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A Personalized Task Allocation Strategy in Mobile Crowdsensing for Minimizing Total Cost.

Hengfei Gao1, Hongwei Zhao1

  • 1College of Computer Science and Technology, Jilin University, Changchun 130012, China.

Sensors (Basel, Switzerland)
|April 12, 2022
PubMed
Summary

This study introduces a dynamic, personalized task allocation strategy for mobile crowdsensing to minimize costs. It models the problem as a traveling salesman problem (TSP), offering greedy and genetic algorithms for efficient, optimal task assignment.

Keywords:
minimizing costmobile crowdsensingpersonalized task allocationtraveling salesman problem

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Area of Science:

  • Computer Science
  • Distributed Systems
  • Optimization

Background:

  • Mobile crowdsensing relies on efficient task allocation for optimal performance.
  • Existing methods often use static allocation, ignoring dynamic user states and preferences.
  • This overlooks the combinatorial optimization nature of task assignment.

Purpose of the Study:

  • To develop a personalized task allocation strategy for mobile crowdsensing.
  • To minimize total costs by considering both user movement and task preference.
  • To address the dynamic and combinatorial nature of task allocation.

Main Methods:

  • Formulated task allocation as a heterogeneous, asymmetric, multiple traveling salesman problem (TSP).
  • Transformed the multiple-TSP into a single-TSP, proving their equivalency.
  • Developed and compared a greedy algorithm with a bounded optimal solution and a genetic algorithm for solving the single-TSP.

Main Results:

  • The proposed personalized strategy effectively minimizes total costs in mobile crowdsensing.
  • The greedy algorithm provides a bounded optimal solution, while the genetic algorithm achieves lower costs with increased computation.
  • Simulation results using real-world datasets (roma/taxi, epfl, geolife) validate the theoretical analysis.

Conclusions:

  • Dynamic and personalized approaches are crucial for efficient mobile crowdsensing task allocation.
  • Modeling the problem as a TSP offers a robust framework for optimization.
  • Both greedy and genetic algorithms present viable solutions for practical implementation.