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.
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Aldunate, R., Ochoa, S., Pena-Mora, F., and Nussbaum, M. 2006. Robust mobile ad hoc space for collaboration to support disaster relief efforts involving critical physical infrastructure. J. Comp. in Civ. Engrg. ASCE, 20(1):13–27.
Boyd, S. and Vandenberghe, L. 2004. Convex Optimization. Cambridge University Press, Cambridge, UK.
Bulusu, N., Heidemann, J., and Estrin, D. 2000. GPS-less low-cost outdoor localization for very small devices. IEEE Personal Communications, 7(5):28–34.
Business Roundtable. 1982. Modern management systems. Construction Industry Cost Effectiveness Report A-6.
Caldas, C.H., Haas, C.T., Torrent, D.G., Wood, C.R., and Porter, R. 2004. Field trials of GPS technology for locating fabricated pipe in laydown yards. Smart Chips Project Report, FIATECH, Austin, TX.
Caron, F., Haas, C., Vanheeghe, P., et Duflos, E., “Modélisation de mesures de proximité par la théorie des fonctions de croyance; Application à la localisation de matériaux de construction équipés d’étiquettes RFID,” Journée d’étude SEE: La théorie des fonctions de croyance : de nouveaux horizons pour l'aide à la décision - 8-9 décembre 2005, Paris.
Dempster, A.P. 1968. A generalization of Bayesian inference. Journal of the Royal Statistical Society, 30.
Doherty, L., Pister, K., and Ghaoui, L. 2001. Convex position estimation in wireless sensor networks. In Proc., INFOCOM, IEEE, pp. 1655–1663.
Furlani, K. and Stone, W. 1999. Architecture for discrete construction component tracking. In Proc., ISARC ’99, IAARC, Madrid, pp. 289–294.
Glaser, S., Shoureshi, R., and Pescovitz, D. 2005. Frontiers in sensors and sensing systems. Smart Structures and Systems, 1(1):103–120.
He, T., Huang, Ch., Blum, B., Stankovic, J., and Abdelzaher, T. 2003. Range-free localization schemes in large scale sensor networks. In Proc. ACM/IEEE 9th Annu. Int. Conf. Mobile Computing and Networking (MobiCom'03), pp. 81–95.
Hightower, J., Want, R., and Borriello, G. 2000. SpotOn: An indoor 3D location sensing technology based on RF signal strength. Technical Report No. UW CSE 00-02-02, Department of Computer Science and Eng., University of Washington, Seattle, WA.
Hightower, J. and Borriello, G. 2001. Location Sensing Techniques. Technical Report, Computer Science and Engineering, University of Washington.
Jaselskis, E.J. and El-Misalami, T. 2003. Implementing radio frequency identification in the construction process. ASCE J. Constr. Engrg. Manag., 129(6):680–688.
Jaselskis, E.J., Anderson, M.R., Jahren, C.T., Rodriguez, Y., and Njos, S. 1995. Radio-frequency identification applications in construction industry. J. Constr. Eng. Manage., 121(2):189–196.
Kini, D.U. 1999. Materials management: The key to successful project management. J. Manage. Eng., ASCE, 15(1):30–34.
Navon, R. and Goldschmidt, E. 2003. Can labor inputs be measured and controlled automatically? J. Constr. Eng. Manage., ASCE, 129(4):437–445.
Patwari, N., O'Dea, R.J., and Wang, Y. 2001. Relative location in wireless networks. In Proc., IEEE Vehicular Technology Conference, vol. 2, pp. 1149–1153.
Peyret, F. and Tasky, R. 2002. Asphalt quality parameters traceability using electronic tags and GPS. In Proc., ISARC ’02, Washington, DC, pp. 155–160.
Reinhardt, J., Garrett, J., and Akinci, B. 2005. Framework for providing customized data representations for effective and efficient interaction with mobile computing solutions on construction sites. J. Comp. in Civ. Engrg., ASCE, 19(2):109–118.
Sacks, R., Navon, R., and Goldschmidt, E. 2003. Building project model support for automated labor monitoring. J. Comp. in Civ. Engrg., ASCE, 17(1):19–27.
Sacks, R., Navon, R., Brodetskaia, I., and Shapira, A. 2005. Feasibility of automated monitoring of lifting equipment in support of project control. J. Constr. Eng. Manage., 131(5):604–614.
Shafer, G. 1976. A Mathematical Theory of Evidence. Princeton University Press, Princeton.
Shafer, G. 1992. The Dempster-Shafer theory. Encyclopedia of Artificial Intelligence, 2nd edition, Stuart C. Shapiro (ed.). Wiley, pp. 330–331
Simic, S.N. and Sastry, S. 2002. Distributed localization in wireless ad hoc networks. Technical Report UCB/ERL M02 /26, Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA.
Smets, P. and Kennes, R. 1994. The transferable belief model. Artificial Intelligence, 66:191–234.
Song, J., Haas, C., Caldas, C., and Liapi, K. 2005. Locating materials on construction sites using proximity techniques. ASCE Construction Research Congress, San Diego, CA.
Song, J., Haas, C., Caldas, C., Ergen, E., and Akinci, B. 2006a. Automating the task of tracking the delivery and receipt of fabricated pipe spools in industrial projects. Automation in Construction, Elsevier, 15(2):166–177.
Song, J., Haas, C.T., and Caldas, C.H. 2006b. A proximity-based method for locating RFID tagged objects. Accepted for publication in Special Issue of Journal of Advanced Engineering Informatics on RFID Applications in Engineering.
Thomas, H.R., Sanvido, V.E., and Sanders, S.R. 1989. Impact of material management on productivity—a case study. J. Constr. Eng. Manage., 115(3):370–384.
Tommelein, I.D. 1998. Pull-driven scheduling for pipe-spool installation: Simulation of a lean construction technique. J. Constr. Engrg. Manag., ASCE, 124(4):279–288.
Vorster, M.C. and Lucko, G. 2002. Construction technology needs assessment update. Research Report 173-11, Construction Industry Institute, Austin, TX.
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Caron, F., Razavi, S.N., Song, J. et al. Locating sensor nodes on construction projects. Auton Robot 22, 255–263 (2007). https://doi.org/10.1007/s10514-006-9720-1
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DOI: https://doi.org/10.1007/s10514-006-9720-1