Abstract: With the popularity of mobile intelligent devices, the mobile crowdsensing (MCS) network based on wireless sensor networks and crowdsourcing technology came into being. There is more and more research on MCS, and it has been applied in many scenarios. Due to the increase in data volume of the MCS platform, the task shows exponential growth. Among them, there will be irreplaceable tasks that belong to the same category, that is, tasks with correlation. If the related tasks can be allocated to the same person for execution, the overhead will be greatly reduced, and the success probability of task allocation will be improved. Firstly, the spatio-temporal distribution of tasks and users is predicted by fuzzy logic to divide spatio-temporal scenarios in this study, and a more suitable multi-task allocation algorithm is selected. Then, when allocating multi-tasks, considering the correlation of tasks, the greedy algorithm is used to allocate multi-tasks according to different scenarios. The experimental results show that compared with the benchmark scheme, the proposed related multi-task allocation scheme based on the greedy algorithm improves the task allocation completion rate by 25.2%, and significantly improves the task allocation success rate in MCS.

Xia Zhuoyue and Raja Kumar Murugesan. “Related Multi-Task Allocation Scheme Based on Greedy Algorithm in Mobile Crowdsensing”. International Journal of Advanced Computer Science and Applications (IJACSA) 16.12 (2025). http://dx.doi.org/10.14569/IJACSA.2025.0161247

@article{Zhuoyue2025,
title = {Related Multi-Task Allocation Scheme Based on Greedy Algorithm in Mobile Crowdsensing},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0161247},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0161247},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {12},
author = {Xia Zhuoyue and Raja Kumar Murugesan}
}