Spatial Region Estimation for Autonomous CoT Clustering Using Hidden Markov Model
This paper proposes a hierarchical dual filtering (HDF) algorithm to estimate the spatial region between a Cloud of Things (CoT) gateway and an Internet of Things (IoT) device. The accuracy of the spatial region estimation is important for autonomous CoT clustering. We conduct spatial region estimat...
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Format: | Article |
Language: | English |
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Electronics and Telecommunications Research Institute (ETRI)
2018-02-01
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Series: | ETRI Journal |
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Online Access: | https://doi.org/10.4218/etrij.18.0117.0142 |
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author | Joon‐young Jung Okgee Min |
author_facet | Joon‐young Jung Okgee Min |
author_sort | Joon‐young Jung |
collection | DOAJ |
description | This paper proposes a hierarchical dual filtering (HDF) algorithm to estimate the spatial region between a Cloud of Things (CoT) gateway and an Internet of Things (IoT) device. The accuracy of the spatial region estimation is important for autonomous CoT clustering. We conduct spatial region estimation using a hidden Markov model (HMM) with a raw Bluetooth received signal strength indicator (RSSI). However, the accuracy of the region estimation using the validation data is only 53.8%. To increase the accuracy of the spatial region estimation, the HDF algorithm removes the high‐frequency signals hierarchically, and alters the parameters according to whether the IoT device moves. The accuracy of spatial region estimation using a raw RSSI, Kalman filter, and HDF are compared to evaluate the effectiveness of the HDF algorithm. The success rate and root mean square error (RMSE) of all regions are 0.538, 0.622, and 0.75, and 0.997, 0.812, and 0.5 when raw RSSI, a Kalman filter, and HDF are used, respectively. The HDF algorithm attains the best results in terms of the success rate and RMSE of spatial region estimation using HMM. |
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format | Article |
id | doaj.art-6cf24aa723ac45dc97dc16f245e4b118 |
institution | Directory Open Access Journal |
issn | 1225-6463 2233-7326 |
language | English |
last_indexed | 2024-12-20T21:17:13Z |
publishDate | 2018-02-01 |
publisher | Electronics and Telecommunications Research Institute (ETRI) |
record_format | Article |
series | ETRI Journal |
spelling | doaj.art-6cf24aa723ac45dc97dc16f245e4b1182022-12-21T19:26:22ZengElectronics and Telecommunications Research Institute (ETRI)ETRI Journal1225-64632233-73262018-02-0140112213210.4218/etrij.18.0117.014210.4218/etrij.18.0117.0142Spatial Region Estimation for Autonomous CoT Clustering Using Hidden Markov ModelJoon‐young JungOkgee MinThis paper proposes a hierarchical dual filtering (HDF) algorithm to estimate the spatial region between a Cloud of Things (CoT) gateway and an Internet of Things (IoT) device. The accuracy of the spatial region estimation is important for autonomous CoT clustering. We conduct spatial region estimation using a hidden Markov model (HMM) with a raw Bluetooth received signal strength indicator (RSSI). However, the accuracy of the region estimation using the validation data is only 53.8%. To increase the accuracy of the spatial region estimation, the HDF algorithm removes the high‐frequency signals hierarchically, and alters the parameters according to whether the IoT device moves. The accuracy of spatial region estimation using a raw RSSI, Kalman filter, and HDF are compared to evaluate the effectiveness of the HDF algorithm. The success rate and root mean square error (RMSE) of all regions are 0.538, 0.622, and 0.75, and 0.997, 0.812, and 0.5 when raw RSSI, a Kalman filter, and HDF are used, respectively. The HDF algorithm attains the best results in terms of the success rate and RMSE of spatial region estimation using HMM.https://doi.org/10.4218/etrij.18.0117.0142CoT clusteringHidden Markov modelHierarchical dual filteringRegion estimation |
spellingShingle | Joon‐young Jung Okgee Min Spatial Region Estimation for Autonomous CoT Clustering Using Hidden Markov Model ETRI Journal CoT clustering Hidden Markov model Hierarchical dual filtering Region estimation |
title | Spatial Region Estimation for Autonomous CoT Clustering Using Hidden Markov Model |
title_full | Spatial Region Estimation for Autonomous CoT Clustering Using Hidden Markov Model |
title_fullStr | Spatial Region Estimation for Autonomous CoT Clustering Using Hidden Markov Model |
title_full_unstemmed | Spatial Region Estimation for Autonomous CoT Clustering Using Hidden Markov Model |
title_short | Spatial Region Estimation for Autonomous CoT Clustering Using Hidden Markov Model |
title_sort | spatial region estimation for autonomous cot clustering using hidden markov model |
topic | CoT clustering Hidden Markov model Hierarchical dual filtering Region estimation |
url | https://doi.org/10.4218/etrij.18.0117.0142 |
work_keys_str_mv | AT joonyoungjung spatialregionestimationforautonomouscotclusteringusinghiddenmarkovmodel AT okgeemin spatialregionestimationforautonomouscotclusteringusinghiddenmarkovmodel |