TY - GEN
T1 - Content-Oriented Surveillance System Based on ICN in Disaster Scenarios
AU - Okamoto, Koki
AU - Mochida, Toru
AU - Nozaki, Daichi
AU - Wen, Zheng
AU - Qi, Xin
AU - Sato, Takuro
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - This paper deals with an efficient image object detection method for use in a disaster prevention network that uses information-centric networking (ICN). In ICN for disaster prevention, a large number of surveillance cameras are arranged, and disaster image contents corresponding to the user's requests are distributed directly from the node attached to the surveillance camera. At this time, the name of the content requested by the user does not necessarily match the name of the image acquired by the surveillance camera. In this paper, the content requested by the user is processed and named using natural language processing (NLP). In addition, the image content from the surveillance camera is named using artificial intelligence technology. In this way, a method for improving the hit ratio between users and cameras was established. Furthermore, the volume of the interest packets decreases based on the information which area often occurs disaster. As a result, the network efficiency of ICN can be improved.
AB - This paper deals with an efficient image object detection method for use in a disaster prevention network that uses information-centric networking (ICN). In ICN for disaster prevention, a large number of surveillance cameras are arranged, and disaster image contents corresponding to the user's requests are distributed directly from the node attached to the surveillance camera. At this time, the name of the content requested by the user does not necessarily match the name of the image acquired by the surveillance camera. In this paper, the content requested by the user is processed and named using natural language processing (NLP). In addition, the image content from the surveillance camera is named using artificial intelligence technology. In this way, a method for improving the hit ratio between users and cameras was established. Furthermore, the volume of the interest packets decreases based on the information which area often occurs disaster. As a result, the network efficiency of ICN can be improved.
KW - Artificial Intelligence (AI)
KW - Image Recognition
KW - Information Centric Network (ICN)
KW - Internet of Things (IoT)
KW - Named Data Networking (NDN)
KW - Natural Language processing (NLP)
UR - http://www.scopus.com/inward/record.url?scp=85066296393&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85066296393&partnerID=8YFLogxK
U2 - 10.1109/WPMC.2018.8712852
DO - 10.1109/WPMC.2018.8712852
M3 - Conference contribution
AN - SCOPUS:85066296393
T3 - International Symposium on Wireless Personal Multimedia Communications, WPMC
SP - 484
EP - 489
BT - 2018 21st International Symposium on Wireless Personal Multimedia Communications, WPMC 2018
PB - IEEE Computer Society
T2 - 21st International Symposium on Wireless Personal Multimedia Communications, WPMC 2018
Y2 - 25 November 2018 through 28 November 2018
ER -