Locating sensor nodes on construction projects

  • Authors:
  • François Caron;Saiedeh Navabzadeh Razavi;Jongchul Song;Philippe Vanheeghe;Emmanuel Duflos;Carlos Caldas;Carl Haas

  • Affiliations:
  • Laboratoire d'Automatique, Génie Informatique et Signal, Villeneuve d'Ascq Cedex, France F59651;University of Waterloo, Canada N2L 3G1;Department of Civil Enginering, University of New Mexico, Albuquerque 87131;Laboratoire d'Automatique, Génie Informatique et Signal, Villeneuve d'Ascq Cedex, France F59651;Laboratoire d'Automatique, Génie Informatique et Signal, Villeneuve d'Ascq Cedex, France F59651;Constr. Engrg. and Proj. Mgmt. (CEPM), Civil, Architectural and Environmental Engineering, 1 University Station C1752, University of Texas at Austin, Austin 78712;Canada Research Chair in Sustainable Infrastructure, M. ASCE, University of Waterloo, Canada N2L 3G1

  • Venue:
  • Autonomous Robots
  • Year:
  • 2007

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Abstract

Localization of randomly distributed wireless sensor nodes is a significant and fundamental problem in a broad range of emerging civil engineering applications. Densely deployed in physical environments, they are envisioned to form ad hoc communication networks and provide sensed data without relying on a fixed communications infrastructure. To establish ad hoc communication networks among wireless sensor nodes, it is useful and sometimes necessary to determine sensors' positions in static and dynamic sensor arrays. As well, the location of sensor nodes becomes of immediate use if construction resources, such as materials and components, are to be tracked. Tracking the location of construction resources enables effortless progress monitoring and supports real-time construction state sensing. This paper compares several models for localizing RFID nodes on construction job sites. They range from those based on triangulation with reference to transmission space maps, to roving RFID reader and tag systems using multiple proximity constraints, to approaches for processing uncertainty and imprecision in proximity measurements. They are compared qualitatively on the basis of cost, flexibility, scalability, computational complexity, ability to manage uncertainty and imprecision, and ability to handle dynamic sensor arrays. Results of field experiments and simulations are also presented where applicable.