基于改进精英克隆选择算法的B样条曲线逼近方法

时间同步是多智能体网络协同工作的基础,具有重要意义。低成本无线传感器网络节点时钟易受到环境因素的影响,导致节点时间同步误差增大,网络信道负载增加。针对上述问题,提出了一种低时钟再同步周期、自适应温度补偿的无线传感器网络时间同步方法。首先,基于双向通信时间同步模型,提出了温度补偿的节点频移量动态估计模型;然后,采用Almon函数加权求和的方式对温度和频移量数据进行融合,解决数据采样率不匹配和模型维度高的问题;其次,为进一步提高时间同步精度,采用Kalman滤波器对频移量和相移量估计值进行滤波,并采用系统状态后验估计值对节点本地时间进行补偿;最后,参照IEEE 802.15.4标准对时间同步精度的要求,设计了一种失效风险最小化的再同步决策函数,最大限度提高节点再同步周期,减少信道负载。在高低温箱、室内和室外3种环境下进行实验以验证所提的方法。试验结果表明,与洪泛时钟同步协议(FTSP)时间同步协议相比,自适应温度补偿的时间同步(ATCTS)算法平均时间同步误差降低了97.4%,室外环境下平均时间同步周期为324 min。

B spline curve approximation method based on an improved elitist clonal selection algorithm

In this paper, an improved elitist clonal selection algorithm (ECSA) is proposed to realize the automatic knot adjustment of the B spline curve approximation. In order to improve the search efficiency and solution quality of the algorithm, an adaptive chaotic mutation operator is designed, and an antibody reselection strategy based on the antibody concentration and antigen affinity vectorial moment is proposed. Then Bayesian Information Criterion (BIC) is used as the affinity metric to weigh and judge the goodness of fitting and computational complexity. Further, the improved algorithm achieves a balance between depth search and breadth optimization, and can automatically and accurately calculate the number and locations of internal knots, thus the B spline curve approximation of the data points is completed. Simulation and experiment results show that the proposed algorithm not only can efficiently and accurately realize the automatic B spline curve approximation of the noisy complex data with the features of continuity, discontinuity and cusps, but also possesses better global convergence and convergence speed compared with current researches.