论文标题
群集内有效节点的有效，有效且有理的估计
Efficient, Effective and Well Justified Estimation of Active Nodes within a Cluster
论文作者
论文摘要
对物联网中动态变化群集的大小的可靠，有效估计对于其名义操作至关重要。大多数以前的估计方案的框架尺寸和大量的圆形效果相对较小。在这里，我们提出了一个名为\ TextQuotedBlleft高斯节点的估计器\ TextQuotedBlright（Gean）的新估计器，该估计器可与足够大的框架大小一起使用，在该框架下，测试统计量近似为高斯变量，因此需要更少的频率范围，因此需要更少的频道插槽来达到所需的准确率估算中的准确度。更具体地说，框架大小的选择是根据三角形阵列中心限制定理完成的，这也使我们能够量化近似误差。较大的帧大小有助于统计平均值更快地收敛到估计器的整体平均值，并且近似误差的量化有助于确定回合的数量，以跟上准确性要求。我们在两个不同的频道模型下介绍了对方案的分析，即$ \ {0,1 \} $和$ \ {0,1，e \} $，而所有以前的方案仅在$ \ {0,1 \} $ Channel模型下工作。考虑到达到给定的估计准确性所需的估算所需的插槽数量，GEAN的总体性能比以前提出的方案要好。
Reliable and efficient estimation of the size of a dynamically changing cluster in an IoT network is critical in its nominal operation. Most previous estimation schemes worked with relatively smaller frame size and large number of rounds. Here we propose a new estimator named \textquotedblleft Gaussian Estimator of Active Nodes,\textquotedblright (GEAN), that works with large enough frame size under which testing statistics is well approximated as a Gaussian variable, thereby requiring less number of frames, and thus less total number of channel slots to attain a desired accuracy in estimation. More specifically, the selection of the frame size is done according to Triangular Array Central Limit Theorem which also enables us to quantify the approximation error. Larger frame size helps the statistical average to converge faster to the ensemble mean of the estimator and the quantification of the approximation error helps to determine the number of rounds to keep up with the accuracy requirements. We present the analysis of our scheme under two different channel models i.e. $ \{0,1 \} $ and $ \{0,1,e \} $, whereas all previous schemes worked only under $ \{0,1 \} $ channel model. The overall performance of GEAN is better than the previously proposed schemes considering the number of slots required for estimation to achieve a given level of estimation accuracy.
