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Distributed configuration management is imperative for wirelessinfrastructureless networks where each node adjusts locally its physical andlogical configuration through information exchange with neighbors. Two issuesremain open. The first is the optimality. The second is the complexity. Westudy these issues through modeling, analysis, and randomized distributedalgorithms. Modeling defines the optimality. We first derive a globalprobabilistic model for a network configuration which characterizes jointly thestatistical spatial dependence of a physical- and a logical-configuration. Wethen show that a local model which approximates the global model is a two-layerMarkov Random Field or a random bond model. The complexity of the local modelis the communication range among nodes. The local model is near-optimal whenthe approximation error to the global model is within a given error bound. Weanalyze the trade-off between an approximation error and complexity, and derivesufficient conditions on the near-optimality of the local model. We validatethe model, the analysis and the randomized distributed algorithms also throughsimulation.

Randomized Distributed Configuration Management of Wireless Networks: Multi-layer Markov Random Fields and Near-Optimality