Double-Blind Data Discovery Using Double Cross for Large-Scale Wireless Sensor Networks With Mobile Sinks
Double-Blind Data Discovery Using Double Cross for Large-Scale Wireless Sensor Networks With Mobile Sinks
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DOI：
10.1109/tvt.2012.2194310
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发表时间：
2012-04
影响因子：
6.8
通讯作者：
Gaotao Shi;Jun Zheng;Jinfeng Yang;Z. Zhao
中科院分区：
文献类型：
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作者：
Gaotao Shi;Jun Zheng;Jinfeng Yang;Z. Zhao
具有移动的汇聚节点的大规模无线传感器网络（WSNs）给数据发现带来了新的挑战，因为移动的汇聚节点的位置无法预先确定。本文研究了无线传感器网络中的双盲数据发现问题，其中移动的sink和传感数据不知道彼此的位置先验。为了解决这个问题，我们首先提出了一个随机线行走（RLW）的消息转发机制，并在此转发机制的基础上，我们进一步提出了一个高效的数据发现机制称为双交叉。Double Cross利用了平面的简单几何性质，即，对于平面中的几对正交线，它们在平面内相交的概率大于99%。然而，它不依赖于节点上的地理位置或方向信息，这在这样的网络中是难以获得的。相反，每个传感器节点只需要知道其传输范围内的邻居节点之间的距离。分析和仿真结果表明，双交叉可以达到更高的成功发现率，较低的能耗，与现有的工作。
Large-scale wireless sensor networks (WSNs) with mobile sinks present new challenges on data discovery because the locations of the mobile sinks cannot be predetermined. In this paper, we study the double-blind data discovery problem in a WSN, where the mobile sink(s) and the sensed data do not know the locations of each other a priori. To address this problem, we first propose a Random Line Walk (RLW) mechanism for message forwarding, and based on this forwarding mechanism, we further propose an efficient data discovery mechanism called Double Cross. Double Cross exploits a simple geometric property of a planar, i.e., for a couple of pairs of orthogonal lines in a planar, the probability that they intersect within the planar is larger than 99%. However, it does not depend on the geographic location or directional information on a node, which is difficult to obtain in such networks. Instead, each sensor node only needs to know the distances between the neighbor nodes within its transmission range. Analytical and simulation results show that Double Cross can achieve a higher successful discovery rate with lower energy consumption, compared existing work.