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We present a novel, easy to use virtual testbed for the evaluation of localization algorithms. Our testbed enables researchers to easily run tests on a huge body of real world range-based indoor localization data. The data consists of a dense grid of reference points belonging to one or multiple maps. Each point consists of a ground truth value and an arbitrary number of ranging values. Each ranging...
We present a novel, easy to use virtual testbed enabling researchers to evaluate their localization algorithms based on distance measurements in indoor environments. We provide precise ground truth information collected by our previously presented reference system, based on a mobile robot, in combination with range measurements from a Wireless Sensor Network (WSN) in multiple buildings. The user can...
We introduce the Membership Degree Min-Max (MD-Min-Max) localization algorithm as a precise and simple lateration algorithm for indoor localization. MD-Min-Max is based on the well known Min-Max algorithm that uses a bounding box to compute the position. We present an analysis of the Min-Max algorithm and show strengths and weaknesses in the spatial distribution of the position error. MD-Min-Max uses...
We introduce a simulation engine to visually evaluate and compare distance based lateration algorithms and deployments called the FU Berlin Parallel Lateration-Algorithm Simulation and Visualization Engine (LS2). Our engine simulates a scenario which consists of given anchor positions and evaluates all positions of a playing field in parallel, instead of only randomly selected positions. At the end...
Several ways to estimate the position of a Wireless-Sensor-Network (WSN) node have been discussed in the past years. Unlike in outdoor solutions where the Global Positioning System (GPS) could be used in most applications, a general solution for indoor usage has not been found. The few existing indoor localization solutions on the market are highly specialized and rely on infrastructure or on very...
The MANIAC Challenge is a competition for cooperation strategies in wireless ad-hoc networks with the focus on experimental evaluation. We present the results of the MANIAC Challenge and discuss characteristics of real networks such as link instability and mobility, which are often simplified in common network simulators. We introduce our strategy Friendly Clustering that won the Performance Award...
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