期刊
ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY
卷 9, 期 2, 页码 -出版社
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3131671
关键词
Crowdsensing; task allocation; data quality
资金
- NSFC [61572048, 71601106, 61602067]
- State Language Commission Key Program [ZDI135-18]
- Hong Kong ITF Grant [ITS/391/15FX]
- European Research Council (ERC) under the European Union's Horizon research and innovation program [683253/GraphInt]
- Missouri S&T Startup Funding for Smart Cyber-Physical Systems Cluster Hire
Data quality and budget are two primary concerns in urban-scale mobile crowdsensing. Traditional research on mobile crowdsensing mainly takes sensing coverage ratio as the data quality metric rather than the overall sensed data error in the target-sensing area. In this article, we propose to leverage spatiotemporal correlations among the sensed data in the target-sensing area to significantly reduce the number of sensing task assignments. In particular, we exploit both intradata correlations within the same type of sensed data and interdata correlations among different types of sensed data in the sensing task. We propose a novel crowdsensing task allocation framework called SPACE-TA (SPArse Cost-Effective Task Allocation), combining compressive sensing, statistical analysis, active learning, and transfer learning, to dynamically select a small set of subareas for sensing in each timeslot (cycle), while inferring the data of unsensed subareas under a probabilistic data quality guarantee. Evaluations on real-life temperature, humidity, air quality, and traffic monitoring datasets verify the effectiveness of SPACE-TA. In the temperature-monitoring task leveraging intradata correlations, SPACE-TA requires data from only 15.5% of the subareas while keeping the inference error below 0.25 degrees C in 95% of the cycles, reducing the number of sensed subareas by 18.0% to 26.5% compared to baselines. When multiple tasks run simultaneously, for example, for temperature and humidity monitoring, SPACE-TA can further reduce similar to 10% of the sensed subareas by exploiting interdata correlations.
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