Joint Annotator Clustering and Power Control for Energy-Efficient Wireless Crowd Labelling

Li，Xiaoyang1; Zhu，Guangxu2; Han，Kaifeng3; Gong，Yi1; Huang，Kaibin4

10.1109/GCWkshps52748.2021.9682025

2021

978-1-6654-2391-5

2021 IEEE Globecom Workshops, GC Wkshps 2021 - Proceedings

1-6

7-11 Dec. 2021

Madrid, Spain

The unprecedented growth of mobile data traffic has fueled the deployment of intelligence applications at the network edge, while distilling the intelligence from raw data by supervised learning requires tremendous labelling effort. To overcome this challenge, wireless crowd labelling (WCL) is proposed for efficient data labelling by exploiting billions of available mobile annotators and the multicasting property of wireless channels. A WCL system is considered in this paper where unlabelled data (objects) are multicast to different clusters of annotators for repeated labelling to improve accuracy. Given the number of objects to be labelled with the required accuracy, the annotator clustering and multicast power control are jointly optimized for energy consumption minimization, leading to a challenging combinatorial problem. An efficient algorithm is designed to derive the optimal solutions based on tree search and examined by the simulations.

Annotator Clustering Energy Efficiency Multicast Power Control Wireless Crowd Labelling

第一

英语

EI

20221111785920

Energy utilization ; Multicasting ; Power control ; Trees (mathematics)

Energy Conservation:525.2 ; Energy Utilization:525.3 ; Telecommunication; Radar, Radio and Television:716 ; Optical Communication:717 ; Telephone Systems and Related Technologies; Line Communications:718 ; Specific Variables Control:731.3 ; Combinatorial Mathematics, Includes Graph Theory, Set Theory:921.4

2-s2.0-85126134185

Scopus

1.Southern University of Science and Technology,Shenzhen,China
2.Shenzhen Research Institute of Big Data,Shenzhen,China
3.China Academy of Information and Communications Technology,Beijing,China
4.The University of Hong Kong,Hong Kong

Li，Xiaoyang,Zhu，Guangxu,Han，Kaifeng,et al. Joint Annotator Clustering and Power Control for Energy-Efficient Wireless Crowd Labelling[C],2021:1-6.