# Robust Simulation of Cyber-Physical Systems for Environmental Monitoring on Construction Sites
## 3 Research Framework  
According to the characteristics of robustness, the inputs-processes-outputs (I-P-O) paradigm is used to develop a robust simulation I-P-O framework for the construction of site monitoring and decision-making. With the deepening of ontology theory and construction technology, ontology has been utilized in the construction field to realize the integration and sharing of knowledge information on construction sites. The existing studies attempted to identify construction safety risks by developing domain ontology during the construction period [39, 40]. Based on these studies, [PERSON] et al. considered more issues on health and well-being to promote safety and information management on construction sites [41]. Moreover, [PERSON] and [PERSON] combined ontology with CPS to conduct information modeling and decision support on construction sites [42]. This study focuses on environmental issues on construction sites and constructs an environmental monitoring and evaluation model through ontology and CPS. Therefore, this study creates a workflow (Figure 1) that illustrates the prototypical framework to measure the robustness of the E-CPS. Considering that the E-CPS framework has four main layers (perception, transportation fusion, and decision-making layers), the ontology is first used to develop an E-CPS model since the ontology-based model can perform the integration of physical space, cyber space, and social space, as well as assess the semantic collaboration. This can describe the relationships among ontologies and perform the sharing and reuse of data, so that the system can make efficient decisions when facing uncertainty [43]. More precisely, the ontology objects, including projects, equipment, and sensors in the perception layer and the object properties, are created and saved in the ontology library. After the database and rule base are established, the Jena inference engine is used for ontology reasoning. In addition, the inference results are obtained accordingly in Eclipse, in order to perform the early warning and control of TSP, PM2.5, PM10, and noise on construction sites. The data from the environmental monitoring and evaluation indicators are then fused in the fusion layer. Finally, they enter the decision-making layer for project managers, in order to conduct data processing and make decisions based on experts' evaluation and stakeholder interaction. The E-CPS model contains three environmental-monitoring systems with PM10, PM2.5, TSP, and other sensors, as well as five video monitoring sites with three dust reduction controllers: fog cannon, enclosure spray, and tower crane spray. Afterward, the proposed ontology-based E-CPS model faces different uncertainties on construction sites: (1) uncertainties of anomalous data from the ontology objects such as sensors and equipment in the perception layer and the transportation layer; (2) uncertainties of multi-source perception data fusion of rule reasoning and expert scoring in the fusion layer; (3) uncertainties of experts' decision fusion results for project managers in the decision-making layer. Therefore, three categories of uncertainty scenarios of the E-CPS are created according to the uncertainty factors of the four layers in the E-CPS. Figure 1: Overall research framework. As for the anomalous data in the perception layer and the transportation layer, uncertainty detection and processing are conducted in the ontology-based E-CPS model using a spatio-temporal correlation anomaly-recognition algorithm. This improves the E-CPS robustness since the algorithm can use the temporal and spatial correlation of the monitoring data to identify anomalous data [44]. Previous studies have explored some algorithms, including belief functions, as well as D-S evidence theory to conduct uncertainty detection and processing [45; 46; 47].