Abstract
Mobile Crowdsensing (MCS) has shown the greatest potential that allows smart devices to collect and share different sensing data. Mobile users (or participants) send the desired sensing data to the service providers and collect rewards. However, the reward needs to be given such as, it does not increase platform costs. On the other hand, the unsatisfactory reward may reduce the interest of the participant which may degrade the quality data. Therefore, increasing sensing data quality with a constrained budget is a crucial challenge. There has been extensive research on the reward mechanism for MCS, but, most of the work is on the basic assumption that participant will complete their assigned task positively. In this paper, we propose an efficient user selection mechanism for Mobile Crowdsensing System (MCS) by considering the Probability of Success (PoS) of users (i.e. participant may fail to complete the assigned task with some probability). For the selection of an efficient user, the proposed mechanism also accounts the parameters like data quality and platform cost. We also propose a reward calculation model for the selected users. Minimizing the platform cost with a constrained budget is an NP-hard problem. To provide a sub-optimal solution to this problem Chaotic Krill-Herd optimization algorithm is used. The extensive simulation results reveal that the proposed method outperforms the existing work by considerable margins.
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Data availability
The website for downloading the dataset https://www.kaggle.com/datasets/arashnic/tdriver.
Change history
15 July 2023
A Correction to this paper has been published: https://doi.org/10.1007/s00542-023-05512-y
Abbreviations
- n :
-
Number of mobile users (or participants)
- m :
-
Number of tasks
- b j :
-
Allocated budget for task j
- S i :
-
Set of tasks coverd by user i
- \(p_i^j\) :
-
Probability of success of user i for the task j
- \(r_{i}^{s}\) :
-
Sensing range of user i
- \(\mathcal P\) :
-
Set of locations
- \(c^j\) :
-
Platform cost to complete task j
- \(d^{j^{'}}\) :
-
Required minimum data quality for task j
- \(\mathcal D_Q\) :
-
Total data quality
- \(\omega _{n}\) :
-
Inertia weight of the induced motion
- \(\omega _{f}\) :
-
Inertia weight of the foraging motion
- \(\alpha _{i}\) :
-
Direction of the induced motion
- \(V^f\) :
-
Foraging speed
- \(\beta _{i}^{best}\) :
-
Best fitness effect of the ith Krill
- PoI:
-
Point of Interest
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Mondal, S., Das, A. Quality aware cost efficient reward mechanism in mobile crowdsensing system with uncertainty constraints. Microsyst Technol 30, 381–390 (2024). https://doi.org/10.1007/s00542-023-05495-w
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DOI: https://doi.org/10.1007/s00542-023-05495-w