The Challenges of Model Objective Selection and Estimation for Ad-hoc Network Data Sets

Abstract

We introduce a new methodology for determining the difficulty of selecting the modeling objective function and estimating the parameters for an ad-hoc network data set. The method utilizes formulation of the underlying optimization problem instance that consists of an objective function and a set of constraints. The method is illustrated on real distance measurement data used for estimating the locations of wireless nodes that is the most studied and a representative problem for ad-hoc networks estimation. The properties of the data set that could affect the quality of optimization are categorized. In large optimization problems with multiple properties (characteristics) that contribute to the solution quality, it is practically impossible to analytically study the effect of each property. A number of metrics for evaluating the effectiveness of the optimization on each data set are proposed. Using the well known Plackett and Burmann fast simulation methodology, for each metric, the impact of the categorized properties of the data are determined for the specified optimization. A new approach for combining the impacts resulting from different properties on various metrics is described. We emphasize that the method is generic and has the potential to be more broadly applicable to other parameter estimation problems.