TY - GEN
T1 - Poster Abstract
T2 - 2019 INFOCOM IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2019
AU - Zhang, Daniel
AU - Zhang, Yang
AU - Wang, Dong
N1 - Funding Information:
ACKNOWLEDGMENTS This research is supported in part by the National Science Foundation under Grant No. CNS-1831669, CBET-1637251, CNS-1566465 and IIS-1447795, Army Research Office under Grant W911NF-17-1-0409. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Office or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/4
Y1 - 2019/4
N2 - A fundamental problem in collaborative sensing lies in providing an accurate prediction of critical events (e.g., hazardous environmental condition, urban abnormalities, economic trends). However, due to the resource constraints, collaborative sensing applications normally only collect measurements from a subset of physical locations and predict the measurements for the rest of locations. This problem is referred to as sparse collaborative sensing prediction. In this poster, we present a novel closed-loop prediction model by leveraging topic modeling and online learning techniques. We evaluate our scheme using a realworld collaborative sensing dataset. The initial results show that our proposed scheme outperforms the state-of-the-art baselines.
AB - A fundamental problem in collaborative sensing lies in providing an accurate prediction of critical events (e.g., hazardous environmental condition, urban abnormalities, economic trends). However, due to the resource constraints, collaborative sensing applications normally only collect measurements from a subset of physical locations and predict the measurements for the rest of locations. This problem is referred to as sparse collaborative sensing prediction. In this poster, we present a novel closed-loop prediction model by leveraging topic modeling and online learning techniques. We evaluate our scheme using a realworld collaborative sensing dataset. The initial results show that our proposed scheme outperforms the state-of-the-art baselines.
UR - http://www.scopus.com/inward/record.url?scp=85073247943&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85073247943&partnerID=8YFLogxK
U2 - 10.1109/INFCOMW.2019.8845087
DO - 10.1109/INFCOMW.2019.8845087
M3 - Conference contribution
AN - SCOPUS:85073247943
T3 - INFOCOM 2019 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2019
SP - 1063
EP - 1064
BT - INFOCOM 2019 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2019
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 29 April 2019 through 2 May 2019
ER -