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arXiv 1708.07044cs.NI
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EZ-AG:使用推辅助自排斥随机游走的移动自组网中的无结构数据聚合

EZ-AG: Structure-free data aggregation in MANETs using push-assisted self-repelling random walks

Vinod Kulathumani, Masahiro Nakagawa, Anish Arora

摘要:

本文介绍了EZ-AG,一个无结构的协议,重复不敏感的数据聚集在移动自组网。EZ-AG的关键思想是引入一个令牌,该令牌在网络中执行自排斥随机游走,并在节点第一次访问时聚合来自节点的信息。图上令牌的自排斥随机游走是其中在每一步中,令牌移动到最不经常访问的邻居。虽然自排斥随机游走访问网络中的所有节点比普通随机游走快得多,但当大多数节点已经被访问时,它们往往会减慢。在本文中,我们表明,在每个节点上单步的推阶段可以显着加快聚合,并消除这种放缓。通过这样做,EZ-AG仅在O(N)时间和消息中实现聚合。在开销方面,EZ-AG优于现有的无结构数据聚合,至少是log(N)的因子,并达到聚合消息开销的下限。我们证明了EZ-AG的可扩展性和鲁棒性,使用NS-3模拟网络从100到4000个节点在不同的移动模型和节点速度。我们还描述了EZ-AG的层次扩展,可以在每个节点上只使用O(NlogN)消息,这是一个多对数因子的改进,在现有的技术,可以产生多分辨率聚合。

英文摘要:

This paper describes EZ-AG, a structure-free protocol for duplicate insensitive data aggregation in MANETs. The key idea in EZ-AG is to introduce a token that performs a self-repelling random walk in the network and aggregates information from nodes when they are visited for the first time. A self-repelling random walk of a token on a graph is one in which at each step, the token moves to a neighbor that has been visited least often. While self-repelling random walks visit all nodes in the network much faster than plain random walks, they tend to slow down when most of the nodes are already visited. In this paper, we show that a single step push phase at each node can significantly speed up the aggregation and eliminate this slow down. By doing so, EZ-AG achieves aggregation in only O(N) time and messages. In terms of overhead, EZ-AG outperforms existing structure-free data aggregation by a factor of at least log(N) and achieves the lower bound for aggregation message overhead. We demonstrate the scalability and robustness of EZ-AG using ns-3 simulations in networks ranging from 100 to 4000 nodes under different mobility models and node speeds. We also describe a hierarchical extension for EZ-AG that can produce multi-resolution aggregates at each node using only O(NlogN) messages, which is a poly-logarithmic factor improvement over existing techniques.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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