Mobile crowdsensing: A survey on privacy-preservation, task management, assignment models, and incentives mechanisms

Fazlullah Khan, Ateeq Ur Rehman, Jiangbin Zheng*, Mian Ahmad Jan, Muhammad Alam

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

74 Citations (Scopus)

Abstract

Mobile crowdsensing is a useful technique to collect detailed information from mobile devices of the participants. The participants need to participate to sense and transmit valuable information to the servers. Due to the technological growth, various components of mobile devices such as accelerometer, gyroscope, camera and inertial, collect vast volumes of data in a quick, efficient, and cost-effective manner. However, a mobile crowdsensing paradigm may result in serious privacy and security breaches by exposing the mobile devices to various threats and vulnerabilities. This leakage of privacy has an adverse impact on the usage and participation of mobile devices. Motivated by these threats and privacy challenges, we investigate the current approaches used for preserving privacy in mobile crowdsensing applications. After a generic description of mobile crowdsensing systems and their components, we discuss critical issues related to privacy preservation, such as task management, task assignment models, and incentive mechanisms. We also discuss various mobile crowdsensing mechanisms available in the literature. Finally, we highlight numerous research challenges that need to be addressed to improve the performance of future privacy-preserving mechanisms for mobile crowdsensing applications.

Original languageEnglish
Pages (from-to)456-472
Number of pages17
JournalFuture Generation Computer Systems
Volume100
DOIs
Publication statusPublished - Nov 2019

Keywords

  • Assignment models
  • Incentives
  • Mobile crowdsensing
  • Privacy
  • Security
  • Task management

Fingerprint

Dive into the research topics of 'Mobile crowdsensing: A survey on privacy-preservation, task management, assignment models, and incentives mechanisms'. Together they form a unique fingerprint.

Cite this