Self-triggered Control with Energy Harvesting Sensor Nodes
Authors: 
Naomi Stricker, Yingzhao Lian, Yuning Jiang, Colin N. Jones, Lothar ThieleAuthors Info & Claims
Naomi Stricker, Yingzhao Lian, Yuning Jiang, Colin N. Jones, Lothar ThieleAuthors Info & ClaimsArticle No.: 20, Pages 1 - 31
Abstract
Distributed embedded systems are pervasive components jointly operating in a wide range of applications. Moving toward energy harvesting powered systems enables their long-term, sustainable, scalable, and maintenance-free operation. When these systems are used as components of an automatic control system to sense a control plant, energy availability limits when and how often sensed data are obtainable and therefore when and how often control updates can be performed. The time-varying and non-deterministic availability of harvested energy and the necessity to plan the energy usage of the energy harvesting sensor nodes ahead of time, on the one hand, have to be balanced with the dynamically changing and complex demand for control updates from the automatic control plant and thus energy usage, on the other hand. We propose a hierarchical approach with which the resources of the energy harvesting sensor nodes are managed on a long time horizon and on a faster timescale, self-triggered model predictive control controls the plant. The controller of the harvesting-based nodes’ resources schedules the future energy usage ahead of time and the self-triggered model predictive control incorporates these time-varying energy constraints. For this novel combination of energy harvesting and automatic control systems, we derive provable properties in terms of correctness, feasibility, and performance. We evaluate the approach on a double integrator and demonstrate its usability and performance in a room temperature and air quality control case study.
References
[1]
Ferran Adelantado, Xavier Vilajosana, Pere Tuset-Peiro, Borja Martinez, Joan Melia-Segui, and Thomas Watteyne. 2017. Understanding the limits of LoRaWAN. IEEE Commun. Mag. 55, 9 (2017), 34–40.
[2]
Abdul Afram and Farrokh Janabi-Sharifi. 2014. Theory and applications of HVAC control systems—A review of model predictive control (MPC). Build. Environ. 72 (2014), 343–355.
[3]
Kaiser Ahmed, Jarek Kurnitski, and Bjarne Olesen. 2017. Data for occupancy internal heat gain calculation in main building categories. Data Brief 15 (2017), 1030–1034.
[4]
Patroklos Anagnostou, Andres Gomez, Pascal A. Hager, Hamed Fatemi, J. Pineda de Gyvez, Lothar Thiele, and Luca Benini. 2019. Energy and power awareness in hardware schedulers for energy harvesting IoT SoCs. Integration 67 (2019), 33–43.
[5]
Dominik Baumann, Fabian Mager, Ulf Wetzker, Lothar Thiele, Marco Zimmerling, and Sebastian Trimpe. 2021. Wireless control for smart manufacturing: Recent approaches and open challenges. Proc. IEEE 109, 4 (2021), 441–467.
[6]
Simon Benninger, Michele Magno, Andres Gomez, and Luca Benini. 2019. Edgeeye: A long-range energy-efficient vision node for long-term edge computing. In 10th International Green and Sustainable Computing Conference (IGSC’19). IEEE, 1–8.
[7]
J. D. J. Barradas Berglind, T. M. P. Gommans, and W. P. M. H. Heemels. 2012. Self-triggered MPC for constrained linear systems and quadratic costs. IFAC Proc. Vol. 45, 17 (2012), 342–348.
[8]
Anass Berouine, Radouane Ouladsine, Mohamed Bakhouya, and Mohamed Essaaidi. 2020. Towards a real-time predictive management approach of indoor air quality in energy-efficient buildings. Energies 13, 12 (2020), 3246.
[9]
J. Frédéric Bonnans and Alexander Shapiro. 2013. Perturbation Analysis of Optimization Problems. Springer.
[10]
Florian David Brunner, Maurice Heemels, and Frank Allgöwer. 2016. Robust self-triggered MPC for constrained linear systems: A tube-based approach. Automatica 72 (2016), 73–83.
[11]
B. Buchli, P. Kumar, and L. Thiele. 2015. Optimal power management with guaranteed minimum energy utilization for solar energy harvesting systems. In 2015 International Conference on Distributed Computing in Sensor Systems, IEEE, 147–158.
[12]
Bernhard Buchli, Felix Sutton, Jan Beutel, and Lothar Thiele. 2014. Dynamic power management for long-term energy neutral operation of solar energy harvesting systems. In Proceedings of the ACM Conference on Embedded Networked Sensor Systems (SenSys’14).31–45.
[13]
Bernhard Buchli, Felix Sutton, Jan Beutel, and Lothar Thiele. 2014. Towards enabling uninterrupted long-term operation of solar energy harvesting embedded systems. In European Conference on Wireless Sensor Networks. Springer, 66–83.
[14]
A. Caruso, S. Chessa, S. Escolar, X. del Toro, and J. C. López. 2018. A dynamic programming algorithm for high-level task scheduling in energy harvesting IoT. IEEE IoT J. 5, 3 (2018), 2234–2248.
