CONNECT: Consociating opportunistic network neighbors for constructing a consistent and connected virtual backbone

CONNECT: Consociating opportunistic network neighbors for constructing a consistent and connected virtual backbone
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CONNECT:联合机会性网络邻居,构建一致且互联的虚拟骨干网

DOI:
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发表时间:
2014
期刊:
Proceeding of IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks 2014
影响因子:
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通讯作者:
Sajal K. Das
Sajal K. Das
中科院分区:
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文献类型:
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作者:
Mehrab Shahriar;Sajal K. Das

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迄今为止,机会主义网络本质上大多被视为具有延迟容忍性。因此,需要一致和即时的网络连接的实时和软实时应用程序在机会通信环境中通常被认为是无形的。在本文中,我们试图通过网络中邻居的联合来揭示机会网络中固有的连接虚拟骨干网。该主干可以为设计实时社交应用程序的架构铺平道路。骨干网可能会随着时间、地点和人群密度的变化而变化。通过对现实世界以及合成的人类移动轨迹和暂停时间进行实验,我们首先构建了热门地点的人类停留持续时间的模式。通过注入这种模式,我们证明了这些网络环境中内在骨干的存在,在这些网络环境中,人们的活动表现出规律性。应用最小连通支配集和单元节点加权斯坦纳树等图论概念,我们进一步优化并确保主干的鲁棒性。模拟结果表明我们的方法在以机会​​网络中实时交互前景的形式揭示新维度方面的有效性。
Opportunistic networks have so far been seen mostly as delay tolerant in nature. As a result, real-time and soft real-time applications, which demand consistent and instant connectivity of the network, are usually considered intangible in opportunistic communication environments. In this paper we seek to reveal the inherent connected virtual backbone in an opportunistic network through the consociation of the neighbors in the network. This backbone can pave the way for designing an architecture for real-time social applications. The backbone may change in terms of time, location and crowd density. Experimenting on real world as well as synthetic human mobility traces and pause times, we first structure the pattern of human halt durations at popular places. Infusing this pattern, we then prove the existence of the intrinsic backbone in those networking environments, where people show regularity in their movements. Applying graph-theoretic concepts like Minimum Connected Dominating Set and Unit Node Weighted Steiner Tree we further optimize and ensure the robustness of the backbone. Simulation results show the effectiveness of our approach in exposing a newer dimension in the form of real time interaction prospects in opportunistic networks.