Optimizing deployment of sensors with self-healing ability is an efficient way to solve the problems of cov-erage, connectivity and the dead nodes in WSNs. This work discusses the particular relationship between the m...Optimizing deployment of sensors with self-healing ability is an efficient way to solve the problems of cov-erage, connectivity and the dead nodes in WSNs. This work discusses the particular relationship between the monitoring range and the communication range, and proposes an optimal deployment with self-healing movement algorithm for closed or semi-closed area with irregular shape, which can not only satisfy both coverage and connectivity by using as few nodes as possible, but also compensate the failure of nodes by mobility in WSNs. We compute the maximum efficient range of several neighbor sensors based on the dif-ferent relationships between monitoring range and communication range with consideration of the complex boundary or obstacles in the region, and combine it with the Euclidean Minimum Spanning Tree (EMST) algorithm to ensure the coverage and communication of Region of Interest (ROI). Besides, we calculate the location of dead nodes by Geometry Algorithm, and move the higher priority nodes to replace them by an-other Improved Virtual Force Algorithm (IVFA). Eventually, simulation results based-on MATLAB are presented, which do show that this optimal deployment with self-healing movement algorithm can ensure the coverage and communication of an entire region by requiring the least number of nodes and effectively compensate the loss of the networks.展开更多
针对大跨度斜拉桥的索力优化问题,该文基于径向基神经网络(Radial Basis Function Neural Network,RBFNN)拟合响应面,提出了一种考虑斜拉索可靠度指标的索力优化方法。通过RBFNN训练拟合结构隐式功能函数,建立求解可靠度指标的RBFNN响...针对大跨度斜拉桥的索力优化问题,该文基于径向基神经网络(Radial Basis Function Neural Network,RBFNN)拟合响应面,提出了一种考虑斜拉索可靠度指标的索力优化方法。通过RBFNN训练拟合结构隐式功能函数,建立求解可靠度指标的RBFNN响应面模型,采用改进粒子群算法对考虑可靠度指标的索力优化模型进行寻优求解。研究结果表明:RBFNN可以精确预测结构响应并拟合结构隐式功能函数,20个测试集的平均拟合误差仅为3.25%;改进粒子群算法对索力优化问题具有良好的适应性。相较于标准粒子群算法,改进后的算法收敛精度更高,收敛速度更快,优化后整体索力分布趋势与原索力分布趋势大致相同,采用优化索力计算得到的跨中位置斜拉索可靠度指标明显提升,各斜拉索平均可靠度增幅达3%左右,主梁线形得到大幅改善,最大挠度降幅高达36%。展开更多
文摘Optimizing deployment of sensors with self-healing ability is an efficient way to solve the problems of cov-erage, connectivity and the dead nodes in WSNs. This work discusses the particular relationship between the monitoring range and the communication range, and proposes an optimal deployment with self-healing movement algorithm for closed or semi-closed area with irregular shape, which can not only satisfy both coverage and connectivity by using as few nodes as possible, but also compensate the failure of nodes by mobility in WSNs. We compute the maximum efficient range of several neighbor sensors based on the dif-ferent relationships between monitoring range and communication range with consideration of the complex boundary or obstacles in the region, and combine it with the Euclidean Minimum Spanning Tree (EMST) algorithm to ensure the coverage and communication of Region of Interest (ROI). Besides, we calculate the location of dead nodes by Geometry Algorithm, and move the higher priority nodes to replace them by an-other Improved Virtual Force Algorithm (IVFA). Eventually, simulation results based-on MATLAB are presented, which do show that this optimal deployment with self-healing movement algorithm can ensure the coverage and communication of an entire region by requiring the least number of nodes and effectively compensate the loss of the networks.
文摘针对大跨度斜拉桥的索力优化问题,该文基于径向基神经网络(Radial Basis Function Neural Network,RBFNN)拟合响应面,提出了一种考虑斜拉索可靠度指标的索力优化方法。通过RBFNN训练拟合结构隐式功能函数,建立求解可靠度指标的RBFNN响应面模型,采用改进粒子群算法对考虑可靠度指标的索力优化模型进行寻优求解。研究结果表明:RBFNN可以精确预测结构响应并拟合结构隐式功能函数,20个测试集的平均拟合误差仅为3.25%;改进粒子群算法对索力优化问题具有良好的适应性。相较于标准粒子群算法,改进后的算法收敛精度更高,收敛速度更快,优化后整体索力分布趋势与原索力分布趋势大致相同,采用优化索力计算得到的跨中位置斜拉索可靠度指标明显提升,各斜拉索平均可靠度增幅达3%左右,主梁线形得到大幅改善,最大挠度降幅高达36%。
