research-article

Open access

Efficient Holistic Control: Self-awareness across Controllers and Wireless Networks

Authors:

Chenyang Lu, and

Yebin WangAuthors Info & Claims

ACM Transactions on Cyber-Physical Systems, Volume 4, Issue 4

Article No.: 41, Pages 1 - 27

https://doi.org/10.1145/3371500

Published: 18 June 2020 Publication History

All formats PDF

Abstract

Industrial automation is embracing wireless sensor-actuator networks (WSANs). Despite the success of WSANs for monitoring applications, feedback control poses significant challenges due to data loss and stringent energy constraints in WSANs. Holistic control adopts a cyber-physical system approach to overcome the challenges by orchestrating network reconfiguration and process control at run time. Fundamentally, it leverages self-awareness across control and wireless boundaries to enhance the resiliency of wireless control systems. In this article, we explore efficient holistic control designs to maintain control performance while reducing the communication cost. The contributions of this work are five-fold: (1) We introduce a holistic control architecture that integrates Low-power Wireless Bus (LWB) and two control strategies, rate adaptation and self-triggered control; (2) We present heuristics-based and optimal rate selection algorithms for rate adaptation; (3) We design novel network adaptation mechanisms to support rate adaptation and self-triggered control in a multi-hop WSAN; (4) We build WCPS-RT, a real-time network-in-the-loop simulator that integrates MATLAB/Simulink and a physical WSAN testbed to evaluate wireless control systems; (5) We empirically explore the tradeoff between communication cost and control performance in holistic control approaches. Our studies show that rate adaptation and self-triggered control offer advantages in control performance and energy efficiency, respectively, in normal operating conditions. The advantage in energy efficiency of self-triggered control, however, may diminish under harsh physical and wireless conditions due to the cost of recovering from data loss and physical disturbances.

References

[1]

Pangun Park et al. 2018. Wireless network design for control systems: A survey. Commun. Surv. Tut. 20, 2 (2018).

[2]

ABB. 2020. WirelessHART Networks: 7 Myths That Cloud Their Consideration for Process Control Measurement Made Easy. Retrieved from https://new.abb.com/products/measurement-products/pl/produkty-i-rozwiazania-wireless/wirelesshart-networks-seven-myths-that-cloud-their-consideration-for-process-controll-en.

[3]

Emerson. 2017. White Paper: Emerson Wireless Security – WirelessHart and Wi-Fi. Retrieved from https://www.emerson.com/documents/automation/white-paper-emerson-wireless-security-wirelesshart-wi-fi-security-deltav-en-41260.pdf.

[4]

Yehan Ma et al. 2018. Holistic cyber-physical management for dependable wireless control systems. ACM Trans. Cyber-Phys. Syst. 3, 1 (2018).

[5]

Federico Ferrari et al. 2012. Low-power wireless bus. In Proceedings of the ACM Conference on Embedded Network Sensor Systems.

[6]

Xiaomin Li et al. 2017. A review of industrial wireless networks in the context of Industry 4.0. Wirel. Netw. 23, 1 (2017).

[7]

Chenyang Lu et al. 2016. Real-time wireless sensor-actuator networks for industrial cyber-physical systems. Proc. IEEE 104, 5 (2016).

[8]

Bruno Sinopoli et al. 2004. Kalman filtering with intermittent observations. IEEE Trans. Autom. Control 49, 9 (2004).

[9]

Manuel Mazo et al. 2008. On event-triggered and self-triggered control over sensor/actuator networks. In Proceedings of the IEEE Conference on Decision and Control.

[10]

Manuel Mazo et al. 2011. Decentralized event-triggered control over wireless sensor/actuator networks. IEEE Trans. Autom. Contr. 56, 10 (2011).

[11]

Manuel Mazo Jr et al. 2010. An ISS self-triggered implementation of linear controllers. Automatica 46, 8 (2010).

[12]

José Araújo et al. 2014. System architectures, protocols and algorithms for aperiodic wireless control systems. IEEE Trans. Ind. Inf. 10, 1 (2014).

