 投递率 延迟 能耗 DiGs[96] 网状 分布式 是 周期流 多信道 $\surd$ $\surd$ $\surd$ DistributedHART[106] 网状 分布式 是 周期流和事件流 多信道 $\surd$ $\surd$ $\surd$ ALICE[97] 树形 分布式 是 周期流 多信道 $\surd$ $\surd$ $\surd$ OST[99] 树形 分布式 是 周期流 多信道 $\surd$ $\surd$ $\surd$ Diva[105] 网状 分布式 是 周期流 多信道 $\surd$ Wave[110] 树状 分布式 是 周期流 多信道 $\surd$ OLS[73] 树状 集中式 是 事件流 多信道 $\surd$ Feasible[79] 网状 集中式 是 事件流 多信道 $\surd$ LDF[63] 树状 集中式 否 周期流 多信道 $\surd$ $\surd$ SAandPSO[83] 树状 集中式 是 事件流 单信道 $\surd$ GCSA [107] 树状 分布式 是 周期流 单信道 $\surd$ TASA[87] 树状 集中式 是 周期流 多信道 $\surd$ $\surd$ OBSSA[75] 网状 集中式 是 周期流和事件流 多信道 $\surd$ RS[72] 树状 集中式 是 周期流和事件流 多信道 $\surd$ DRAND[103] 网状 分布式 是 周期流 多信道 $\surd$ $\surd$ Tinka[104] 网状 分布式 是 周期流 多信道 $\surd$ DeTAS[109] 网状 分布式 是 周期流 多信道 $\surd$ Orchestra[21] 网状 分布式 是 周期流 多信道 $\surd$ $\surd$ $\surd$ TSMP[77] 网状 集中式 是 周期流和事件流 多信道 $\surd$ $\surd$ $\surd$ C-LLF[81] 树状 集中式 是 周期流 多信道 $\surd$ Util-base[52] 树状 集中式 是 周期流 多信道 $\surd$ TDMH[60] 网状 集中式 是 周期流 多信道 $\surd$ node-base[20] 网状 集中式 是 周期流 多信道 $\surd$ level-base[20] 网状 集中式 是 周期流 多信道 $\surd$ DDFS[20] 网状 集中式 是 周期流 多信道 $\surd$ MinMax[108] 树状 集中式 是 周期流 多信道 $\surd$ SchedEX[54] 树状 集中式 是 周期流 多信道 $\surd$ RateSelection[50] 网状 集中式 是 周期流 多信道 TESLA[98] 树状 分布式 是 周期流 多信道 $\surd$ $\surd$ $\surd$ JiTS[9] 树状 分布式 是 周期流 单信道 $\surd$ SSEvent[74] 网状 集中式 是 周期流和事件流 多信道 $\surd$ Bit[61] 网状 集中式 是 周期流 多信道 $\surd$ $\surd$ $\surd$ SRDR[59] 网状 集中式 是 周期流 多信道 $\surd$ $\surd$ $\surd$ Hierarchic[76] 树状 集中式 是 周期流和事件流 多信道 $\surd$ $\surd$ Segment[90] 网状 集中式 是 周期流和事件流 单信道 $\surd$ $\surd$ $\surd$ RLSchedule[67] 树状 集中式 是 周期流 多信道 $\surd$ $\surd$ $\surd$ OSCAR[101] 网状 分布式 是 周期流 多信道 $\surd$ $\surd$ $\surd$ EDFS[111] 树状 分布式 是 周期流 多信道 $\surd$ $\surd$ $\surd$
下载: 导出CSV
-
[1] Venkata P M, Abusayeed S, Sanjay M. Distributed graph routing for wirelesshart networks. Association for Computing Machinery, 2018, 27(4): 1669-1682 [2] Wu C J, Dolvara G, Abusayeed S, Mo S. Maximizing network lifetime of wirelesshart networks under graph routing. 2016 IEEE First International Conference on Internet-of-Things Design and Implementation, 2016, DOI: 10.1109/IoTDI.2015.43 [3] He Y, Guo X Z, Zheng X L, Yu Z H, Zhang J, Jiang H T, et al. Cross-technology communication for the Internet of Things: A survey. ACM Computing Surveys, 2022, 55(5): 1-29 [4] Daniele P, Martin H. Multipath fading in wireless sensor networks: measurements and interpretation. In: Proceedings of the International Wireless Communications and Mobile Computing Conference. Amman, Jordan: IEEE, 2006. 1039−1044 [5] Krijn L, Jan H F. The capture effect in fm receivers. IEEE Transactions on Communications, 1976, 24(5): 531-539 doi: 10.1109/TCOM.1976.1093327 [6] Federico F, Marco Z, Lothar T, Olga S. Efficient network flooding and time synchronization with glossy. In: Proceedings of the 10th ACM/IEEE International Conference on Information Processing in Sensor Networks. Chicago, IL, USA: IEEE, 2011. 73−84 [7] Luís M B, Fernando J V, António S L. Survey on the characterization and classification of wireless sensor networks applications. IEEE Communications Surveys Tutorials, 2014, 16(4): 1860-1890 doi: 10.1109/COMST.2014.2320073 [8] 张晓玲, 梁炜, 于海斌, 封锡盛. 无线传感器网络传输调度方法综述. 通信学报, 2012, 33(5): 143-156 doi: 10.3969/j.issn.1000-436X.2012.05.019Zhang Xiao-Ling, Liang Wei, Yu Hai-Bin, Feng Xi-Sheng. Survey of transmission scheduling methods in wireless sensor networks. Journal on Communications. 