Mobility-Aware Participant Recruitment for Vehicle-Based Mobile Crowdsensing

Nowadays, vehicles have been increasingly adopted in mobile crowdsensing applications. Due to their predictable mobility trajectories, vehicles as participants bring new insight in improving the crowdsensing quality. The predictable mobility of vehicles provides not only the current locations of the...

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Bibliographic Details
Published in:IEEE transactions on vehicular technology Vol. 67; no. 5; pp. 4415 - 4426
Main Authors: Wang, Xiumin, Wu, Weiwei, Qi, Deyu
Format: Journal Article
Language:English
Published: New York IEEE 01.05.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Approximation
Computer simulation
Greedy algorithms
Heuristic methods
LP-relaxation
Mobile communication
participant recruitment
Performance evaluation
Prediction algorithms
Probabilistic logic
Probabilistic models
Recruitment
Sensors
Trajectories
Trajectory
Vehicle-based crowdsensing
Vehicles
ISSN:0018-9545, 1939-9359
Online Access:Get full text
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Summary:Nowadays, vehicles have been increasingly adopted in mobile crowdsensing applications. Due to their predictable mobility trajectories, vehicles as participants bring new insight in improving the crowdsensing quality. The predictable mobility of vehicles provides not only the current locations of the vehicles, but also their future mobility trajectory. In this context, the existing participant recruitment solutions, which are mainly based on the current locations of the participants, cannot be directly used in vehicle-based mobile crowdsensing. Utilizing the predicted mobility trajectory of vehicles, this paper aims to propose efficient vehicle recruitment algorithms for mobile crowdsensing, so as to minimize the overall recruitment cost. Specifically, we study two mobility trajectory models of the vehicles, named deterministic and probabilistic models. We first prove that the vehicle recruitment problem under both models is NP-hard. Then, for the deterministic trajectory model, an efficient LP-relaxation-based heuristic algorithm is proposed, and an approximation ratio is analyzed. For the probabilistic trajectory model, we propose a greedy algorithm and analyze its performance with a guaranteed approximation ratio. Finally, we evaluate the performance of the proposed schemes through simulations.
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ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2017.2787750