[13]

Pangun Park et al. 2011. Breath: An adaptive protocol for industrial control applications using wireless sensor networks. IEEE Trans. Mob. Comput. 10, 6 (2011).

[14]

Felix Dobslaw et al. 2016. End-to-end reliability-aware scheduling for wireless sensor networks. IEEE Trans. Ind. Inf. 12, 2 (2016).

[15]

Hyung-Sin Kim et al. 2017. Load balancing under heavy traffic in RPL routing protocol for low power and lossy networks. IEEE Trans. Mob. Comput. 16, 4 (2017).

Digital Library

[16]

Marco Zimmerling et al. 2017. Adaptive real-time communication for wireless cyber-physical systems. ACM Trans. Cyber-Phys. Syst. 1, 2 (2017).

[17]

Zheng Li et al. 2006. Adaptive multiple sampling rate scheduling of real-time networked supervisory control system-part II. In Proceedings of the Annual Conference on IEEE Industrial Electronics. IEEE.

[18]

Dip Goswami et al. 2012. Time-triggered implementations of mixed-criticality automotive software. In Proceedings of the Conference on Design, Automation and Test in Europe. EDA Consortium.

[19]

Burak Demirel et al. 2014. Modular design of jointly optimal controllers and forwarding policies for wireless control. IEEE Trans. Autom. Contr. 59, 12 (2014).

[20]

Abusayeed Saifullah et al. 2014. Near optimal rate selection for wireless control systems. ACM Trans. Embed. Comput. Syst. 13, 4s (2014).

[21]

Dohwan Kim et al. 2019. Sampling rate optimization for IEEE 802.11 wireless control systems. In Proceedings of the ACM/IEEE International Conference on Cyber-Physical Systems.

[22]

Bo Li et al. 2016. Wireless routing and control: A cyber-physical case study. In Proceedings of the ACM/IEEE International Conference on Cyber-Physical Systems.

[23]

Jia Bai et al. 2012. Optimal cross-layer design of sampling rate adaptation and network scheduling for wireless networked control systems. In Proceedings of the ACM/IEEE International Conference on Cyber-Physical Systems.

[24]

Lei Bao et al. 2009. Rate allocation for quantized control over noisy channels. In Proceedings of the International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks. IEEE.

[25]

J. Colandairaj et al. 2007. Wireless networked control systems with QoS-based sampling. IET Contr. Theor. Applic. 1, 1 (2007).

[26]

Anton Cervin et al. 2003. How does control timing affect performance? Analysis and simulation of timing using Jitterbug and TrueTime. IEEE Contr. Syst. Mag. 23, 3 (2003).

[27]

Emeka Eyisi et al. 2012. NCSWT: An integrated modeling and simulation tool for networked control systems. Simul. Model. Pract. Theor. 27 (2012).

[28]

Kannan Srinivasan et al. 2010. An empirical study of low-power wireless. ACM Trans. Sens. Netw. 6, 2 (2010).

[29]

Carlos Santos et al. 2015. Aperiodic linear networked control considering variable channel delays: Application to robots coordination. Sensors 15, 6 (2015).

[30]

Bo Li et al. 2015. Incorporating emergency alarms in reliable wireless process control. In Proceedings of the ACM/IEEE International Conference on Cyber-Physical Systems.

[31]

Philip Levis et al. 2003. TOSSIM: Accurate and scalable simulation of entire TinyOS applications. In Proceedings of the International Conference on Embedded Networked Sensor Systems. ACM.

[32]

Miroslav Pajic et al. 2012. Closing the loop: A simple distributed method for control over wireless networks. In Proceedings of the ACM/IEEE International Conference on Information Processing in Sensor Networks. IEEE.

[33]

Fabian Mager et al. 2019. Feedback control goes wireless: Guaranteed stability over low-power multi-hop networks. In Proceedings of the ACM/IEEE International Conference on Cyber-Physical Systems.

