TY - GEN
T1 - Edge-centric field monitoring system for energy-efficient and network-friendly field sensing
AU - Ogawa, Keigo
AU - Kanai, Kenji
AU - Takeuchi, Masaru
AU - Katto, Jiro
AU - Tsuda, Toshitaka
N1 - Funding Information:
ACKNOWLEDGMENT This research is supported by "Research and Development on Fundamental and Utilization Technologies for Social Big Data," NICT, Japan and Grant-in-Aid for Scientific Research (A) (15H01684) of JSPS, Japan.
Publisher Copyright:
© 2018 IEEE.
PY - 2018/3/16
Y1 - 2018/3/16
N2 - To provide energy-efficient (i.e., longer lifetime of sensors) and network-friendly (i.e., reducing network traffic) field sensing, we propose an edge-centric field monitoring system which applies efficient sensors and camera control. The proposed system detects conditions in a monitoring area and controls sensing frequency (sampling rate) of sensors, and capture rate and encoding rate of surveillance cameras, according to the detected conditions. In addition, the system applies a Multi-access Edge Computing (MEC) platform to provide fast feedback control to the sensors and cameras. In performance evaluations, we assume that the monitoring target is landslide detection and create a miniature 'artificial landslide generation' environment in our laboratory. By using the environment, we evaluate the system performance, and evaluation results indicate that the proposed system can reduce network traffic and save energy consumption efficiently.
AB - To provide energy-efficient (i.e., longer lifetime of sensors) and network-friendly (i.e., reducing network traffic) field sensing, we propose an edge-centric field monitoring system which applies efficient sensors and camera control. The proposed system detects conditions in a monitoring area and controls sensing frequency (sampling rate) of sensors, and capture rate and encoding rate of surveillance cameras, according to the detected conditions. In addition, the system applies a Multi-access Edge Computing (MEC) platform to provide fast feedback control to the sensors and cameras. In performance evaluations, we assume that the monitoring target is landslide detection and create a miniature 'artificial landslide generation' environment in our laboratory. By using the environment, we evaluate the system performance, and evaluation results indicate that the proposed system can reduce network traffic and save energy consumption efficiently.
KW - Field monitoring
KW - Internet of Things
KW - Multi-access Edge Computing
KW - landslide detection
UR - http://www.scopus.com/inward/record.url?scp=85046940745&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85046940745&partnerID=8YFLogxK
U2 - 10.1109/CCNC.2018.8319243
DO - 10.1109/CCNC.2018.8319243
M3 - Conference contribution
AN - SCOPUS:85046940745
T3 - CCNC 2018 - 2018 15th IEEE Annual Consumer Communications and Networking Conference
SP - 1
EP - 6
BT - CCNC 2018 - 2018 15th IEEE Annual Consumer Communications and Networking Conference
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th IEEE Annual Consumer Communications and Networking Conference, CCNC 2018
Y2 - 12 January 2018 through 15 January 2018
ER -
