【摘要】 基于社会的机会网络中,节点趋向于和自己关系亲密的节点进行接触,因此可以通过评价节点之间的亲密程度来帮助预测节点之间的接触概率。由于节点亲密性的时间相关性和区域相关性,利用节点的历史接触信息构造节点亲密程度的评价模型时,考虑节点接触区域和节点接触时间对亲密程度的影响,从而得到节点在相应区域的亲密节点——区域朋友(Regional Friends,RF)。最后结合节点当前位置和区域朋友关系提出一种基于区域朋友关系的机会路由算法(Opportunistic Routing algorithm based on Regional Friendship,ORRF)。实验数据表明,在节点分布密度以及数据可容忍时延变化的网络环境下,该算法得到较高的数据送达率和较低的转发能耗。更多还原

【Abstract】 In the opportunistic networks,especially the mobile social networks,the nodes tend to maintain contacts with their closer neighbors,which helps to predict the contacting probabilities among nodes through evaluating the node closeness degrees.Due to node closeness relating with both the time and the located regions,when constructing the closeness evaluation model exploiting the node contacting histories,we considered the influence of contact region and contact time on node closeness degrees.And thus close neighbors of the node in the corresponding regions-regional friends can be obtained.Finally,an opportunistic routing algorithm based on regional friendship was presented combining current location of the node and its regional friendship.The experimental results indicate that the proposed algorithm can get a higher delivery ratio and a lower forwarding energy consumption than others,under the different deployment density of nodes and tolerant delay.更多还原