Spatiotemporal Correlation-Based Environmental Monitoring System in Energy Harvesting Internet of Things (IoT)

Haneul Ko, Sangheon Pack, Victor Leung

Research output: Contribution to journalArticle

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 (PMF). 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 languageEnglish
JournalIEEE Transactions on Industrial Informatics
DOIs
Publication statusAccepted/In press - 2018 Jan 1

Fingerprint

Energy harvesting
Gateways (computer networks)
Monitoring
Factorization
Mean square error
Electron energy levels
Restoration
Internet of things

Keywords

  • Correlation
  • Energy harvesting
  • energy harvesting
  • Environmental monitoring
  • Internet of Things
  • Internet of Things (IoT)
  • Markov decision process (MDP)
  • monitoring service
  • probabilistic matrix factorization (PMF)
  • Spatiotemporal correlation
  • Spatiotemporal phenomena
  • Temperature sensors

ASJC Scopus subject areas

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

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 (PMF). 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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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 (PMF). 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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