Spatiotemporal correlation-based environmental monitoring system in energy harvesting internet of things (IoT)

Haneul Ko; Sangheon Pack; Victor C.M. Leung

doi:10.1109/TII.2018.2889778

Spatiotemporal correlation-based environmental monitoring system in energy harvesting internet of things (IoT)

Haneul Ko, Sangheon Pack, Victor C.M. Leung

School of Electrical Engineering

Research output: Contribution to journal › Article › peer-review

14 Citations (Scopus)

Abstract

To provide an accurate environmental map (EM) while avoiding unnecessary transmissions of Internet of Things (IoT) devices, we propose a spatiotemporal correlation-based environmental monitoring system (ST-EMS). In ST-EMS, IoT devices decide whether to transmit the sensed data to an IoT gateway (GW) or not by considering the temporal correlation in the sensed data and energy level. Through a Markov decision process (MDP) formulation, the optimal policy is obtained and it is proved that the optimal policy of MDP has an implementation-friendly threshold structure by using the submodularity concept. Also, the IoT GW in ST-EMS restores EM and improves its accuracy by exploiting the spatial correlation among sensed data using probabilistic matrix factorization. Evaluation results demonstrate that ST-EMS can improve the expected total reward significantly compared with other schemes and achieve low mean square error of 1% in EM restoration.

Original language	English
Article number	8588359
Pages (from-to)	2958-2968
Number of pages	11
Journal	IEEE Transactions on Industrial Informatics
Volume	15
Issue number	5
DOIs	https://doi.org/10.1109/TII.2018.2889778
Publication status	Published - 2019 May

Bibliographical note

Funding Information:
Manuscript received July 19, 2018; revised November 12, 2018; accepted December 17, 2018. Date of publication December 25, 2018; date of current version May 2, 2019. This work was supported in part by the Ministry of Science and ICT (MSIT), Korea, under the Information Technology Research Center Support Program under Grant IITP-2018-2017-0-01633 supervised by the Institute for Information and Communications Technology Promotion and in part by the National Research Foundation of Korea funded by the Korean Government (MSIP) under Grant 2017R1E1A1A01073742. Paper no. TII-18-1877. (Corresponding author: Sangheon Pack.) H. Ko is with the Department of Computer Convergence Software, Korea University, Sejong 30019, South Korea (e-mail:, st_basket@ korea.ac.kr).

Funding Information:
This work was supported in part by the Ministry of Science and ICT (MSIT), Korea, under the Information Technology Research Center Support Program under Grant IITP-2018-2017-0-01633 supervised by the Institute for Information and Communications Technology Promotion and in part by the National Research Foundation of Korea funded by the Korean Government (MSIP) under Grant 2017R1E1A1A01073742.

Publisher Copyright:
© 2018 IEEE.

Keywords

Energy harvesting
Internet of Things (IoT)
Markov decision process (MDP)
monitoring service
probabilistic matrix factorization (PMF)
spatiotemporal correlation

ASJC Scopus subject areas

Control and Systems Engineering
Information Systems
Computer Science Applications
Electrical and Electronic Engineering

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10.1109/TII.2018.2889778

Cite this

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title = "Spatiotemporal correlation-based environmental monitoring system in energy harvesting internet of things (IoT)",

abstract = "To provide an accurate environmental map (EM) while avoiding unnecessary transmissions of Internet of Things (IoT) devices, we propose a spatiotemporal correlation-based environmental monitoring system (ST-EMS). In ST-EMS, IoT devices decide whether to transmit the sensed data to an IoT gateway (GW) or not by considering the temporal correlation in the sensed data and energy level. Through a Markov decision process (MDP) formulation, the optimal policy is obtained and it is proved that the optimal policy of MDP has an implementation-friendly threshold structure by using the submodularity concept. Also, the IoT GW in ST-EMS restores EM and improves its accuracy by exploiting the spatial correlation among sensed data using probabilistic matrix factorization. Evaluation results demonstrate that ST-EMS can improve the expected total reward significantly compared with other schemes and achieve low mean square error of 1% in EM restoration.",

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author = "Haneul Ko and Sangheon Pack and Leung, {Victor C.M.}",

note = "Funding Information: Manuscript received July 19, 2018; revised November 12, 2018; accepted December 17, 2018. Date of publication December 25, 2018; date of current version May 2, 2019. This work was supported in part by the Ministry of Science and ICT (MSIT), Korea, under the Information Technology Research Center Support Program under Grant IITP-2018-2017-0-01633 supervised by the Institute for Information and Communications Technology Promotion and in part by the National Research Foundation of Korea funded by the Korean Government (MSIP) under Grant 2017R1E1A1A01073742. Paper no. TII-18-1877. (Corresponding author: Sangheon Pack.) H. Ko is with the Department of Computer Convergence Software, Korea University, Sejong 30019, South Korea (e-mail:, st_basket@ korea.ac.kr). Funding Information: This work was supported in part by the Ministry of Science and ICT (MSIT), Korea, under the Information Technology Research Center Support Program under Grant IITP-2018-2017-0-01633 supervised by the Institute for Information and Communications Technology Promotion and in part by the National Research Foundation of Korea funded by the Korean Government (MSIP) under Grant 2017R1E1A1A01073742. Publisher Copyright: {\textcopyright} 2018 IEEE.",

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N2 - To provide an accurate environmental map (EM) while avoiding unnecessary transmissions of Internet of Things (IoT) devices, we propose a spatiotemporal correlation-based environmental monitoring system (ST-EMS). In ST-EMS, IoT devices decide whether to transmit the sensed data to an IoT gateway (GW) or not by considering the temporal correlation in the sensed data and energy level. Through a Markov decision process (MDP) formulation, the optimal policy is obtained and it is proved that the optimal policy of MDP has an implementation-friendly threshold structure by using the submodularity concept. Also, the IoT GW in ST-EMS restores EM and improves its accuracy by exploiting the spatial correlation among sensed data using probabilistic matrix factorization. Evaluation results demonstrate that ST-EMS can improve the expected total reward significantly compared with other schemes and achieve low mean square error of 1% in EM restoration.

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Spatiotemporal correlation-based environmental monitoring system in energy harvesting internet of things (IoT)

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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