Dislocation detection in field environments: A belief functions contribution q S.N. Razavi b , E. Duflos a,1 , C. Haas b,⇑ , P. Vanheeghe a a Laboratoire d’Automatique, de Génie Informatique et Signal (LAGIS UMR CNRS 8219), Ecole Centrale de Lille, Cité Scientifique, BP 48, 59651 Villeneuve d’Ascq Cedex, France b Department of Civil and Environmental Engineering, University of Waterloo, 200 University Avenue, West Waterloo, Ontario, Canada N2L 3G1 article info Keywords: Dislocation detection belief functions Sensors network Construction materials RFID GPS abstract Dislocation is defined as the change between discrete sequential locations of critical items in field envi- ronments such as large construction projects. Dislocations on large sites of materials and critical items for which discrete time position estimates are available represent critical state changes. The ability to detect dislocations automatically for tens of thousands of items can ultimately improve project performance sig- nificantly. Detecting these dislocations in a noisy information environment where low cost radio fre- quency identification tags are attached to each piece of material, and the material is moved sometimes only a few meters, is the main focus of this study. We propose in this paper a method developed in the frame of belief functions to detect dislocations. The belief function framework is well-suited for such a problem where both uncertainty and imprecision are inherent to the problem. We also show how to deal with the calculations. This method has been implemented in a controlled experimental setting. The results of these experiments show the ability of the proposed method to detect materials dislocation over the site reliably. Broader application of this approach to both animate and inanimate objects is possible. Ó 2012 Published by Elsevier Ltd. 1. Introduction Material tracking is a key performance element in many field environments such as construction. For example, the unavailability of construction materials at the right place and at the right time has been recognized as having a major negative impact on construction productivity. Moreover, poor site materials management poten- tially delays construction activities, thus threatens project comple- tion dates and raises total installed costs (Grau & Caldas, 2007). While automated controls are often established for engineered and other critical materials during the design and procurement stages of large industrial projects, on-site control practices are still typically based on necessarily fallible direct human observation, manual data entry, and adherence to processes. These are inade- quate for overcoming the dynamic and unpredictable nature of construction sites. Node location approaches using signal strength and based on triangulation or relaxation algorithms (Bulusu, Heidemann, & Estrin, 2000; Boyd & Vandenberghe, 2004; Doherty & Ghaoui, 2001) are limited because of the cost of required node electronics and their site mobilisation (no current high volume de- mand exists), and because the anisotropic, dynamic transmission space on a construction site, for example, cannot feasibly be mapped at the temporal or spatial resolution required. In addition, even sophisticated and expensive solutions experience multipath, dead space, and environmentally-related interference to some ex- tent. For example, the Wi-Fi RTLS (real time location systems), such as commercial solutions from AeroScout Ó , Ubisense Ó , Ekahau Ó , and the PanGo Ó Network, require extensive and periodic calibration to map the Wi-Fi signals to locations throughout a building site while the existence of 802.11 access points is not guaranteed for any facility being built. Thus we have selected a more cost-effective approach that is applicable to field environment specifications. However, developing a method for location estimation that is robust to measurement noise but still has a reasonable implemen- tation cost is a challenge. Wireless sensor network-based data col- lection technologies that leverage the complementary strengths of GPS (high accuracy but high cost) and radio frequency identifica- tion (RFID – low cost but low accuracy) are being developed for a wide spectrum of applications. Specifically, more recent research is demonstrating that, coupled with mobile computers, data collec- tion technologies and sensors can provide a cost-effective, scalable, and easy-to-implement materials location sensing system in real world construction sites (Akinci, Patton, & Ergen, 2002; Caldas, Grau, & Haas, 2006; Grau & Caldas, 2007; Jaselskis & El-Misalami, 0957-4174/$ - see front matter Ó 2012 Published by Elsevier Ltd. doi:10.1016/j.eswa.2011.12.014 q This paper results from the collaboration between the Laboratoire d’Automa- tique Génie Informatique et Signal (UMR CNRS 8219, Lille, France) and the Department of Civil and Environmental Engineering of the University of Waterloo (Canada). The research work was sponsored by a CNRS International Scientific Collaboration Program (PICS). ⇑ Corresponding author. E-mail addresses: snavabza@engmail.uwaterloo.ca (S.N. Razavi), emmanuel.du- flos@ec-lille.fr (E. Duflos), chaas@civmail.uwaterloo.ca (C. Haas), philippe.vanhee- ghe@ec-lille.fr (P. Vanheeghe). 1 Principal corresponding author Expert Systems with Applications 39 (2012) 8505–8513 Contents lists available at SciVerse ScienceDirect Expert Systems with Applications journal homepage: www.elsevier.com/locate/eswa