2012, 33(5): 143-156. doi: 10.3969/j.issn.1000-436X.2012.05.019 [9] Liu K, Nael A G, Kyoung D K. Jits: Just-in-time scheduling for real-time sensor data dissemination. In: Proceedings of the Fourth Annual IEEE International Conference on Pervasive Computing and Communications. Pisa, Italy: IEEE, 2006. 42−46 [10] Tengfei C, Thomas W, Xavier V, Pedro H G. Constructive interference in 802.15.4: A tutorial. IEEE Communications Surveys Tutorials, 2019, 21(1): 217-237 doi: 10.1109/COMST.2018.2870643 [11] Zhang K W, Shi Y, Karnouskos S, Sauter T, Fang H Z, Colombo A W. Advancements in industrial cyber-physical systems: an overview and perspectives. IEEE Transactions on Industrial Informatics, 2022, 19(1): 716-729 [12] Brummet R, Hossain M K, Chipara O, Herman T, Goddard S. WARP: On-the-fly program synthesis for agile, real-time, and reliable wireless networks. In: Proceedings of the 20th International Conference on Information Processing in Sensor Networks. Nashville, TN, USA: Association for Computing Machinery, 2021. 254−267 [13] Romain J, Marco Z, Huang P C, Jan B, Lothar T. End-to-end real-time guarantees in wireless cyber-physical systems. In: Proceedings of the IEEE Real-Time Systems Symposium. Porto, Portugal: IEEE, 2016. 167−178 [14] Chen Y, Zhang H W, Nathan F, Wang L Y, George Y. Probabilistic per-packet real-time guarantees for wireless networked sensing and control. IEEE Transactions on Industrial Informatics, 2018, 14(5): 2133-2145 doi: 10.1109/TII.2018.2795567 [15] Kayan H, Nunes M, Rana O, Burnap P, Perera C. Cybersecurity of industrial cyber-physical systems: A review. ACM Computing Surveys, 2022, 54(11): 1-35 [16] Shen W, Zhang T T, Mikael G. Prioritymac: A priority-enhanced mac protocol for critical traffic in industrial wireless sensor and actuator networks. IEEE Transactions on Industrial Informatics, 2014, 10(1): 824-835 doi: 10.1109/TII.2013.2280081 [17] Adil M, Menon V G, Balasubramanian V, Alotaibi S R, Song H, Jin Z p, et al. Survey: Self-Empowered Wireless Sensor Networks Security Taxonomy, Challenges and Future Research Directions. IEEE Sensors Journal, DOI: 10.1109/JSEN.2022.3216824 [18] Milanez G, Vieira M, Vieira L, Nacif J. VariBan: A variable bandwidth channel allocation algorithm for IEEE 802.15. 4e-based networks. Computer Networks, DOI: 10.1016/j.comnet.2023.109774 [19] Wang Q, Jiang J. Comparative examination on architecture and protocol of industrial wireless sensor network standards. IEEE Communications Surveys and Tutorials, 2016, 18(3): 2179-2219 [20] Sinem C E, Pravin V. TDMA scheduling algorithms for wireless sensor networks. Wireless Networks, DOI: 10.1007/s11276-009-0183-0 [21] Simon D, Beshr A N, Olaf L, Thomas W. Orchestra: Robust mesh networks through autonomously scheduled tsch. In: Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems. Seoul, South Korea: Association for Computing Machinery, 2015. 337−350 [22] Dominik B, Fabian M, Marco Z, Sebastian T. Control-guided communication: Efficient resource arbitration and allocation in multi-hop wireless control systems. IEEE Control Systems Letters, 2020, 4(1): 127-132 doi: 10.1109/LCSYS.2019.2922188 [23] Bang A O, Rao U P, Kaliyar P, Conti M. Assessment of routing attacks and mitigation techniques with RPL control messages: A survey. ACM Computing Surveys, 2022, 55(2): 1-36 [24] Javan N T, Sabaei M, Hakami V. Adaptive channel hopping for IEEE 802.15. 