TY - GEN
T1 - Selecting sensing location leveraging spatial and cross-domain correlations
AU - Chang, Huijuan
AU - Yu, Zhiyong
AU - Yu, Zhiwen
AU - Guo, Bin
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/8
Y1 - 2019/8
N2 - In environmental monitoring applications, selecting appropriate locations to sense is important relating to data quality and Sensing cost. This paper addresses the challenge by collecting data from a subset of locations, then leveraging the spatial and cross-domain correlations to deduce data of other locations, thus can obtain acceptable data quality with lower sensing cost. Referring to active learning, the proposed framework is constructed by two types modules (i.e., estimators and selectors) and a cyclic process of estimating and selecting. Estimators based on kriging interpolation and regression tree are implemented, and their corresponding selectors are designed. We evaluate the effectiveness of the framework by taking air quality sensing as an example. Results show that to reach data quality of about 25% MAPE, the framework only needs 15% locations, while random selector needs 25% locations.
AB - In environmental monitoring applications, selecting appropriate locations to sense is important relating to data quality and Sensing cost. This paper addresses the challenge by collecting data from a subset of locations, then leveraging the spatial and cross-domain correlations to deduce data of other locations, thus can obtain acceptable data quality with lower sensing cost. Referring to active learning, the proposed framework is constructed by two types modules (i.e., estimators and selectors) and a cyclic process of estimating and selecting. Estimators based on kriging interpolation and regression tree are implemented, and their corresponding selectors are designed. We evaluate the effectiveness of the framework by taking air quality sensing as an example. Results show that to reach data quality of about 25% MAPE, the framework only needs 15% locations, while random selector needs 25% locations.
KW - Active learning
KW - Kriging interpolation
KW - Location selection
KW - Regression tree
UR - http://www.scopus.com/inward/record.url?scp=85083574491&partnerID=8YFLogxK
U2 - 10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00149
DO - 10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00149
M3 - 会议稿件
AN - SCOPUS:85083574491
T3 - Proceedings - 2019 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Internet of People and Smart City Innovation, SmartWorld/UIC/ATC/SCALCOM/IOP/SCI 2019
SP - 661
EP - 666
BT - Proceedings - 2019 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Internet of People and Smart City Innovation, SmartWorld/UIC/ATC/SCALCOM/IOP/SCI 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Internet of People and Smart City Innovation, SmartWorld/UIC/ATC/SCALCOM/IOP/SCI 2019
Y2 - 19 August 2019 through 23 August 2019
ER -