TY - GEN
T1 - A Light-Weight and Quality-Aware Online Adaptive Sampling Approach for Streaming Social Sensing in Cloud Computing
AU - Zhang, Yang
AU - Zhang, Daniel
AU - Vance, Nathan
AU - Li, Qi
AU - Wang, Dong
N1 - Funding Information:
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, Google 2017 Faculty Research Award. 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:
© 2018 IEEE.
PY - 2019/2/19
Y1 - 2019/2/19
N2 - Social Sensing has emerged as a new distributed application paradigm in networked sensing where streaming data is constantly collected from humans or devices on their behalf. A key challenge in social sensing is that the applications are often both data-intensive and delay-sensitive, which often require extensive resources in the cloud to support the data processing and analytics tasks. To address this problem, this paper focuses on a quality-aware online adaptive sampling problem where the goal is to judiciously sample a small set of representative sensing measurements (i.e., representative set) that effectively represent the key information of the social sensing data stream. However, two technical challenges exist in identifying such a representative set: (i) "quality-aware quantification": data collected in the social sensing applications is often unstructured (e.g., text, image, video) where the key information is so deeply embedded in the raw data that can not be easily quantified to ensure the desirable quality of the selected representative set; (ii) "online adaptive sampling": the sampling decision has to be made in real-time and be adaptive to the dynamics of the streaming data in social sensing. To address these challenges, this paper develops a Light-weight and Quality-aware Online Adaptive Sampling (LQOAS) scheme to dynamically identify the representative set of measurements from the streaming social sensing data using a submodular maximization approach. We evaluate the LQOAS scheme using a real world social sensing dataset from Twitter. The evaluation results show that the LQOAS scheme significantly outperforms the state-of-the-art sampling schemes.
AB - Social Sensing has emerged as a new distributed application paradigm in networked sensing where streaming data is constantly collected from humans or devices on their behalf. A key challenge in social sensing is that the applications are often both data-intensive and delay-sensitive, which often require extensive resources in the cloud to support the data processing and analytics tasks. To address this problem, this paper focuses on a quality-aware online adaptive sampling problem where the goal is to judiciously sample a small set of representative sensing measurements (i.e., representative set) that effectively represent the key information of the social sensing data stream. However, two technical challenges exist in identifying such a representative set: (i) "quality-aware quantification": data collected in the social sensing applications is often unstructured (e.g., text, image, video) where the key information is so deeply embedded in the raw data that can not be easily quantified to ensure the desirable quality of the selected representative set; (ii) "online adaptive sampling": the sampling decision has to be made in real-time and be adaptive to the dynamics of the streaming data in social sensing. To address these challenges, this paper develops a Light-weight and Quality-aware Online Adaptive Sampling (LQOAS) scheme to dynamically identify the representative set of measurements from the streaming social sensing data using a submodular maximization approach. We evaluate the LQOAS scheme using a real world social sensing dataset from Twitter. The evaluation results show that the LQOAS scheme significantly outperforms the state-of-the-art sampling schemes.
KW - Adaptive Sampling
KW - Cloud Computing
KW - Light-weight
KW - Quality-aware
KW - Streaming Data
KW - Submodular Maximization
UR - http://www.scopus.com/inward/record.url?scp=85063326651&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85063326651&partnerID=8YFLogxK
U2 - 10.1109/PADSW.2018.8644560
DO - 10.1109/PADSW.2018.8644560
M3 - Conference contribution
AN - SCOPUS:85063326651
T3 - Proceedings of the International Conference on Parallel and Distributed Systems - ICPADS
SP - 1
EP - 8
BT - Proceedings - 2018 IEEE 24th International Conference on Parallel and Distributed Systems, ICPADS 2018
PB - IEEE Computer Society
T2 - 24th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2018
Y2 - 11 December 2018 through 13 December 2018
ER -