4 TSCH-based networks: a dynamic bernoulli bandit approach. IEEE Sensors Journal, 2021, 21(20): 23667-23681 doi: 10.1109/JSEN.2021.3110720 [25] Diego D, Thomas W, Xavier V, Pascal T. 6TiSCH: deterministic IP-enabled industrial internet (of things). IEEE Communications Magazine, 2014, 25(12): 1-36 [26] Gutierrez J A, Naeve M, Callaway E, Bourgeois M. IEEE 802.15.4: A developing standard for low-power low-cost wireless personal area networks. ACM Transactions on Internet of Things, 2001, 15(5): 12-19 [27] Wireless LAN Medium Access Control (MAC) and Physical layer (PHY) specifications, IEEE Standard 802.11, Piscataway, NJ, USA, 1997 [28] Foundation H C. WirelessHART Specification 75: TDMA Data-Link Layer, HART Communication Foundation Standard, Sydney NSW, Australia, 2008 [29] ISA-100.11 a-2011 Wireless Systems for Industrial Automation: Process Control and Related Applications, Process Control and Related Applications Standard, San Diego, CA, USA, 2011 [30] 802.15. 4e-2012—IEEE Standard for Local and Metropolitan Area Networks—Part 15.4: Low-Rate Wireless Personal Area Networks (LR-WPANs), IEEE Standard Association, Piscataway, NJ, USA, 2012 [31] Moon S, Park H, Chwa H S, Park K. AdaptiveHART: An Adaptive Real-Time MAC Protocol for Industrial Internet-of-Things. IEEE Systems Journal, 2022, 16(3): 4849-4860 doi: 10.1109/JSYST.2022.3171962 [32] Chilukuri S, Gupta A, Sai P, Hemanth S. SmartHART: A priority-aware scheduling and routing scheme for ⅡoT networks using deep reinforcement learning. In: Proceedings of the 15th International Conference on COMmunication Systems and NETworkS. Bangalore, India: IEEE, 2023. 452−456 [33] Liang We, Zheng M, Zhang J L, Shi H G, Yu H B, Yang Y T, et al. WIA-FA and its applications to digital factory: A wireless network solution for factory automation. Proceedings of the IEEE, 2019, 107(6): 1053-1073 doi: 10.1109/JPROC.2019.2897627 [34] Wun C J, Junhee L. Performance evaluation of IEEE 802.15.4e DSME MAC protocol for wireless sensor networks. In: Proceedings of the The First IEEE Workshop on Enabling Technologies for Smartphone and Internet of Things. Seoul, Korea: IEEE, 2012. 452−456 [35] Gianluca C, Lucia S, Adriano V, Claudio Z. On the Performance of IEEE 802.11e Wireless Infrastructures for Soft-Real-Time Industrial Applications. IEEE Transactions on Industrial Informatics, 2010, 6(3): 425-437 doi: 10.1109/TII.2010.2052058 [36] Michal S, Martin M, Zdenek H. Experiments for real-time communication contracts in ieee 802.11e edca networks. In: Proceedings of the IEEE International Workshop on Factory Communication Systems. Dresden, Germany: IEEE, 2008. 89−92 [37] Junyoung H, Jiman H, Yookun C. Earq: Energy aware routing forreal-time and reliable communication in wireless industrial sensornetworks. IEEE Transactions on Industrial Informatics, 2009, 5(1): 3-11 doi: 10.1109/TII.2008.2011052 [38] Wei Y H, Leng Q, Han S, Aloysius K M, Zhang W L. Rt-wifi: Real-time high-speed communication protocol for wireless cyber-physical control applications. In: Proceedings of the IEEE 34th Real-Time Systems Symposium. Vancouver, Canada: IEEE, 2013. 