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Service Benefit Aware Multi-Task Assignment Strategy for Mobile Crowd Sensing

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Τίτλος: Service Benefit Aware Multi-Task Assignment Strategy for Mobile Crowd Sensing
Συγγραφείς: Zhidu Li, Hailiang Liu, Ruyan Wang
Πηγή: Sensors, Vol 19, Iss 21, p 4666 (2019)
Στοιχεία εκδότη: MDPI AG
Έτος έκδοσης: 2019
Συλλογή: Directory of Open Access Journals: DOAJ Articles
Θεματικοί όροι: mobile crowd sensing, task assignment, service benefit, users clustering, gradient descent algorithm, Chemical technology, TP1-1185
Περιγραφή: Mobile crowd sensing (MCS) systems usually attract numerous participants with widely varying sensing costs and interest preferences to perform tasks, where accurate task assignment plays an indispensable role and also faces many challenges (e.g., how to simplify the complicated task assignment process and improve matching accuracy between tasks and participants, while guaranteeing submitted data credibility). To overcome these challenges, we propose a service benefit aware multi-task assignment (SBAMA) strategy in this paper. Firstly, service benefits of participants are modeled based on their task difficulty, task history, sensing capacity, and sensing positivity to meet differentiated requirements of various task types. Subsequently, users are then clustered by enhanced fuzzy clustering method. Finally, a gradient descent algorithm is designed to match task types to participants achieving the maximum service benefit. Simulation results verify that the proposed task assignment strategy not only effectively reduces matching complexity but also improves task completion rate.
Τύπος εγγράφου: article in journal/newspaper
Γλώσσα: English
Relation: https://www.mdpi.com/1424-8220/19/21/4666; https://doaj.org/toc/1424-8220; https://doaj.org/article/91363be88ba1450d8c46de19ea1bef21
DOI: 10.3390/s19214666
Διαθεσιμότητα: https://doi.org/10.3390/s19214666
https://doaj.org/article/91363be88ba1450d8c46de19ea1bef21
Αριθμός Καταχώρησης: edsbas.3AA0505D
Βάση Δεδομένων: BASE
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  – Url: https://doi.org/10.3390/s19214666#
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  Data: Service Benefit Aware Multi-Task Assignment Strategy for Mobile Crowd Sensing
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  Data: <searchLink fieldCode="AR" term="%22Zhidu+Li%22">Zhidu Li</searchLink><br /><searchLink fieldCode="AR" term="%22Hailiang+Liu%22">Hailiang Liu</searchLink><br /><searchLink fieldCode="AR" term="%22Ruyan+Wang%22">Ruyan Wang</searchLink>
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  Data: Sensors, Vol 19, Iss 21, p 4666 (2019)
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  Data: MDPI AG
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  Label: Publication Year
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  Data: 2019
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  Data: Directory of Open Access Journals: DOAJ Articles
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  Data: <searchLink fieldCode="DE" term="%22mobile+crowd+sensing%22">mobile crowd sensing</searchLink><br /><searchLink fieldCode="DE" term="%22task+assignment%22">task assignment</searchLink><br /><searchLink fieldCode="DE" term="%22service+benefit%22">service benefit</searchLink><br /><searchLink fieldCode="DE" term="%22users+clustering%22">users clustering</searchLink><br /><searchLink fieldCode="DE" term="%22gradient+descent+algorithm%22">gradient descent algorithm</searchLink><br /><searchLink fieldCode="DE" term="%22Chemical+technology%22">Chemical technology</searchLink><br /><searchLink fieldCode="DE" term="%22TP1-1185%22">TP1-1185</searchLink>
– Name: Abstract
  Label: Description
  Group: Ab
  Data: Mobile crowd sensing (MCS) systems usually attract numerous participants with widely varying sensing costs and interest preferences to perform tasks, where accurate task assignment plays an indispensable role and also faces many challenges (e.g., how to simplify the complicated task assignment process and improve matching accuracy between tasks and participants, while guaranteeing submitted data credibility). To overcome these challenges, we propose a service benefit aware multi-task assignment (SBAMA) strategy in this paper. Firstly, service benefits of participants are modeled based on their task difficulty, task history, sensing capacity, and sensing positivity to meet differentiated requirements of various task types. Subsequently, users are then clustered by enhanced fuzzy clustering method. Finally, a gradient descent algorithm is designed to match task types to participants achieving the maximum service benefit. Simulation results verify that the proposed task assignment strategy not only effectively reduces matching complexity but also improves task completion rate.
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  Data: https://www.mdpi.com/1424-8220/19/21/4666; https://doaj.org/toc/1424-8220; https://doaj.org/article/91363be88ba1450d8c46de19ea1bef21
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  Data: 10.3390/s19214666
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      – SubjectFull: mobile crowd sensing
        Type: general
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      – SubjectFull: service benefit
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      – SubjectFull: users clustering
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      – SubjectFull: gradient descent algorithm
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