Recursive approximation of complex behaviours with IoT-data imperfections
This paper presents an approach to recursively estimate the simplest linear model that approximates the time-varying local behaviors from imperfect (noisy and incomplete) measurements in the internet of things (IoT) based distributed decision-making problems. We first show that the problem of findin...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2021
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/145763 |
| _version_ | 1826119288967135232 |
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| author | Bekiroglu, Korkut Srinivasan, Seshadhri Png, Ethan Su, Rong Lagoa, Constantino |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Bekiroglu, Korkut Srinivasan, Seshadhri Png, Ethan Su, Rong Lagoa, Constantino |
| author_sort | Bekiroglu, Korkut |
| collection | NTU |
| description | This paper presents an approach to recursively estimate the simplest linear model that approximates the time-varying local behaviors from imperfect (noisy and incomplete) measurements in the internet of things (IoT) based distributed decision-making problems. We first show that the problem of finding the lowest order model for a multi-input single-output system is a cardinality (ℓ 0 ) optimization problem, known to be NP-hard. To solve the problem a simpler approach is proposed which uses the recently developed atomic norm concept and the modified Frank-Wolfe (mFW) algorithm is introduced. Further, the paper computes the minimum data-rate required for computing the models with imperfect measurements. The proposed approach is illustrated on a building heating, ventilation, and air-conditioning (HVAC) control system that aims at optimizing energy consumption in commercial buildings using IoT devices in a distributed manner. The HVAC control application requires recursive thermal dynamical model updates due to frequently changing conditions and non-linear dynamics. We show that the method proposed in this paper can approximate such complex dynamics on single-board computers interfaced to sensors using unreliable communication channels. Real-time experiments on HVAC systems and simulation studies are used to illustrate the proposed method. |
| first_indexed | 2024-10-01T04:57:28Z |
| format | Journal Article |
| id | ntu-10356/145763 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:57:28Z |
| publishDate | 2021 |
| record_format | dspace |
| spelling | ntu-10356/1457632021-01-07T05:21:07Z Recursive approximation of complex behaviours with IoT-data imperfections Bekiroglu, Korkut Srinivasan, Seshadhri Png, Ethan Su, Rong Lagoa, Constantino School of Electrical and Electronic Engineering Engineering::Electrical and electronic engineering Adaptability Distributed Decision Systems This paper presents an approach to recursively estimate the simplest linear model that approximates the time-varying local behaviors from imperfect (noisy and incomplete) measurements in the internet of things (IoT) based distributed decision-making problems. We first show that the problem of finding the lowest order model for a multi-input single-output system is a cardinality (ℓ 0 ) optimization problem, known to be NP-hard. To solve the problem a simpler approach is proposed which uses the recently developed atomic norm concept and the modified Frank-Wolfe (mFW) algorithm is introduced. Further, the paper computes the minimum data-rate required for computing the models with imperfect measurements. The proposed approach is illustrated on a building heating, ventilation, and air-conditioning (HVAC) control system that aims at optimizing energy consumption in commercial buildings using IoT devices in a distributed manner. The HVAC control application requires recursive thermal dynamical model updates due to frequently changing conditions and non-linear dynamics. We show that the method proposed in this paper can approximate such complex dynamics on single-board computers interfaced to sensors using unreliable communication channels. Real-time experiments on HVAC systems and simulation studies are used to illustrate the proposed method. National Research Foundation (NRF) Published version This work was supported by the Building and Construction Authority through the NRF GBIC Program (NRF2015ENC-GBICRD001-057). Recommended by Associate Editor Xin Luo. (Corresponding author: Korkut Bekiroglu.) 2021-01-07T05:21:07Z 2021-01-07T05:21:07Z 2020 Journal Article Bekiroglu, K., Srinivasan, S., Png, E., Su, R., & Lagoa, C. (2020). Recursive approximation of complex behaviours with IoT-data imperfections. IEEE/CAA Journal of Automatica Sinica, 7(3), 656-667. doi:10.1109/jas.2020.1003126 2329-9266 https://hdl.handle.net/10356/145763 10.1109/JAS.2020.1003126 3 7 656 667 en NRF2015ENC-GBICRD001-057 IEEE/CAA Journal of Automatica Sinica © 2020 The Chinese Association of Automation. All rights reserved. This paper was published in IEEE/CAA Journal of Automatica Sinica and is made available with permission of the Chinese Association of Automation. application/pdf |
| spellingShingle | Engineering::Electrical and electronic engineering Adaptability Distributed Decision Systems Bekiroglu, Korkut Srinivasan, Seshadhri Png, Ethan Su, Rong Lagoa, Constantino Recursive approximation of complex behaviours with IoT-data imperfections |
| title | Recursive approximation of complex behaviours with IoT-data imperfections |
| title_full | Recursive approximation of complex behaviours with IoT-data imperfections |
| title_fullStr | Recursive approximation of complex behaviours with IoT-data imperfections |
| title_full_unstemmed | Recursive approximation of complex behaviours with IoT-data imperfections |
| title_short | Recursive approximation of complex behaviours with IoT-data imperfections |
| title_sort | recursive approximation of complex behaviours with iot data imperfections |
| topic | Engineering::Electrical and electronic engineering Adaptability Distributed Decision Systems |
| url | https://hdl.handle.net/10356/145763 |
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