The active learning multi-task allocation method in mobile crowd sensing based on normal cloud model

2020 ◽  
Vol 67 ◽  
pp. 101181
Author(s):  
Jian Wang ◽  
Yanli Wang ◽  
Guosheng Zhao ◽  
Zhongnan Zhao
Keyword(s):  
Active Learning ◽  
Task Allocation ◽  
Cloud Model ◽  
Mobile Crowd Sensing ◽  
Crowd Sensing ◽  
Allocation Method ◽  
Mobile Crowd

Task allocation based on time optimization in Mobile crowd sensing

2021 ◽  
Author(s):  
Zhi Gang Jia ◽  
Weiwei Zhao ◽  
Ming Chi ◽  
Jie Luo ◽  
Bing Ren
Keyword(s):  
Task Allocation ◽  
Mobile Crowd Sensing ◽  
Crowd Sensing ◽  
Time Optimization ◽  
Mobile Crowd

scholarly journals HyTasker: Hybrid Task Allocation in Mobile Crowd Sensing

2020 ◽  
Vol 19 (3) ◽  
pp. 598-611 ◽  
Author(s):  
Jiangtao Wang ◽  
Feng Wang ◽  
Yasha Wang ◽  
Leye Wang ◽  
Zhaopeng Qiu ◽  
...  
Keyword(s):  
Task Allocation ◽  
Mobile Crowd Sensing ◽  
Crowd Sensing ◽  
Mobile Crowd

scholarly journals Task Allocation in Mobile Crowd Sensing: State-of-the-Art and Future Opportunities

2018 ◽  
Vol 5 (5) ◽  
pp. 3747-3757 ◽  
Author(s):  
Jiangtao Wang ◽  
Leye Wang ◽  
Yasha Wang ◽  
Daqing Zhang ◽  
Linghe Kong
Keyword(s):  
Task Allocation ◽  
State Of The Art ◽  
Mobile Crowd Sensing ◽  
Crowd Sensing ◽  
Mobile Crowd

Multi-Task Allocation in Mobile Crowd Sensing with Individual Task Quality Assurance

2018 ◽  
Vol 17 (9) ◽  
pp. 2101-2113 ◽  
Author(s):  
Jiangtao Wang ◽  
Yasha Wang ◽  
Daqing Zhang ◽  
Feng Wang ◽  
Haoyi Xiong ◽  
...  
Keyword(s):  
Quality Assurance ◽  
Task Allocation ◽  
Mobile Crowd Sensing ◽  
Crowd Sensing ◽  
Individual Task ◽  
Mobile Crowd

scholarly journals Social-Aware Task Allocation in Mobile Crowd Sensing

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Weiping Zhu ◽  
Wenzhong Guo ◽  
Zhiyong Yu
Keyword(s):  
Task Allocation ◽  
Allocation Problem ◽  
Seed Selection ◽  
Selection Algorithm ◽  
Mobile Crowd Sensing ◽  
Expected Number ◽  
Crowd Sensing ◽  
The Poor ◽  
Sensing Cost ◽  
Mobile Crowd

Task allocation is a significant issue in crowd sensing, which trades off the data quality and sensing cost. Existing task allocation works are based on the assumption that there is plenty of users available in the candidate pool. However, for some specific applications, there may be only a few candidate users, resulting in the poor completion of tasks. To tackle this problem, in this paper, we investigate the task allocation problem with the assistance of social networks. We select a subset of users; if a user can not complete the task, he can propagate the task information to his friends. The object of this problem is to maximize the expected number of completed tasks. We prove that the task allocation problem is an NP-hard and submodular problem and then propose a native greedy selection (NGS) algorithm, which selects the user with maximum margin gain in each round. To improve the efficiency of the NGS algorithm, we further propose a fast greedy selection algorithm (FGS), which selects the user who can actually complete the maximum number of tasks. Experimental results show that although FGS gets slightly worse results in terms of the expected number of completed tasks, it can greatly reduce the running time of seed selection.

scholarly journals Multi-objective Optimization for Multi-task Allocation in Mobile Crowd Sensing

2019 ◽  
Vol 155 ◽  
pp. 360-368 ◽  
Author(s):  
Mingchu Li ◽  
Yuan Gao ◽  
Mingliang Wang ◽  
Cheng Guo ◽  
Xing Tan
Keyword(s):  
Task Allocation ◽  
Multi Objective Optimization ◽  
Mobile Crowd Sensing ◽  
Multi Objective ◽  
Crowd Sensing ◽  
Mobile Crowd
Sign in / Sign up

Export Citation Format

Share Document