Improving the quality of service indices of task allocation in mobile crowd sensing with fuzzy-based inverse stackelberg game theory
This paper introduces a comprehensive strategy for heterogeneously allocating tasks, aiming to optimize mobile crowd sensing through the use of fuzzy logic and thus achieving superior coverage quality. We employed a deep learning method to address the diverse range of requests. Recognizing the insta...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-11-01
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| Series: | Intelligent Systems with Applications |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667305323001163 |
| _version_ | 1797526178058207232 |
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| author | Zohreh Vahedi Seyyed Javad Seyyed Mahdavi Chabok Gelareh Veisi |
| author_facet | Zohreh Vahedi Seyyed Javad Seyyed Mahdavi Chabok Gelareh Veisi |
| author_sort | Zohreh Vahedi |
| collection | DOAJ |
| description | This paper introduces a comprehensive strategy for heterogeneously allocating tasks, aiming to optimize mobile crowd sensing through the use of fuzzy logic and thus achieving superior coverage quality. We employed a deep learning method to address the diverse range of requests. Recognizing the instability during the learning process, we utilized an approximation function for the Q-values, thereby preventing divergence during the training phase of the model. A prominent challenge is ensuring robust user participation in mobile crowd sensing initiatives. Essentially, a higher number of monitoring nodes within an area correlates with improved coverage quality. We employed fuzzy logic to estimate participation density, taking into account both the duration of users' presence in the study region and the geographical density. Our results are compelling: the proposed method boosts coverage levels by over 17% compared to standard techniques. Additionally, with an accuracy spanning 91.5% to 95.3% for the correct allocation of resources using a dataset from Google, the efficacy of our approach is further underscored. |
| first_indexed | 2024-03-10T09:25:31Z |
| format | Article |
| id | doaj.art-4f8b4d48085d4c3bbd53980a29121bf4 |
| institution | Directory Open Access Journal |
| issn | 2667-3053 |
| language | English |
| last_indexed | 2024-03-10T09:25:31Z |
| publishDate | 2023-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Intelligent Systems with Applications |
| spelling | doaj.art-4f8b4d48085d4c3bbd53980a29121bf42023-11-22T04:49:39ZengElsevierIntelligent Systems with Applications2667-30532023-11-0120200291Improving the quality of service indices of task allocation in mobile crowd sensing with fuzzy-based inverse stackelberg game theoryZohreh Vahedi0Seyyed Javad Seyyed Mahdavi Chabok1Gelareh Veisi2Department of Computer Engineering, Mashhad Branch, Islamic Azad University, Mashhad, IranDepartment of Electrical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran; Corresponding author.Department of Computer Engineering, Mashhad Branch, Islamic Azad University, Mashhad, IranThis paper introduces a comprehensive strategy for heterogeneously allocating tasks, aiming to optimize mobile crowd sensing through the use of fuzzy logic and thus achieving superior coverage quality. We employed a deep learning method to address the diverse range of requests. Recognizing the instability during the learning process, we utilized an approximation function for the Q-values, thereby preventing divergence during the training phase of the model. A prominent challenge is ensuring robust user participation in mobile crowd sensing initiatives. Essentially, a higher number of monitoring nodes within an area correlates with improved coverage quality. We employed fuzzy logic to estimate participation density, taking into account both the duration of users' presence in the study region and the geographical density. Our results are compelling: the proposed method boosts coverage levels by over 17% compared to standard techniques. Additionally, with an accuracy spanning 91.5% to 95.3% for the correct allocation of resources using a dataset from Google, the efficacy of our approach is further underscored.http://www.sciencedirect.com/science/article/pii/S2667305323001163Heterogeneous task allocationMobile crowd sensingFuzzy method, Internet of ThingsCoverage |
| spellingShingle | Zohreh Vahedi Seyyed Javad Seyyed Mahdavi Chabok Gelareh Veisi Improving the quality of service indices of task allocation in mobile crowd sensing with fuzzy-based inverse stackelberg game theory Intelligent Systems with Applications Heterogeneous task allocation Mobile crowd sensing Fuzzy method, Internet of Things Coverage |
| title | Improving the quality of service indices of task allocation in mobile crowd sensing with fuzzy-based inverse stackelberg game theory |
| title_full | Improving the quality of service indices of task allocation in mobile crowd sensing with fuzzy-based inverse stackelberg game theory |
| title_fullStr | Improving the quality of service indices of task allocation in mobile crowd sensing with fuzzy-based inverse stackelberg game theory |
| title_full_unstemmed | Improving the quality of service indices of task allocation in mobile crowd sensing with fuzzy-based inverse stackelberg game theory |
| title_short | Improving the quality of service indices of task allocation in mobile crowd sensing with fuzzy-based inverse stackelberg game theory |
| title_sort | improving the quality of service indices of task allocation in mobile crowd sensing with fuzzy based inverse stackelberg game theory |
| topic | Heterogeneous task allocation Mobile crowd sensing Fuzzy method, Internet of Things Coverage |
| url | http://www.sciencedirect.com/science/article/pii/S2667305323001163 |
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