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。这些人群由普通市民组成。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基于社区的交通和导航的移动应用程序，它可以帮助司机根据其他司机提供的信息选择最有效的路线。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。一个众包系统可能是一个信息物理系统（CPS），它包括鼓励用户参与特定任务的激励机制。这种系统的典型元素是数据购买者、贡献者和数据存储和处理平台。这些贡献者可能是使用智能手机收集数据的人，也可能是带有传感器的自动驾驶汽车。这些贡献者有自然的日常运动模式，在特定的时间只覆盖目标区域的特定路径。然而，传感任务可能需要在不同时间从目标区域的所有部分获取数据，以确保代表性采样。因此，空间和时间的覆盖对于众包应用程序来说可能是至关重要的。该建议解决了目标地区采样的时空覆盖问题，特别是参与者密度非常低的孤立分区域的覆盖问题。这个问题是通过一种奖励机制来解决的，该机制根据目标地区的分区域的数据提供者密度，动态地为该分区域的数据收集分配补偿。为了实现这一目标，使用博弈论方法对传感市场进行建模。在传感市场中，数据买方在每个子区域宣布一个任务和相应的补偿。然后，有兴趣的参与者决定访问该地区，提交他们目前的位置和最终目的地，以及他们愿意花在感知任务上的时间。与任何其他市场类似，CS市场的成员都希望最大化他们的效用。贡献者通过规划他们的轨迹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优奖励来最大化他们的效用。所提出的激励机制的结果信息可能会被人们和其他理性参与者（如自动驾驶汽车）利用，以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动车辆调度和导航、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等学科之间的跨学科合作。具体地说，跨学科的课程和实验室将被开发，同时采用点对点的网络技术，如维基页面，以促进即时和直接访问与项目有关的想法和数据。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.