A Vehicular Crowd-sensing Incentive Mechanism for Temporal Coverage
A Vehicular Crowd-sensing Incentive Mechanism for Temporal Coverage
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DOI：
10.1109/ccnc49032.2021.9369634
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发表时间：
2021-01
期刊：
影响因子：
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通讯作者：
H. Chintakunta;Janar Kahr;L. Jaimes
中科院分区：
文献类型：
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作者：
H. Chintakunta;Janar Kahr;L. Jaimes
车辆人群传感（VCS）是一种新的数据收集模式，它利用车辆移动的独特特性来收集传感数据。VCS面临的主要挑战之一是如何在参与者之间分配感知任务，以保持所需的感知数据质量（QoSD），同时保持参与有利可图。我们通过设计一种激励机制来解决这一挑战，该机制鼓励高QoSD的收集，同时提高参与者的效用。提议的机制包括一个发布一系列传感任务和相关奖励的平台，以及一组配备传感器的参与车辆，它们竞争这些奖励。我们将这种竞争建模为一种非合作的游戏，其中一组交通工具是参与者，他们的轨迹是他们的策略。通过使用开放街道地图、SUMO车辆交通模拟器和广泛的模拟，我们表明我们的算法在QoSD、平均车辆效用、空间覆盖率和道路利用率方面明显优于贪婪方法。
Vehicular Crowd-sensing (VCS) is a new data collection paradigm that leverages the unique characteristics of vehicular mobility to collect sensing data. One of the main challenges for VCS is how to assign sensing tasks among participant so as to maintain the required Quality of Sensing Data (QoSD), while keeping participation profitable. We tackle this challenge by designing an incentive mechanism that encourages the collection of high QoSD, while improve participant utilities. The proposed mechanism includes a platform which post a set of sensing tasks and the associated rewards, and a set of participant vehicles equipped with sensors, who compete for these rewards. We model this competition as a non-cooperative game in which the set of vehicles are the players, and their trajectories are their strategies. Using open-street maps, SUMO vehicular traffic simulator, and extensive simulations, we show that our algorithm significantly outperforms a greedy approach in terms of QoSD, average vehicle utility, spatial coverage, and road utilization.