Context-aware reliable crowdsourcing in social networks
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 com...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2020
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| Online Access: | https://hdl.handle.net/10356/139994 |
| _version_ | 1811677318188367872 |
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| author | Jiang, Jiuchuan An, Bo Jiang, Yichuan Lin, Donghui |
| author2 | School of Computer Science and Engineering |
| author_facet | School of Computer Science and Engineering Jiang, Jiuchuan An, Bo Jiang, Yichuan Lin, Donghui |
| author_sort | Jiang, Jiuchuan |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T02:35:27Z |
| format | Journal Article |
| id | ntu-10356/139994 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T02:35:27Z |
| publishDate | 2020 |
| record_format | dspace |
| spelling | ntu-10356/1399942020-05-26T03:03:06Z Context-aware reliable crowdsourcing in social networks Jiang, Jiuchuan An, Bo Jiang, Yichuan Lin, Donghui School of Computer Science and Engineering Engineering::Computer science and engineering Context-aware Crowdsourcing 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. 2020-05-26T03:03:06Z 2020-05-26T03:03:06Z 2017 Journal Article 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 2168-2216 https://hdl.handle.net/10356/139994 10.1109/TSMC.2017.2777447 2-s2.0-85039801180 2 50 617 632 en IEEE Transactions on Systems, Man, and Cybernetics: Systems © 2017 IEEE. All rights reserved. |
| spellingShingle | Engineering::Computer science and engineering Context-aware Crowdsourcing Jiang, Jiuchuan An, Bo Jiang, Yichuan Lin, Donghui Context-aware reliable crowdsourcing in social networks |
| title | Context-aware reliable crowdsourcing in social networks |
| title_full | Context-aware reliable crowdsourcing in social networks |
| title_fullStr | Context-aware reliable crowdsourcing in social networks |
| title_full_unstemmed | Context-aware reliable crowdsourcing in social networks |
| title_short | Context-aware reliable crowdsourcing in social networks |
| title_sort | context aware reliable crowdsourcing in social networks |
| topic | Engineering::Computer science and engineering Context-aware Crowdsourcing |
| url | https://hdl.handle.net/10356/139994 |
| work_keys_str_mv | AT jiangjiuchuan contextawarereliablecrowdsourcinginsocialnetworks AT anbo contextawarereliablecrowdsourcinginsocialnetworks AT jiangyichuan contextawarereliablecrowdsourcinginsocialnetworks AT lindonghui contextawarereliablecrowdsourcinginsocialnetworks |