Towards optimally efficient field estimation with threshold-based pruning in real robotic sensor networks

Abstract

Abstract-The efficiency of distributed sensor networks depends on an optimal trade-off between the usage of resources and data quality. The work in this paper addresses the problem of optimizing this trade-off in a self-configured distributed robotic sensor network, with respect to a user-defined objective function. We investigate a quadtree network topology and implement a fully distributed threshold-based field estimation algorithm. Simulations with field data as well as real robot experiments are performed, validating our distributed control strategy and evaluating the threshold-based formula for real world scenarios. We propose a theoretical analysis that predicts the system's behavior in real world case studies. The experiments and this prediction show very good correspondence, enabling the accurate employment of the objective function, optimizing the trade-off based on user needs.

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“…Prorok et al study hierarchical network topologies based on spatial clustering [4]. In this approach, cluster heads may choose to prune their children if the part of the monitored field they represent is highly isotropic as defined by some statistically computed threshold.…”

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