用于无线传感器网络数据聚合的分布式持久生成树

Jen-Yeu Chen, Da-Wei Juan, Cheng-Sen Huang
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引用次数: 3

摘要

本文提出了一种能量感知的分布式算法,用于构建无线传感器网络数据聚合的持久生成树。在构建的聚合树上,将剩余能量较高的节点布置在靠近主干的位置,使树的生命周期最大化,并保持聚合数据的完整性。数据聚合是一种从传感器节点收集数据的节能方式,其中一个节点处理从其所有子节点收集的数据,然后通过一条消息将处理结果传输给父节点。然而,在数据聚合的树状结构中,靠近树的根节点(传感器网络的汇聚节点)的节点故障将导致严重的数据丢失(该故障节点的下游所有数据将丢失)。这促使我们的算法将剩余能量较高的节点安排在靠近聚合树根的位置,并减轻剩余能量较小的节点的责任。我们的树形构造算法是分布式的;每个节点通过与相邻节点交换信息来做出自己的决定。实验结果表明,与文献中具有代表性的能量感知聚合树相比,本文算法构建的分布式持久生成树(Distributed endurance Spanning tree, DEST)是最持久的,即在最长的时间内由于节点耗尽能量而导致一定程度的数据丢失。这也表明数据聚合树的重构频率较低,进一步降低了传感器网络的能耗。(6页)
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DEST: Distributed endurant spanning tree for data aggregation on wireless sensor networks
In this paper, an energy-aware distributed algorithm is proposed to construct an endurant spanning tree for data aggregation on wireless sensor networks. On the constructed aggregation tree, nodes with higher residual energy are arranged close to the trunk to maximize the lifetime of the tree, and maintain the integrity of aggregated data. Data aggregation, in which a node processes the data collected from all its children nodes and then transmits the processed result to its parent node by a single message, is an energy efficient way to collect data from sensor nodes. However, in the tree hierarchy for data aggregation, a node failure close to the root of the tree (the sink node of a sensor network) will cause severe data loss (all data from the downstream of this failure node will be lost.). This motivates our algorithm to arrange those nodes with higher residual energy close to the root of aggregation tree and relieve the responsibility of nodes with less residual energy. Our algorithm for tree construction is distributed; each node makes its own decision by exchanging information with its neighbouring nodes. The experiment results show that the constructed aggregation tree by our algorithm, named Distributed Endurant Spanning Tree (DEST) is the most endurant, i.e., of the longest time to reach a certain level of data loss due to node failures from running out of energy, compared to other representative energy-aware aggregation trees in the literature. This also indicates a less frequent re-construction of data aggregation tree, which further reduces the energy consumption on sensor networks. (6 pages)
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