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A Dynamic Task Allocation Framework in Mobile Crowd Sensing with D3QN.

Yanming Fu1, Yuming Shen1, Liang Tang1

  • 1School of Computer and Electronic Information, Guangxi University, No. 100, University East Road, Nanning 530004, China.

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Summary
This summary is machine-generated.

Mobile crowd sensing (MCS) uses smart devices for data collection. A new deep reinforcement learning approach improves task assignment, boosting platform profit and task completion rates.

Keywords:
deep reinforcement learning (DRL)double deep Q network (D3QN)dueling dqndynamic task allocationmobile crowd sensing (MCS)multi-objective

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Area of Science:

  • Computer Science
  • Mobile Computing
  • Artificial Intelligence

Background:

  • Mobile crowd sensing (MCS) leverages smart devices for large-scale data collection.
  • Efficient task assignment is crucial for MCS system performance.
  • Traditional methods struggle with the dynamic, multi-constraint nature of MCS task allocation.

Purpose of the Study:

  • To develop a more efficient task assignment solution for MCS systems.
  • To address the limitations of greedy and heuristic approaches in dynamic MCS environments.
  • To optimize multiple objectives including platform profit and participant satisfaction.

Main Methods:

  • Utilized deep reinforcement learning, specifically a double deep Q network (D3QN) with a dueling architecture.
  • Developed a weighted approach to optimize multiple objectives.
  • Modeled the problem as a dynamic task allocation problem under constraints like travel distance, rewards, and task arrival.

Main Results:

  • The proposed D3QN-based solution significantly outperformed standard baseline solutions.
  • Demonstrated improvements in platform profit and task completion rates.
  • Enhanced the overall utility and attractiveness of the MCS platform.

Conclusions:

  • Deep reinforcement learning offers a powerful solution for complex MCS task assignment.
  • The D3QN approach effectively handles dynamic and multi-objective optimization in MCS.
  • The findings suggest a more sustainable and profitable model for MCS platforms.