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首页> 外文期刊>IEEE Transactions on Parallel and Distributed Systems >Toward Quasiregular Sensor Networks: Topology Control Algorithms for Improved Energy Efficiency
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Toward Quasiregular Sensor Networks: Topology Control Algorithms for Improved Energy Efficiency

机译：走向准规则传感器网络：提高能源效率的拓扑控制算法

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Uniformly random or Poisson distributions are widely accepted models for the location of the nodes in wireless sensor networks if nodes are deployed in large quantities and there is little control over where they are dropped. On the other hand, by placing nodes in regular topologies, we expect benefits both in coverage and efficiency of communication. We describe and analyze a basic localized algorithm and three modifications for topology control that provide a tradeoff between performance and deployment cost. The objective is to regularize the topology for improved energy efficiency. The basic algorithm produces quasiregular networks, which only use nodes as sentries and relays that are approximately evenly spaced, thereby emulating a regular grid topology. It is shown that quasiregular networks have a significant energy and lifetime advantage compared with purely random networks. We consider two specific types of quasiregular networks: the ones that are based on a Gaussian deviation about an ideal grid point (type A), and the ones that consist of a subset of nodes taken from a Poisson point process (type B). We show that the two types are equivalent for a certain density of the Poisson point process and, in particular, that in both cases the deviation from the ideal regular grid follows a Rayleigh distribution, whereas the distance between nearest neighbors is Ricean.
机译：如果节点被大量部署并且对其放置位置几乎没有控制，则均匀随机分布或泊松分布是无线传感器网络中节点位置的广泛接受的模型。另一方面，通过将节点放置在常规拓扑中，我们期望在通信的覆盖范围和效率方面都受益。我们描述并分析了基本的本地化算法和拓扑控制的三个修改，这些修改在性能和部署成本之间进行了权衡。目的是调整拓扑结构以提高能效。基本算法生成准规则网络，该网络仅将节点用作哨兵和中继，它们之间的间隔近似均匀，从而模拟规则的网格拓扑。结果表明，与纯随机网络相比，准规则网络具有明显的能量和生命周期优势。我们考虑两种特定类型的准规则网络：一种基于关于理想网格点的高斯偏差（类型A），另一种由从泊松点过程中获取的节点子集组成（类型B）。我们表明，对于一定密度的泊松点过程，这两种类型是等效的，特别是在两种情况下，与理想规则网格的偏差均遵循瑞利分布，而最近邻居之间的距离为莱斯。

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