随机无线传感器网络性能评估分析模型

Fabian Giertzsch, Leo Krüger, A. Timm‐Giel
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摘要

今天,无线传感器网络(wsn)可以在许多应用领域中找到,例如,它们用于智能家居系统或工业环境中,以监视和控制机械。后者通常需要保证网络的性能。这主要解决了网络的实时能力,即需要了解在发送方和接收方之间的多跳路由上可能发生的延迟。本文提出了一种新的多跳网络建模方法,该网络具有固定数目的节点均匀分布在一个正方形区域上。因此,不需要明确的路由和拓扑知识。也就是说,没有反映特定的网络,而是对一类具有共同属性的随机网络进行了建模。在我们的模型中,所有节点以相同的速率根据泊松过程生成帧,并将它们发送到公共网关。在此基础上,我们推导了假设通用CSMA MAC协议实现的同时传输概率的数学模型。我们使用这个结果来推导出在IEEE 802.15.4 MAC和PHY协议的特殊情况下,链路上产生的平均延迟的表达式,即多跳路由的中等访问和排队延迟。如果提供的流量不超过一定的限制,则模型结果与omnet++仿真结果很好地对应。超过这个限制,直到网络的容量,模型仍然捕获模拟的行为,但偏差增加。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analytical Model for Performance Evaluation of Random Wireless Sensor Networks
Today Wireless Sensor Networks (WSNs) can be found in many application areas, e.g. they are used in smart home systems or in industrial settings in order to monitor and control machinery. The latter usually requires a guaranteed performance of the network. This mainly addresses the real-time capability of the network, i.e. knowledge of possible delays which occur on the multi-hop route between sender and receiver is needed. In this paper, a novel approach to model multi-hop networks with a fixed number of nodes uniformly distributed on a square area is proposed. Accordingly, no explicit knowledge of the routing and the topology is needed. That is, no specific network is reflected, but a class of random networks sharing common properties is modelled. In our model, all nodes generate frames according to a Poisson process with the same rate and send them to a common gateway. Based on this scenario, we deduce a mathematical model for the probability of simultaneous transmissions assuming that a generic CSMA MAC protocol is implemented. We use this result in order to derive an expression for the mean delays arising on the links, i.e. the medium access and queueing delay, of the multi-hop route in the special case of the IEEE 802.15.4 MAC and PHY protocols being used. The model results correspond well to an OMNeT++ simulation if the offered traffic does not exceed a certain limit. Beyond this limit, up to the capacity of the network, the model still captures the behaviour of the simulation but the deviation grows.
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