Special Issue Information Special Issue Call for Paper Other Special Issues on this journal Closed Special Issues
Mobile Crowdsourcing (MCS)

Mobile Crowdsourcing (MCS)

Journal
Impact Score 3.79

OFFICIAL WEBSITE

Special Issue Information

Submission Deadline: 31-01-2017
Journal Impact Score: 3.79
Journal Name: World Wide Web
Publisher: World Wide Web
Journal & Submission Website: https://www.springer.com/journal/11280

Special Issue Call for Papers

In general, crowdsourcing is defined as the practice of obtaining needed services or
content by soliciting contributions from a large group of people, and especially
from an online community. Recently, with the rapid development of mobile
Internet and mobile social networking techniques, the scope of crowd
problem-solving systems using mobile devices has been broadened and the
traditional Internet Crowdsourcing is evolving into a new paradigm, i.e.,
Mobile
Crowdsourcing
(
MCS
), which facilitates the increasing number of mobile device
users to participate crowdsourcing tasks. On one hand, compared with Internet
crowdsourcing, mobile crowdsourcing leverages both sensory data from mobile
devices (offline community) and user-contributed data from mobile social
networking services (online community). On the other hand, mobile crowdsourcing
extends the original user participation scheme of crowdsourcing tasks, from
explicit participation to implicit partic
ipation. As a result, quite a number of
crowdsourcing tasks that are difficult to complete based on Internet crowdsourcing
has now become feasible, e.g., monitoring pollution level or noise level at the
city-scale, predicting the arrival time of buses, collecting the truth happenings after
a disaster, etc. Meanwhile, mobile crowdsourcing also brings a number of
challenges: How to build efficient infrastructure or framework to support MCS
systems? How to fuse online and offline data to facilitate MCS applications? How
to select workers and allocate tasks given the inherent mobility of potential MCS
participants? How to accomplish MCS tasks unintentionally or with minimum user
effort? How to ensure the performance of
MCS (e.g., accuracy and coverage) with
low quality data contributed by volunteers? What are the incentive mechanisms to
encourage MCS participants? How to protect the privacy of MCS participants?

Closed Special Issues

Publisher
Journal Details
Closing date
G2R Score
Decision Making in Heterogeneous Network Data Scenarios and Applications

Decision Making in Heterogeneous Network Data Scenarios and Applications

World Wide Web
Closing date: 15-10-2021 G2R Score: 3.79
Synthetic Media on the Web

Synthetic Media on the Web

World Wide Web
Closing date: 01-09-2021 G2R Score: 3.79
Resource Management at the Edge for Future Web, Mobile and IoT Applications

Resource Management at the Edge for Future Web, Mobile and IoT Applications

World Wide Web
Closing date: 31-07-2021 G2R Score: 3.79
Computational Aspects of Network Science

Computational Aspects of Network Science

World Wide Web
Closing date: 01-03-2021 G2R Score: 3.79
Large Scale Graph Data Analytics

Large Scale Graph Data Analytics

World Wide Web
Closing date: 31-01-2021 G2R Score: 3.79
Explainability in Web

Explainability in Web

World Wide Web
Closing date: 28-10-2020 G2R Score: 3.79
Deep Mining Big Social Data

Deep Mining Big Social Data

World Wide Web
Closing date: 30-06-2017 G2R Score: 3.79
Mobile Crowdsourcing (MCS)

Mobile Crowdsourcing (MCS)

World Wide Web
Closing date: 31-01-2017 G2R Score: 3.79
Security and Privacy of IoT

Security and Privacy of IoT

World Wide Web
Closing date: 30-09-2016 G2R Score: 3.79