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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.
Sensors (Basel, Switzerland)
|November 14, 2019
Summary
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.
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.
