Community-Aware Opportunistic Routing in Mobile Social Networks
Community-Aware Opportunistic Routing in Mobile Social Networks
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DOI：
10.1109/tc.2013.55
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发表时间：
2014-07
影响因子：
3.7
通讯作者：
Mingjun Xiao;Jie Wu;Liusheng Huang
中科院分区：
文献类型：
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作者：
Mingjun Xiao;Jie Wu;Liusheng Huang
移动社交网络（MSN）是一种由大量具有社交特征的移动节点组成的延迟容忍网络。最近，许多社交感知算法被提出来解决 MSN 中的路由问题。然而，这些算法倾向于将消息转发到具有局部最优社会特性的节点，因此无法达到最优性能。在本文中，我们提出了一种分布式最优社区感知机会路由（CAOR）算法。我们的主要贡献是提出了一种家庭感知社区模型，将 MSN 转变为仅包含社区家庭的网络。我们证明，在社区家庭网络中，我们仍然可以通过逆Dijkstra算法计算节点的最小期望传递延迟，并实现最优的机会路由性能。由于社区数量在量级上远小于节点数量，因此大大降低了联系信息的计算成本和维护成本。我们基于真实的 MSN 跟踪和合成的 MSN 跟踪，通过广泛的模拟，展示了我们的算法如何显着优于以前的算法。
Mobile social networks (MSNs) are a kind of delay tolerant network that consists of lots of mobile nodes with social characteristics. Recently, many social-aware algorithms have been proposed to address routing problems in MSNs. However, these algorithms tend to forward messages to the nodes with locally optimal social characteristics, and thus cannot achieve the optimal performance. In this paper, we propose a distributed optimal Community-Aware Opportunistic Routing (CAOR) algorithm. Our main contributions are that we propose a home-aware community model, whereby we turn an MSN into a network that only includes community homes. We prove that, in the network of community homes, we can still compute the minimum expected delivery delays of nodes through a reverse Dijkstra algorithm and achieve the optimal opportunistic routing performance. Since the number of communities is far less than the number of nodes in magnitude, the computational cost and maintenance cost of contact information are greatly reduced. We demonstrate how our algorithm significantly outperforms the previous ones through extensive simulations, based on a real MSN trace and a synthetic MSN trace.