关键词: 机会网络, 时间敏感, 社会关系拓扑演化, 马尔可夫模型

Abstract: At present, routing algorithms based on community for the opportunistic network are on the base of static social relations, community is seen as steady state. But in practice, due to the mobility of opportunistic network nodes, the social relation topology dynamically evolutes with time, community division based on static social relations will cause the problems of message low delivery rate and large transmission delay, etc. In order to solve the problem of inaccurate social relationship, this paper studies the social relations topology dynamic evolution of the opportunistic network, proposes a prediction model based on Markov chain for the social relations of the opportunistic network. First of all, dividing run time of the opportunistic network into time slices of equal length, then constructing social relations at each time slice; secondly, decomposing the social relations of the network into the social relations between the nodes; constituting a state sequence for the social relations between the nodes; finally, according to the meeting status sequence sample data of node pairs based on time slice sequence, establishing the meeting state transition probability model for the node pairs. The experimental results show that in the social relations of the time sensitive opportunistic network, the evolution model based on Markov chain proposed in this paper can predict the changes of topology, the accurate rate reaches above 80%.

Key words: opportunistic networks, time sensitive, social relation topology evolution, Markov model