140−149 [39] Tian G S, Seyit C, Tian Y C. A deadline-constrained 802.11mac protocol with qos differentiation for soft real-time control. IEEE Transactions on Industrial Informatics, 2016, 12(2): 544-554 doi: 10.1109/TII.2016.2520398 [40] Lucia S, Gianluca C, Stefano S, Adriano V, Claudio Z. Enhancing communication determinism in wi-fi networks for softreal-time industrial applications. IEEE Transactions on Industrial Informatics, 2009, 13(2): 866-876 [41] Leng Q, Wei Y H, Han S, Aloysius K, Zhang W L. Improving control performance by minimizing jitter in rt-wifi networks. In: Proceedings of the IEEE Real-Time Systems Symposium. Rome, Italy: IEEE, 2014. 63−73 [42] Seijo O, Val I, Fernandez J L. w-SHARP: Implementation of a High-Performance Wireless Time-Sensitive Network for Low Latency and Ultra-low Cycle Time Industrial Applications IEEE Transactions on Industrial Informatics, 2020, 17(5): 3651-3662 [43] Yun Z L, Wu P, Zhou S L, Mok A K, Mark N, Han S. RT-WiFi on Software-Defined Radio: Design and Implementation. In: Proceedings of the IEEE 28th Real-Time and Embedded Technology and Applications Symposium. Milano, Italy: IEEE, 2022. 254−266 [44] Morgado A, Huq K M S, Mumtaz S. A survey of 5G technologies: regulatory, standardization and industrial perspectives. Digital Communications and Networks, 2018, 4(2): 87-97 doi: 10.1016/j.dcan.2017.09.010 [45] Garcia-Rodriguez A, Lopez-Perez D, Galati-Giordano L. IEEE 802.11 be: Wi-Fi 7 strikes back. IEEE Communications Magazine, 2021, 59(4): 102-108 doi: 10.1109/MCOM.001.2000711 [46] Azzino T, Ropitault T, Zorzi M. Scheduling the data transmission interval in IEEE 802.11 ad: A reinforcement learning approach. In: Proceedings of the International Conference on Computing, Networking and Communications. Big Island, HI, USA: IEEE, 2020. 602−607 [47] Chen C, Li J C, Balasubramaniam V, Wu Y Q, Zhang Y R and Wan S H. Contention resolution in Wi-Fi 6-enabled Internet of Things based on deep learning. IEEE Internet of Things Journal, 2021, 8(7): 5309-5320 doi: 10.1109/JIOT.2020.3037774 [48] Cheng C Y, Li C Y, Chiu C H. An experience driven design for IEEE 802.11 ac rate adaptation based on reinforcement learning. In: Proceedings of the IEEE INFOCOM 2021 - IEEE Conference on Computer Communications. Vancouver, British Columbia, Canad: IEEE, 2021. 1−10 [49] Sha M, Dolvara G, Wu C J, Lu C Y. Empirical study and enhancements of industrial wireless sensor actuator network protocols. IEEE Internet of Things Journal, 2017, 4(3): 696-704 doi: 10.1109/JIOT.2017.2653362 [50] Abusayeed S, Chengjie Wu, Paras B Ti, Xu Y, Fu Y, Lu C Y. Near optimal rate selection for wireless control systems. IEEE Real-Time and Embedded Technology and Applications Symposium, 2012, 13(45): 231-240 [51] Pablo S, Zhang H B, Johansson M. Deadline-constrained transmission scheduling and data evacuation in wirelesshart networks. In: Proceedings of the European Control Conference. Budapest, Hungary: IEEE, 2009. 4320−4325 [52] Venkata P M, Dali I, Mahbubur R, and Abusayeed S. A utilization-based approach for schedulability analysis in wireless control systems. In: Proceedings of the IEEE International Conference on Industrial Internet. Bellevue, Washington, USA: IEEE, 2008. 