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Evaluating Collaborative and Autonomous Agents in Data-Stream-Supported Coordination of Mobile Crowdsourcing

dc.contributor.authorBruns, Ralf
dc.contributor.authorDötterl, Jeremias
dc.contributor.authorDunkel, Jürgen
dc.contributor.authorOssowski, Sascha
dc.date.accessioned2023-12-22T08:28:12Z
dc.date.available2023-12-22T08:28:12Z
dc.date.issued2023-01-05
dc.identifier.citationBruns, R.; Dötterl, J.; Dunkel, J.; Ossowski, S. Evaluating Collaborative and Autonomous Agents in Data-Stream-Supported Coordination of Mobile Crowdsourcing. Sensors 2023, 23, 614es
dc.identifier.issn1424-8220
dc.identifier.urihttps://hdl.handle.net/10115/27713
dc.descriptionThis work was supported by the German Niedersächsisches Ministerium für Wissenschaftund Kultur (MWK) in the programme PROFILinternational, as well as the Spanish Ministry of Science and Innovation, co-funded by EU FEDER Funds, through grants RTI2018-095390-B-C33, PID2021-123673OB-C32 and TED2021-131295B-C33.es
dc.description.abstractMobile 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.es
dc.language.isoenges
dc.publisherMDPIes
dc.rightsAtribución 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectcrowdsourcinges
dc.subjectdata stream learninges
dc.subjectmultiagent systemses
dc.subjectcollaborative coordinationes
dc.subjectmarket-based coordinationes
dc.titleEvaluating Collaborative and Autonomous Agents in Data-Stream-Supported Coordination of Mobile Crowdsourcinges
dc.typeinfo:eu-repo/semantics/articlees
dc.identifier.doi10.3390/s23020614es
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses


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Atribución 4.0 InternacionalExcept where otherwise noted, this item's license is described as Atribución 4.0 Internacional