Overlapping Coalition Formation Game via Multi-Objective Optimization for Crowdsensing Task Allocation
With the rapid development of sensor technology and mobile services, the service model of mobile crowd sensing (MCS) has emerged. In this model, user groups perceive data through carried mobile terminal devices, thereby completing large-scale and distributed tasks. Task allocation is an important li...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/12/16/3454 |
| _version_ | 1797584939097522176 |
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| author | Yanming Fu Xiao Liu Weigeng Han Shenglin Lu Jiayuan Chen Tianbing Tang |
| author_facet | Yanming Fu Xiao Liu Weigeng Han Shenglin Lu Jiayuan Chen Tianbing Tang |
| author_sort | Yanming Fu |
| collection | DOAJ |
| description | With the rapid development of sensor technology and mobile services, the service model of mobile crowd sensing (MCS) has emerged. In this model, user groups perceive data through carried mobile terminal devices, thereby completing large-scale and distributed tasks. Task allocation is an important link in MCS, but the interests of task publishers, users, and platforms often conflict. Therefore, to improve the performance of MCS task allocation, this study proposes a repeated overlapping coalition formation game MCS task allocation scheme based on multiple-objective particle swarm optimization (ROCG-MOPSO). The overlapping coalition formation (OCF) game model is used to describe the resource allocation relationship between users and tasks, and design two game strategies, allowing users to form overlapping coalitions for different sensing tasks. Multi-objective optimization, on the other hand, is a strategy that considers multiple interests simultaneously in optimization problems. Therefore, we use the multi-objective particle swarm optimization algorithm to adjust the parameters of the OCF to better balance the interests of task publishers, users, and platforms and thus obtain a more optimal task allocation scheme. To verify the effectiveness of ROCG-MOPSO, we conduct experiments on a dataset and compare the results with the schemes in the related literature. The experimental results show that our ROCG-MOPSO performs superiorly on key performance indicators such as average user revenue, platform revenue, task completion rate, and user average surplus resources. |
| first_indexed | 2024-03-10T23:59:45Z |
| format | Article |
| id | doaj.art-ec24954b6a654574a176d98cf3578ccd |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T23:59:45Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-ec24954b6a654574a176d98cf3578ccd2023-11-19T00:53:54ZengMDPI AGElectronics2079-92922023-08-011216345410.3390/electronics12163454Overlapping Coalition Formation Game via Multi-Objective Optimization for Crowdsensing Task AllocationYanming Fu0Xiao Liu1Weigeng Han2Shenglin Lu3Jiayuan Chen4Tianbing Tang5School of Computer, Electronics and Information, Guangxi University, No. 100, University East Road, Nanning 530004, ChinaSchool of Computer, Electronics and Information, Guangxi University, No. 100, University East Road, Nanning 530004, ChinaSchool of Computer, Electronics and Information, Guangxi University, No. 100, University East Road, Nanning 530004, ChinaSchool of Computer, Electronics and Information, Guangxi University, No. 100, University East Road, Nanning 530004, ChinaSchool of Computer, Electronics and Information, Guangxi University, No. 100, University East Road, Nanning 530004, ChinaSchool of Computer, Electronics and Information, Guangxi University, No. 100, University East Road, Nanning 530004, ChinaWith the rapid development of sensor technology and mobile services, the service model of mobile crowd sensing (MCS) has emerged. In this model, user groups perceive data through carried mobile terminal devices, thereby completing large-scale and distributed tasks. Task allocation is an important link in MCS, but the interests of task publishers, users, and platforms often conflict. Therefore, to improve the performance of MCS task allocation, this study proposes a repeated overlapping coalition formation game MCS task allocation scheme based on multiple-objective particle swarm optimization (ROCG-MOPSO). The overlapping coalition formation (OCF) game model is used to describe the resource allocation relationship between users and tasks, and design two game strategies, allowing users to form overlapping coalitions for different sensing tasks. Multi-objective optimization, on the other hand, is a strategy that considers multiple interests simultaneously in optimization problems. Therefore, we use the multi-objective particle swarm optimization algorithm to adjust the parameters of the OCF to better balance the interests of task publishers, users, and platforms and thus obtain a more optimal task allocation scheme. To verify the effectiveness of ROCG-MOPSO, we conduct experiments on a dataset and compare the results with the schemes in the related literature. The experimental results show that our ROCG-MOPSO performs superiorly on key performance indicators such as average user revenue, platform revenue, task completion rate, and user average surplus resources.https://www.mdpi.com/2079-9292/12/16/3454mobile crowd sensingtask allocationmulti-objective optimizationoverlapping coalition formation |
| spellingShingle | Yanming Fu Xiao Liu Weigeng Han Shenglin Lu Jiayuan Chen Tianbing Tang Overlapping Coalition Formation Game via Multi-Objective Optimization for Crowdsensing Task Allocation Electronics mobile crowd sensing task allocation multi-objective optimization overlapping coalition formation |
| title | Overlapping Coalition Formation Game via Multi-Objective Optimization for Crowdsensing Task Allocation |
| title_full | Overlapping Coalition Formation Game via Multi-Objective Optimization for Crowdsensing Task Allocation |
| title_fullStr | Overlapping Coalition Formation Game via Multi-Objective Optimization for Crowdsensing Task Allocation |
| title_full_unstemmed | Overlapping Coalition Formation Game via Multi-Objective Optimization for Crowdsensing Task Allocation |
| title_short | Overlapping Coalition Formation Game via Multi-Objective Optimization for Crowdsensing Task Allocation |
| title_sort | overlapping coalition formation game via multi objective optimization for crowdsensing task allocation |
| topic | mobile crowd sensing task allocation multi-objective optimization overlapping coalition formation |
| url | https://www.mdpi.com/2079-9292/12/16/3454 |
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