| Recursive Approximation of Complex Behaviours With IoT-Data Imperfections |
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| 作者姓名： | Korkut Bekiroglu Seshadhri Srinivasan Ethan Png Rong Su Constantino Lagoa |
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| 作者单位： | College of Engineering;Berkeley Education Alliance for Research in Singapore（BEARS）;School of Electrical and Electronic Engineering;IEEE;Electrical Engineering Department of Pennsylvania State University |
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| 基金项目： | supported by the Building and Construction Authority through the NRF GBIC Program(NRF2015ENC-GBICRD001-057)。 |
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| 摘 要： | 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(l0) 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.
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| 关 键 词： | ADAPTABILITY distributed decision systems imperfect measurements internet of things(IoT) low order model identification |
Recursive Approximation of Complex Behaviours With IoT-Data Imperfections |
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| Authors: | Korkut Bekiroglu Seshadhri Srinivasan Ethan Png Rong Su Constantino Lagoa |
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| Abstract: | 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 (?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. |
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| Keywords: | Adaptability distributed decision systems imperfect measurements internet of things (IoT) low order model identification |
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