Survivability-oriented optimal node density for randomly deployed wireless sensor networks

来源 :Science China(Information Sciences) | 被引量 : 0次 | 上传用户:wocaodouji
下载到本地 , 更方便阅读
声明 : 本文档内容版权归属内容提供方 , 如果您对本文有版权争议 , 可与客服联系进行内容授权或下架
论文部分内容阅读
The survivability is usually critical for wireless sensor networks,which are often deployed in the unattended harsh environment.Lots of technical schemes to improve the survivability depend on the redundant resources of the sensor networks.The amount of resources is usually determined by the node density during the deployment phase.For the random deployment and random node failures,the quantitative relation between the survivability and the node density is studied.Based on the conditional survivability,the node density to meet the required survivability level is presented.Finally,the survivability for unpredictable failures and the optimal node density to maximize the price–performance ratio are also discussed. The survivability is usually critical for wireless sensor networks, which are often deployed in the unattended harsh environment. Lots of technical schemes to improve the survivability depend on the redundant resources of the sensor networks the amount of resources is usually determined by the node density during the deployment phase. For the random deployment and random node failures, the quantitative relation between the survivability and the node density is studied.Based on the conditional survivability, the node density to meet the required survivability level is presented .Finally, the survivability for unpredictable failures and the optimal node density to maximize the price-performance ratio are also discussed.
其他文献
学位
该学位论文主要开展以下三方面的工作:1)从微观结构参数预测宏观物化性质;2)自适应映射图法是神经网络中一种很重要的模型,其特点是能把多维空间中的向量投射在二维平面上,并
该文提出人工神经网络阳级溶出伏安(ANN-ASV)化学形态分析法,用于Pb-Cd-OH-Cl水体系,取得了满意的结果.