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首页> 外文会议>Information Sciences and Systems, 2009. CISS 2009 >Sleeping policy cost analysis for sensor nodes collecting heterogeneous data
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Sleeping policy cost analysis for sensor nodes collecting heterogeneous data

机译：传感器节点收集异构数据的睡眠策略成本分析

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摘要

Sleeping policies are widely used in many contexts to reduce energy consumption in sensor nodes, ad-hoc terminals or mobile cell phones. Although there is extensive literature on energy saving in sensor networks, little has been done with respect to comparing additional costs, such as packet drop probability and buffer occupancies of sleeping versus non-sleeping policies, especially for multiple classes of data. In this paper, we employ discrete time vacation modeling results from queueing theory to obtain the quantities of interest, i.e., blocking probabilities, and queue lengths incurred by sleeping policies for a sensor node with a finite buffer.We propose a 2-class Geo/G/1/K vacation model, where two classes of data, each with its own buffer, are collected by a sensor node following a sleeping policy. At any time instant, only one packet can be transferred and the transmission order is selected by a scheduling function. The sleeping, transfer and selection processes have general distributions. To the best of our knowledge, this is the first work on modeling and analyzing the performance characteristics of a sensor node with a sleeping policy, collecting heterogeneous data. First, we present a Markov renewal process embedded at the time instant of transfer completion and then obtain the marginal occupancy distributions at an arbitrary time instant. Simulation results validate our theoretical derivation and demonstrate how the results can be applied to understand the behavior of sensor nodes with heterogeneous data.
机译：睡眠策略在许多情况下被广泛使用，以减少传感器节点，ad hoc终端或移动手机中的能耗。尽管有大量关于传感器网络中节能的文献，但是在比较额外成本方面几乎没有做任何事情，例如丢包概率以及睡眠策略和非睡眠策略的缓冲区占用率，尤其是对于多类数据。在本文中，我们利用排队论中的离散时间休假建模结果来获得感兴趣的数量，即阻塞概率和休眠策略对具有有限缓冲区的传感器节点产生的队列长度。我们提出了2类Geo / G / 1 / K休假模型，其中传感器节点遵循睡眠策略收集两类数据，每类数据都有自己的缓冲区。在任何时候，只能传输一个数据包，并且通过调度功能选择传输顺序。睡眠，转移和选择过程具有一般分布。据我们所知，这是第一个使用睡眠策略对传感器节点的性能特征进行建模和分析，收集异构数据的工作。首先，我们提出了在转移完成的瞬间嵌入的马尔可夫更新过程，然后在任意时刻获得边际占用率分布。仿真结果验证了我们的理论推导，并演示了如何将结果应用于了解异构数据的传感器节点的行为。

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