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Probabilistic Coverage Constraint Task Assignment with Privacy Protection in Vehicular Crowdsensing

The increasing popularity of portable smart devices has led to the emergence of vehicular crowdsensing as a novel approach for real-time sensing and environmental data collection, garnering significant attention across various domains. Within vehicular crowdsensing, task assignment stands as a funda...

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Detalles Bibliográficos
Autores principales: Li, Zhe, Liu, Xiaolong, Huang, Yang, Chen, Honglong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10536900/
https://www.ncbi.nlm.nih.gov/pubmed/37765855
http://dx.doi.org/10.3390/s23187798
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author Li, Zhe
Liu, Xiaolong
Huang, Yang
Chen, Honglong
author_facet Li, Zhe
Liu, Xiaolong
Huang, Yang
Chen, Honglong
author_sort Li, Zhe
collection PubMed
description The increasing popularity of portable smart devices has led to the emergence of vehicular crowdsensing as a novel approach for real-time sensing and environmental data collection, garnering significant attention across various domains. Within vehicular crowdsensing, task assignment stands as a fundamental research challenge. As the number of vehicle users and perceived tasks grows, the design of efficient task assignment schemes becomes crucial. However, existing research solely focuses on task deadlines, neglecting the importance of task duration. Additionally, the majority of privacy protection mechanisms in the current task assignment process emphasize safeguarding user location information but overlook the protection of user-perceived duration. This lack of protection exposes users to potential time-aware inference attacks, enabling attackers to deduce user schedules and device information. To address these issues in opportunistic task assignment for vehicular crowdsensing, this paper presents the minimum number of participants required under the constraint of probability coverage and proposes the User-Based Task Assignment (UBTA) mechanism, which selects the smallest set of participants to minimize the payment cost while measuring the probability of accomplishing perceived tasks by user combinations. To ensure privacy protection during opportunistic task assignment, a privacy protection method based on differential privacy is introduced. This method fuzzifies the sensing duration of vehicle users and calculates the probability of vehicle users completing sensing tasks, thus avoiding the exposure of users’ sensitive data while effectively assigning tasks. The efficacy of the proposed algorithm is demonstrated through theoretical analysis and a comprehensive set of simulation experiments.
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spelling pubmed-105369002023-09-29 Probabilistic Coverage Constraint Task Assignment with Privacy Protection in Vehicular Crowdsensing Li, Zhe Liu, Xiaolong Huang, Yang Chen, Honglong Sensors (Basel) Article The increasing popularity of portable smart devices has led to the emergence of vehicular crowdsensing as a novel approach for real-time sensing and environmental data collection, garnering significant attention across various domains. Within vehicular crowdsensing, task assignment stands as a fundamental research challenge. As the number of vehicle users and perceived tasks grows, the design of efficient task assignment schemes becomes crucial. However, existing research solely focuses on task deadlines, neglecting the importance of task duration. Additionally, the majority of privacy protection mechanisms in the current task assignment process emphasize safeguarding user location information but overlook the protection of user-perceived duration. This lack of protection exposes users to potential time-aware inference attacks, enabling attackers to deduce user schedules and device information. To address these issues in opportunistic task assignment for vehicular crowdsensing, this paper presents the minimum number of participants required under the constraint of probability coverage and proposes the User-Based Task Assignment (UBTA) mechanism, which selects the smallest set of participants to minimize the payment cost while measuring the probability of accomplishing perceived tasks by user combinations. To ensure privacy protection during opportunistic task assignment, a privacy protection method based on differential privacy is introduced. This method fuzzifies the sensing duration of vehicle users and calculates the probability of vehicle users completing sensing tasks, thus avoiding the exposure of users’ sensitive data while effectively assigning tasks. The efficacy of the proposed algorithm is demonstrated through theoretical analysis and a comprehensive set of simulation experiments. MDPI 2023-09-11 /pmc/articles/PMC10536900/ /pubmed/37765855 http://dx.doi.org/10.3390/s23187798 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Li, Zhe
Liu, Xiaolong
Huang, Yang
Chen, Honglong
Probabilistic Coverage Constraint Task Assignment with Privacy Protection in Vehicular Crowdsensing
title Probabilistic Coverage Constraint Task Assignment with Privacy Protection in Vehicular Crowdsensing
title_full Probabilistic Coverage Constraint Task Assignment with Privacy Protection in Vehicular Crowdsensing
title_fullStr Probabilistic Coverage Constraint Task Assignment with Privacy Protection in Vehicular Crowdsensing
title_full_unstemmed Probabilistic Coverage Constraint Task Assignment with Privacy Protection in Vehicular Crowdsensing
title_short Probabilistic Coverage Constraint Task Assignment with Privacy Protection in Vehicular Crowdsensing
title_sort probabilistic coverage constraint task assignment with privacy protection in vehicular crowdsensing
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10536900/
https://www.ncbi.nlm.nih.gov/pubmed/37765855
http://dx.doi.org/10.3390/s23187798
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