49−58 [53] 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 doi: 10.16383/j.aas.c170722Wang 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 doi: 10.16383/j.aas.c170722 [54] Felix D, Zhang T T, Mikael G. End-to-end reliability-aware scheduling for wireless sensor networks. IEEE Transactions on Industrial Informatics, 2016, 12(2): 758-767 doi: 10.1109/TII.2014.2382335 [55] Alharbi N, Mackenzie L, Pezaros D. Enhancing graph routing algorithm of industrial wireless sensor networks using the covariance-matrix adaptation evolution strategy. Sensors, DOI: 10.3390/s22197462 [56] Park M, Paek J. On-demand scheduling of command and responses for low-power multihop wireless networks. Sensors, 2021, 21(3): 738 doi: 10.3390/s21030738 [57] Abusayeed S, Xu Y, Lu C Y, Chen Y X. End-to-end communication delay analysisin industrial wireless networks. IEEE Transactions on Computers, 2015, 64(5): 1361-1374 doi: 10.1109/TC.2014.2322609 [58] Abusayeed S, Dolvara G, Paras T, Mo S, Lu C Y. Schedulability analysis under graph routing in wirelesshart networks. In: Proceedings of the IEEE Real-Time Systems Symposium. San Antonio, Texas, USA: IEEE, 2015. 165−174 [59] Song H, Zhu X M, Aloysius K M, Chen D J, Nixon M. Reliable and real-time communication in industrial wireless meshnetworks. In: Proceedings of the IEEE Real-Time and Embedded Technology and Applications Symposiumn. Chicago, Illinois, USA : IEEE, 2011: 3−12 [60] Terraneo F, Polidori P, Leva A, Fornaciari W. Tdmh-mac: Real-time and multi-hop in the same wireless mac. In: Proceedings of the IEEE Real-Time Systems Symposium. Nashville, TN, USA: IEEE, 2018: 277−287 [61] Yu G, Tian H, Lin M G, Xu J H. Spatiotemporal delay control for low-duty-cycle sensor networks. In: Proceedings of the IEEE Real-Time Systems Symposium. Washington DC, USA: IEEE, 2009. 127−137 [62] Kiszka J, Wagner B. Rtnet - a flexible hard real-time networking framework. In: Proceedings of the IEEE Conference on Emerging Technologies and Factory Automation. Prague, Czech Republic: IEEE, 2005. 449−456 [63] Kang X H, Wang W N, José J, Ying L. On the performance of largest-deficit-first for scheduling real-time traffic in wireless networks. ACM Transactions on Networking, 2016, 24(1): 72-84 doi: 10.1109/TNET.2014.2360365 [64] Abusayeed S, You X, Lu C Y, Chen Y X. Real-time scheduling for wirelesshart networks. In: Proceedings of the 31st IEEE Real-Time Systems Symposium. San Diego, USA: IEEE, 2010. 150−159 [65] Harms O, Landsiedel O. MASTER: Long-term stable routing and scheduling in low-power wireless networks In: Proceedings of the 16th International Conference on Distributed Computing in Sensor Systems. Marina del Rey, USA: IEEE, 2020. 86-94 [66] Harms O, Landsiedel O. Opportunistic routing and synchronous transmissions meet TSCH. In: Proceedings of the IEEE 46th Conference on Local Computer Networks. Edmonton, AB, Canada: IEEE, 2021.107−114 [67] Chilukuri S, Piao G, Lugones D. Deadline-aware TDMA scheduling for multihop networks using reinforcement learning. In: Proceedings of the IFIP Networking Conference. Espoo, Finland: IEEE, 2021. 1−9 [68] Chilukuri S, Pesch D. RECCE: Deep reinforcement learning for joint routing and scheduling in time-constrained wireless networks. IEEE Access, DOI: 10.1109/ACCESS.2021.3114967 [69] Paulo T. Event-triggered real-time scheduling of stabilizing control tasks. IEEE Transactions on Automatic Control, 2007, 52(9): 1680-1685 doi: 10.1109/TAC.2007.904277 [70] Wang X F, Michael D L. Self-triggered feedback control systems with finite-gain l2 stability, IEEE Transactions on Automatic Control, 2009, 54(3): 452-467 doi: 10.1109/TAC.2009.2012973 [71] Lunze J, Lehmann D. A state-feedback approach to event-based control. Automatica, 2010, 46(1): 211-215 doi: 10.1016/j.automatica.2009.10.035 [72] Xi Jin, Abusayeed Saifullah, Chenyang Lu, Peng Zeng. Real-time scheduling for event-triggered and time-triggered flows in industrial wireless sensor-actuator networks. In: Proceedings of the IEEE INFOCOM IEEE Conference on Computer Communications. Paris, France: IEEE, 2019: 1684−1692 [73] Hong S Y, Han X B, Sharon H G. On-line data link layer scheduling in wireless networked control systems. In: Proceedings of the 27th Euromicro Conference on Real-Time Systems. Lund, Sweden: IEEE, 2015. 