Quality-aware multi-task allocation based on location importance in mobile crowdsensing
Authors:
- Yuping LiuYLYuping Liu
- Honglong ChenHCHonglong Chen
- Xiang LiuXLXiang Liu
- Wentao WeiWWWentao Wei
- Guoqi MaGMGuoqi Ma
- Xiaolong LiuXiaolong Liu
- Duannan YeDYDuannan Ye
- 22 January 2025
- journal article
- Published by Elsevier in Journal of Network and Computer Applications
- Vol. 236
Abstract
No abstract availableFunding Information
- National Natural Science Foundation of China (ZR2023ZD32, 62111530052)
- Natural Science Foundation of Shandong Province (ZR2022YQ61)
- Major Research Plan (61772551)
This publication has 39 references indexed in Scilit:
- Multitask Data Collection With Limited Budget in Edge-Assisted Mobile CrowdsensingIEEE Internet of Things Journal, 2024
- Utility-Based Heterogeneous User Recruitment of Multitask in Mobile CrowdsensingIEEE Internet of Things Journal, 2023
- Towards a privacy-preserving smart contract-based data aggregation and quality-driven incentive mechanism for mobile crowdsensingJournal of Network and Computer Applications, 2022
- Dynamic Task Pricing in Mobile Crowdsensing: An Age-of-Information-Based Queueing Game SchemeIEEE Internet of Things Journal, 2022
- HSM-SMCS: Task Assignment Based on Hybrid Sensing Modes in Sparse Mobile CrowdsensingIEEE Internet of Things Journal, 2022
- Privacy-preserving mechanisms for location privacy in mobile crowdsensing: A surveyJournal of Network and Computer Applications, 2022
- Cost-Effective Active Sparse Urban Sensing: Adversarial Autoencoder ApproachIEEE Internet of Things Journal, 2021
- Optimal Mobile Crowdsensing Incentive Under Sensing InaccuracyIEEE Internet of Things Journal, 2020
- A Learning-Based Credible Participant Recruitment Strategy for Mobile Crowd SensingIEEE Internet of Things Journal, 2020
- Data correlation based crowdsensing enhancement for environment monitoring2016