Incentive Mechanism for Vehicular Crowdsensing with Budget Constrains
Incentive Mechanism for Vehicular Crowdsensing with Budget Constrains
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DOI：
10.1109/southeastcon44009.2020.9249696
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发表时间：
2020-03
期刊：
影响因子：
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通讯作者：
Xin Wang;Quentin Goss;M. Akbaş;Alireza Chakeri;J. Calderon;L. Jaimes
中科院分区：
文献类型：
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作者：
Xin Wang;Quentin Goss;M. Akbaş;Alireza Chakeri;J. Calderon;L. Jaimes
提出了一种基于循环逆向拍卖的车载人群感知激励机制。拟议的方法鼓励参与者的车辆使用他们的传感器收集数据，同时也最大化了他们的效用。这种方法解决了VCS中的重要问题，如成本爆炸和区域传感覆盖。使用OpenStreetMaps的真实街道网络进行了大量的SUMO(城市交通模拟)仿真，我们的VCS算法在感知覆盖率和活跃参与者数量方面分别比基线方法提高了3倍和8倍。
In this paper, we present an incentive mechanism for vehicular crowdsensing (VCS) based on a recurrent reverse auction. The proposed approach encourages participant's vehicles to used their sensors to collect data while also maximizing their utility. This approach tackles important issues in VCS such as cost explosion, and area sensing coverage. Using a realistic street network from OpenStreetMaps with extensive SUMO (Simulation of Urban Mobility) simulations, we show our VCS algorithm significantly o utperforms the baseline approach in terms of sensing coverage and active number of participants by three and eight times respectively.