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.

众包应用的成功在很大程度上取决于能否招募到有能力的人群来承担相应的任务。对于大多数应用程序，成本也是一个大问题。最近的研究主要集中在移动众包的激励机制上，这主要是基于他们的价格/成本来决定如何招募人群。但是，很少注意到所招募的人群的能力，即每个移动用户和整个人群可能能够提供的服务/数据的质量。项目团队的目标是为移动众包设计一种新的生活方式。其基本理念是保持（相对）稳定的移动用户群，并学会全面了解他们的生活模式（即他们的能力），这样一旦收到移动众包任务，就可以迅速找到能力与任务匹配的优秀人群。本研究将显著提高移动众包服务的质量，降低移动众包服务的能耗，使移动众包服务的提供商和用户受益，更重要的是对全球环境有益。此外，拟议的项目有望填补文献空白，并激发更多的研究沿着这条线。本项目的目标是通过设计一种新颖的、整体的人群招募解决方案，实现高质量的移动众包，该方案利用智能手机感知的节能框架来学习移动用户的生活方式，然后使用生活方式感知算法和激励机制来进行人群招募。为实现上述目标，本研究分为三个有凝聚力的研究方向：第一，高效节能的生活方式学习：通过描述移动用户的行为和习惯，并预测他们未来的活动，开发一个统一的、高效节能的框架来学习移动用户的生活方式；重点2：基于生活方式的人群招募和激励机制：将开发人群质量（QoC）模型和基于生活方式的算法，以实现有效的人群招募；推力3实施和性能评估：提出的方法将在智能手机传感平台上实施，并将通过广泛的模拟和实验进行验证和评估。