[34]

Dominik Baumann et al. 2018. Evaluating low-power wireless cyber-physical systems. In Proceedings of the IEEE Workshop on Benchmarking Cyber-Physical Networks and Systems.

[35]

Dominik Baumann et al. 2019. Control-guided communication: Efficient resource arbitration and allocation in multi-hop wireless control systems. IEEE Contr. Syst. Lett. 4, 1 (2019).

[36]

Anders Ahlen et al. 2019. Toward wireless control in industrial process automation: A case study at a paper mill. IEEE Contr. Syst. Mag. 39, 5 (2019).

[37]

Yehan Ma et al. 2019. Optimal dynamic scheduling of wireless networked control systems. In Proceedings of the ACM/IEEE International Conference on Cyber-Physical Systems.

[38]

Kemin Zhou et al. 1996. Robust and Optimal Control. Prentice Hall, Upper Saddle River, NJ.

[39]

Federico Ferrari et al. 2011. Efficient network flooding and time synchronization with glossy. In Proceedings of the International Conference on Information Processing in Sensor Networks. IEEE.

[40]

Gang Zhao. 2011. Wireless sensor networks for industrial process monitoring and control: A survey. Netw. Prot. Algor. 3, 1 (2011).

[41]

Timofei Istomin et al. 2016. Data prediction+ synchronous transmissions= ultra-low power wireless sensor networks. In Proceedings of the ACM Conference on Embedded Network Sensor Systems. ACM.

[42]

Tongwen Chen et al. 2012. Optimal Sampled-data Control Systems. Springer Science 8 Business Media.

[43]

Bruce Francis. 1979. The optimal linear-quadratic time-invariant regulator with cheap control. IEEE Trans. Autom. Contr. 24, 4 (1979).

[44]

Hai Lin et al. 2009. Stability and stabilizability of switched linear systems: A survey of recent results. IEEE Trans. Autom. Contr. 54, 2 (2009).

[45]

Wei Zhang et al. 2001. Stability of networked control systems. IEEE Contr. Syst. Mag. 21, 1 (2001).

[46]

Huijun Gao et al. 2008. A new delay system approach to network-based control. Automatica 44, 1 (2008).

[47]

Merid Lješnjanin et al. 2014. Packetized MPC with dynamic scheduling constraints and bounded packet dropouts. Automatica 50, 3 (2014).

[48]

Jing Wu et al. 2007. Design of networked control systems with packet dropouts. IEEE Trans. Autom. Contr. 52, 7 (2007).

[49]

Jamal Daafouz et al. 2002. Stability analysis and control synthesis for switched systems: A switched Lyapunov function approach. IEEE Trans. Autom. Contr. 47, 11 (2002).

[50]

Daniel Liberzon et al. 1999. Basic problems in stability and design of switched systems. IEEE Contr. Syst. Mag. 19, 5 (1999).

[51]

S.-H. Lee et al. 2000. A new stability analysis of switched systems. Automatica 36, 6 (2000).

Digital Library

[52]

Dragan Nesic et al. 2004. Input-output stability properties of networked control systems. IEEE Trans. Autom. Contr. 49, 10 (2004).

[53]

Bin Hu et al. 2019. Co-design of safe and efficient networked control systems in factory automation with state-dependent wireless fading channels. Automatica 105 (2019).

[54]

Simulink. 2020. Simulink Desktop Real-Time. Retrieved from https://www.mathworks.com/products/simulink-desktop-real-time.html.

[55]

Mo Sha et al. 2015. Implementation and experimentation of industrial wireless sensor-actuator network protocols. In Proceedings of the European Conference on Wireless Sensor Networks. Springer.

[56]

Mo Sha et al. 2017. Empirical study and enhancements of industrial wireless sensor–actuator network protocols. IEEE Internet Things J. (2017).

[57]

PTP. 2020. PTP Protocol. Retrieved from https://www.nist.gov/el/intelligent-systems-division-73500/ieee-1588.

