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首页> 外文期刊>IEEE transactions on mobile computing >Efficient Rostering of Mobile Nodes in Intermittently Connected Passive RFID Networks
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Efficient Rostering of Mobile Nodes in Intermittently Connected Passive RFID Networks

机译：间歇连接的无源RFID网络中移动节点的高效排班

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This paper focuses on the problem of rostering in intermittently connected passive RFID networks. It aims to report a list of tagged mobile nodes that appear in given interested area(s) and time interval(s). Such rostering faces several unique challenges. First, the network consists of two dramatically different types of nodes: powerful static readers and extremely resource-constrained mobile tags. Communication can be established from a reader to a tag only, but not tags to tags or readers to readers. Therefore, the connectivity is very low and intermittent. Besides connectivity, the tag's computation power is also intermittent. It is available only for a short interval when the tag is powered up by a nearby reader, rendering any continuous functions impossible. Moreover, the capacity of tags is so limited that it becomes the critical network resource and communication bottleneck. To address the above challenges, we propose a rostering algorithm that employs a dynamic space-efficient coding scheme to construct hypothetic packet candidates, appraises their values according to information redundancy and tag mobility, and establishes a 0-1 Knapsack model to choose the best set of packets, which together maximize their total (redundancy-excluded) value, but do not exceed the capacity of a tag. We carry out experiments that involve 38 volunteers for nine days and perform large-scale simulations to evaluate the proposed rostering scheme.
机译：本文关注间歇性连接的无源RFID网络中的排班问题。它旨在报告出现在给定感兴趣区域和时间间隔中的带标记的移动节点的列表。这种名册面临几个独特的挑战。首先，网络由两种截然不同的节点组成：功能强大的静态读取器和资源极为受限的移动标签。只能从阅读器与标签建立通信，而不能将标签与标签或阅读器与阅读器建立通信。因此，连接性非常低并且是间歇性的。除了连通性之外，标签的计算能力也是断断续续的。当标签由附近的阅读器加电时，它仅在很短的时间间隔内可用，从而使任何连续功能都变得不可能。而且，标签的容量是如此有限，以至于它成为关键的网络资源和通信瓶颈。为了解决上述挑战，我们提出了一种排班算法，该算法采用动态空间高效编码方案来构造假设的分组候选者，根据信息冗余和标签移动性评估其值，并建立0-1背包模型以选择最佳集合。个数据包，这些数据加起来使其总值（排除冗余）达到最大，但不超过标签的容量。我们进行了涉及38名志愿者的实验，为期9天，并进行了大规模模拟，以评估拟议的名册计划。

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