NeTS: Small: Resource-Aware Crowdsourcing in Wireless Networks

NetS：小型：无线网络中的资源感知众包

• 批准号: 1421578
• 负责人: Guohong Cao
• 金额: $ 48.75万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-10-01 至 2018-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1421578&HistoricalAwards=false
• 关键词: NeTS; Small; Resource; Aware; Crowdsourcing

Transmitting large numbers of photos in a wireless environment with bandwidth constraints is challenging. In this project, the research team aims to develop a framework to quantify the quality of crowdsourced photos based on the accessible geographical and geometrical information (called metadata) including the smartphone's orientation, position and all related parameters of the built-in camera. From the metadata, information such as where and how the photo is taken can be inferred, and then only the most useful photos may be transmitted. Specifically, the project addresses three closely intertwined issues in resource-aware crowdsourcing. The first part investigates how to select photos based on the collected metadata by considering two scenarios: Point of Interest where the selected photos should be about a specific location or object, and Area of Interest where selected photos are related to an area. For both cases, various algorithms are designed to quantify the coverage of the photos based on the metadata, and then to select the minimum number of photos based on the coverage requirement, or to select a predefined number of photos to maximize the photo coverage. The second part focuses on metadata transmission and redundancy removal when crowdsourcing is based on peer-to-peer (P2P) communications. The third part investigates techniques to automatically and accurately generate metadata based on sensors available on most off-the-shelf smartphones.The proposed research could potentially have substantial impacts on emergency responders and service providers by allowing them to identify the most useful information from crowdsourced photos under resource constraints. The results from this research are likely to foster new research directions on supporting resource-aware crowdsourcing in wireless networks. The project will engage under-represented students in the proposed research. The scholarly discovery of this project will be disseminated broadly to the community.

在具有带宽约束的无线环境中传输大量照片是具有挑战性的。在这个项目中，研究团队旨在根据可访问的地理和几何信息（称为元数据）制定框架来量化众包照片的质量，包括智能手机的方向，位置以及内置相机的所有相关参数。从元数据中，可以推断出诸如何处和如何拍摄照片之类的信息，然后只能传输最有用的照片。具体而言，该项目解决了资源感知众包的三个紧密相互交织的问题。第一部分通过考虑两种情况：所选照片应与特定位置或对象有关的兴趣点以及感兴趣的区域，其中所选照片与某个区域相关的区域，研究了如何根据收集的元数据选择照片。在这两种情况下，各种算法均设计旨在根据元数据量化照片的覆盖范围，然后根据覆盖范围要求选择最少的照片数量，或选择预定义数的照片以最大程度地覆盖照片。当众包基于对等通信（P2P）通信时，第二部分侧重于元数据传输和冗余。第三部分研究了基于大多数现成的智能手机上可用的传感器自动，准确地生成元数据的技术。拟议的研究可能会通过允许在资源约束下从人群中识别出众群的最有用的信息来对紧急响应者和服务提供商产生重大影响。这项研究的结果很可能促进了有关支持无线网络中资源感知众包的新研究指示。该项目将与代表性不足的学生一起参与拟议的研究。该项目的学术发现将广泛传播给社区。