CPS: Small: RUI: Incentive Mechanisms for Mobile Crowdsourcing, Reaching Spatial and Temporal Coverage Under Budget Constraints

众包是一种计算范式，通过外包特定任务的解决方案来利用群体的力量。这些人都是普通市民。众包的潜力已经在环境科学、交通系统和社会科学等领域得到了证明。众所周知的例子包括用于基于社区的交通和导航的移动的应用程序，这些应用程序帮助驾驶员根据其他驾驶员提供的信息采取最有效的路线。其他一些众包应用包括环境变量的定期测量、道路、交通和民用基础设施的监测。众包系统可以是网络物理系统（CPS），其包括鼓励用户参与给定任务的激励机制。这种系统的典型要素是数据购买者、贡献者以及数据存储和处理平台。贡献者可以是使用智能手机收集数据的人，也可以是带有传感器的自动驾驶汽车。这些贡献者有着自然的日常活动模式，只在特定时间在目标地区的特定路径上活动。然而，感测任务可能需要来自不同时间的目标区域的所有部分的数据以确保代表性采样。因此，在空间和时间方面的覆盖对于众包应用程序可能至关重要。这一建议涉及在目标区域进行采样的空间和时间覆盖问题，特别是涉及参与者密度很低的孤立次区域的覆盖问题。这个问题是通过一个激励机制来解决的，该机制根据该子区域中贡献者的密度动态地分配对目标区域的子区域中的数据收集的补偿。为了实现这一目标，传感市场建模使用博弈论的方法。在传感市场中，数据购买者宣布每个子区域的任务和相应的补偿。然后，决定访问该地区的感兴趣的参与者提交他们的当前位置和最终目的地，以及他们愿意在感知任务上花费的时间。与任何其他市场类似，CS市场的成员希望最大化其效用。贡献者通过制定策略来最大化他们的效用，而数据购买者通过预测贡献者的行为和设置每个子区域的最佳奖励来最大化他们的效用。人们和其他理性参与者（如自动驾驶汽车）可以利用所提出的激励机制的结果信息来更好地规划他们的日常活动。例如，个人可以避免对他们的健康构成风险的环境条件，或者改变他们的日常通勤以产生最低的压力水平。其他潜在的应用包括自主车辆调度和导航、智能机器人导航和交通资源的智能利用。拟议的项目将促进和鼓励计算机科学、运输工程和环境科学学科之间的跨学科合作。具体而言，将开发跨学科课程和实验室，同时采用对等网络技术，如维基网页，以促进即时和直接访问的想法和数据有关的项目。该奖项反映了NSF的法定使命，并已被认为是值得通过评估使用基金会的智力价值和更广泛的影响审查标准的支持。

Crowdsourcing is a computational paradigm to leverage the power of crowds by outsourcing the solution of a specific task. These crowds are composed of regular citizens. The potential of crowdsourcing has been proven in fields such as environmental sciences, transportation systems, and social sciences. Well-known examples include the mobile applications for the community-based traffic and navigation, which help drivers take the most efficient routes based on information provided by other drivers. Some other crowdsourced applications are the periodic measurement of environmental variables, monitoring of roads, traffic, and civil infrastructure. A crowdsourced system may be a Cyber-Physical System (CPS), which includes incentive mechanisms to encourage user participation in a given task. The typical elements of such a system are the data buyers, contributors and a platform for data storage and processing. The contributors may take the form of people who use their smartphones for data collection or autonomous vehicles with the attached sensors. These contributors have natural patterns of daily movement, which covers only specific paths in the target area at specific times. However, a sensing task may require data from all parts of the target area in different times to ensure representative sampling. Therefore, coverage in terms of both space and time may be critical for crowdsourced applications. This proposal addresses the problem of spatial and temporal coverage for sampling in a target area, in particular the coverage of isolated sub-regions where participants' density is very low. This problem is tackled by an incentive mechanism that dynamically assigns compensation for data collection in the sub-regions of the target area based on the density of the contributors in that sub-region. To achieve this goal, a sensing market is modeled using a game-theoretic approach. In the sensing market, a data buyer announces a task per sub-region and the corresponding compensation. Then, the interested participants who decide to visit that region, submit their current locations and final destinations as well as the amount of time they are willing to spend on the sensing task. Similar to any other market, the members of a CS market want to maximize their utilities. The contributors maximize their utility by strategizing their trajectories while data buyers maximize their utility by predicting the contributors' behavior and setting the optimal rewards per sub-region. The resulting information of the proposed incentive mechanism may be leveraged by people and other rational participants such as autonomous vehicles to better plan their daily activities. For example, individuals can avoid environmental conditions that represent a risk for their health or change their daily commute to produce the lowest stress level. Other potential applications include autonomous vehicle scheduling and navigation, smart robots navigation and smart utilization of transportation resources. The proposed project will facilitate and encourage interdisciplinary collaboration among the disciplines of computer science, transportation engineering and environmental science. Specifically, interdisciplinary courses and laboratories will be developed while employing peer-to-peer Web technology, such as Wiki pages, to facilitate instant and direct access to ideas and data related to the project.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.