TY  - JOUR
U1  - Wissenschaftlicher Artikel
A1  - Bruns, Ralf
A1  - Dötterl, Jeremias
A1  - Dunkel, Jürgen
A1  - Ossowski, Sascha
T1  - Evaluating Collaborative and Autonomous Agents in Data-Stream-Supported Coordination of Mobile Crowdsourcing
JF  - Sensors
N2  - 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.
KW  - data stream learning
KW  - multiagent systems
KW  - collaborative coordination
KW  - market-based coordination
KW  - Crowdsourcing
KW  - Datenstrom
KW  - Sensor
Y1  - 2023
UN  - https://nbn-resolving.org/urn:nbn:de:bsz:960-opus4-24436
SN  - 1424-8220
SS  - 1424-8220
U6  - https://doi.org/10.25968/opus-2443
DO  - https://doi.org/10.25968/opus-2443
VL  - 23
IS  - 2
SP  - 20
S1  - 20
ER  -