Jaimes Luis G, White Craig, Abedin Paniz
Department of Computer Science, Florida Polytechnic University, Lakeland, FL 33805, USA.
Sensors (Basel). 2024 Nov 9;24(22):7191. doi: 10.3390/s24227191.
In this paper, we investigate whether greedy algorithms, traditionally used for pedestrian-based crowdsensing, remain effective in the context of vehicular crowdsensing (VCS). Vehicular crowdsensing leverages vehicles equipped with sensors to gather and transmit data to address several urban challenges. Despite its potential, VCS faces issues with user engagement due to inadequate incentives and privacy concerns. In this paper, we use a dynamic incentive mechanism based on a recurrent reverse auction model, incorporating vehicular mobility patterns and realistic urban scenarios using the Simulation of Urban Mobility (SUMO) traffic simulator and OpenStreetMap (OSM). By selecting a representative subset of vehicles based on their locations within a fixed budget, our mechanism aims to improve coverage and reduce data redundancy. We evaluate the applicability of successful participatory sensing approaches designed for pedestrian data and demonstrate their limitations when applied to VCS. This research provides insights into adapting greedy algorithms for the particular dynamics of vehicular crowdsensing.
在本文中，我们研究了传统上用于基于行人的众包感知的贪婪算法在车辆众包感知（VCS）环境下是否仍然有效。车辆众包感知利用配备传感器的车辆来收集和传输数据，以应对若干城市挑战。尽管VCS具有潜力，但由于激励不足和隐私问题，它面临着用户参与度方面的问题。在本文中，我们使用基于循环反向拍卖模型的动态激励机制，利用城市交通仿真（SUMO）交通模拟器和开放街道地图（OSM）纳入车辆移动模式和现实城市场景。通过在固定预算内根据车辆位置选择具有代表性的车辆子集，我们的机制旨在提高覆盖范围并减少数据冗余。我们评估了为行人数据设计的成功参与式感知方法的适用性，并展示了将其应用于VCS时的局限性。这项研究为使贪婪算法适应车辆众包感知的特定动态特性提供了见解。
