EAGER: Enabling In-Network Crowd-Sensing

EAGER：启用网络内人群感知

• 批准号: 1346782
• 负责人: Robin Kravets
• 金额: $ 10万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-10-01 至 2015-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1346782&HistoricalAwards=false
• 关键词: EAGER; Enabling; Network; Crowd; Sensing

Proliferation of mobile smartphones has opened up possibilities of leveraging the people and devices in a crowd, (i.e., crowd-sourcing) to gather data from and monitor large crowds. However, current solutions either put unpredictable stress on the wireless or cellular infrastructure to a cloud and on energy-constrained smartphones or do not accurately capture crowd behavior. In response, our CrowdWatch project will investigate monitoring crowds from the ?inside-out? via a scalable, distributed and energy-efficient in-network crowd-sourcing framework. Local energy-efficient coordination and processing will enable the off-loading of some of the processing to the devices by establising a hierarchy of participants? multi-radio devices (i.e., Wi-Fi and Bluetooth). Through probabilistic monitoring of a crowd, CrowdWatch will reduce the demand on the bandwidth to the cloud and enhance traditional crowd-sourcing by enabling information to be delivered back to and among people within the crowd. The main challenges of this project are (1) deployment and validation of the hierarchical crowd-sourcing system architecture, (2) design and validation of adaptive sampling algorithms to enable distributed sensing and control using Bluetooth and Wi-Fi sensing, (3) role selection algorithms based on resource availability (i.e., energy, bandwidth). Validation of CrowdWatch will entail experimentation and measurements of performance metrics such as resource usage, crowd density and user mobility, effectiveness of information distribution and monitoring of interaction frequency among users during two crowed events on our campus, the Engineering Open House and a University Basketball Game.

移动智能手机的激增开启了利用人群中的人和设备(即，众包)从大量人群中收集数据和监控的可能性。然而，当前的解决方案要么将不可预测的压力放在无线或蜂窝基础设施、云和能源受限的智能手机上，要么无法准确捕获人群行为。作为回应，我们的CrowdWatch项目将从内到外调查监控人群。通过可扩展、分布式和高能效的网络内众包框架。当地的能效协调和处理将通过建立参与者的层次结构来实现将一些处理卸载到设备上？多无线电设备(即Wi-Fi和蓝牙)。通过对人群的概率监控，CrowdWatch将减少对到云的带宽的需求，并通过使信息能够传递回人群中的人以及人群中的人之间来增强传统的人群外包。该项目的主要挑战是(1)分层众包系统架构的部署和验证，(2)自适应采样算法的设计和验证，以实现使用蓝牙和Wi-Fi感知的分布式感知和控制，(3)基于资源可用性(即，能量、带宽)的角色选择算法。CrowdWatch的验证将需要对性能指标进行实验和测量，如资源使用率、人群密度和用户移动性、信息分发的有效性以及在我们校园的两个拥挤活动--工程开放日和一场大学篮球比赛期间监测用户之间的互动频率。