Special Issue Information Special Issue Call for Paper Other Special Issues on this journal Closed Special Issues
Active Crowd Sensing

Active Crowd Sensing

Journal
Impact Score 4.62

OFFICIAL WEBSITE

Special Issue Information

Submission Deadline: 15-10-2020
Journal Impact Score: 4.62
Journal Name: Personal and Ubiquitous Computing
Publisher: Personal and Ubiquitous Computing
Journal & Submission Website: https://www.springer.com/journal/779

Special Issue Call for Papers

Guest Editors

Zhiyong Yu, Fuzhou University (China), [email protected]
Jiangtao Wang, Lancaster University (UK), [email protected]
Jordán Pascual Espada, University of Oviedo (Spain), [email protected]

Mobile Crowd Sensing (MCS) has emerged as a paradigm for gathering information about the physical world. Using smartphones, networked vehicles and other sensor-rich mobile, portable and wearable devices, massive volumes of data can be gathered, converged and mined to enable applications in intelligent traffic, environmental monitoring, urban planning or public safety management, amongst many others.

The cost of crowdsensed data and the quality of data are two key factors for MCS. There has been a great deal of work to optimize them separately, such as recruiting participants that can meet a task completion requirement, or utilizing data already collected to infer uncollected data with minimal error. However, optimizing them jointly is a promising direction to optimize both cost and quality and is based on active learning which selects the most useful samples and so reduces the number of labeled samples needed. This approach has been successfully used in other research fields including signal and image processing.

In MCS, this is known as ‘Active Crowd Sensing’, and presents several challenges including: the crowdsensed data are spatiotemporally autocorrelated, while traditional active learning assumes the data are independently identically distributed; and that data from different applications require different learning algorithms and so active selection strategies should be based on specific learning algorithms.

This special issue of Personal and Ubiquitous Computing provides an opportunity for researchers and product developers to review and discuss the state-of-the-art in Active Crowd Sensing, to explore novel application areas and demonstrate the benefits of Active Crowd Sensing and to identify open issues sensing and learning in MCS.

Topics may include (but are not limited to):

Important Dates

Manuscript Submission:       June 15, 2020
Decision Notification:           September 15, 2020
Final Manuscript Due Date:  October 15, 2020
Publication Date (tentative): 2021

Submission Guidelines

Papers must address one or more of the above issues and should be original and not be under consideration in other publication venues.
All papers will be peer reviewed by at least two independent reviewers in addition to the editors.
Extended versions of conference papers that are already published may be considered as long as the additional contribution is at least 30% new or additional content from the original.
Authors must follow the formatting and submission instructions of the Personal and Ubiuitous Computing journal at https://www.springer.com/journal/779.
During the first step in the submission system Editorial Manager, please select “Original article” as article type. In further steps please confirm that your submission belongs to a special issue and choose from the drop-down menu the appropriate special issue title.



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