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A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
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Preference-Matched Multitask Assignment for Group Socialization under Mobile Crowdsensing.

Mingyuan Zhang1, Shiyong Chen1, Zihao Wei1

  • 1School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China.

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|February 28, 2023
PubMed
Summary
This summary is machine-generated.

Mobile crowdsensing (MCS) addresses Internet of Things (IoT) challenges with a new group-oriented adjustable bidding task assignment (GO-ABTA) algorithm. This approach enhances task matching by considering user preferences within social groups for efficient IoT development.

Keywords:
group-orientedheterogeneous multitaskingmobile crowdsensingpreference matching

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Network Engineering

Background:

  • Mobile crowdsensing (MCS) is a key sensing paradigm for the Internet of Things (IoT).
  • Efficient task assignment in MCS is crucial for supporting diverse IoT services.
  • Existing methods struggle with heterogeneous tasks and user preferences in social networks.

Purpose of the Study:

  • To develop a novel algorithm for group-oriented multitask assignment in MCS.
  • To address the challenge of matching user preferences with heterogeneous tasks.
  • To solve the group-oriented bilateral preference-matching problem under budget constraints.

Main Methods:

  • Introduced the group-oriented adjustable bidding task assignment (GO-ABTA) algorithm.
  • Selected initial leaders and collaborative groups within social networks.
  • Modeled task requester and user preference assignment as a stable matching problem.
  • Implemented a tunable bidding process considering budget constraints.

Main Results:

  • Demonstrated the individual reasonableness, stability, and convergence of the GO-ABTA algorithm.
  • Verified the effectiveness of GO-ABTA through simulation.
  • Showcased the superiority of GO-ABTA compared to other existing algorithms.

Conclusions:

  • The GO-ABTA algorithm effectively solves the group-oriented bilateral preference-matching problem in MCS.
  • The proposed method enhances task assignment efficiency by incorporating social structures and user preferences.
  • GO-ABTA offers a promising solution for advancing IoT services through improved mobile crowdsensing task allocation.