Mobile Information Systems

Big Data Management and Analytics for Mobile Crowd Sensing


Publishing date
29 Apr 2016
Status
Published
Submission deadline
11 Dec 2015

Lead Editor

1California State Polytechnic University, Pomona, Pomona, USA

2Shanghai Jiao Tong University, Shanghai, China

3Institute for Infocomm Research, Singapore

4University of Calgary, Calgary, Canada

5Institute for Infocomm Research, A*STAR, Singapore


Big Data Management and Analytics for Mobile Crowd Sensing

Description

With the fast increasing popularity of mobile smart devices, mobile crowd sensing has become a new paradigm of applications that enables the ubiquitous mobile devices with enhanced sensing capabilities, such as smartphones and wearable devices, to collect and to share local information towards a common goal. Most of the smart devices are equipped with a rich set of cheap and powerful sensors, for example, accelerometer, digital compass, GPS, microphone, and camera. These sensors can be utilized to monitor mobile users’ surrounding environment and infer human activities and contexts. In recent years, a wide variety of applications have been developed to realize the potential of crowd sensing throughout everyday life, such as environmental monitoring, noise pollution assessment, road and traffic condition monitoring, road-side parking statistics, and indoor localization. The data acquired through mobile crowd sensing exhibits a number of important characteristics, such as being large in scale (Volume), being fast generated (Velocity), being different in forms (Variety), and being uncertain in quality (Veracity). The 4Vs of crowd sensing data make it extremely interesting and challenging in designing participatory and opportunistic sensing technologies, human centric data management and analytics models, and novel visualization tools.

The objective of this special issue is to invite authors to submit original manuscripts that demonstrate and explore current advances in all aspects of big data management in mobile crowd sensing environments. The special issue solicits novel papers.

Potential topics include, but are not limited to:

  • Architecture and framework design for crowd sensed data management
  • Theoretic foundations of data analytics for crowd sensing
  • Human centric data management and analytics models
  • Data mining and machine learning algorithms and applications for crowd sensed data
  • Distributed and parallel algorithms for understanding big crowd sensed data
  • Participatory and opportunistic sensing and data collection
  • Crowd sensing data communication and sharing
  • Algorithm design for sensing scheduling
  • Big crowd sensed data processing, storage, and mining
  • Sensing resource management in crowd sensing
  • Economic systems and incentive mechanisms for crowd sensing
  • Crowd sensed data quality evaluation and pricing
  • Security, data privacy preservation, and trust management in crowd sensing
  • Social and psychological issues on crowd sensed data management
  • Novel crowd sensing and human centric data management applications
  • Novel crowd sensed data visualization tools
  • Experience reports and studies of crowd sensing systems with big data

Articles

  • Special Issue
  • - Volume 2016
  • - Article ID 8731802
  • - Editorial

Big Data Management and Analytics for Mobile Crowd Sensing

Tingting Chen | Fan Wu | ... | Qirong Ho
  • Special Issue
  • - Volume 2016
  • - Article ID 6804379
  • - Research Article

How Dangerous Are Your Smartphones? App Usage Recommendation with Privacy Preserving

Konglin Zhu | Xiaoman He | ... | Achille Pattavina
  • Special Issue
  • - Volume 2016
  • - Article ID 1763416
  • - Research Article

A Perturbed Compressed Sensing Protocol for Crowd Sensing

Zijian Zhang | Chengcheng Jin | ... | Liehuang Zhu
  • Special Issue
  • - Volume 2016
  • - Article ID 9825820
  • - Research Article

Outdoor Air Quality Level Inference via Surveillance Cameras

Zheng Zhang | Huadong Ma | ... | Cheng Zhang
  • Special Issue
  • - Volume 2016
  • - Article ID 6406981
  • - Research Article

Share the Crowdsensing Data with Local Crowd by V2V Communications

Chao Song | Ming Liu | Xili Dai
  • Special Issue
  • - Volume 2016
  • - Article ID 8793025
  • - Research Article

Privacy Leakage in Mobile Sensing: Your Unlock Passwords Can Be Leaked through Wireless Hotspot Functionality

Jie Zhang | Xiaolong Zheng | ... | Feng Chen
  • Special Issue
  • - Volume 2016
  • - Article ID 8686945
  • - Research Article

Cooperation Dynamics on Mobile Crowd Networks of Device-to-Device Communications

Yong Deng | Guiyi Wei | ... | Jun Shao
  • Special Issue
  • - Volume 2016
  • - Article ID 7908328
  • - Research Article

ODMBP: Behavior Forwarding for Multiple Property Destinations in Mobile Social Networks

Jia Xu | Jin Xin Xiang | ... | Jing Jie Yu
Mobile Information Systems
 Journal metrics
Acceptance rate37%
Submission to final decision102 days
Acceptance to publication42 days
CiteScore3.900
Impact Factor1.508
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