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Location and Time Aware Multitask Allocation in Mobile Crowd-Sensing Based on Genetic Algorithm.

Aridegbe A Ipaye1, Zhigang Chen1, Muhammad Asim1

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Summary
This summary is machine-generated.

This study introduces a genetic algorithm for efficient mobile crowd-sensing task allocation, maximizing worker rewards and task completion within time constraints. The proposed Worker Multi-task Allocation-Genetic Algorithm (WMTA-GA) outperforms existing methods.

Keywords:
crowd-sensinggenetic algorithmincentive mechanismmultitask allocationtime-sensitive

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Area of Science:

  • Computer Science
  • Data Science
  • Mobile Computing

Background:

  • Mobile crowd-sensing (MCS) systems leverage smart device sensors for data collection.
  • Increasing sensing tasks and participants necessitate efficient task allocation strategies.
  • Worker incentives are crucial for ensuring task completion in MCS.

Purpose of the Study:

  • To develop an efficient task allocation approach for mobile crowd-sensing (MCS).
  • To assist workers in selecting multiple tasks considering time constraints and task requirements.
  • To maximize worker welfare through a novel pricing and reward mechanism.

Main Methods:

  • The study addresses task allocation as a non-deterministic polynomial (NP)-complete problem.
  • A Worker Multi-task Allocation-Genetic Algorithm (WMTA-GA) is proposed.
  • A pricing mechanism determines task budgets and worker payments based on willingness.

Main Results:

  • The WMTA-GA effectively solves the NP-complete task allocation problem.
  • Theoretical analysis confirms the algorithm's effectiveness.
  • The proposed algorithm demonstrates superior performance compared to state-of-the-art methods in average performance, worker welfare, and task assignment.

Conclusions:

  • The WMTA-GA offers an effective solution for mobile crowd-sensing task allocation.
  • The approach successfully balances worker time constraints with task requirements.
  • This method enhances overall system efficiency and worker satisfaction in MCS.