EAGER: Enabling In-Network Crowd-Sensing

移动智能手机的普及为利用人群中的人和设备（即众包）来收集数据和监控大量人群提供了可能性。然而，目前的解决方案要么对无线或蜂窝基础设施施加不可预测的压力，要么对云计算和能源有限的智能手机施加压力，要么不能准确捕捉人群的行为。作为回应，我们的CrowdWatch项目将调查从内到外监控人群的情况。通过可扩展、分布式和节能的网络众包框架。通过建立参与者的层次结构，本地节能协调和处理将能够将一些处理工作卸载到设备上。多无线电设备（即Wi-Fi和蓝牙）。通过对人群的概率监测，CrowdWatch将减少对云带宽的需求，并通过将信息传递给人群中的人以及在人群中传递来增强传统的众包。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传感的分布式传感和控制，(3)基于资源可用性（即能源，带宽）的角色选择算法。CrowdWatch的验证将需要在我们校园的两个拥挤事件（工程开放日和大学篮球比赛）中进行实验和性能指标的测量，如资源使用、人群密度和用户移动性、信息分发的有效性和用户之间互动频率的监测。

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.