[15]
Lucian Coroianu. 2016. Best lipschitz constants of solutions of quadratic programs. J. Optim. Theory Appl. 170 (2016), 853–875.
[16]
Ying Ding, Liang Wang, Yongwei Li, and Daoliang Li. 2018. Model predictive control and its application in agriculture: A review. Comput. Electr. Agric. 151 (2018), 104–117.
[17]
Stefan Draskovic and Lothar Thiele. 2021. Optimal power management for energy harvesting systems with a backup power source. In 10th Mediterranean Conference on Embedded Computing (MECO’21). IEEE, 1–9.
[18]
Simon Duquennoy, Atis Elsts, Beshr Al Nahas, and George Oikonomo. 2017. Tsch and 6tisch for contiki: Challenges, design and evaluation. In Proceedings of the 13th International Conference on Distributed Computing in Sensor Systems (DCOSS’17). IEEE, 11–18.
[19]
EMPA, Swisscom AG, Decentlab GmbH, and Swiss Data Science Center. 2019. Carbosense T and RH Data—Release October 2019.
[20]
L. M. Fernández-Ahumada, J. Ramírez-Faz, R. López-Luque, A. Márquez-García, and M. Varo-Martínez. 2019. A Methodology for buildings access to solar radiation in sustainable cities. Sustainability 11, 23 (2019).
[21]
Alessandro Franco and Francesco Leccese. 2020. Measurement of CO2 concentration for occupancy estimation in educational buildings with energy efficiency purposes. J. Build. Eng. 32 (2020), 101714.
[22]
Markus-Philipp Gherman, Yun Cheng, Andres Gomez, and Olga Saukh. 2021. Compensating Altered Sensitivity of duty-cycled MOX gas sensors with machine learning. In Proceedings of the 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON’21). IEEE, 1–9.
[23]
W. P. M. H. Heemels, Karl Henrik Johansson, and Paulo Tabuada. 2012. An introduction to event-triggered and self-triggered control. In Proceedings of the 51st IEEE Conference on Decision and Control (CDC’12). IEEE, 3270–3285.
[24]
Roy Chaoming Hsu and Tzu-Hao Lin. 2018. A fuzzy q-learning based power management for energy harvest wireless sensor node. In Proceedings of the International Conference on High Performance Computing & Simulation (HPCS’18). IEEE, 957–961.
[25]
S. R. Jino Ramson and D. J. Moni. 2017. Applications of wireless sensor networks—A survey. In Proceedings of the International Conference on Innovations in Electrical, Electronics, Instrumentation and Media Technology (ICEEIMT’17). 325–329.
[26]
Ola M. Johansson and Rolf Johansson. 2008. Model predictive control for scheduling and routing in a solid waste management system. IFAC Proc. Vol. 41, 2 (2008), 4481–4486.
[27]
Aman Kansal, Jason Hsu, Sadaf Zahedi, and Mani B. Srivastava. 2007. Power management in energy harvesting sensor networks. ACM Trans. Embed. Comput. Syst. 6, 4 (2007), 32–es.
[28]
H. Li, W. Yan, and Y. Shi. 2018. Triggering and control codesign in self-triggered model predictive control of constrained systems: With guaranteed performance. IEEE Trans. Automat. Contr. 63, 11 (2018), 4008–4015.
[29]
Yingzhao Lian, Yuning Jiang, Naomi Stricker, Lothar Thiele, and Colin N. Jones. 2021. Resource-aware stochastic self-triggered model predictive control. IEEE Control Systems Letters 6 (2021), 1262–1267.
[30]
Y. Lian, S. Wildhagen, Y. Jiang, B. Houska, F. Allgöwer, and C.N. Jones. 2020. Resource-aware asynchronous multi-agent coordination via self-triggered MPC. In Proceeding of the 59th IEEE Conference on Decision and Control (CDC’20). IEEE, 685–690.
[31]
Marcel Macarulla, Miquel Casals, Matteo Carnevali, Núria Forcada, and Marta Gangolells. 2017. Modelling indoor air carbon dioxide concentration using grey-box models. Build. Environ. 117 (2017), 146–153.
[32]
Fabian Mager, Dominik Baumann, Romain Jacob, Lothar Thiele, Sebastian Trimpe, and Marco Zimmerling. 2019. Feedback control goes wireless: Guaranteed stability over low-power multi-hop networks. In Proceedings of the ACM/IEEE 14th International Conference on Cyber-Physical Systems (ICCPS’19).ACM, 12 pages.
[33]
A. Murad, F. A. Kraemer, K. Bach, and G. Taylor. 2019. Autonomous management of energy-harvesting IoT nodes using deep reinforcement learning. In Proceedings of the IEEE International Conferences on Self-Adaptive and Self-Organizing System (SASO’19), IEEE, 43–51.
[34]
Zoltán Nagy, Fah Yik Yong, Mario Frei, and Arno Schlueter. 2015. Occupant centered lighting control for comfort and energy efficient building operation. Energy Build. 94 (2015), 100–108.
