Name
Abstract | ||
|---|---|---|
Rain observations with fine spatio-temporal granularity are significant for professional researches, decision-making, and our daily lives. However, the existing rain gauges can only cover less than 1% of the earth surface, and its amount is still decreasing. Even with the help of several other limited and immature supplementary techniques, rain observations today are still not precise enough. In such context, crowdsourcing paves the avenues toward a fault-tolerant rain observation network with unprecedented resolution and coverage, based on an alternative, nowadays omnipresent source, smartphones, which are integrated with abundant advanced sensors and are becoming more and more ubiquitous around us. In this paper, we propose Chaac, a novel system that exploits opportunistically crowdsourced audio clips from smartphone users to achieve precise detection and intensity measurement of rain. The evaluation results of performing Chaac on 1-s long audio segments demonstrate that it can detect and measure rain with 92.0% and 93.9% true positive rates, respectively. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/JIOT.2018.2866690 | IEEE Internet of Things Journal |
Keywords | Field | DocType |
Rain,Sensors,Smart phones,Real-time systems,Internet of Things,Electronic mail | Computer science,Crowdsourcing,Internet of Things,Real-time computing,Exploit,Granularity,Distributed computing | Journal |
Volume | Issue | ISSN |
6 | 1 | 2327-4662 |
Citations | PageRank | References |
1 | 0.37 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hansong Guo | 1 | 14 | 3.39 |
| He Huang | 2 | 829 | 65.14 |
| Yu-e Sun | 3 | 33 | 7.07 |
| Youlin Zhang | 4 | 1 | 0.71 |
| Shiping Chen | 5 | 190 | 25.84 |
| Liusheng Huang | 6 | 473 | 64.55 |