상세정보
학술지
Spatial Region Estimation for Autonomous CoT Clustering Using Hidden Markov Model
- 발행일
- 201802
- 출처
- ETRI Journal, v.40 no.1, pp.122-132
- ISSN
- 1225-6463
- 출판사
- 한국전자통신연구원 (ETRI)
- 협약과제
- 16MS4600, (ICBMS-1세부) CoT(Cloud of Things) 환경에서 실시간 반응성 향상을 위한 계층적 데이터 스트림 분석 SW 기술 개발, 민옥기
- 초록
- 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.
- KSP 제안 키워드
- Cloud of Things, High Frequency(HF), Internet of thing(IoT), IoT Devices, Radio signal strength(RSS), Region estimation, Root mean square(RMS), Success rate, Validation data, hidden Markov Model, high frequency signal
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