Advanced Methods for 3D Magnetic Localization in Industrial Process Distributed Data-Logging with a Sparse Distance Matrix

  • Abhaya Chandra  Kammara
  • Andreas König
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 223)


Wireless sensor networks/data-logging devices are increasingly applied for distributed measurement and acquiring additional contextual data. These have been applied in large scale indoor and outdoor systems with solutions based on RF, light based and ultra sound based systems. Data-loggers in liquid filled containers pose new challenges for localization because of the high reflectivity of containers and high attenuation due to the liquids obstructing communication between wireless nodes. Magnetic localization techniques have been used in many places including military research [14]. This approach was adapted for use in liquid filled containers. In this project, two prototypes, a laboratory and an industrial installation have been conceived and served for acquisition of experimental data for localization. In our paper, we exploit the sparsity met in the particular magnetic MEMS sensor swarm localization concept by introducing NLMR which is a simplified form of Sammon’s mapping (NLM) and we combine it with different meta-heuristics and soft-computing techniques, e.g., gradient descent, Simulated Annealing and PSO. We compare this with Multilateration and conventional NLM localization technique. Our approach has improved the localization from a mean error of 20 cm in the first cut analysis for the industrial setup using conventional NLM down to 11 cm without and to 9 cm with apriori knowledge. Future improvements are to be expected from a thorough calibration of all system components. in [5]. The modified algorithm is capable of distributed localization producing mean localization error of 10 cm for the Warstein experiment data.


Magnetic sensor localization Sammon’s mapping NLMR Particle swarm optimization PSO Indoor localization 



This work was partly supported by the Federal Ministry of Education and Research (BMBF) in the program mst-AVS, in the project ROSIG grant no. 16SV3604 of the PAC4PT consortium (Partners were UST GmbH, IMST GmbH, Krohne, Warsteiner, and microTEC Ges. für Mikrotechn. GmbH (Coord.). The industrial environment data, employed here for the algorithmic studies, has been acquired and first-cut analysed by conventional NLM in the project work by S. Carrella and D. Groben. All algorithms were implemented using python (numpy, scipy, and matplotlib packages) and Matlab.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Institute of Integrated Sensor SystemsTU KaiserslauternKaiserslauternGermany

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