Abstract
Disaster detection is an important research topic and draws great attentions from both industry and academia. In this paper, we study the hybrid landslide detection system, which utilizes the video surveillance camera and multiple kinds of sensors and can detect the landslide automatically. Edge processing is adopted in this hybrid system to fuse the sensor data based on the Dempster-Shafer (D-S) theory, i.e. utilizing multiple sensors' information to calculate the possibility of the landslide. Edge processing can make faster decisions than the control center, the results of the edge processing are then used to schedule the sensor's transmission frequency and video transmission under the network constraints in this system. The simulation results show that the proposed scheme outperforms the competing schemes in typical network scenarios.
| Original language | English |
|---|---|
| Title of host publication | 2017 IEEE International Conference on Smart Computing, SMARTCOMP 2017 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9781509065172 |
| DOIs | |
| Publication status | Published - 2017 Jun 12 |
| Event | 2017 IEEE International Conference on Smart Computing, SMARTCOMP 2017 - Hong Kong, China Duration: 2017 May 29 → 2017 May 31 |
Other
| Other | 2017 IEEE International Conference on Smart Computing, SMARTCOMP 2017 |
|---|---|
| Country | China |
| City | Hong Kong |
| Period | 17/5/29 → 17/5/31 |
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Keywords
- Dempster-Shafer (DS)
- disaster
- landslide
- video streaming
- WSN
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Networks and Communications
- Computer Science Applications
Cite this
Adaptive Video Streaming in Hybrid Landslide Detection System with D-S Theory. / Liu, Zhi; Kanai, Kenji; Takeuchi, Masaru; Tsuda, Toshitaka; Watanabe, Hiroshi.
2017 IEEE International Conference on Smart Computing, SMARTCOMP 2017. Institute of Electrical and Electronics Engineers Inc., 2017. 7946984.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Adaptive Video Streaming in Hybrid Landslide Detection System with D-S Theory
AU - Liu, Zhi
AU - Kanai, Kenji
AU - Takeuchi, Masaru
AU - Tsuda, Toshitaka
AU - Watanabe, Hiroshi
PY - 2017/6/12
Y1 - 2017/6/12
N2 - Disaster detection is an important research topic and draws great attentions from both industry and academia. In this paper, we study the hybrid landslide detection system, which utilizes the video surveillance camera and multiple kinds of sensors and can detect the landslide automatically. Edge processing is adopted in this hybrid system to fuse the sensor data based on the Dempster-Shafer (D-S) theory, i.e. utilizing multiple sensors' information to calculate the possibility of the landslide. Edge processing can make faster decisions than the control center, the results of the edge processing are then used to schedule the sensor's transmission frequency and video transmission under the network constraints in this system. The simulation results show that the proposed scheme outperforms the competing schemes in typical network scenarios.
AB - Disaster detection is an important research topic and draws great attentions from both industry and academia. In this paper, we study the hybrid landslide detection system, which utilizes the video surveillance camera and multiple kinds of sensors and can detect the landslide automatically. Edge processing is adopted in this hybrid system to fuse the sensor data based on the Dempster-Shafer (D-S) theory, i.e. utilizing multiple sensors' information to calculate the possibility of the landslide. Edge processing can make faster decisions than the control center, the results of the edge processing are then used to schedule the sensor's transmission frequency and video transmission under the network constraints in this system. The simulation results show that the proposed scheme outperforms the competing schemes in typical network scenarios.
KW - Dempster-Shafer (DS)
KW - disaster
KW - landslide
KW - video streaming
KW - WSN
UR - http://www.scopus.com/inward/record.url?scp=85022338350&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85022338350&partnerID=8YFLogxK
U2 - 10.1109/SMARTCOMP.2017.7946984
DO - 10.1109/SMARTCOMP.2017.7946984
M3 - Conference contribution
AN - SCOPUS:85022338350
BT - 2017 IEEE International Conference on Smart Computing, SMARTCOMP 2017
PB - Institute of Electrical and Electronics Engineers Inc.
ER -