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具有挑战性的。在这个项目中，研究团队旨在开发一个框架，基于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是在哪里以及如何拍摄的信息，然后可以只传输最有用的照片。具体地说，该项目解决了资源意识众包中紧密交织的三个问题。第一部分通过考虑两个场景来研究如何基于收集的元数据来选择照片：兴趣点，其中所选照片应该是关于特定位置或对象的；以及感兴趣区域，其中所选照片与区域相关。对于这两种情况，设计了各种算法来基于元数据来量化照片的覆盖范围，然后根据覆盖要求来选择最小数量的照片，或者选择预定数量的照片来最大化照片覆盖。第二部分重点研究了基于P2P通信的众包时的元数据传输和冗余消除。第三部分研究了基于大多数现成智能手机上的传感器自动、准确地生成元数据的技术。拟议中的研究可能会对应急人员和服务提供商产生重大影响，因为他们可以在资源受限的情况下从众包照片中识别最有用的信息。这项研究的结果可能会在支持无线网络中的资源感知众包方面形成新的研究方向。该项目将邀请代表人数不足的学生参与拟议的研究。这一项目的学术发现将向社会广泛传播。

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.