Table of Contents
Advances in Artificial Neural Systems
Volume 2014, Article ID 394038, 17 pages
Research Article

Neural Virtual Sensors for Adaptive Magnetic Localization of Autonomous Dataloggers

Institute of Integrated Sensor Systems, EIT, TU Kaiserslautern, Erwin-Schrödinger-Straße 12, 67663 Kaiserslautern, Germany

Received 12 June 2014; Revised 16 October 2014; Accepted 19 October 2014; Published 30 December 2014

Academic Editor: Manwai Mak

Copyright © 2014 Dennis Groben et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The surging advance in micro- and nanotechnologies allied with neural learning systems allows the realization of miniaturized yet extremely powerful multisensor systems and networks for wide application fields, for example, in measurement, instrumentation, automation, and smart environments. Time and location context is particularly relevant to sensor swarms applied for distributed measurement in industrial environment, such as, for example, fermentation tanks. Common RF solutions face limits here, which can be overcome by magnetic systems. Previously, we have developed the electronic system for an integrated data logger swarm with magnetic localization and sensor node timebase synchronization. The focus of this work is on an approach to improving both localization accuracy and flexibility by the application of artificial neural networks applied as virtual sensors and classifiers in a hybrid dedicated learning system. Including also data from an industrial brewery environment, the best investigated neural virtual sensor approach has achieved an advance in localization accuracy of a factor of 4 compared to state-of-the-art numerical methods and, thus, results in the order of less than 5 cm meeting industrial expectations on a feasible solution for the presented integrated localization system solution.