57−65 [74] Li B, Nie L S, Wu C J, Humberto G, Lu C Y. Incorporating emergency alarms in reliable wireless process control. In: Proceedings of the IEEE International Conference on Cyber-Physical System. Seattle Washington, USA: Association for Computing Machinery, 2015. 218−227 [75] Xia C Q, Xi J, Kong L H, Zeng P. Scheduling for emergency tasks in industrial wireless sensor networks. Sensors, DOI: 10.3390/s17071674 [76] Jin X, Kong F X, Kong L H, Wang H H, Xia C Q. A hierarchical data transmission framework for industrial wireless sensor and actuator networks. IEEE Transactions on Industrial Informatics, 2017, 13(4): 2019-2029 doi: 10.1109/TII.2017.2685689 [77] Kristofer S J P, Lance D. Tsmp: Time synchronized mesh protocol. In: Proceedings of the IASTED Distributed Sensor Networks. Crete, Greece: IEEE, 2008. 391−398 [78] Dimitri B. Nonlinear Programming: 3nd Edition. Athena Scientific. Nashua: Athena Scientific, 2016. 97−123 [79] Hou I H, Borkar V, Kumar P R. A Theory of QoS for Wireless. In: Proceedings of the IEEE INFOCOM 2009. Rio de Janeiro, Brazil: IEEE, 2009. 486−494 [80] Ma Y H, Guo J L, Wang Y B, Chakrabarty A, Ahn H J, Lu C Y. Optimal dynamic scheduling of wireless networked control systems. In: Proceedings of the 10th ACM/IEEE International Conference on Cyber-Physical Systems. Montreal, Quebec, Canada: Association for Computing Machinery, 2019. 77−89 [81] Saifullah A, Xu Y, Lu C Y, Chen Y X. Real-time scheduling for wirelesshart networks. In: Proceedings of the Real-time Systems Symposium. San Diego, USA: IEEE, 2011. 150−159 [82] Karadag G, Iqbal M S, Coleri S. Optimal Power Control, Scheduling, and Energy Harvesting for Wireless Networked Control Systems. IEEE Transactions on Communications, 2021, 69(3): 1789-1801 doi: 10.1109/TCOMM.2020.3042792 [83] Yang G K, Myung J L. Scheduling multi-channel and multi-timeslot in time constrained wireless sensor networks via simulated annealing and particle swarm optimization. IEEE Communications Magazine, 2014, 52(1): 122-129 doi: 10.1109/MCOM.2014.6710073 [84] Thong H, Fabrice T, Won J H. On the interest of opportunistic anycast scheduling for wireless low power lossy networks. Computer Communications, DOI: 10.1016/j.comcom.2016.06.001 [85] Iqbal M S, Sadi Y, Coleri S. Minimum Length Scheduling for Discrete-Rate Full-Duplex Wireless Powered Communication Networks. IEEE Transactions on Wireless Communications, 2022, 21(1): 135-148 doi: 10.1109/TWC.2021.3094138 [86] Zoppi S, Champati J P, Gross J, Kellerer W. Scheduling of Wireless Edge Networks for Feedback-Based Interactive Applications. IEEE Transactions on Communications, 2022, 70(5): 3295-3309 doi: 10.1109/TCOMM.2022.3163761 [87] Palattella M, Accettura N, Dohler M, Grieco L, Boggia G. Traffic aware scheduling algorithm for reliable low-power multi-hop ieee 802.15.4e networks. In: Proceedings of the 23rd IEEE International Symposium on Personal, Indoor and Mobile Radio Communications. Sydney, Australia: IEEE, 2012. 