Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/139994
Title: Context-aware reliable crowdsourcing in social networks
Authors: Jiang, Jiuchuan
An, Bo
Jiang, Yichuan
Lin, Donghui
Keywords: Engineering::Computer science and engineering
Issue Date: 2017
Source: Jiang, J., An, B., Jiang, Y., & Lin, D. (2020). Context-aware reliable crowdsourcing in social networks. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 50(2), 617-632. doi:10.1109/TSMC.2017.2777447
Journal: IEEE Transactions on Systems, Man, and Cybernetics: Systems
Abstract: There are two problems in the traditional crowdsourcing systems for handling complex tasks. First, decomposing complex tasks into a set of micro-subtasks requires the decomposition capability of the requesters; thus, some requesters may abandon using crowdsourcing to accomplish a large number of complex tasks since they cannot bear such heavy burden by themselves. Second, tasks are often assigned redundantly to multiple workers to achieve reliable results, but reliability may not be ensured when there are many malicious workers in the crowd. Currently, it is observed that the workers are often connected through social networks, a feature that can significantly facilitate task allocation and task execution in crowdsourcing. Therefore, this paper investigates crowdsourcing in social networks and presents a novel context-aware reliable crowdsourcing approach. In our presented approach, the two problems in traditional crowdsourcing are addressed as follows: 1) the complex tasks can be performed through autonomous coordination between the assigned worker and his contextual workers in the social network; thus, the requesters can be exempt from a heavy computing load for decomposing complex tasks into subtasks and combing the partial results of subtasks, thereby enabling more requesters to accomplish a large number of complex tasks through crowdsourcing, and 2) the reliability of a worker is determined not only by the reputation of the worker himself but also by the reputations of the contextual workers in the social network; thus, the unreliability of transient or malicious workers can be effectively addressed. The presented approach addresses two types of social networks including simplex and multiplex networks. Based on theoretical analyses and experiments on a real-world dataset, we find that the presented approach can achieve significantly higher task allocation and execution efficiency than the previous benchmark task allocation approaches; moreover, the presented contextual reputation mechanism can achieve relatively higher reliability when there are many malicious workers in the crowd.
URI: https://hdl.handle.net/10356/139994
ISSN: 2168-2216
DOI: 10.1109/TSMC.2017.2777447
Rights: © 2017 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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