A Semantic Data-Based Distributed Computing Framework to Accelerate Digital Twin Services for Large-Scale Disasters

• Published in: Sensors (Basel, Switzerland) Switzerland), 2022-09, Vol.22 (18), p.6749
• Main Authors: Kwon, Jin-Woo, Yun, Seong-Jin, Kim, Won-Tae
• Format: Article
• Language: English
• Subjects: Algorithms; balanced partitioning; computing load prediction; digital twin; Digital twins; Disasters; distributed computing; Distributed processing; Distributed processing (Computers); Emergency management; Environment; Environmental impact; Forest & brush fires; Humidity; large-scale disasters; Load; load balancing; Machine learning; Management; Natural disasters; Semantics; Sensors; Simulation; Simulation models; Technology application; Wildfires
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22186749

As natural disasters become extensive, due to various environmental problems, such as the global warming, it is difficult for the disaster management systems to rapidly provide disaster prediction services, due to complex natural phenomena. Digital twins can effectively provide the services using high-fidelity disaster models and real-time observational data with distributed computing schemes. However, the previous schemes take little account of the correlations between environmental data of disasters, such as landscapes and weather. This causes inaccurate computing load predictions resulting in unbalanced load partitioning, which increases the prediction service times of the disaster management agencies. In this paper, we propose a novel distributed computing framework to accelerate the prediction services through semantic analyses of correlations between the environmental data. The framework combines the data into disaster semantic data to represent the initial disaster states, such as the sizes of wildfire burn scars and fuel models. With the semantic data, the framework predicts computing loads using the convolutional neural network-based algorithm, partitions the simulation model into balanced sub-models, and allocates the sub-models into distributed computing nodes. As a result, the proposal shows up to 38.5% of the prediction time decreases, compared to the previous schemes.