As it strongly affects the system performance in measuring 3D point coordinates, beacon positioning represents a challenging issue in large scale metrology applications based on wireless sensor networks. This paper presents a software-assisted procedure for efficient placement of ultrasonic beacons in a wireless distributed network-based system for medium–large sized object measurements. A regular grid-based strategy and a genetic algorithm-based approach to the deployment problem are presented. The resulting network configurations are compared in terms of overall costs, sensor availability and measurement precision. The genetic algorithm outperforms the regular deployment solution, optimizing the objective functions and providing additional capabilities to represent a realistic working environment. The novelty here is the approach to the “pre-processing” phase of a sensor network deployment, involving working environment constraints, system functional characteristics, measurand geometry, and measurement task definition in the three-dimensional network design.

Optimal Sensor Positioning for Large Scale Metrology Applications / Galetto, Maurizio; Pralio, Barbara. - In: PRECISION ENGINEERING. - ISSN 0141-6359. - STAMPA. - 34:3(2010), pp. 563-577. [10.1016/j.precisioneng.2010.02.001]

Optimal Sensor Positioning for Large Scale Metrology Applications

GALETTO, Maurizio;PRALIO, Barbara
2010

Abstract

As it strongly affects the system performance in measuring 3D point coordinates, beacon positioning represents a challenging issue in large scale metrology applications based on wireless sensor networks. This paper presents a software-assisted procedure for efficient placement of ultrasonic beacons in a wireless distributed network-based system for medium–large sized object measurements. A regular grid-based strategy and a genetic algorithm-based approach to the deployment problem are presented. The resulting network configurations are compared in terms of overall costs, sensor availability and measurement precision. The genetic algorithm outperforms the regular deployment solution, optimizing the objective functions and providing additional capabilities to represent a realistic working environment. The novelty here is the approach to the “pre-processing” phase of a sensor network deployment, involving working environment constraints, system functional characteristics, measurand geometry, and measurement task definition in the three-dimensional network design.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2303807
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