NeTS: Small: Enabling High-Quality Mobile Crowdsourcing with Lifestyle-aware and Energy-efficient Control

NetS：小型：通过生活方式感知和节能控制实现高质量移动众包

• 批准号: 1525920
• 负责人: Jian Tang
• 金额: $ 35.44万
• 依托单位: Syracuse University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-10-01 至 2019-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1525920&HistoricalAwards=false
• 关键词: NeTS; Small; Enabling; Quality; Mobile

The success of a crowdsourcing application highly depends on whether a capable crowd can be recruited to undertake the corresponding tasks. For most applications, the cost is also a big concern. Recent research has been focused on incentive mechanisms for mobile crowdsourcing, which determine how to recruit a crowd mainly based on their prices/costs. However, scant attention has been paid to capability of the recruited crowd, i.e., the quality of services/data each individual mobile user and the whole crowd are potentially capable of providing. The project team aims to design a new lifestyle-aware approach for mobile crowdsourcing. The basic idea is to maintain a (relatively) stable set of mobile users and learn to gain a comprehensive view of their life patterns (i.e., their capabilities) such that once a mobile crowdsourcing task is received, a good crowd, whose capabilities well match the task, can be quickly found. The proposed research will significantly improve quality of mobile crowdsourcing services and reduce their energy usages, which can substantially benefit mobile crowdsourcing service providers and users, and more importantly, is good for global environment. Moreover, the proposed project is expected to fill the literature gap and inspire more research along this line.The objective of this project to enable high-quality mobile crowdsourcing by designing a novel and holistic solution for crowd recruitment, which utilizes an energy-efficient framework for learning lifestyles of mobile users via smartphone sensing, and then employs lifestyle-aware algorithms and incentive mechanisms for crowd recruitment. To achieve the above objective, the proposed research is organized into three cohesive research thrusts: Thrust 1 Energy-efficient Lifestyle Learning: a unified and energy-efficient framework will be developed to learn lifestyles of mobile users by characterizing their behaviors and habits, and predicting their future activities; Thrust 2 Lifestyle-aware Crowd Recruitment and Incentive Mechanisms: Quality of Crowd (QoC) models and lifestyle-aware algorithms will be developed for effective crowd recruitment; Thrust 3 Implementation and Performance Evaluation: the proposed approaches will be implemented on a smartphone sensing platform and will be validated and evaluated via extensive simulation and experiments.

众包应用程序的成功很大程度上取决于是否可以招募有能力的人群来执行相应的任务。对于大多数应用程序，成本也是一个很大的问题。最近的研究集中在用于移动众包的激励机制上，这些机制决定了如何主要根据其价格/成本招募人群。但是，已经注意到招募人群的能力，即服务/数据的质量，每个移动用户和整个人群都有可能提供的能力。该项目团队旨在为移动众包设计一种新的生活方式感知方法。基本想法是要维护一组稳定的移动用户集，并学会对其生活模式（即他们的功能）进行全面了解，以便一旦收到移动众包任务，就可以快速找到一项良好的人群，其功能符合任务的良好功能。拟议的研究将显着提高移动众包服务的质量并减少其能源使用情况，这可以实质上使移动众包服务提供商和用户受益，更重要的是，对全球环境有益。此外，预计该拟议的项目将填补文献差距并激发更多的研究。该项目的目的是通过为人群招聘设计一种新颖而整体的解决方案来实现高质量的移动众包，该解决方案利用能够通过智能手机传感学习移动使用者的能源效率框架，然后使用智能手机的传感，然后使用生活方式的型号和群体的机构来进行群众群体和群众群体的机构。为了实现上述目标，提出的研究被组织成三个凝聚力的研究推力：1个节能的生活方式学习：将开发一个统一和节能的框架，以通过表征其行为和习惯来学习移动用户的生活方式，并预测其未来活动；推力2生活方式感知的人群招募和激励机制：将开发人群（QOC）模型和生活方式感知算法的质量，以进行有效的人群招募；推力3实施和绩效评估：所提出的方法将在智能手机传感平台上实施，并将通过广泛的仿真和实验进行验证和评估。