NeTS: Small: Resource-Aware Crowdsourcing in Wireless Networks

在带宽受限的无线环境中传输大量照片是一项挑战。在这个项目中，研究团队的目标是开发一个框架，根据可访问的地理和几何信息（称为元数据）来量化众包照片的质量，包括智能手机的方向、位置和内置摄像头的所有相关参数。从元数据中，可以推断出照片的拍摄地点和方式等信息，然后只传输最有用的照片。具体而言，该项目解决了资源意识众包中三个密切相关的问题。第一部分研究如何根据收集到的元数据选择照片，考虑两种场景：兴趣点（Point of Interest），选择的照片应该是关于特定位置或物体的，以及兴趣区域（Area of Interest），选择的照片与一个区域相关。针对这两种情况，设计了各种算法，根据元数据量化照片的覆盖范围，然后根据覆盖要求选择最小的照片数量，或者选择预定义的照片数量以最大化照片覆盖范围。第二部分侧重于基于点对点（P2P）通信的众包时的元数据传输和冗余去除。第三部分研究了基于大多数现成智能手机上可用的传感器自动准确地生成元数据的技术。拟议的研究可能会对应急人员和服务提供商产生重大影响，使他们能够在资源有限的情况下从众包照片中识别出最有用的信息。这项研究的结果可能会促进支持无线网络中资源感知众包的新研究方向。该项目将邀请代表性不足的学生参与拟议的研究。该项目的学术发现将广泛传播到社区。

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.