面向高动态移动自组织网络的生物启发分簇算法

分簇可以有效地提高大规模移动自组织网络的性能.但高动态的移动自组织网络具有节点移动性强、网络拓扑变化快的特点，应用传统的分簇算法会造成网络性能迅速下降，频繁的簇拓扑更新造成了簇结构的不稳定和控制开销的增加.为了解决传统分簇算法无法适应高动态的大规模移动自组织网络的问题，提出了一种基于生物启发的移动感知分簇算法，该算法对多头绒泡菌的觅食模型进行了改进，使其适用于移动自组织网络领域.由于该算法与节点的移动特性进行了结合，所以该算法可以有效地在高动态移动自组织网络中进行簇的建立与维护.实验结果表明，相较于其他传统分簇算法，本文算法提高了平均链路连接保持时间和平均簇首保持时间，使得簇结构更加稳定，提高了对高动态、大规模移动自组织网络的适应能力.

Bio-Inspired Clustering Algorithm for Highly Dynamic Mobile Ad Hoc Networks

Clustering can increase the performance of large-scale mobile ad hoc networks effectively.But the highly dynamic mobile ad hoc networks have some of characteristics,such as high mobility and fast network topology change;applying traditional clustering algorithms will cause the sharp decrease of the performance of the network,frequent updates of cluster topology will cause the instability of cluster structure and the increase of control overhead.For purpose of solving the problems that traditional clustering algorithms cannot fit the highly dynamic large-scale mobile ad hoc networks,BIMAC (Bio-Inspired Mobility-Aware Clustering) algorithm is proposed.This algorithm ameliorates the forage model of physarum polycephalum,which can make it adapt to the domain of mobile ad hoc networks.On account of this algorithm includes the mobility characteristic of the node,we can carry through the cluster formation and maintenance effectively.Experimental results have indicated that the BIMAC algorithm increases average link connection lifetime and average cluster head lifetime compared with other traditional clustering algorithms;BIMAC algorithm can make the cluster structure more stable.This algorithm can increase the adaptive capacity for highly dynamic large-scale mobile ad hoc networks.