TY - JOUR
T1 - Data-Importance-Aware Bandwidth-Allocation Scheme for Point-Cloud Transmission in Multiple LIDAR Sensors
AU - Otsu, Ryo
AU - Shinkuma, Ryoichi
AU - Sato, Takehiro
AU - Oki, Eiji
N1 - Funding Information:
This work was supported in part by the Japan Science and Technology Agency as PRESTO under Grant JPMJPR1854, and in part by the National Institute of Information and Communications Technology (NICT), Japan.
Publisher Copyright:
© 2013 IEEE.
PY - 2021
Y1 - 2021
N2 - This paper addresses bandwidth allocation to multiple light detection and ranging (LIDAR) sensors for smart monitoring, which a limited communication capacity is available to transmit a large volume of point-cloud data from the sensors to an edge server in real time. To deal with the limited capacity of the communication channel, we propose a bandwidth-allocation scheme that assigns multiple point-cloud compression formats to each LIDAR sensor in accordance with the spatial importance of the point-cloud data transmitted by the sensor. Spatial importance is determined by estimating how objects, such as cars, trucks, bikes, and pedestrians, are likely to exist since regions where objects are more likely to exist are more useful for smart monitoring. A numerical study using a real point-cloud dataset obtained at an intersection indicates that the proposed scheme is superior to the benchmarks in terms of the distributions of data volumes among LIDAR sensors and quality of point-cloud data received by the edge server.
AB - This paper addresses bandwidth allocation to multiple light detection and ranging (LIDAR) sensors for smart monitoring, which a limited communication capacity is available to transmit a large volume of point-cloud data from the sensors to an edge server in real time. To deal with the limited capacity of the communication channel, we propose a bandwidth-allocation scheme that assigns multiple point-cloud compression formats to each LIDAR sensor in accordance with the spatial importance of the point-cloud data transmitted by the sensor. Spatial importance is determined by estimating how objects, such as cars, trucks, bikes, and pedestrians, are likely to exist since regions where objects are more likely to exist are more useful for smart monitoring. A numerical study using a real point-cloud dataset obtained at an intersection indicates that the proposed scheme is superior to the benchmarks in terms of the distributions of data volumes among LIDAR sensors and quality of point-cloud data received by the edge server.
KW - LIDAR sensor
KW - Smart monitoring
KW - bandwidth allocation
KW - point cloud compression
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U2 - 10.1109/ACCESS.2021.3075275
DO - 10.1109/ACCESS.2021.3075275
M3 - Article
AN - SCOPUS:85104584102
SN - 2169-3536
VL - 9
SP - 65150
EP - 65161
JO - IEEE Access
JF - IEEE Access
M1 - 9411830
ER -