327−332 [88] Wu C J, Gunatilaka D, Sha M, Lu C Y. Real-time wireless routing for industrial internet of things. In: Proceedings of the IEEE International Conference on Internet-of-Things Design and Implementation. Orlando, USA: IEEE, 2018. 261−266 [89] Jonsson M, Kunert K. Towards reliable wireless industrial communication with real-time guarantees. IEEE Transactions on Industrial Informatics, 2009, 5(4): 429-442 doi: 10.1109/TII.2009.2031921 [90] Yang D, Xu Y Z, Wang H C, Zheng T, Zhang H, Zhang H K, Gidlund M. Assignment of segmented slots enabling reliable real-time transmission in industrial wireless sensor networks. IEEE Transactions on Industrial Electronics, 2015, 62(6): 3966-3977 [91] Munir S, Lin S, Hoque E, Shahriar S M. Addressing burstiness for reliable communicationand latency bound generation in wireless sensor networks. In: Proceedings of the International Conference on Information Processing in Sensor Networks. Stockholm, Sweden: Association for Computing Machinery, 2010. 303−314 [92] Gamba G, Tramarin F, Willigt A. Retransmission strategies for cyclic polling over wireless channels in the presence of interference. IEEE Transactions on Industrial Informatics, 2009, 6(3): 405-415 [93] Shi H, Zheng M and Liang W. AODR: an automatic on-demand retransmission scheme for WIA-FA networks. IEEE Transactions on Vehicular Technology, 2021, 70(6): 6094-6107 doi: 10.1109/TVT.2021.3076988 [94] Venkata P. M, Abusayeed S, Sanjay M. A Distributed Real-time Scheduling System for Industrial Wireless Networks. ACM Transactions on Embedded Computing Systems, 2021, 20(5): 1-28 [95] Rekik S, Baccour N, Jmaiel M, Drira K, Grieco L. Autonomous and traffic-aware scheduling for tsch networks. Computer Networks, DOI: 10.1016/j.comnet.2018.02.023 [96] Shi J Y, Sha M, Yang Z C. Distributed graph routing and scheduling for industrial wireless sensor-actuator networks. IEEE/ACM Transactions on Networking, 2019, 27(4): 1669-1682 doi: 10.1109/TNET.2019.2925816 [97] Kim S, Kim H S, Kim C. Alice: Autonomous link-based cell scheduling for tsch. In: Proceedings of the 18th ACM/IEEE International Conference on Information Processing in Sensor Networks. Montreal, Quebec, Canada: Association for Computing Machinery, 2019: 121−132 [98] Jeong S, Paek J, Kim H S, Bahk S. Tesla: Traffic-aware elastic slotframe adjustment in tsch networks. IEEE Access, DOI: 10.1109/ACCESS.2019.2940457 [99] Jeong S, Kim H S, Paek J, Bahk S. Ost: On-demand tsch scheduling with traffic-awareness. In: Proceedings of the IEEE Conference on Computer Communications. Toronto, ON, Canada: IEEE, 2020. 69−78 [100] Cheng X, Sha M. Autonomous traffic-aware scheduling for industrial wireless sensor-actuator networks. Association for Computing Machinery, 2021, 19(2): 1-25 [101] Osman M, Nabki F. OSCAR: An optimized scheduling cell allocation algorithm for convergecast in IEEE 802.15. 4e TSCH networks. Sensors, DOI: 10.3390/s21072493 [102] Kim S, Kim H S, Kim C. A3: Adaptive autonomous allocation of TSCH slots. In: Proceedings of the 20th International Conference on Information Processing in Sensor Networks. Nashville, TN, USA : Association for Computing Machinery, 2021. 299−314 [103] Rhee I, Warrier A, Min J, Xu L. Drand: Distributed randomized tdma scheduling for wireless ad hoc networks. In: Proceedings of the 7th ACM international symposium on Mobile ad hoc networking and computing. Florence Italy: Association for Computing Machinery, 2006. 