The Internet of Things creates opportunities to develop data-driven design methodologies for smart cities. However, effects rather than causes are often measured in complex urban systems, requiring robust data-interpretation methodologies. Additionally, effective monitoring of large urban components, such as civil infrastructure, often involves multiple sensor devices and invasive sensor systems. In these situations, the design of measurement systems is an important task. Usually, this task is carried out by engineers using only qualitative rules of thumb and experience. Recently, researchers have developed quantitative sensor-placement methodologies to maximize the information gain of measurement systems. Nonetheless, these methodologies are only weakly validated using field measurements due to the small amount of data collected and the difficulties comparing the predicted information gain with observations. This paper proposes a validation strategy for sensor-placement methodologies. In this strategy, predictions of both individual sensor and sensor-configuration performances are compared with observations using statistical tests and hypothesis testing. The validation procedure is illustrated through three full-scale-bridge case studies. This strategy helps engineers select an appropriate methodology to design measurement systems in order to optimize data collection using sensors.