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Using Greedy Random Adaptive Procedure to Solve the User Selection Problem in Mobile Crowdsourcing.

Jian Yang1, Xiaojuan Ban2, Chunxiao Xing3

  • 1School of Computer and Communication Engineering, Beijing Key Laboratory of Knowledge Engineering for Materials Science, University of Science and Technology Beijing, Beijing 100083, China.

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|July 21, 2019
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Summary
This summary is machine-generated.

This study introduces a new approach for mobile crowdsourcing user selection, maximizing task quality and minimizing costs. The proposed marginalism-based method, proven NP-hard, utilizes a greedy random adaptive procedure for efficient selection.

Keywords:
GRASP-ARmarginalism principlemobile crowdsourcinguser selection

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Operations Research

Background:

  • Mobile crowdsourcing systems are increasingly prevalent due to advancements in mobile networks and smart devices.
  • Existing user selection methods in mobile crowdsourcing often fail to simultaneously optimize for task quality and cost-efficiency.
  • There is a need for improved user selection strategies that balance maximizing service quality with minimizing operational expenses.

Purpose of the Study:

  • To propose a novel user selection scheme for mobile crowdsourcing systems based on the principle of marginalism.
  • To address the limitations of existing methods that focus on either maximizing quality under budget or minimizing cost for a required quality.
  • To develop an efficient algorithm for the NP-hard mobile crowdsourcing user selection (MCUS) problem.

Main Methods:

  • The study models the user selection problem as a marginalism problem in mobile crowdsourcing user selection (MCUS-marginalism).
  • The NP-hard nature of the MCUS-marginalism problem is rigorously proven.
  • A greedy random adaptive procedure with annealing randomness (GRASP-AR) is proposed to solve the problem.

Main Results:

  • The proposed MCUS-marginalism framework effectively balances maximizing task quality and minimizing costs.
  • The GRASP-AR algorithm demonstrates superior performance in achieving optimal user selection.
  • Extensive experimental evaluations on real-world and synthetic datasets validate the effectiveness and efficiency of the proposed approach.

Conclusions:

  • The marginalism-based approach offers a significant improvement over existing user selection strategies in mobile crowdsourcing.
  • The GRASP-AR algorithm provides an efficient and effective solution for the complex MCUS-marginalism problem.
  • This research contributes to the optimization of mobile crowdsourcing systems by enhancing user selection mechanisms.