Name
Abstract | ||
|---|---|---|
Mobile crowdsensing recruits a group of mobile users to cooperatively perform a common sensing job with their smart devices. As a special issue, photo crowdsensing allows users to utilize the built-in cameras of mobile devices to take photos for an event or a target. Then, the photos can be used in numerous application areas, such as target reconstruction, scenario reduction, and so on. Therefore, photo crowdsensing has attracted considerable attention recently due to the rich information that can be provided by images. In this paper, we focus on using the photos to make reconstructions for specific targets. Furthermore, we develop a user recruitment system for efficient photo collecting in mobile crowdsensing (RSMC), where the task requesters publish a sensing task to the users, and the map is gridded according to the locations of the sensing targets. Then, we use a semi-Markov model to calculate the user's utility for the sensing task. Finally, a user recruitment strategy is devised to recruit the optimal
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users for finishing the sensing task. We conduct extensive simulations based on three widely used real-world traces:
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,
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, and
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. The results show that, compared with other recruitment strategies, RSMC takes the largest number of efficient photos for the sensing task. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/THMS.2019.2912509 | IEEE Transactions on Human-Machine Systems |
Keywords | Field | DocType |
Task analysis,Sensors,Recruitment,Buildings,Publishing,Image reconstruction,Cameras | Computer science,Crowdsensing,Human–computer interaction,Artificial intelligence,Machine learning | Journal |
Volume | Issue | ISSN |
50 | 1 | 2168-2291 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| En Wang | 1 | 21 | 8.13 |
| Yongjian Yang | 2 | 39 | 14.05 |
| Jie Wu | 3 | 8307 | 592.07 |
| Kaihao Lou | 4 | 5 | 2.41 |
| Dongming Luan | 5 | 3 | 2.74 |
| Hengzhi Wang | 6 | 0 | 2.37 |