190−201 [104] Tinka A, Watteyne T, Kristofer S J, Bayen A M. A decentralized scheduling algorithm for time synchronized channel hopping. EAI Endorsed Transactions Mobile Communications Application, DOI: 10.4108/icst.trans.mca.2011.e5 [105] Bilgili A K. Diva: a distributed divergecast scheduling algorithm for ieee 802.15.4e tsch networks. Wireless Networks, 2019, 25(2): 625-635 [106] Modekurthy V P, Saifullah A, Madria S. Distributedhart: A distributed real-time scheduling system for wirelesshart networks. In: Proceedings of the IEEE Real-Time and Embedded Technology and Applications Symposium. Montreal, Canada: IEEE, 2019. 216−227 [107] Kang H, Zhao Y N, Mei F. A graph coloring based tdma scheduling algorithm for wireless sensor networks. Wireless Personal Communications, DOI: 10.1007/s11277-013-1052-9 [108] Abusayeed S, Xu Y, Lu C Y, Chen Y X. Distributed channel allocation protocolsfor wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems, 2014, 25(9): 2264-2274 doi: 10.1109/TPDS.2013.185 [109] Nicola A, Palattella M R, Gennaro B, Alfredo G, Mischa D. Decentralized traffic aware scheduling for multi-hop low power lossy networks in the internet of things. In: Proceedings of the IEEE 14th International Symposium on “A World of Wireless, Mobile and Multimedia Networks”. Madrid, Spain: IEEE, 2013. 1−6 [110] Soua R, Minet P, Livolant E. Wave: a distributed scheduling algorithm for convergecast in ieee 802.15.4e tsch networks. European transactions on telecommunications, 2016, 27(4): 557-575 [111] Emiliano S, Paolo F, Fernandes C D, Stefano R, Pasetti Marco, Alessandra Flammini, and Alessandro Depari. EDSF: Efficient Distributed Scheduling Function for IETF 6TiSCH-based Industrial Wireless Networks. Mobile Networks and Applications, DOI: 0.1007/s11036-022-02004-7 [112] Marco Z, Luca M, Silvia S. Synchronous transmissions in low-power wireless: A survey of communication protocols and network services. ACM Computing Surveys, 2020, 53(6): 1-39 [113] Jonathan O, Yang F, Sam M, Danny H. Zero-wire: A deterministic and low-latency wireless bus through symbol-synchronous transmission of optical signals. In Proceedings of the 18th Conference on Embedded Networked Sensor Systems. Virtual Event Japan: Association for Computing Machinery, 2020. 164−178 [114] Matteo T, Gabriel G, Timofei I, Manuel M J, Pietro P G. The wireless control bus: Enabling efficient multi-hop event-triggered control with concurrent transmissions. ACM Transactions on Cyber-Physical Systems, 2022, 6(1): 1-25 [115] Lorawan 1.1 specification. [Online], available: https://lora-alliance.org/resource_hub/lorawan-specification-v1-1. 2017 [116] Luca L, Filippo B, Lucia L B. Rt-lora: A medium access strategy to support real-time flows over lora-based networks for industrial iot applications. IEEE Internet of Things Journal, 2019, 6(6): 10812-10823 doi: 10.1109/JIOT.2019.2942776 [117] Quy L H, Hoon O. A real-time lora protocol using logical frame partitioning for periodic and aperiodic data transmission. IEEE Internet of Things Journal, 2022, 9(16). 15401-15412 doi: 10.1109/JIOT.2022.3162019 [118] Emiliano S, Paolo F, Fernandes C D, Stefano R, Pasetti Marco, Alessandra Flammini, and Alessandro Depari. Lorawan range extender for industrial iot. IEEE Transactions on Industrial Informatics, 2019, 16(8): 5607-5616 -
点击查看大图
计量
- 文章访问数: 387
- HTML全文浏览量: 305
- 被引次数: 0
