Skip to main content

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 Original, unpublished contributions and invited articles, reflecting various aspects of mobile multimedia cloud computing are encouraged.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. For satisfying the network trend and intelligent demand of biopharmaceutical, we proposed the energy optimization consumption and management scheme of the drug green crowd data in the biological pharmaceutical...

    Authors: Shujuan Wang, Long He and Guiru Cheng
    Citation: EURASIP Journal on Embedded Systems 2017 2017:21
  2. 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...

    Authors: Ya-chun Tang, Xiao-bo Guo and Xiang-dong Yin
    Citation: EURASIP Journal on Embedded Systems 2016 2016:23
  3. 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...

    Authors: Xue-liang Ma and Ming-min Lv
    Citation: EURASIP Journal on Embedded Systems 2016 2016:19
  4. The opportunistic cooperative platform and adaptive cooperative control scheme were proposed based on the redundancy degree and priority of cooperative data packet, as well as selective characteristics of time...

    Authors: Yong Jin, Huan Dai, Canghai Sui, Anqi Liu and Ping Xu
    Citation: EURASIP Journal on Embedded Systems 2016 2016:5