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논문 기본 정보

자료유형
학술저널
저자정보
Jalsan, Khash-Erdene (Structural Engineering Research Laboratory, Empa, Swiss Federal Laboratories for Materials Science and Technology) Soman, Rohan N. (Department of Civil Engineering and Geomatics, Cyprus University of Technology) Flouri, Kallirroi (Structural Engineering Research Laboratory, Empa, Swiss Federal Laboratories for Materials Science and Technology) Kyriakides, Marios A. (Department of Civil Engineering and Geomatics, Cyprus University of Technology) Feltrin, Glauco (Structural Engineering Research Laboratory, Empa, Swiss Federal Laboratories for Materials Science and Technology) Onoufriou, Toula (Department of Civil Engineering and Geomatics, Cyprus University of Technology)
저널정보
테크노프레스 Smart structures and systems Smart structures and systems 제14권 제1호
발행연도
2014.1
수록면
39 - 54 (16page)

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Node layout optimization of structural wireless systems is investigated as a means to prolong the network lifetime without, if possible, compromising information quality of the measurement data. The trade-off between these antagonistic objectives is studied within a multi-objective layout optimization framework. A Genetic Algorithm is adopted to obtain a set of Pareto-optimal solutions from which the end user can select the final layout. The information quality of the measurement data collected from a heterogeneous WSN is quantified from the placement quality indicators of strain and acceleration sensors. The network lifetime or equivalently the network energy consumption is estimated through WSN simulation that provides realistic results by capturing the dynamics of the wireless communication protocols. A layout optimization study of a monitoring system on the Great Belt Bridge is conducted to evaluate the proposed approach. The placement quality of strain gauges and accelerometers is obtained as a ratio of the Modal Clarity Index and Mode Shape Expansion values that are computed from a Finite Element model of the monitored bridge. To estimate the energy consumption of the WSN platform in a realistic scenario, we use a discrete-event simulator with stochastic communication models. Finally, we compare the optimization results with those obtained in a previous work where the network energy consumption is obtained via deterministic communication models.

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