Most current methods for 802.11-based indoor localization depend on surveys conducted by experts or skilled technicians. Some recent systems have incorporated surveying by users. Structuring localization systems "organically," however, introduces its own set of challenges: conveying uncertainty, determining when user input is actually required, and discounting erroneous and stale data. Through deployment of an organic location system in our nine-story building, which contains nearly 1,400 distinct spaces, we evaluate new algorithms for addressing these challenges. We describe the use of Voronoi regions for conveying uncertainty and reasoning about gaps in coverage, and a clustering method for identifying potentially erroneous user data. Our algorithms facilitate rapid coverage while maintaining positioning accuracy comparable to that achievable with survey-driven indoor deployments.

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We show that the fixed power, synchronous Interference Avoidance (IA) scheme of [3] employing the (greedy) eigen-iteration can be modeled as the recently developed potential game of [10]. Motivated by the fact that receivers can make small mistakes, we consider the convergence of the eigeniteration when noise is added in a manner similar to [2]. Further, we restrict ourselves to a class of signal environments that we call levelable environments. Applying game-theory, we obtain a convergence result similar to that of [2] for levelable environments: arbitrarily small noise assures that the eigeniteration almost surely converges to a neighborhood of the optimum signature set.