Efficient Data Collection Trees in Sensor Networks with Redundancy Removal
Efficient Data Collection Trees in Sensor Networks with Redundancy Removal
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DOI：
10.1007/978-3-540-28634-9_20
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发表时间：
2004-07
期刊：
影响因子：
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通讯作者：
Shoudong Zou;I. Nikolaidis;J. Harms
中科院分区：
文献类型：
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作者：
Shoudong Zou;I. Nikolaidis;J. Harms
在传感器网络中，重叠的感测区域会导致部分冗余数据的收集。沿着数据收集树的内部传感器节点可以消除冗余并减少聚合数据所需的带宽。假设感知到的信息与感知到的面积成正比，我们提出了启发式算法来构建数据收集树，以降低通信成本。该算法是完全分布式的，基于寻找最短跳路径和采用特定的断线机制。这些机制可以分为两类：一类是使用邻居的邻居集的基数的简单知识，另一类是依赖于邻居的距离单调度量的知识。我们比较了基于通信成本以及能源和加工成本的五种启发式方法。对于较小的配置，还将启发式的性能与最优配置进行比较。利用所提出的方法，可以建立一个经济有效的数据收集树，该树利用了距离较近的传感器之间的数据冗余。
In sensor networks, overlapping sensed areas result in the collection of partially redundant data. Interior sensor nodes along a data collection tree can remove redundancies and reduce the required bandwidth for the aggregate data. Assuming that the sensed information is proportional to the sensed area, we propose heuristic algorithms to build data gathering trees in order to reduce communication costs. The algorithms are completely distributed and are based on finding shortest hop paths and employing particular tie-breaking mechanisms. The mechanisms can be divided into two categories: those that use simple knowledge of the cardinality of a neighbor’s neighbor set and those that rely on knowledge of a distance–monotonic metric to a neighbor. We compare five heuristics based on communication cost and also on energy and processing costs. For smaller configurations, the performance of the heuristics are also compared to the optimal. With the proposed approaches, it is possible to build a cost efficient data gathering tree which takes advantage of the data redundancy among closely located sensors.