[58]

Xiangheng Liu et al. 2004. Kalman filtering with partial observation losses. In Proceedings of the Conference on Decision and Control. IEEE.

[59]

Yang Shi et al. 2010. Kalman filter-based identification for systems with randomly missing measurements in a network environment. Int. J. Contr. (2010).

[60]

Vijay Shilpiekandula et al. 2012. Load positioning in the presence of base vibrations. In Proceedings of the American Control Conference.

[61]

Yu Jiang et al. 2015. Optimal codesign of nonlinear control systems based on a modified policy iteration method. IEEE Trans. Neural Netw. Learn. Syst. (2015).

[62]

Chayan Sarkar. 2016. LWB and FS-LWB implementation for Sky platform using Contiki. arXiv preprint arXiv:1607.06622 (2016).

[63]

Adam Dunkels et al. 2007. Software-based on-line energy estimation for sensor nodes. In Proceedings of the Workshop on Embedded Networked Sensors. ACM.

[64]

Chengjie Wu et al. 2016. Maximizing network lifetime of WirelessHART networks under graph routing. In Proceedings of the IEEE International Conference on Internet-of-Things Design and Implementation.

Cited By

Moon SLee SJeon WPark K(2024)Learning-Enabled Network-Control Co-Design for Energy-Efficient Industrial Internet of ThingsIEEE Transactions on Network and Service Management10.1109/TNSM.2023.332428221:2(1478-1489)Online publication date: Apr-2024
https://doi.org/10.1109/TNSM.2023.3324282
Ma YWang YCairano SKoike-Akino TGuo JOrlik PGuan XLu C(2024)Smart Actuation for End-Edge Industrial Control SystemsIEEE Transactions on Automation Science and Engineering10.1109/TASE.2022.321621721:1(269-283)Online publication date: Jan-2024
https://doi.org/10.1109/TASE.2022.3216217
Ali SElsaid SAteya AElAffendi MEl-Latif A(2023)Enabling Technologies for Next-Generation Smart Cities: A Comprehensive Review and Research DirectionsFuture Internet10.3390/fi1512039815:12(398)Online publication date: 9-Dec-2023
https://doi.org/10.3390/fi15120398
Show More Cited By

Index Terms

Efficient Holistic Control: Self-awareness across Controllers and Wireless Networks
1. Computer systems organization
  1. Embedded and cyber-physical systems
    1. Sensor networks
2. Networks
  1. Network types
    1. Cyber-physical networks
      1. Sensor networks

Recommendations

Holistic Cyber-Physical Management for Dependable Wireless Control Systems
Special Issue on Dependability in CPS

Wireless sensor-actuator networks (WSANs) are gaining momentum in industrial process automation as a communication infrastructure for lowering deployment and maintenance costs. In traditional wireless control systems, the plant controller and the ...
Read More
Flexible Energy Efficient Density Control on Wireless Sensor Networks
Heterogenous Wireless Ad Hoc and Sensor Networks

In dense wireless sensor networks, density control is an important technique for prolonging the network's lifetime while providing sufficient sensing coverage. In this paper, we develop three new density control protocols by considering the tradeoff ...
Read More
Feedback control goes wireless: guaranteed stability over low-power multi-hop networks
ICCPS '19: Proceedings of the 10th ACM/IEEE International Conference on Cyber-Physical Systems

Closing feedback loops fast and over long distances is key to emerging applications; for example, robot motion control and swarm coordination require update intervals of tens of milliseconds. Low-power wireless technology is preferred for its low cost, ...
Read More

Comments

Information & Contributors

Information

Published In

cover image ACM Transactions on Cyber-Physical Systems

ACM Transactions on Cyber-Physical Systems Volume 4, Issue 4

Special Issue on Self-Awareness in Resource Constrained CPS and Regular Papers

October 2020

293 pages

ISSN:2378-962X

EISSN:2378-9638

DOI:10.1145/3407233

Editor:
Tei-Wei Kuo
City University of Hong Kong and National Taiwan University, Taiwan

