| Autor: |
Seongseop Kim, Jeonghun Cho, Daejin Park |
| Jazyk: |
angličtina |
| Rok vydání: |
2018 |
| Předmět: |
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| Zdroj: |
Applied Sciences, Vol 8, Iss 9, p 1466 (2018) |
| Druh dokumentu: |
article |
| ISSN: |
2076-3417 |
| DOI: |
10.3390/app8091466 |
| Popis: |
Discrete event system specification (DEVS) has been widely used in event-driven simulations for sensor-driven Internet of things (IoT) applications, such as monitoring the spread of fire disaster. Event-driven models for IoT sensor nodes and their communication is described in DEVS and they have to be integrated with continuous models of fire-spreading dynamics so that the hybrid system modeling and simulation approach have to be considered for both continuous behavior of fire-spreading and event-driven communications by large-scale IoT sensor devices. The hybrid-integrated modelling and simulation for fire-spreading in wide area and large-scale IoT devices result in more complex model evaluation, including simulation time synchronization, so that simulation acceleration is important by considering scalability in large-scale IoT-driven applications that sense fire-spreading. In this study, we proposed a scalable simulation acceleration of a DEVS-based hybrid system using heterogeneous architecture based on multi-cores and graphic processing units (GPUs). We evaluated the power consumption comparison of the proposed accelerated-simulation approach in terms of the composition of the event-driven IoT models and continuous fire-spreading models, which are tightly described in differential equations across a large number of cellular models. The demonstrated result shows that the full utilization of CPU-GPU integrated computing resources, on which event-driven models and continuous models are efficiently deployed and optimally distributed, could enable an advantage for high-performance simulation speedup in terms of execution time, although more power consumption is required, but the total energy consumption could be reduced due to fast simulation time. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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