Towards Trust and Time-sharing Task Allocation Scheme in Mobile Crowdsensing

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TowardsTrustandTimesharingTaskAllocationSchemeinMobileCrowdsensing.pdf (1.25 MB)

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https://ieeexplore.ieee.org/abstract/document/11196935

Permanent Link

https://doi.org/10.1109/JIOT.2025.3619083
 http://hdl.handle.net/11603/40833

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UMBC Faculty Collection
UMBC Information Systems Department

Author/Creator

Author/Creator ORCID

https://orcid.org/0000-0003-2631-9223

Date

2025-10-08

Type of Work

journal articles
postprints
Text

Department

Program

Citation of Original Publication

Liu, Yuxin, Ziyi He, Zichao Xie, Neal N. Xiong, Tian Wang, and Houbing Herbert Song. “Towards Trust and Time-Sharing Task Allocation Scheme in Mobile Crowdsensing.” IEEE Internet of Things Journal, October 8, 2025, 1–1. https://doi.org/10.1109/JIOT.2025.3619083.

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© 2025 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Subjects

UMBC Security and Optimization for Networked Globe Laboratory (SONG Lab)
Electronic mail
Crowdsensing
Time-sharing task allocation
Reliability
Sensors
Resource management
Accuracy
Costs
Data collection
Mobile Crowdsensing
Classification based trust evaluation
Data integrity
Optimization
Trust truth data discovery

Abstract

Assigning tasks to reliable workers to obtain reliable data is a critical issue in Mobile CrowdSensing (MCS). The challenge is compounded by the problem of Information Elicitation Without Verification (IEWV), which renders traditional data quality evaluation methods ineffective. While some studies attempt to address this, they often struggle to assess workers’ dynamic trustworthiness, resulting in unreliable data. To overcome these challenges, we propose the Trust and Time-sharing Task Allocation based Truth Discovery (TTTA-TD) scheme, designed to ensure reliable data collection in MCS. This scheme includes three components: (a) Classification-based Trust Evaluation (CTE) that classifies workers based on behavior and applies tailored penalties—lenient for honest workers and stricter for malicious ones, (b) Trust-based Truth Data Discovery (TTDD), which improves truth data accuracy by integrating trust scores, and (c) Trust and Time-sharing Task Allocation (TTTA) which allocates tasks to ensure data reliability and minimize time-sharing disparities. Experimental results show that the TTTA algorithm reduces average time-sharing by 93.95%. The TTDD algorithm improves truth estimates across all dataset qualities, and the TTTA-TD scheme enhances data reliability by 0.35%, 2.06%, and 7.41% in high, medium, and low-quality datasets respectively.