ESD: A Novel Optimization Algorithm for Positioning Estimation in Wireless Sensor Networks - Analysis and Experimental Validation via a Testbed Platform

In recent contributions [Tennina, S., et al., 2008], [Tennina, S., 2008], we have provided a comparative analysis of various optimization algorithms, which can be used for atomic location estimation, and suggested an enhanced version of the steepest descent (ESD) algorithm, which we have shown to be competitive with well-known distributed localization algorithms in terms of estimation accuracy and numerical complexity. Moreover, in [Tennina, S., 2008] we have conducted a statistical characterization of the positioning error distribution of the ESD algorithm, and shown that the latter error can be well approximated by the family of Pearson distributions. However, the analysis in [Tennina, S., et al., 2008; Tennina, S., 2008; Tennina, S., 2008] is mainly based on numerical (i.e., computer-based) simulations, which only in part allows to predict the system performance in a realistic environment where sensor nodes are expected to operate. As a consequence, the aim of this contribution is twofold: i) to analyze the error performance of the ESD algorithm in a real testbed platform working in a typical indoor environment, and ii) to compare experimental and simulated results to substantiate via real measurements our previous findings useful for network setup and analysis. In particular, we will first report on the implementation issues related on mapping the ESD algorithm on the CrossBow's MICAz sensor node platform [http://www.xbow.com/Products/wproductsoverview.aspx.], and, then, we will investigate, via real experiments, on the effect of network topology and ranging errors in estimating the final position of an unknown sensor node.