[35]
Truong X. Nghiem and Rahul Mangharam. 2015. Scalable scheduling of energy control systems. In Proceedings of the SIGBED International Conference on Embedded Software (EMSOFT’15). IEEE, 137–146.
[36]
Alessandra Parisio, Damiano Varagnolo, Marco Molinari, Giorgio Pattarello, Luca Fabietti, and Karl H. Johansson. 2014. Implementation of a scenario-based mpc for hvac systems: An experimental case study. IFAC Proc. Vol. 47, 3 (2014), 599–605.
[37]
Pangun Park, Sinem Coleri Ergen, Carlo Fischione, Chenyang Lu, and Karl Henrik Johansson. 2017. Wireless network design for control systems: A survey. IEEE Commun. Surv. Tutor. 20, 2 (2017), 978–1013.
[38]
Gianni Pasolini, Chiara Buratti, Luca Feltrin, Flavio Zabini, Cristina De Castro, Roberto Verdone, and Oreste Andrisano. 2018. Smart city pilot projects using LoRa and IEEE802. 15.4 technologies. Sensors 18, 4 (2018), 1118.
[39]
Gianluca Serale, Massimo Fiorentini, Alfonso Capozzoli, Daniele Bernardini, and Alberto Bemporad. 2018. Model predictive control (MPC) for enhancing building and HVAC system energy efficiency: Problem formulation, applications and opportunities. Energies 11, 3 (2018), 631.
[40]
Amandeep Sharma and Ajay Kakkar. 2020. A review on solar forecasting and power management approaches for energy-harvesting wireless sensor networks. Int. J. Commun. Syst. 33, 8 (2020), e4366.
[41]
Shaswot Shresthamali, Masaaki Kondo, and Hiroshi Nakamura. 2017. Adaptive power management in solar energy harvesting sensor node using reinforcement learning. ACM Trans. Embed. Comput. Syst. 16, 5s, Article 181 (September2017), 21 pages.
[42]
Lukas Sigrist, Andres Gomez, and Lothar Thiele. 2019. Dataset: Tracing indoor solar harvesting. In Proceedings of the 2nd Workshop on Data Acquisition to Analysis (DATA’19). Association for Computing Machinery, New York, NY, 47–50.
[43]
Naomi Stricker, Yingzhao Lian, Yunning Jiang, Colin Jones, and Lothar Thiele. 2021. Joint energy management for distributed energy harvesting systems. In Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems (SenSys’21). ACM, 575–577.
[44]
Naomi Stricker and Lothar Thiele. 2020. Analysing and improving robustness of predictive energy harvesting systems. In Proceedings of the 8th International Workshop on Energy Harvesting and Energy-Neutral Sensing Systems. ACM, 64–70.
[45]
Naomi Stricker and Lothar Thiele. 2022. Accurate onboard predictions for indoor energy harvesting using random forests. In Proceedings of the 11th Mediterranean Conference on Embedded Computing (MECO’22). IEEE, 1–6.
[46]
D. Sturzenegger, D. Gyalistras, V. Semeraro, M. Morari, and R. S. Smith. 2014. BRCM matlab toolbox: Model generation for model predictive building control. In Proceedings of the American Control Conference. 1063–1069.
[47]
Sebastian Trimpe and Dominik Baumann. 2019. Resource-aware IoT control: Saving communication through predictive triggering. IEEE IoT J. 6, 3 (2019), 5013–5028.
[48]
Roman Trüb. 2022. Design and Testing of Mixed-Range Low-Power Wireless Networks. Ph.D. Dissertation. ETH Zurich.
[49]
Stefan Wildhagen, Colin N. Jones, and Frank Allgöwer. 2020. A resource-aware approach to self-triggered model predictive control: Extended version. (2020). arxiv:eess.SY/1911.10799. Retrieved from https://arxiv.org/abs/1911.10799.
[50]
Fan Yang, Ashok Samraj Thangarajan, Gowri Sankar Ramachandran, Wouter Joosen, and Danny Hughes. 2021. AsTAR: Sustainable energy harvesting for the internet of things through adaptive task scheduling. ACM Trans. Sens. Netw. 18, 1 (2021), 1–34.
[51]
Jingyuan Zhan, Xiang Li, and Zhong-Ping Jiang. 2017. Self-triggered robust output feedback model predictive control of constrained linear systems. In Proceedings of the American Control Conference (ACC’17). IEEE, 3066–3071.
[52]
M. S. Zuraimi, A. Pantazaras, K. A. Chaturvedi, J. J. Yang, K. W. Tham, and S. E. Lee. 2017. Predicting occupancy counts using physical and statistical Co2-based modeling methodologies. Build. Environ. 123 (2017), 517–528.
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July 2023
154 pages
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Association for Computing Machinery
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Publication History
Published: 13 July 2023
Online AM: 17 May 2023
Accepted: 03 May 2023
Revised: 28 March 2023
Received: 06 January 2022
Published in TCPS Volume 7, Issue 3
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