Ms. Susu Xu Profile

Susu Xu

at Carnegie Mellon Univ

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Proceedings Article | 27 March 2019 Paper

Incentivizing vehicular crowdsensing system for large scale smart city applications

Susu Xu, Xinlei Chen, Xidong Pi, Carlee Joe-Wong, Pei Zhang, Hae Young Noh

Proceedings Volume 10970, 109701C (2019) https://doi.org/10.1117/12.2514021

KEYWORDS: Sensing systems, Sensors, Optimization (mathematics)

Read Abstract +

Mobile crowd sensing (MCS) enables many smart city applications (e.g., transportation monitoring/management, environmental monitoring, etc.). Recently, MCS systems built on non-dedicated vehicular platforms like taxis have become popular due to their large-scale coverage and low-cost deployment and maintenance. However, the goal of MCS may be inconsistent with the goal of vehicles. For example, MCS expects to get large and balanced sensing coverage over the city, while the taxis gather in busy areas to search for new ride requests. This inconsistency between the goals of MCS and vehicles results in a low sensing coverage and decreases the quality of the collected information. To address this inconsistency and optimize the sensing coverage, this paper presents an incentivizing system to optimize the sensing coverage of the sampled data. Key challenges to resolving this inconsistency include limited budget constraining the ability to incentivize more vehicles and complicate vehicle and trajectory selection problem making it difficult to obtain the incentivizing strategy. To address these challenges, we design a customized incentive by combining monetary incentives and potential ride request at the destination to reduce the cost of incentivizing vehicles and utilize the budget efficiently. Meanwhile, we formulate the problem of incentivizing trajectory planning as a non-linear multiple-choice knapsack problem, and propose a heuristic algorithm to approximate the optimal incentivizing strategy. The experiments based on the real-world data show that our system achieves up to 26.99% improvement in the sensing coverage compared to benchmark methods.

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