Next Article in Journal
A Review of Distributed Optical Fiber Sensors for Civil Engineering Applications
Next Article in Special Issue
Distance-Based Opportunistic Mobile Data Offloading
Previous Article in Journal
Wireless Sensor Array Network DoA Estimation from Compressed Array Data via Joint Sparse Representation
Previous Article in Special Issue
Optimizing Retransmission Threshold in Wireless Sensor Networks
Article Menu

Export Article

Open AccessArticle
Sensors 2016, 16(5), 746; doi:10.3390/s16050746

A Task-Centric Cooperative Sensing Scheme for Mobile Crowdsourcing Systems

1
State Key Laboratory of Software Engineering, Wuhan University, Wuhan 430072, China
2
Computer School, Wuhan University, Wuhan 430072, China
3
Institute of Seismology, China Earthquake Administration, Wuhan 430071, China
*
Author to whom correspondence should be addressed.
Academic Editor: Leonhard M. Reindl
Received: 15 February 2016 / Revised: 1 May 2016 / Accepted: 17 May 2016 / Published: 23 May 2016
(This article belongs to the Special Issue Identification, Information & Knowledge in the Internet of Things)
View Full-Text   |   Download PDF [5069 KB, uploaded 23 May 2016]   |  

Abstract

In a densely distributed mobile crowdsourcing system, data collected by neighboring participants often exhibit strong spatial correlations. By exploiting this property, one may employ a portion of the users as active participants and set the other users as idling ones without compromising the quality of sensing or the connectivity of the network. In this work, two participant selection questions are considered: (a) how to recruit an optimal number of users as active participants to guarantee that the overall sensing data integrity is kept above a preset threshold; and (b) how to recruit an optimal number of participants with some inaccurate data so that the fairness of selection and resource conservation can be achieved while maintaining sufficient sensing data integrity. For question (a), we propose a novel task-centric approach to explicitly exploit data correlation among participants. This subset selection problem is regarded as a constrained optimization problem and we propose an efficient polynomial time algorithm to solve it. For question (b), we formulate this set partitioning problem as a constrained min-max optimization problem. A solution using an improved version of the polynomial time algorithm is proposed based on (a). We validate these algorithms using a publicly available Intel-Berkeley lab sensing dataset and satisfactory performance is achieved. View Full-Text
Keywords: mobile crowd sensing; task-centric; participant selection; data integrity; data prediction mobile crowd sensing; task-centric; participant selection; data integrity; data prediction
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Liu, Z.; Niu, X.; Lin, X.; Huang, T.; Wu, Y.; Li, H. A Task-Centric Cooperative Sensing Scheme for Mobile Crowdsourcing Systems. Sensors 2016, 16, 746.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top