Open AccessSpatial Region Estimation for Autonomous CoT Clustering Using Hidden Markov Model
Author(s) -
Jung Joonyoung,
Min Okgee
Publication year - 2018
Publication title -
etri journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.295
H-Index - 46
eISSN - 2233-7326
pISSN - 1225-6463
DOI - 10.4218/etrij.18.0117.0142
Subject(s) - cluster analysis , mean squared error , computer science , kalman filter , hidden markov model , bluetooth , markov chain , artificial intelligence , pattern recognition (psychology) , statistics , wireless , mathematics , machine learning , telecommunications
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 .