Anonymous Temporal-Spatial Joint Estimation at Category Level Over Multiple Tag Sets
Anonymous Temporal-Spatial Joint Estimation at Category Level Over Multiple Tag Sets
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DOI：
10.1109/infocom.2018.8485885
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发表时间：
2018-04
期刊：
影响因子：
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通讯作者：
Youlin Zhang;Shigang Chen;You Zhou;Yuguang Fang
中科院分区：
文献类型：
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作者：
Youlin Zhang;Shigang Chen;You Zhou;Yuguang Fang
射频识别（RFID）技术已广泛应用于库存管理、目标跟踪和供应链管理。其中一个基本的系统功能被称为基数估计，即估计覆盖区域内的标签数量。我们从两个方面对该函数进行了扩展研究。首先，我们对出现在不同地理位置和不同时间的标签进行联合基数估计。此外，我们收集类别级别的信息，这在需要监视许多不同类型的标记对象的实际场景中更为重要。其次，我们在信息收集过程中要求匿名，以保护被标记对象的隐私。这些功能将支持新的应用，例如跟踪产品如何在大型分布式供应网络中移动。为了满足多标签集匿名类别级联合估计的要求，提出了一种新的协议设计。我们正式分析了估计器的性能并确定了最优系统参数。大量的仿真结果表明，该协议在保持标签匿名性的同时，能够有效地获得准确的类别级别估计。
Radio-frequency identification (RFID) technologies have been widely used in inventory management, object tracking and supply chain management. One of the fundamental system functions is called cardinality estimation, which is to estimate the number of tags in a covered area. We extend the research of this function in two directions. First, we perform joint cardinality estimation among tags that appear at different geographical locations and at different times. Moreover, we collect category-level information, which is more significant in practical scenarios where we need to monitor the tagged objects of many different types. Second, we require anonymity in the process of information gathering in order to preserve the privacy of the tagged objects. These capabilities will enable new applications such as tracking how products are moved in a large, distributed supply network. We propose a novel protocol design to meet the requirements of anonymous category-level joint estimation over multiple tag sets. We formally analyze the performance of our estimator and determine the optimal system parameters. Extensive simulations show that the proposed protocol can efficiently obtain accurate category-level estimation, while preserving tags' anonymity.