【摘要】：提出了一種新的基于興趣挖掘的機會網(wǎng)絡內容分發(fā)策略(Interest Mining Based Scheme,IMBS),通過貝葉斯理論分析節(jié)點的興趣以及節(jié)點基于興趣的相遇頻率,挖掘移動節(jié)點隨機運動背后所蘊含的人類社交特征和情感特征.此外,IMBS采用發(fā)布/訂閱機制,收集節(jié)點的訂閱信息,以獲取消息在整個網(wǎng)絡中的需求量.在轉發(fā)消息的時候,IMBS把消息的需求總量和節(jié)點的情感特征以及社交特征結合起來選擇下一跳節(jié)點.仿真實驗及算法復雜度分析表明,在不提高算法復雜度的情況下,IMBS可顯著減少消息的傳輸延時和網(wǎng)絡開銷,并提高消息傳輸?shù)某晒β?
[Abstract]:In this paper, a new opportunity network content distribution strategy based on interest mining (Interest Mining Based Scheme,IMBS) is proposed. The interest of nodes and the frequency of encounter based on interest are analyzed by Bayesian theory. Mining the human social and emotional characteristics behind the random motion of mobile nodes. In addition, IMBS uses publish / subscribe mechanism to collect the subscription information of nodes to obtain the demand of messages in the whole network. When forwarding a message, IMBS combines the total amount of message requirements with the emotional and social characteristics of the node to select the next hop node. Simulation results and algorithm complexity analysis show that IMBS can significantly reduce message transmission delay and network overhead, and improve the success rate of message transmission without increasing the complexity of the algorithm.
【作者單位】： 華南師范大學物理與電信工程學院;
【基金】：國家自然科學基金項目(61471175) 教育部新世紀優(yōu)秀人才支持計劃項目(NCET-13-0805)
【分類號】：TN929.5

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