Issue’s Table of Contents

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Journal Family

ACM Journals for the Design of Smart and Connected Systems

Publication History

Published: 18 June 2020

Online AM: 07 May 2020

Accepted: 01 November 2019

Revised: 01 August 2019

Received: 01 December 2018

Published in TCPS Volume 4, Issue 4

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed

Funding Sources

Fullgraf Foundation
National Science Foundation

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

10
Total Citations
View Citations
336
Total Downloads

Downloads (Last 12 months)87
Downloads (Last 6 weeks)4

Other Metrics

View Author Metrics

Citations

Cited By

Moon SLee SJeon WPark K(2024)Learning-Enabled Network-Control Co-Design for Energy-Efficient Industrial Internet of ThingsIEEE Transactions on Network and Service Management10.1109/TNSM.2023.332428221:2(1478-1489)Online publication date: Apr-2024
https://doi.org/10.1109/TNSM.2023.3324282
Ma YWang YCairano SKoike-Akino TGuo JOrlik PGuan XLu C(2024)Smart Actuation for End-Edge Industrial Control SystemsIEEE Transactions on Automation Science and Engineering10.1109/TASE.2022.321621721:1(269-283)Online publication date: Jan-2024
https://doi.org/10.1109/TASE.2022.3216217
Ali SElsaid SAteya AElAffendi MEl-Latif A(2023)Enabling Technologies for Next-Generation Smart Cities: A Comprehensive Review and Research DirectionsFuture Internet10.3390/fi1512039815:12(398)Online publication date: 9-Dec-2023
https://doi.org/10.3390/fi15120398
Samaddar AEaswaran A(2023)Online Distributed Schedule Randomization to Mitigate Timing Attacks in Industrial Control SystemsACM Transactions on Embedded Computing Systems10.1145/362458422:6(1-39)Online publication date: 16-Sep-2023
https://dl.acm.org/doi/10.1145/3624584
Xu HWu JPan QLiu XVerikoukis C(2023)Digital Twin and Meta RL Empowered Fast-Adaptation of Joint User Scheduling and Task Offloading for Mobile Industrial IoTIEEE Journal on Selected Areas in Communications10.1109/JSAC.2023.331008141:10(3254-3266)Online publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1109/JSAC.2023.3310081
Ma YChen CZeng SGuan XLu C(2023)Data-Driven Edge Offloading for Wireless Control SystemsIEEE Internet of Things Journal10.1109/JIOT.2023.324277010:12(10802-10816)Online publication date: 15-Jun-2023
https://doi.org/10.1109/JIOT.2023.3242770
Jo HJin HKim J(2023)Self-adaptive end-to-end resource management for real-time monitoring in cyber–physical systemsComputer Networks10.1016/j.comnet.2023.109669225(109669)Online publication date: Apr-2023
https://doi.org/10.1016/j.comnet.2023.109669
Hu BTamba T(2022)Optimal Transmission Power and Controller Design for Networked Control Systems Under State-Dependent Markovian ChannelsIEEE Transactions on Automatic Control10.1109/TAC.2022.318175867:10(5669-5676)Online publication date: Oct-2022
https://doi.org/10.1109/TAC.2022.3181758
Moon SPark HChwa HPark K(2022)AdaptiveHART: An Adaptive Real-Time MAC Protocol for Industrial Internet-of-ThingsIEEE Systems Journal10.1109/JSYST.2022.317196216:3(4849-4860)Online publication date: Sep-2022
https://doi.org/10.1109/JSYST.2022.3171962
Kim SPark KLu C(2022)A Survey on Network Security for Cyber–Physical Systems: From Threats to Resilient DesignIEEE Communications Surveys & Tutorials10.1109/COMST.2022.318753124:3(1534-1573)Online publication date: 1-Jul-2022
https://dl.acm.org/doi/10.1109/COMST.2022.3187531

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

Media

Figures

Other

Tables

View Issue’s Table of Contents