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摘要翻译：
对于无基础设施的无线网络，分布式配置管理是必要的，每个节点通过与邻居的信息交换来本地调整其物理和逻辑配置。两个问题仍然悬而未决。首先是最优性。二是复杂性。我们通过建模、分析和随机分布式算法来研究这些问题。建模定义了最优性。我们首先导出了一个网络结构的全局概率模型，该模型共同表征了物理结构和逻辑结构的统计空间依赖性。然后我们证明了近似全局模型的局部模型是二层马尔可夫随机场或随机键模型。局部模型的复杂度是节点间的通信范围。当对全局模型的逼近误差在给定的误差范围内时，局部模型是近最优的。我们分析了近似误差与复杂度之间的权衡，并给出了局部模型接近最优的充分条件。通过仿真对模型、分析和随机分布式算法进行了验证。
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英文标题：
《Randomized Distributed Configuration Management of Wireless Networks:
Multi-layer Markov Random Fields and Near-Optimality》
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作者：
Sung-eok Jeon, and Chunayi Ji
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最新提交年份：
2008
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分类信息：
一级分类：Computer Science 计算机科学
二级分类：Distributed, Parallel, and Cluster Computing 分布式、并行和集群计算
分类描述：Covers fault-tolerance, distributed algorithms, stabilility, parallel computation, and cluster computing. Roughly includes material in ACM Subject Classes C.1.2, C.1.4, C.2.4, D.1.3, D.4.5, D.4.7, E.1.
包括容错、分布式算法、稳定性、并行计算和集群计算。大致包括ACM学科类C.1.2、C.1.4、C.2.4、D.1.3、D.4.5、D.4.7、E.1中的材料。
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一级分类：Computer Science 计算机科学
二级分类：Artificial Intelligence 人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
Distributed configuration management is imperative for wireless infrastructureless networks where each node adjusts locally its physical and logical configuration through information exchange with neighbors. Two issues remain open. The first is the optimality. The second is the complexity. We study these issues through modeling, analysis, and randomized distributed algorithms. Modeling defines the optimality. We first derive a global probabilistic model for a network configuration which characterizes jointly the statistical spatial dependence of a physical- and a logical-configuration. We then show that a local model which approximates the global model is a two-layer Markov Random Field or a random bond model. The complexity of the local model is the communication range among nodes. The local model is near-optimal when the approximation error to the global model is within a given error bound. We analyze the trade-off between an approximation error and complexity, and derive sufficient conditions on the near-optimality of the local model. We validate the model, the analysis and the randomized distributed algorithms also through simulation.
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PDF链接：
https://arxiv.org/pdf/0809.1916
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