Evaluating Collaborative and Autonomous Agents in Data-Stream-Supported Coordination of Mobile Crowdsourcing
Mobile crowdsourcing refers to systems where the completion of tasks necessarily requires physical movement of crowdworkers in an on-demand workforce. Evidence suggests that in such systems, tasks often get assigned to crowdworkers who struggle to complete those tasks successfully, resulting in high...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/2/614 |
| _version_ | 1827622211753607168 |
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| author | Ralf Bruns Jeremias Dötterl Jürgen Dunkel Sascha Ossowski |
| author_facet | Ralf Bruns Jeremias Dötterl Jürgen Dunkel Sascha Ossowski |
| author_sort | Ralf Bruns |
| collection | DOAJ |
| description | Mobile crowdsourcing refers to systems where the completion of tasks necessarily requires physical movement of crowdworkers in an on-demand workforce. Evidence suggests that in such systems, tasks often get assigned to crowdworkers who struggle to complete those tasks successfully, resulting in high failure rates and low service quality. A promising solution to ensure higher quality of service is to continuously adapt the assignment and respond to failure-causing events by transferring tasks to better-suited workers who use different routes or vehicles. However, implementing task transfers in mobile crowdsourcing is difficult because workers are autonomous and may reject transfer requests. Moreover, task outcomes are uncertain and need to be predicted. In this paper, we propose different mechanisms to achieve outcome prediction and task coordination in mobile crowdsourcing. First, we analyze different data stream learning approaches for the prediction of task outcomes. Second, based on the suggested prediction model, we propose and evaluate two different approaches for task coordination with different degrees of autonomy: an opportunistic approach for crowdshipping with collaborative, but non-autonomous workers, and a market-based model with autonomous workers for crowdsensing. |
| first_indexed | 2024-03-09T11:18:21Z |
| format | Article |
| id | doaj.art-893994568b9b48c6a17182dcab45d07a |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T11:18:21Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-893994568b9b48c6a17182dcab45d07a2023-12-01T00:24:39ZengMDPI AGSensors1424-82202023-01-0123261410.3390/s23020614Evaluating Collaborative and Autonomous Agents in Data-Stream-Supported Coordination of Mobile CrowdsourcingRalf Bruns0Jeremias Dötterl1Jürgen Dunkel2Sascha Ossowski3Computer Science Department, Hannover University of Applied Sciences and Arts, 30459 Hannover, GermanyComputer Science Department, Hannover University of Applied Sciences and Arts, 30459 Hannover, GermanyComputer Science Department, Hannover University of Applied Sciences and Arts, 30459 Hannover, GermanyCETINIA, University Rey Juan Carlos, Móstoles, 28933 Madrid, SpainMobile crowdsourcing refers to systems where the completion of tasks necessarily requires physical movement of crowdworkers in an on-demand workforce. Evidence suggests that in such systems, tasks often get assigned to crowdworkers who struggle to complete those tasks successfully, resulting in high failure rates and low service quality. A promising solution to ensure higher quality of service is to continuously adapt the assignment and respond to failure-causing events by transferring tasks to better-suited workers who use different routes or vehicles. However, implementing task transfers in mobile crowdsourcing is difficult because workers are autonomous and may reject transfer requests. Moreover, task outcomes are uncertain and need to be predicted. In this paper, we propose different mechanisms to achieve outcome prediction and task coordination in mobile crowdsourcing. First, we analyze different data stream learning approaches for the prediction of task outcomes. Second, based on the suggested prediction model, we propose and evaluate two different approaches for task coordination with different degrees of autonomy: an opportunistic approach for crowdshipping with collaborative, but non-autonomous workers, and a market-based model with autonomous workers for crowdsensing.https://www.mdpi.com/1424-8220/23/2/614crowdsourcingdata stream learningmultiagent systemscollaborative coordinationmarket-based coordination |
| spellingShingle | Ralf Bruns Jeremias Dötterl Jürgen Dunkel Sascha Ossowski Evaluating Collaborative and Autonomous Agents in Data-Stream-Supported Coordination of Mobile Crowdsourcing Sensors crowdsourcing data stream learning multiagent systems collaborative coordination market-based coordination |
| title | Evaluating Collaborative and Autonomous Agents in Data-Stream-Supported Coordination of Mobile Crowdsourcing |
| title_full | Evaluating Collaborative and Autonomous Agents in Data-Stream-Supported Coordination of Mobile Crowdsourcing |
| title_fullStr | Evaluating Collaborative and Autonomous Agents in Data-Stream-Supported Coordination of Mobile Crowdsourcing |
| title_full_unstemmed | Evaluating Collaborative and Autonomous Agents in Data-Stream-Supported Coordination of Mobile Crowdsourcing |
| title_short | Evaluating Collaborative and Autonomous Agents in Data-Stream-Supported Coordination of Mobile Crowdsourcing |
| title_sort | evaluating collaborative and autonomous agents in data stream supported coordination of mobile crowdsourcing |
| topic | crowdsourcing data stream learning multiagent systems collaborative coordination market-based coordination |
| url | https://www.mdpi.com/1424-8220/23/2/614 |
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