A Group Mobility Model Based On K-means Clustering Algorithm And Wavelet Analysis Of Its Long-Range-Dependence In Opportunistic Networks

Group mobility model, a common human behavior, is very important and received considerable attention in the opportunistic networks, where the data forwarding exhibits the characteristics of burst and long-range dependence which make the performance of routing protocols quite different with that at other mobility models such as random walk mobility. But current researches on group mobility models are focus on describing the group movement behavior such as the group division and group merging, but the relevance of the long-range dependence to the performance of routing protocols is not studied until now. In this work firstly the group mobility model based on K-means clustering algorithm(KGM) is proposed by which group behaviors become closer to the reality, the nodes can be grouped or ungrouped more freely without the involvement of the researchers and free nodes are also considered. Then by the wavelet analysis method we analyze the long-range dependence of data forwarding with various nodes, groups, free nodes and node moving speeds. Further, we analyze the relevance of the long-range dependence to the performance of routing protocol in group mobility model, and find that the long-range dependence causes fluctuations in the routing performance such as delivery ratio and average hop count.