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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 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.

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Author:Ralf BrunsORCiDGND, Jeremias Dötterl, Jürgen DunkelORCiDGND, Sascha OssowskiORCiD
DOI original:https://doi.org/10.3390/s23020614
Parent Title (English):Sensors
Document Type:Article
Year of Completion:2023
Publishing Institution:Hochschule Hannover
Release Date:2023/02/10
Tag:collaborative coordination; data stream learning; market-based coordination; multiagent systems
GND Keyword:Crowdsourcing; Datenstrom; Sensor
Article Number:614
Page Number:20
Link to catalogue:1843779978
Institutes:Fakultät IV - Wirtschaft und Informatik
DDC classes:004 Informatik
Licence (German):License LogoCreative Commons - CC BY - Namensnennung 4.0 International