无线智能设备的普遍使用促进了机会网络的发展。这类网络处于间歇性连接状态，以自组织方式转发数据。路由协议设计时考虑节点携带者的社会特征和日常行为能够提高机会网络的性能。提出了一种基于社会上下文认知的机会路由算法SCOR ，该算法利用网络中的社会上下文信息，通过BP神经网络模型预测节点的移动行为。路由决策过程充分考虑移动节点活动的时间和空间属性，当接收节点与发送节点同时处于网络中的同一连通域时，数据转发采用同步方式，否则采用异步方式。仿真分析和实验结果表明，与其它经典算法相比，SCOR算法提高了数据成功转发的比率，减少了网络的开销。%The pervasive deployment of wireless smart devices stimulates the development of ad hoc networks .Such net-works ,also referred as opportunistic networks ,are intermittently connected and represent a paradigm shift of forwarding data in an ad hoc manner .A recent trend is looking at social relationships ,inferred from the social nature of human mobility ,to bring messages close to a destination .We addressed this challenge by presenting a social context-aware opportunistic routing (SCOR ) .In this novel protocol ,social context information of the network was exploited to predict the mobility patterns of nodes based on the back-propa-gation neural networks model .The routing scheme considered both the spatial and the temporal dimensions .If the recipient was pre-sent in the same connected region of the network as the sender ,the message was delivered by synchronous method ,or else the mes-sage was delivered by asynchronous method .The evaluation analysis and the simulation results indicate that for the social context-based routing algorithms in opportunistic networks ,SCOR solution outperforms other routing solutions due to its ability to maximize the delivery ratio and to minimize the network overhead .
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