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Collaboration enforcement in mobile ad hoc networks.

机译:移动自组织网络中的协作实施。

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摘要

Mobile Ad hoc NETworks (MANETs) have attracted great research interest in recent years. Among many issues, lack of motivation for participating nodes to collaborate forms a major obstacle to the adoption of MANETs. Many contemporary collaboration enforcement techniques employ reputation mechanisms for nodes to avoid and penalize malicious participants. Reputation information is propagated among participants and updated based on complicated trust relationships to thwart false accusation of benign nodes. The aforementioned strategy suffers from low scalability and is likely to be exploited by adversaries. To address these problems, we first propose a finite state model. With this technique, no reputation information is propagated in the network and malicious nodes cannot cause false penalty to benign hosts. Misbehaving node detection is performed on-demand; and malicious node punishment and avoidance are accomplished by only maintaining reputation information within neighboring nodes. This scheme, however, requires that each node equip with a tamper-proof hardware. In the second technique, no such restriction applies. Participating nodes classify their one-hop neighbors through direct observation and misbehaving nodes are penalized within their localities. Data packets are dynamically rerouted to circumvent selfish nodes. In both schemes, overall network performance is greatly enhanced. Our approach significantly simplifies the collaboration enforcement process, incurs low overhead, and is robust against various malicious behaviors. Simulation results based on different system configurations indicate that the proposed technique can significantly improve network performance with very low communication cost.
机译:近年来,移动自组网(MANET)引起了极大的研究兴趣。在许多问题中,参与节点缺乏协作的动力构成了采用MANET的主要障碍。许多当代的协作执行技术都为节点采用信誉机制,以避免和惩罚恶意参与者。信誉信息在参与者之间传播,并基于复杂的信任关系进行更新,以阻止对良性节点的错误指控。前面提到的策略的可伸缩性低,很容易被对手利用。为了解决这些问题,我们首先提出一个有限状态模型。使用此技术,信誉信息不会在网络中传播,并且恶意节点不会对良性主机造成虚假惩罚。行为不当的节点检测是按需执行的;恶意节点的惩罚和避免是通过仅在邻近节点内维护信誉信息来完成的。但是,此方案要求每个节点都配备防篡改硬件。在第二种技术中,没有这样的限制。参与节点通过直接观察对一跳邻居进行分类,行为不当的节点在其所在位置受到惩罚。数据包被动态重新路由到自私节点。在这两种方案中,整体网络性能都得到了极大的提高。我们的方法极大地简化了协作实施过程,降低了开销,并且能够抵抗各种恶意行为。基于不同系统配置的仿真结果表明,所提出的技术可以以非常低的通信成本显着提高网络性能。

著录项

  • 作者

    Jiang, Ning.;

  • 作者单位

    University of Central Florida.;

  • 授予单位 University of Central Florida.;
  • 学科 Computer Science.
  • 学位 Ph.D.
  • 年度 2006
  • 页码 156 p.
  • 总页数 156
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 自动化技术、计算机技术;
  • 关键词

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