Name
Abstract | ||
|---|---|---|
Data quality and budget are two primary concerns in urban-scale mobile crowdsensing applications. In this paper, we leverage the spatial and temporal correlation among the data sensed in different sub-areas to significantly reduce the required number of sensing tasks allocated (corresponding to budget), yet ensuring the data quality. Specifically, we propose a novel framework called CCS-TA, combining the state-of-the-art compressive sensing, Bayesian inference, and active learning techniques, to dynamically select a minimum number of sub-areas for sensing task allocation in each sensing cycle, while deducing the missing data of unallocated sub-areas under a probabilistic data accuracy guarantee. Evaluations on real-life temperature and air quality monitoring datasets show the effectiveness of CCS-TA. In the case of temperature monitoring, CCS-TA allocates 18.0-26.5% fewer tasks than baseline approaches, allocating tasks to only 15.5% of the sub-areas on average while keeping overall sensing error below 0.25°C in 95% of the cycles. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1145/2750858.2807513 | ACM International Conference on Ubiquitous Computing |
Keywords | Field | DocType |
Crowdsensing, Task Allocation, Data Quality | Data mining,Data quality,Active learning,Bayesian inference,Computer science,Crowdsensing,Real-time computing,Air quality index,Probabilistic logic,Missing data,Compressed sensing | Conference |
Citations | PageRank | References |
29 | 0.84 | 27 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Leye Wang | 1 | 551 | 36.79 |
| Daqing Zhang | 2 | 3619 | 217.31 |
| Animesh Pathak | 3 | 335 | 17.96 |
| Chao Chen | 4 | 2032 | 185.26 |
| Haoyi Xiong | 5 | 505 | 44.63 |
| Dingqi Yang | 6 | 542 | 28.79 |
| Ya-sha Wang | 7 | 303 | 37.40 |