A BIM centered indoor localization algorithm to support building fire emergency response operations - Li, Nan; Becerik-Gerber, Burcin; Krishnamachari, Bhaskar; Soibelman, Lucio (Automation in construction, 2014)
The ability to locate people quickly and accurately in buildings is critical to the success of building fire emergency response operations, and can potentially contribute to the reduction of various building fire-caused casualties and injuries. This paper introduces an environment aware beacon deployment algorithm designed by the authors to support a sequence based localization schema for locating first responders and trapped occupants at building fire emergency scenes. The algorithm is designed to achieve dual objectives of improving room-level localization accuracy and reducing the effort required to deploy an ad-hoc sensor network, as the required sensing infrastructure is presumably unavailable at most emergency scenes. The deployment effort is measured by the number of beacons to deploy, and the location accessibility to deploy the beacons. The proposed algorithm is building information modeling (BIM) centered, where BIM is integrated to provide the geometric information of the sensing area as input to the algorithm for computing space division quality, a metric that measures the likelihood of correct room-level estimations and associated deployment effort. BIM also provides a graphical interface for user interaction. Metaheuristics are integrated to efficiently search for a satisfactory solution in order to reduce the computational time, which is crucial for the success of emergency response operations. The algorithm was evaluated by simulation, where two building fire emergency scenarios were simulated. The tabu search, which employs dynamically generated constraints to guide the search for optimum solutions, was found to be the most efficient among three tuned tested metaheuristics. The algorithm yielded an average room-level accuracy of 87.1% and 32.1% less deployment effort on average compared with random beacon placements. The robustness of the algorithm was also examined as the deployed ad-hoc sensor network is subject to various hazards at emergency scenes. Results showed that the room-level accuracy could remain above 80% when up to 54% of all deployed nodes were damaged. The tradeoff between the space division quality and deployment effort was also examined, which revealed the relationship between the total deployment effort and localization accuracy. •Building information modeling is integrated into indoor localization.•Ad-hoc networks are optimized to increase accuracy and reduce deployment effort.•The proposed algorithm bears high robustness against damages on emergency scenes.•The proposed algorithm provides a tradeoff between accuracy and deployment effort.