【作者】 王娅； 许凯华； 刘玉华；

【Author】 Ya Wang, Kaihua Xu, Yuhua Liu ( College of Physical Science and Technology, Central China Normal University, Wuhan 430079) ( Department of Computer Science, Central China Normal University, Wuhan 430079)

【机构】 华中师范大学物理学院； 华中师范大学计算机科学系；

【摘要】 近年来提出了许多有关ad hoc网络和传感器网络的拓扑生成算法。在LCA算法中, 将拥有较大邻居节点数的节点作为簇头节点来管理和协调簇内节点的通信。在DCA算法中,假设各节点类似静止状态,通过设置节点的权值来选择簇头,选择具有较高权值的节点充当簇头。在 WCA算法中,通过综合考虑节点的邻居节点数、传输功率、能量寿命和移动速率四方面因素来选择合适的簇头,同时限定了每个簇内节点的总个数,以确保MAC层协议的高效性能。更多还原

【Abstract】 Many clustering algorithms have been proposed for ad-hoc and sensor networks in the last few years. In Linked Cluster Algorithm, a node becomes the cluster head if it has the highest identify among all nodes within one hop of itself or among all nodes within one hop of one of its neighbors; The Distributed Clustering Algorithm (DCA) assumes quasi-stationary nodes with real-valued weighted; The Weighted Clustering Algorithm (WCA) elects a node as a cluster head based on the number of neighbors, transmission power, battery-life and mobility rate of the node. At same time, it keeps the number of nodes in a cluster around a pre-defined threshold to facilitate the optimal operation of the medium access control (MAC) protocol. In this paper, take the optimum number of cluster heads based on power and the optimizing algorithm of WCA into consideration, and generate the topology framework of WSNs. The number of nodes is combined as iterated number in my algorithm, which is suitable for small and medium networks. Because the nodes are stationary in WSNs, the considered parameters for the optimizing algorithm of WCA are simple relatively. Consider three parameters as follows: (1) the battery-life of nodes Ci-res; (2) the nodes degree-difference △i = |dj - E[N]|; (3) the average distance between node and its neighbors di-ave = Di/di. Consider three parameters above, the combined weight wi for node i in WSNs can be calculated: wi = w1 · ci/ci-res + w2 · △i + w3 · di-ave.Where, Ct is defined as the initial energy of nodes, and w1, w2 , w3 are defined respectively as the weight of three parameters, where w1 + w2 + w3 = 1. When the smaller the weight wi, the bigger probability of node i being elected as a cluster head. At same time, the computed values of p and the corresponding values of the number of nodes n and the intensity of nodes λ for WSNs are provided in the following expression: (?) During the clustering procedure, compute the combined weight wi by the expression, and combine the optimum number of cluster heads to vote the cluster heads. After that, each cluster head broadcasts its states using the same transmit energy to the non-cluster heads in the networks, and each non-cluster head decides the cluster to which it will belong. When every node is the member of the clusters, the framework of WSNs forms.更多还原