Security-oriented sensor placement in intelligent buildings

• Published in: Building and environment Vol. 63; pp. 114 - 121
• Main Authors: Eliades, D.G., Michaelides, M.P., Panayiotou, C.G., Polycarpou, M.M.
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.05.2013; Elsevier
• Subjects: Applied sciences; Building technical equipments; Buildings; Buildings. Public works; Computation methods. Tables. Charts; Exact sciences and technology; Indoor air quality; Intelligent buildings; Measurements. Technique of testing; Miscellaneous; Multi-objective optimization; Pollution indoor buildings; Sensor placement; Structural analysis. Stresses; Intelligent buildings; Sensor placement; Multi-objective optimization; Indoor air quality; Script; Measurement sensor; Indoor climate; Multiobjective programming; Air quality; Modeling; Contamination; Optimization; Design; Case study; Intelligent building; Safety; Localization; Monitoring
• ISSN: 0360-1323; 1873-684X
• DOI: 10.1016/j.buildenv.2013.02.006

Intelligent buildings are beginning to utilize sensor networks for monitoring and protecting indoor air quality against contamination events. This paper presents a methodology for determining where to install such sensors. In particular, a multi-objective optimization problem is formulated for minimizing the sensor cost, the average and the worst-case impact damage corresponding to a set of contamination event scenarios. Each contamination scenario is comprised of parameters characterized by some given probability distribution. Based on these distributions, a set of representative contamination scenarios is constructed through grid and random sampling, and the overall impact of each scenario is computed, thus providing a solution to the sensor placement problem. The proposed methodology is illustrated by two case studies, a simple building with five rooms and a realistic building with 14 rooms. ► Installation of air quality sensors in indoor building environments. ► Multi-objective optimization program that minimizes sensor cost and impact risk. ► Contamination scenarios construction based on probability distributions. ► Sensor placement optimization with respect to the contamination scenarios. ► Evaluation of methodology using building case studies.