Predicting node proximity in ad-hoc networks: a least overhead adaptive model for selecting stable routes
Predicting node proximity in ad-hoc networks: a least overhead adaptive model for selecting stable routes
复制标题
预测自组织网络中的节点邻近度：用于选择稳定路由的最小开销自适应模型
DOI：
10.1109/mobhoc.2000.869210
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发表时间：
2000
期刊：
影响因子：
--
通讯作者：
T. Znati
中科院分区：
文献类型：
--
作者：
A. B. McDonald;T. Znati
本文提出了一种量化自组织网络中相邻节点未来邻近度的策略。邻近模型提供了反映给定链接未来稳定性的定量指标。由于在自组织网络中维护精确信息是不可行的，因此我们的模型被设计为需要最少的信息，并使用自适应学习策略来最小化在不确定条件下做出错误决策相关的成本。在假设随机独立移动性的情况下计算初始基线链路可用性后，该模型根据基于独立假设的预期链路故障时间以及反映环境的参数来调整未来的计算。定义该度量的目的是增强路由算法的性能并更好地促进自组织网络中的移动自适应动态集群。
This paper presents a strategy for quantifying the future proximity of adjacent nodes in an ad-hoc network. The proximity model provides a quantitative metric that reflects the future stability of a given link. Because it is not feasible to maintain precise information in an ad-hoc network, our model is designed to require minimal information and uses an adaptive learning strategy to minimize the cost associated with making a wrong decision under uncertain conditions. After computing the initial baseline link availability assuming random-independent mobility, the model adapts future computations depending on the expected time-to-failure of the link based on the independence assumption, and a parameter that reflects the the environment. The purpose for defining this metric is to enhance the performance of routing algorithms and better facilitate mobility-adaptive dynamic clustering in ad-hoc networks.