XS-WSNet:极端规模无线传感器网络仿真

Ali Asim, S. Tixeuil
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引用次数: 4

摘要

无线传感器网络的最新进展表明,网络的规模和要执行的应用程序的种类都在扩大。在这种情况下,无线网络模拟器在新的无线传感器网络应用和协议的设计、开发和测试中发挥着关键作用。目前大多数可用的无线传感器网络模拟器都可以很好地用于中小型网络,但却无法扩展到真正的大型网络,这主要是由于它们的单一桌面机器架构和有限的资源。在本文中,我们提出了XS-WSNet,一个无线传感器网络模拟器,其设计以极端的可扩展性为主要目标。首先,我们将模拟的无线传感器网络节点分布在各种通过有线网络通信的实际机器上。然后,一个大规模的模拟由几个并发和协作运行的较小规模的模拟来模拟。由于在不同机器上运行的小型仿真实例通常会导致整个系统的异步和不确定性,我们还提出了一种分布式同步协议,负责某些仿真模型的仿真精度。我们对XS-WSNet的实现(包括异步和同步版本)使用可扩展的基准应用程序在各种上下文中进行了全面评估。与单机版本相比,分布式模拟器提供了合理的匹配结果,显示了模拟的放大和加速,并且在多达1000万个模拟节点时执行线性减速。
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XS-WSNet: Extreme scale wireless sensor network simulation
Recent advances in wireless sensor networks show an expansion both in the size of the network and in the variety of the applications that are to be executed. In this context, wireless network simulators play a key role in the design, development and testing of new wireless sensor networks applications and protocols. Most of the currently available wireless sensor network simulators work well for small to medium sized networks yet fail to scale to really large networks, mainly due to their single desktop machine architecture and its limited resources. In this paper, we present XS-WSNet, a wireless sensor network simulator that is designed with extreme scalability as a prime objective. First, we distribute the simulated wireless sensor network nodes on a variety of actual machines that communicate through a wired network. A large-scale simulation is then emulated by several smaller scale simulations that run concurrently and collaboratively. As smaller instances of simulations running on different machines usually induce asynchrony and non-determinism in the whole system, we also propose a distributed synchronization protocol, responsible for simulation accuracy in some simulation models. Our implementation (both asynchronized and synchronized versions) of XS-WSNet is fully evaluated in various contexts using a scalable benchmark application. Against its single machine version, the distributed simulator provides sensible matching results, exhibits both scale-up and speed-up of the simulation, and performs with linear slowdown with up to ten million simulated nodes.
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