IoT-Enabled Particulate Matter Monitoring and Forecasting Method Based on Cluster Analysis
- Authors
- Yun, Jaeseok; Woo, Jiyoung
- Issue Date
- 1-May-2021
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Keywords
- Sensors; Monitoring; Internet of Things; Forecasting; Air pollution; Sensor systems; Predictive models; Hierarchical clustering; LoRa networks; oneM2M platforms; particulate matter (PM); time-series forecasting
- Citation
- IEEE Internet of Things Journal, v.8, no.9, pp 7380 - 7393
- Pages
- 14
- Journal Title
- IEEE Internet of Things Journal
- Volume
- 8
- Number
- 9
- Start Page
- 7380
- End Page
- 7393
- URI
- https://scholarworks.bwise.kr/sch/handle/2021.sw.sch/18851
- DOI
- 10.1109/JIOT.2020.3038862
- ISSN
- 2327-4662
- Abstract
- In recent years, particulate matter (PM) having a diameter smaller than 2.5 mu m has become a significant issue due to its severe impact on human health. With the advent of IoT-enabling technologies, a ubiquitous IoT sensing infrastructure is now used to constantly monitor aspects of our surrounding environment, such as ambient air pollution. In this article, we introduce a PM-sensing system composed of off-the-shelf LoRa-based wireless hardware boards and low-cost PM sensors. By leveraging software platforms that are compliant with an IoT standard called oneM2M, PM data sets can be collected and accessed in a standardized manner, i.e., via oneM2M-defined representational state transfer application programmable interfaces. Also, for reliable PM monitoring, a short-term (i.e., within 2 h) PM forecasting method based on autoregressive integrated moving average and vector autoregressive moving average (VARMA) models is proposed and evaluated with a 30-day PM data set collected from 15 LoRa-based PM sensor nodes installed at a university campus. The experimental results show that the overall root-mean square error and correlation coefficient of the VARMA models integrated with hierarchical clustering are improved by 7.77% and 3.7%, respectively, compared with the single node-based forecast model.
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Collections - SCH Media Labs > Department of Big Data Engineering > 1. Journal Articles
- SCH Media Labs > Department of Internet of Things > 1. Journal Articles
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