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Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment
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Service Benefit Aware Multi-Task Assignment Strategy for Mobile Crowd Sensing.

Zhidu Li1,2,3, Hailiang Liu1,2,3, Ruyan Wang1,2,3

  • 1School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.

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|November 14, 2019
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Summary
This summary is machine-generated.

This study introduces a service benefit aware multi-task assignment (SBAMA) strategy for mobile crowd sensing (MCS). The SBAMA strategy enhances task matching accuracy and reduces complexity for better data credibility and completion rates.

Keywords:
Mobile Crowd Sensinggradient descent algorithmservice benefittask assignmentusers clustering

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Area of Science:

  • Computer Science
  • Distributed Computing
  • Mobile Computing

Background:

  • Mobile crowd sensing (MCS) systems involve numerous participants with diverse sensing costs and preferences.
  • Accurate task assignment is crucial but challenging, requiring simplification and improved matching accuracy while ensuring data credibility.

Purpose of the Study:

  • To propose a novel strategy for multi-task assignment in MCS systems.
  • To address challenges in simplifying task assignment, enhancing participant-task matching, and guaranteeing data credibility.

Main Methods:

  • Developed a service benefit aware multi-task assignment (SBAMA) strategy.
  • Modeled participant service benefits considering task difficulty, history, capacity, and positivity.
  • Employed an enhanced fuzzy clustering method for user segmentation.
  • Utilized a gradient descent algorithm for optimal task-participant matching.

Main Results:

  • The SBAMA strategy effectively reduces the complexity of the task assignment process.
  • Improved matching accuracy between tasks and participants was achieved.
  • Enhanced task completion rates in MCS systems.

Conclusions:

  • The proposed SBAMA strategy offers an effective solution for complex task assignment in MCS.
  • It balances participant needs with task requirements for efficient system operation.
  • The strategy contributes to more reliable and efficient mobile crowd sensing operations.