Continuous Data Collection Capacity of Wireless Sensor Networks under Physical Interference Model
Continuous Data Collection Capacity of Wireless Sensor Networks under Physical Interference Model
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DOI：
10.1109/mass.2011.29
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发表时间：
2011-10
期刊：
影响因子：
--
通讯作者：
S. Ji;R. Beyah;Yingshu Li
中科院分区：
文献类型：
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作者：
S. Ji;R. Beyah;Yingshu Li
数据采集是无线传感器网络(WSNs)的常见操作。数据收集的性能可以通过其可实现的网络容量来衡量。然而，现有的工作大多集中在网络容量上，主要集中在单播、多播或广播等不同于数据采集，尤其是连续数据采集的通信方式上。本文研究了随机部署密集无线传感器网络在物理干扰模型(PhIM)下的快照/连续数据收集(SDC/CDC)问题。对于SDC，我们提出了一种基于网络划分的信元路径调度算法(CBPS)。理论分析表明，其可实现的网络容量为$Omega(W)$($W$是信道上的数据传输速率，即带宽)，这是阶数最优的。对于CDC，我们提出了一种新的基于段的流水线调度算法(SBPS)，该算法显著加快了CDC的进程，并获得了惊人的网络容量，比目前的最佳结果至少提高了$\SQRT{\FRAC{n}{\LOG n}$或$\FRAC{n}{\LOG n}$。
Data collection is a common operation of Wireless Sensor Networks (WSNs). The performance of data collection can be measured by its achievable \emph{network capacity}. However, most existing works focus on the network capacity of \emph{unicast}, \emph{multicast} or/and \emph{broadcast}, which are different communication modes from data collection, especially continuous data collection. In this paper, we study the \emph{Snapshot/Continuous Data Collection} (SDC/CDC) problem under the Physical Interference Model (PhIM) for randomly deployed dense WSNs. For SDC, we propose a \emph{Cell-Based Path Scheduling} (CBPS) algorithm based on network partitioning. Theoretical analysis shows that its achievable network capacity is $\Omega(W)$ ($W$ is the data transmitting rate, \emph{i.e.} bandwidth, over a channel), which is order-optimal. For CDC, we propose a novel \emph{Segment-Based Pipeline Scheduling} (SBPS) algorithm that significantly speeds up the CDC process, and achieves a surprising network capacity, which is at least $\sqrt{\frac{n}{\log n}}$ or $\frac{n}{\log n}$ times better than the current best result.