Work

A multi-dimensional framework for improving data reliability in mobile crowd sensing

Year: 2024

Type: article

Abstract: Mobile Crowd Sensing (MCS) has become a promising new data perception paradigm. It is to be able to easily submit the wrong or untrusted data for the malicious attackers in such an environment. This greatly affects the normal operation of the MCS system and the authenticity of task results. Therefore, ensuring the reliability of data is becoming a key research direction in MCS, especially for real-time application scenarios. For this purpose, we propose a multi-dimensional framework for improving data reliability, named MDF. It integrates three dimensions of temporal, spatial context and sensing measurement. Through a series of experiments, it is demonstrated that MDF outperforms existing methods.

Source: Egyptian Informatics Journal

DOI: 10.1016/j.eij.2024.100518

Authors: Xu Wu, Yanjun Song, Junyu Lai

Institutions: Hainan Normal University, Guangxi University

Language: English

Topic: Mobile Crowdsensing and Crowdsourcing

Subfield: Computer Science Applications

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,200

Funders: National Natural Science Foundation of China, National Aerospace Science Foundation of China, Natural Science Research of Jiangsu Higher Education Institutions of China, Anhui Provincial University Teaching Quality and Teaching Reform Project