Task Assignment Scheme Designed for Online Urban Sensing Based on Sparse Mobile Crowdsensing

Sparse mobile crowdsensing (SMCS) achieves urban-scale environmental sensing by assigning tasks to workers in specific subareas and inferring global data from the collected information. However, the effectiveness of SMCS is often limited because many studies overlook workers' mobility and data...

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Vydáno v:IEEE internet of things journal Ročník 12; číslo 11; s. 17791 - 17806
Hlavní autoři: Zeng, Hongjian, Xiong, Yonghua, She, Jinhua, Yu, Anjun
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 01.06.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithm optimization
Automation
Constraints
Costs
Crowdsensing
Cycle time
Data collection
Data integrity
Effectiveness
Evolutionary algorithms
Evolutionary computation
Genetic algorithms
Inference
Inference algorithms
Internet of Things
Mobile computing
Optimization
Sensors
sparse mobile crowdsensing (SMCS)
subarea evaluation
task assignment
Time factors
ISSN:2327-4662, 2327-4662
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Shrnutí:Sparse mobile crowdsensing (SMCS) achieves urban-scale environmental sensing by assigning tasks to workers in specific subareas and inferring global data from the collected information. However, the effectiveness of SMCS is often limited because many studies overlook workers' mobility and data collection time during subarea selection, as well as the time constraints of the sensing cycle in task assignment. This may affect the task completion timeliness and data quality. To address these issues, we develop a subarea evaluation method based on deep reinforcement learning, considering both the temporal effectiveness of sensing tasks and the importance of subarea selection for data inference. Using the subarea evaluation values derived from this method, we establish an online urban sensing task assignment model which is subject to constraints of sensing cycle time and cost budget. This model aims to find the task assignment result that minimizes data inference error by maximizing the comprehensive utility value. Considering the characteristics of the task assignment model, we propose an evolutionary algorithm named OTA-EA, which is based on an improved genetic algorithm. Its enhanced evolutionary operators can avoid generating infeasible solutions while maintaining robust search and optimization performance. Lastly, we conduct experimental evaluations of these methods on the real-world datasets. The results demonstrate that our subarea evaluation method can significantly reduce the data inference error, and our evolutionary task assignment algorithm can achieve better task assignment results than the baseline algorithms.
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ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2025.3540501