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摘要: 传感器网络分簇机制中, 工作效率与网络覆盖密切相关. 任意时刻激活最小数目工作节点能够有效节省网络能量. 然而, 由于传感器网络的高密度部署, 使得该问题成为一个NP-完全问题. 本文提出一种基于改进的精锐非支配遗传算法以选择网络最优覆盖集. 对比于传统的二进制监测模型, 本文在算法实施过程中采用了概率监测模型. 在保证网络全覆盖的前提下, 令一部分节点进入休眠状态达到节能的目的. 并提出循环重组算子和删除因子以优化算法性能. 大量的仿真实验验证了本文算法的有效性.
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关键词:
- 无线传感器网络 /
- 覆盖集 /
- 监测模型 /
- 改进的NSGA-II
Abstract: The effectiveness of a cluster-based distributed sensor network, to a large extent, depends on the coverage provided by the sensor nodes. To activate only the necessary number of sensor nodes at any particular moment is an efficient way to save the overall energy. However, this is an NP-complete problem because of the high-density deployment of wireless sensor networks. In this paper, a novel searching algorithm based on improved NSGA-II (elitist nondominated sorting genetic algorithm) is proposed to select an optimal cover set. In contrast to the binary detection model used in the previous work, a probabilistic detection model is adopted in combination with the detection error range and coverage threshold. With the full network coverage being guaranteed, a number of nodes are made into dormancy mode to save energy. The circulated combination and delete operators are proposed to enhance the search capability. Extensive simulation results are presented to demonstrate the effectiveness of our approach.-
Key words:
- Wireless sensor networks (WSN) /
- cover set /
- detection model /
- improved NSGA-II
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