Spatiotemporal Correlation-Based Environmental Monitoring System in Energy Harvesting Internet of Things (IoT)
DC Field | Value | Language |
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dc.contributor.author | Ko, Haneul | - |
dc.contributor.author | Pack, Sangheon | - |
dc.contributor.author | Leung, Victor C. M. | - |
dc.date.accessioned | 2021-09-01T15:44:52Z | - |
dc.date.available | 2021-09-01T15:44:52Z | - |
dc.date.created | 2021-06-19 | - |
dc.date.issued | 2019-05 | - |
dc.identifier.issn | 1551-3203 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/65861 | - |
dc.description.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. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.subject | SENSOR NETWORKS | - |
dc.subject | WIRELESS | - |
dc.subject | RECOVERY | - |
dc.subject | MANAGEMENT | - |
dc.subject | SCHEME | - |
dc.title | Spatiotemporal Correlation-Based Environmental Monitoring System in Energy Harvesting Internet of Things (IoT) | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Ko, Haneul | - |
dc.contributor.affiliatedAuthor | Pack, Sangheon | - |
dc.identifier.doi | 10.1109/TII.2018.2889778 | - |
dc.identifier.scopusid | 2-s2.0-85059274658 | - |
dc.identifier.wosid | 000467084400044 | - |
dc.identifier.bibliographicCitation | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, v.15, no.5, pp.2958 - 2968 | - |
dc.relation.isPartOf | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS | - |
dc.citation.title | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS | - |
dc.citation.volume | 15 | - |
dc.citation.number | 5 | - |
dc.citation.startPage | 2958 | - |
dc.citation.endPage | 2968 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Automation & Control Systems | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Automation & Control Systems | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Interdisciplinary Applications | - |
dc.relation.journalWebOfScienceCategory | Engineering, Industrial | - |
dc.subject.keywordPlus | SENSOR NETWORKS | - |
dc.subject.keywordPlus | WIRELESS | - |
dc.subject.keywordPlus | RECOVERY | - |
dc.subject.keywordPlus | MANAGEMENT | - |
dc.subject.keywordPlus | SCHEME | - |
dc.subject.keywordAuthor | Energy harvesting | - |
dc.subject.keywordAuthor | Internet of Things (IoT) | - |
dc.subject.keywordAuthor | Markov decision process (MDP) | - |
dc.subject.keywordAuthor | monitoring service | - |
dc.subject.keywordAuthor | probabilistic matrix factorization (PMF) | - |
dc.subject.keywordAuthor | spatiotemporal correlation | - |
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