无线网络的随机分布式配置管理：多层马尔可夫随机场与近最优性
Randomized Distributed Configuration Management of Wireless Networks: Multi-layer Markov Random Fields and Near-Optimality
摘要：
分布式配置管理对于无线无基础设施网络是必要的，在无线无基础设施网络中，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。第一个是最优性。第二是复杂性。我们通过建模、分析和随机分布式算法研究这些问题。建模定义了最优性。我们首先推导出一个全球概率模型的网络配置，其特征在于联合的物理和逻辑配置的统计空间依赖性。然后，我们证明了一个局部模型，近似的全球模型是一个两层马尔可夫随机场或随机债券模型。局部模型的复杂性在于节点间的通信范围。当全局模型的近似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和复杂性之间的权衡，并推导出局部模型近似最优的充分条件。我们也通过仿真验证了模型、分析和随机分布算法。
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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