poster

Vehicle dispatching for sensing coverage optimization in mobile crowdsensing systems: poster abstract

Authors:

Susu Xu,

Hae Young NohAuthors Info & Claims

IPSN '19: Proceedings of the 18th International Conference on Information Processing in Sensor Networks

Pages 311 - 312

https://doi.org/10.1145/3302506.3312604

Published: 16 April 2019 Publication History

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Abstract

Mobile crowd sensing (MCS) collects city-scale sensing data with low cost and high efficiency. One important goal of MCS is to ensure high quality of sensing coverage to provide sufficient information to data analysis end. However, the goal of the MCS may be inconsistent with the goal of vehicles. This inconsistency between goals results in a bad sensing coverage and decreases the quality of the collected information. Key challenges to resolve this inconsistency include the heterogeneous target desired spatio-temporal distributions, limited budget constraining the ability to incentivize more taxis, and high computational complexity.

This work shows a vehicle dispatching system to optimize the sensing coverage of the sampled data with a limited budget and taxis. Our system models the objective function using the similarity between the collected data distribution and target distribution, and introduces an algorithm combining vehicle mobility prediction and ride request prediction to obtain an approximate-optimal vehicle dispatching strategy in an efficient way. The preliminary experiment based on the real-world data shows a significant improvement of the sensing coverage.

References

[1]

Kashif Ali, Dina Al-Yaseen, Ali Ejaz, Tayyab Javed, and Hossam S Hassanein. 2012. Crowdits: Crowdsourcing in intelligent transportation systems. In Wireless Communications and Networking Conference (WCNC), 2012 IEEE. IEEE, 3307--3311.

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[2]

Solomon Kullback and Richard A Leibler. 1951. On information and sufficiency. The annals of mathematical statistics 22, 1 (1951), 79--86.

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[3]

Susu Xu, Weiguang Mao, Yue Cao, Hae Young Noh, and Nihar B Shah. 2018. An Incentive Mechanism for Crowd Sensing with Colluding Agents. arXiv preprint arXiv:1809.05161 (2018).

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Information & Contributors

Information

Published In

IPSN '19: Proceedings of the 18th International Conference on Information Processing in Sensor Networks

April 2019

365 pages

ISBN:9781450362849

DOI:10.1145/3302506

General Chair:
Rasit Eskicioglu
University of Manitoba, Canada
,
Program Chairs:
Luca Mottola
Politecnico di Milano, Italy
,
Bodhi Priyantha
Microsoft Research

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IEEE-SPS: Signal Processing Society

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 April 2019

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IPSN '19

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SIGBED

IPSN '19: The 18th International Conference on Information Processing in Sensor Networks

April 16 - 18, 2019

Quebec, Montreal, Canada

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IPSN '19 Paper Acceptance Rate 25 of 91 submissions, 27%;

Overall Acceptance Rate 143 of 593 submissions, 24%

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