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人群的力量。这些人群是由普通市民组成的。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。著名的例子包括用于基于社区的交通和导航的移动应用程序，这些应用程序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定期测量环境变量、监控道路、交通和民用基础设施。众包系统可以是网络物理系统(CPS)，它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。贡献者的形式可能是使用智能手机收集数据的人，或者是安装了传感器的自动驾驶汽车。这些贡献者有自然的每日移动模式，只覆盖特定时间目标区域的特定路径。然而，传感任务可能需要来自目标区域所有部分的不同时间的数据，以确保代表性抽样。因此，在空间和时间方面的覆盖对于众包应用来说可能是至关重要的。该建议解决了目标区域采样的空间和时间覆盖问题，特别是参与者密度非常低的孤立子区域的覆盖问题。这个问题是通过激励机制来解决的，该激励机制根据目标区域的子区域中贡献者的密度来动态地分配用于该子区域中的数据收集的补偿。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，数据买家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当前的位置和最终目的地以及他们愿意在传感任务上花费的时间。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，而数据买家通过预测贡献者的行为并设置每个子区域的最优回报来最大化他们的效用。建议的激励机制的结果信息可以被人们和其他理性参与者(如自动驾驶汽车)利用，以更好地计划他们的日常活动。例如，个人可以避免对其健康构成风险的环境条件，或者改变日常通勤方式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各学科之间的跨学科合作。具体来说，将开发跨学科的课程和实验室，同时使用点对点网络技术，如维基页面，以促进即时和直接访问与该项目相关的想法和数据。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.