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首页> 外文期刊>ACM transactions on sensor networks >Topology-Related Metrics and Applications for the Design and Operation of Wireless Sensor Networks
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Topology-Related Metrics and Applications for the Design and Operation of Wireless Sensor Networks

机译：无线传感器网络设计和操作的拓扑相关度量标准和应用

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摘要

The use of topological features, more specifically, the importance of an element related to its structural position, is a subject widely studied in the literature. For instance, the theory of complex networks provides centrality measures that have been applied to a large variety of fields (e.g., social sciences and biology). In this work, we propose a new topological measure, the Sink Betweenness (SBet), which stems from the theory of complex networks but is adapted to Wireless Sensor Networks (WSNs) to capture relevant information for this kind of network. We also provide a distributed algorithm to calculate it, and show its applicability to two different scenarios. The first one is focused on data fusion applications for event-driven WSNs, where we devise a tree-based data collection algorithm that takes advantage of node centrality to improve the data fusion efficiency. The second scenario is focused on energy balancing problems, more specifically in a problem called energy hole, where nodes closer to the sink are more likely to relay a larger number of packets than those that are further. This phenomenon is strongly related to the topology induced by the deployment of nodes along the sensor field, and it can be effectively captured by the SBet metric. Thus, we devise a data collection algorithm that is able to distribute the relay task more evenly. Simulation results show that the SBet metric can be satisfactorily used in both scenarios. We compare the proposed approach with some of the most efficient available data fusion algorithms, and show that the proposed algorithm generates consistently good-quality data collection infrastructures which require significantly smaller overhead. The use of SBet allows to alleviate the energy-hole effects by evenly balancing the relay load, and thus increasing the network lifetime. These two applications illustrate how the topology awareness can be used to improve different network functions in a WSN.
机译：拓扑特征的使用，更具体地说，与元素的结构位置相关的元素的重要性，是文献中广泛研究的主题。例如，复杂网络理论提供了已应用于众多领域（例如，社会科学和生物学）的集中度度量。在这项工作中，我们提出了一种新的拓扑措施，即“沉没中间性”（SBet），它源自复杂网络的理论，但适用于无线传感器网络（WSN）来捕获此类网络的相关信息。我们还提供了一种分布式算法来进行计算，并展示了其在两种不同情况下的适用性。第一个重点是针对事件驱动的WSN的数据融合应用程序，其中我们设计了一种基于树的数据收集算法，该算法利用节点的中心性来提高数据融合效率。第二种情况集中在能量平衡问题上，更具体地说，是在一个称为“能量孔”的问题中，较靠近接收器的节点比更远的节点更有可能中继更大数量的数据包。此现象与传感器节点上节点部署引起的拓扑结构密切相关，可以通过SBet度量有效捕获。因此，我们设计了一种数据收集算法，该算法能够更均匀地分配中继任务。仿真结果表明，在两种情况下，SBet度量都可以令人满意地使用。我们将提出的方法与一些最有效的可用数据融合算法进行了比较，结果表明，提出的算法可生成始终如一的高质量数据收集基础结构，而这些基础结构所需的开销要小得多。 SBet的使用可以通过平均地平衡继电器负载来减轻能量空穴效应，从而延长网络寿命。这两个应用程序说明了如何使用拓扑感知来改善WSN中的不同网络功能。

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