Abstract
For participatory sensing, task allocation is a crucial research problem that embodies a tradeoff between sensing quality and cost. An organizer usually publishes and manages multiple tasks utilizing one shared budget. Allocating multiple tasks to participants, with the objective of maximizing the overall data quality under the shared budget constraint, is an emerging and important research problem. We propose a fine-grained multitask allocation framework (MTPS), which assigns a subset of tasks to each participant in each cycle. Specifically, considering the user burden of switching among varying sensing tasks, MTPS operates on an attention-compensated incentive model where, in addition to the incentive paid for each specific sensing task, an extra compensation is paid to each participant if s/he is assigned with more than one task type. Additionally, based on the prediction of the participants' mobility pattern, MTPS adopts an iterative greedy process to achieve a near-optimal allocation solution. Extensive evaluation based on real-world mobility data shows that our approach outperforms the baseline methods, and theoretical analysis proves that it has a good approximation bound.
Recommended Citation
J. Wang et al., "Fine-Grained Multitask Allocation for Participatory Sensing with a Shared Budget," IEEE Internet of Things Journal, vol. 3, no. 6, pp. 1395 - 1405, article no. 7563897, Institute of Electrical and Electronics Engineers, Dec 2016.
The definitive version is available at https://doi.org/10.1109/JIOT.2016.2608141
Department(s)
Computer Science
Keywords and Phrases
Fine-grained; multitask allocation; participatory sensing (PS)
International Standard Serial Number (ISSN)
2327-4662
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 Dec 2016
