Effective link interference model in topology control of wireless Ad hoc and sensor networks
Effective link interference model in topology control of wireless Ad hoc and sensor networks
复制标题
无线Ad hoc和传感器网络拓扑控制中的有效链路干扰模型
DOI：
10.1016/j.jnca.2015.01.006
复制
发表时间：
2015-06
影响因子：
8.7
通讯作者：
Qiao Guofu
中科院分区：
文献类型：
--
作者：
Sun Guodong;Zhao Lin;Chen Zhibo;Qiao Guofu
在共享信道的无线自组网和传感器网络中，拓扑控制是降低链路干扰的重要方法。链路干扰建模是设计干扰感知拓扑控制的首要步骤，对现有拓扑控制方案的有效性起着至关重要的作用。本文提出了一种新的链路干扰模型，该模型不仅可以方便地用全局或局部算法计算，而且比以往的模型更有效地描述了同信道无线自组网和传感器网络中的链路干扰。基于该模型，设计了一种集中式拓扑控制算法，用于构建干扰最优的拓扑。事实上，我们的模型也可以应用到大多数已有的旨在减少链路干扰的拓扑控制算法中。最后，进行了大量的仿真和分析，对我们的设计进行了详细的评估。仿真结果表明，与前人的工作相比，该模型和算法取得了显著的干扰抑制效果。
Topology control is an important way of reducing the link interference in wireless Ad hoc and sensor networks with shared channel. Modeling link interference is the primary step for designing interference-aware topology controls, and it is very critical to the efficacy of the topology control schemes in use. This paper presents a new link interference model, which can not only be easily calculated with global or local algorithms, but also more effectively profile the link interference in co-channel wireless Ad hoc and sensor networks than previous models. Based on the proposed model, a centralized topology control algorithm is designed to construct interference-optimal topologies. In fact, our model can also be fed into most of the already-existed topology control algorithms aimed at reducing link interferences. Finally, extensive simulations and analysis are conducted to evaluate our designs in detail. The simulation results show the outstanding effectiveness of interference reduction achieved by our model and algorithm, in comparison with the previous work.
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DOI：
10.1109/icdcs.2011.47
发表时间：
2011-06
期刊：
2011 31st International Conference on Distributed Computing Systems
影响因子：
--
作者：
Jun Huang;Shucheng Liu;G. Xing;Hongwei Zhang;Jianping Wang;Liusheng Huang
通讯作者：
Jun Huang;Shucheng Liu;G. Xing;Hongwei Zhang;Jianping Wang;Liusheng Huang
影响因子：
2.3
作者：
Rabia Noor Enam;Rehan Qureshi;S. Misbahuddin
通讯作者：
Rabia Noor Enam;Rehan Qureshi;S. Misbahuddin
影响因子：
2.3
作者：
Kyuhong Lee;Heesang Lee
通讯作者：
Kyuhong Lee;Heesang Lee
影响因子：
6.8
作者：
Kousha Moaveninejad;Xiangyang Li
通讯作者：
Kousha Moaveninejad;Xiangyang Li
影响因子：
3
作者：
Jain, K;Padhye, J;Qiu, LL
通讯作者：
Qiu, LL