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Energy-efficient data collection in WSN: A sink-oriented dynamic backbone

机译:WSN中的节能数据收集:面向接收器的动态主干

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In wireless sensor networks, energy efficiency is generally achieved by turning off some capabilities from a subset of deployed sensors. The set of active nodes must therefore meet the application requirements (e.g. area coverage, data redundancy) while remaining fully connected to allow further data collection. Here, we focus on the case of a nomad sink entering the network and gathering every monitoring data. At the routing layer, minimizing the number of nodes acting as relays requires to construct a maximum leaf spanning tree (MLST). However, optimizing convergecast communications consists in minimizing the hop distance between the sink and all others nodes, leading so to a shortest path tree rooted at the sink. In this paper, we propose a distributed routing protocol that aims at constructing an energy efficient backbone being convergecast efficient at the same time. Our proposal introduces a tradeoff parameter to adjust the compromise “number of relays / routing efficiency” and then constructs a hybrid routing structure based on the combination of variants of the Wu-Li algorithm and a gradient-based routing protocol. For all topologies we simulated, and when tuned for energy saving, our approach outperforms a 2-approximation for constructing a MLST. Furthermore, when tuned for convergecast routing, simulation results show that our solution constructs a routing optimal backbone that involves a small fraction of relays.
机译:在无线传感器网络中,通常通过关闭一部分已部署传感器的功能来实现能效。因此,活动节点集必须满足应用程序要求(例如,区域覆盖范围,数据冗余),同时保持完全连接以允许进一步收集数据。在这里,我们重点关注游牧区接收器进入网络并收集所有监视数据的情况。在路由层,要使充当中继的节点数量最少,就需要构建最大的叶子生成树(MLST)。但是,优化聚合广播通信的方法是最小化接收器和所有其他节点之间的跳距离,从而导致根于接收器的最短路径树。在本文中,我们提出了一种分布式路由协议,旨在构建同时收敛高效的节能骨干网。我们的建议引入了一个折衷的参数来调整折衷的“中继数量/路由效率”,然后基于Wu-Li算法的变体和基于梯度的路由协议的组合来构造混合路由结构。对于我们模拟的所有拓扑,当进行节能调整时,我们的方法都比构造MLST的2逼近好。此外,当针对聚合广播路由进行调谐时,仿真结果表明,我们的解决方案构建了包含一小部分中继的路由最佳主干。

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  • 会议地点 Ottawa(CA);Ottawa(CA)
  • 作者

    Clad, Francois;

  • 作者单位

    Image Sciences Computer Sciences and Remote Sensing Laboratory (UMR CNRS 7005) - University of Strasbourg France;

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