Performance evaluation of distributed self-stabilizing dominating set algorithms in wireless sensor networks

Huseyin Tolga Evcimen, V. Akram, O. Dagdeviren
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引用次数: 2

Abstract

Finding a minimal dominating set (MDS) is a popular problem in wireless sensor networks (WSNs) to provide routing and backbone formation. A self-stabilizing system can tolerate topological changes such as node and edge joining/leaving thus they are very suitable for fault-tolerant and reliable WSNs. In this paper, we provide an extensive performance evaluation of self-stabilizing MDS algorithms for WSNs. To the best of our knowledge, this is the first experimental evaluation study of self-stabilizing MDS algorithms applied in WSN domain. We implemented the algorithms on various node counts and densities, and compared the average energy consumption, wall- clock time, move count and dominator count of the algorithms. The comprehensive simulation results show that the Goddard's algorithm finds the MDS with about 8.8% lower move and 5.8% lower dominator node count while its theoretical move count bound is higher than Chiu's algorithm. Despite of lower move count, the simulation results shows that the energy consumption of Goddard's algorithm is up to 1.5 times higher than Turau's and 2.8 times higher than Chiu's algorithms. The wallclock time, move count and detected dominator count of Turau's algorithm are respectively 84%, 88% and 12% higher than Goddard's algorithm in the networks with 1500 nodes which confirms its 9n move count bound. These measurements reveal that Goddard's algorithm outperforms other algorithms in terms of dominator count and Chiu's algorithm has a better performance for other metrics.
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无线传感器网络中分布式自稳定控制集算法的性能评价
寻找最小支配集(MDS)是无线传感器网络(WSNs)中提供路由和主干网形成的一个常见问题。自稳定系统可以容忍节点和边缘的加入/离开等拓扑变化,因此非常适合用于容错和可靠的wsn。在本文中,我们对wsn的自稳定MDS算法进行了广泛的性能评估。据我们所知,这是第一个应用于WSN领域的自稳定MDS算法的实验评估研究。我们在不同的节点数和密度下实现了算法,并比较了算法的平均能耗、挂钟时间、移动计数和支配数。综合仿真结果表明,Goddard算法找到的MDS的移动数和主导节点数分别比Chiu算法低8.8%和5.8%,而其理论移动数界高于Chiu算法。仿真结果表明,尽管Goddard算法的移动次数较低,但其能耗比Turau算法高1.5倍,比Chiu算法高2.8倍。在1500节点的网络中,Turau算法的挂钟时间、移动次数和检测到的主导数分别比Goddard算法高84%、88%和12%,证实了Turau算法的9n移动次数界限。这些测量结果表明,Goddard算法在支配子数方面优于其他算法,而Chiu算法在其他指标上表现更好。
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