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Embedded Systems for Mobile Crowd Sensing Networks

Rapid developments in mobile intelligent terminal chip system and embedded sensor technology in the recent years have led academia and industry to invest in the research and study of Mobile Crowd Sensing Networks (MCSN). The mobile crowd sensing paradigm has emerged as mobile devices are integrated with more and more embedded sensors and are becoming ever more pervasive, introducing a human- and mobility-centric approach to fine-grained sensing. This new paradigm takes advantage of mobile devices to collect data in a distributed and crowd-sourced manner, enabling numerous largescale applications. From sensing cellular network coverage to carbon-dioxide monitoring in cities, from traffic monitoring to the Internet of Things (IoT), mobile crowd sensing offers unprecedented opportunities. In this regard, MCSN is a key technology in future generation networks and plays an important role in the advancement of embedded system technology.

However, the integration of embedded systems into mobile crowd sensing networks should be well orchestrated to address the many technical challenges such as: the design of the embedded devices on mobile terminals, also taking into consideration the competition, mobile data collection algorithms (with respect to crowdsourcing models), incentive mechanisms and modes of cooperation, processing of data for embedded systems applications, and the intelligent crowd sensing platforms.

This special issue focuses on recent advances in theory and key technologies of embedded systems for Mobile Crowd Sensing Networks. Original, unpublished contributions and invited articles, reflecting various aspects of mobile multimedia cloud computing are encouraged.

Edited by: Yong Jin, Philipp Svodoba, Daniele D'Agostino, Yuexin Li and Chaobo Yan

  1. Research

    Mobile adaptive crowd optimization scheme based on distance field

    For complexity and efficiency of the multi-objective optimization, we proposed the mobile distance field-driven adaptive crowd optimization algorithm. In space, we modify the surface parameters based on the co...

    Ya-chun Tang, Xiao-bo Guo and Xiang-dong Yin

    EURASIP Journal on Embedded Systems 2016 2016:23

    Published on: 15 November 2016

  2. Review

    Vehicle networking data-upload strategy based on mobile cloud services

    While traditional vehicle network communication architecture is based on special short-range communication, it is difficult to meet the demand for quality of service of vehicle networking data transmission. Th...

    Jie Yang, Jin Wang, Li Wan and Xiaobing Liu

    EURASIP Journal on Embedded Systems 2016 2016:22

    Published on: 14 November 2016

  3. Research

    Network overhead crowd management mechanism of virtual mobile Internet

    It has become the hot research issue that solves the bottleneck of resource management in the development of Internet through virtualization. However, there are the challenges of mobility management, resource ...

    Hai-yan Wu, Wei-ping Li and Jin Wang

    EURASIP Journal on Embedded Systems 2016 2016:21

    Published on: 3 November 2016

  4. Research

    Image crowd fusion mechanism based on optical embedded system

    Optical principle embedded image analysis can effectively improve the accuracy of image recognition, but there is a problem of low efficiency and high computational complexity. In view of the above problems, w...

    Xue-liang Ma and Ming-min Lv

    EURASIP Journal on Embedded Systems 2016 2016:19

    Published on: 12 October 2016