SmartPhoto: A Resource-Aware Crowdsourcing Approach for Image Sensing with Smartphones
SmartPhoto: A Resource-Aware Crowdsourcing Approach for Image Sensing with Smartphones
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DOI：
10.1145/2632951.2632979
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发表时间：
2014-08
影响因子：
7.9
通讯作者：
Yi Wang;Wenjie Hu;Yibo Wu;G. Cao
中科院分区：
文献类型：
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作者：
Yi Wang;Wenjie Hu;Yibo Wu;G. Cao
通过众包获得的照片可以用于许多关键应用。由于通信带宽、存储和处理能力的限制，传输大量的众包照片是一个挑战。为了解决这个问题，我们提出了一个框架，称为SmartPhoto，量化的质量（实用程序）的众包照片的基础上访问的地理和几何信息（称为元数据），包括智能手机的方向，位置和所有相关参数的内置摄像头。从元数据中，我们可以推断照片是在哪里以及如何拍摄的，然后只传输最有用的照片。四个优化问题的照片效用和资源约束之间的权衡，即最大效用，在线最大效用，最小选择，最小选择与$k$ -覆盖，进行了研究。提出了有效的算法，并从理论上证明了它们的性能界限。我们已经实现了SmartPhoto在一个测试平台上使用基于Android的智能手机，并提出了技术，以提高收集的元数据的准确性，减少传感器阅读错误和解决对象遮挡问题。基于真实的实现和大量仿真的结果证明了所提出的算法的有效性。
Photos obtained via crowdsourcing can be used in many critical applications. Due to the limitations of communication bandwidth, storage, and processing capability, it is a challenge to transfer the huge amount of crowdsourced photos. To address this problem, we propose a framework, called SmartPhoto, to quantify the quality (utility) 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, we can infer where and how the photo is taken, and then only transmit the most useful photos. Four optimization problems regarding the tradeoffs between photo utility and resource constraints, namely Max-Utility, online Max-Utility, Min-Selection, and Min-Selection with $k$ -coverage, are studied. Efficient algorithms are proposed and their performance bounds are theoretically proved. We have implemented SmartPhoto in a testbed using Android based smartphones, and proposed techniques to improve the accuracy of the collected metadata by reducing sensor reading errors and solving object occlusion issues. Results based on real implementations and extensive simulations demonstrate the effectiveness of the proposed algorithms.