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DOI: 10.14569/IJACSA.2018.090154
PDF

An Efficient Participant’s Selection Algorithm for Crowdsensing

Author 1: Tariq Ali
Author 2: Umar Draz
Author 3: Sana Yasin
Author 4: Javeria Noureen
Author 5: Ahmad shaf
Author 6: Munwar Ali

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 9 Issue 1, 2018.

  • Abstract and Keywords
  • How to Cite this Article
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Abstract: With the advancement of mobile technology the use of Smartphone is greatly increased. Everyone has the mobile phones and it becomes the necessity of life. Today, smart devices are flooding the internet data at every time and in any form that cause the mobile crowdsensing (MCS). One of the key challenges in mobile crowd sensing system is how to effectively identify and select the well-suited participants in recruitments from a large user pool. This research work presents the concept of crowdsensing along with the selection process of participants from a large user pool. MCS provides the efficient selection process for participants that how well suited participant’s selects/recruit from a large user pool. For this, the proposed selection algorithm plays our role in which the recruitment of participants takes place with the availability status from the large user pool. At the end, the graphical result presented with the suitable location of the participants and their time slot.

Keywords: Mobile crowdsensing (MCS); Mobile Sensing Platform (MSP]); crowd sensing; participant; user pool; crowdsourcing

Tariq Ali, Umar Draz, Sana Yasin, Javeria Noureen, Ahmad shaf and Munwar Ali, “An Efficient Participant’s Selection Algorithm for Crowdsensing” International Journal of Advanced Computer Science and Applications(IJACSA), 9(1), 2018. http://dx.doi.org/10.14569/IJACSA.2018.090154

@article{Ali2018,
title = {An Efficient Participant’s Selection Algorithm for Crowdsensing},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2018.090154},
url = {http://dx.doi.org/10.14569/IJACSA.2018.090154},
year = {2018},
publisher = {The Science and Information Organization},
volume = {9},
number = {1},
author = {Tariq Ali and Umar Draz and Sana Yasin and Javeria Noureen and Ahmad shaf and Munwar Ali}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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