Distributed data trading algorithm based on multi-objective utility optimization
The traditional centralized data trading models are not well applicable to the current intelligent era where everything is interconnected and real-time data is generated, and in order to maximize the use of collected data, it is essential to design an effective data trading framework.Therefore, a di...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2021-02-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2021034/ |
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| _version_ | 1841539305190719488 |
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| author | Xiaohong HUANG Yong ZHANG Desheng SHAN Yekui QIAN Lu HAN Dandan LI Qun CONG |
| author_facet | Xiaohong HUANG Yong ZHANG Desheng SHAN Yekui QIAN Lu HAN Dandan LI Qun CONG |
| author_sort | Xiaohong HUANG |
| collection | DOAJ |
| description | The traditional centralized data trading models are not well applicable to the current intelligent era where everything is interconnected and real-time data is generated, and in order to maximize the use of collected data, it is essential to design an effective data trading framework.Therefore, a distributed data trading framework based on consortium blockchain was proposed, which realized P2P data trading without relying on a third party.Aiming at the problem that existing data trading models only consider the factors of the data itself and ignore the factors related to user tasks, a bi-level multi-objective optimization model was constructed based on multi-dimensional factors, such as data quality, data attributes, attribute relevance and consumer competition, to optimize the utilities of data provider (DP) and data consumer (DC).To solve the above model, an improved multi-objective genetic algorithm-collaborative NSGAII was proposed, calculated by the cooperation of DP, DC and data aggregator (AG).The simulation results show that the collaborative NSGAII achieves better performance in terms of the utilities of DP and DC, thus realizing more effective data trading. |
| format | Article |
| id | doaj-art-bb808283c5d14ce29ba865200bf0795b |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2021-02-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-bb808283c5d14ce29ba865200bf0795b2025-01-14T07:21:36ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2021-02-0142526359740080Distributed data trading algorithm based on multi-objective utility optimizationXiaohong HUANGYong ZHANGDesheng SHANYekui QIANLu HANDandan LIQun CONGThe traditional centralized data trading models are not well applicable to the current intelligent era where everything is interconnected and real-time data is generated, and in order to maximize the use of collected data, it is essential to design an effective data trading framework.Therefore, a distributed data trading framework based on consortium blockchain was proposed, which realized P2P data trading without relying on a third party.Aiming at the problem that existing data trading models only consider the factors of the data itself and ignore the factors related to user tasks, a bi-level multi-objective optimization model was constructed based on multi-dimensional factors, such as data quality, data attributes, attribute relevance and consumer competition, to optimize the utilities of data provider (DP) and data consumer (DC).To solve the above model, an improved multi-objective genetic algorithm-collaborative NSGAII was proposed, calculated by the cooperation of DP, DC and data aggregator (AG).The simulation results show that the collaborative NSGAII achieves better performance in terms of the utilities of DP and DC, thus realizing more effective data trading.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2021034/consortium blockchaindistributed data tradingoptimization matching modelmulti-objective genetic algo-rithm |
| spellingShingle | Xiaohong HUANG Yong ZHANG Desheng SHAN Yekui QIAN Lu HAN Dandan LI Qun CONG Distributed data trading algorithm based on multi-objective utility optimization Tongxin xuebao consortium blockchain distributed data trading optimization matching model multi-objective genetic algo-rithm |
| title | Distributed data trading algorithm based on multi-objective utility optimization |
| title_full | Distributed data trading algorithm based on multi-objective utility optimization |
| title_fullStr | Distributed data trading algorithm based on multi-objective utility optimization |
| title_full_unstemmed | Distributed data trading algorithm based on multi-objective utility optimization |
| title_short | Distributed data trading algorithm based on multi-objective utility optimization |
| title_sort | distributed data trading algorithm based on multi objective utility optimization |
| topic | consortium blockchain distributed data trading optimization matching model multi-objective genetic algo-rithm |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2021034/ |
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