Abstract
Task allocation is a key problem in Mobile Crowd Sensing (MCS). Prior works have mainly assumed that participants can complete tasks once they arrive at the location of tasks. However, this assumption may lead to poor reliability in sensing data because the heterogeneity among participants is disregarded. In this study, we investigate a multitask allocation problem that considers the heterogeneity of participants (i.e., different participants carry various devices and accomplish different tasks). A greedy discrete particle swarm optimization with genetic algorithm operation is proposed in this study to address the abovementioned problem. This study is aimed at maximizing the number of completed tasks while satisfying certain constraints. Simulations over a real-life mobile dataset verify that the proposed algorithm outperforms baseline methods under different settings.
Recommended Citation
W. Zhu et al., "Multitask Allocation to Heterogeneous Participants in Mobile Crowd Sensing," Wireless Communications and Mobile Computing, vol. 2018, article no. 7218061, Hindawi, Jan 2018.
The definitive version is available at https://doi.org/10.1155/2018/7218061
Department(s)
Computer Science
Publication Status
Open Access
International Standard Serial Number (ISSN)
1530-8677; 1530-8669
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2024 The Authors, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution 4.0 License.
Publication Date
01 Jan 2018
Comments
National Natural Science Foundation of China, Grant 2018J07005