Name
Abstract | ||
|---|---|---|
The distributed nature of cyber-physical systems makes reliable communication essential. Hybrid Error Coding (HEC) allows the adaptation of transmission schemes to application requirements (i.e., reliability and latency) and network conditions. However, picking an efficient HEC configuration is a computationally complex search task that must be repeated when network conditions change. In this paper, we introduce DeepHEC, a deep-learning-based approach for inferring coding configurations. Results indicate that DeepHEC is on par with search-based approaches in configuration efficiency, while significantly reducing inference time. In addition, DeepHEC decouples solution space size and inference time, thereby achieving much more predictable inference times that enable adaptive HEC on real-time systems with strict timing requirements. This is especially advantageous for cyber-physical systems that could not previously benefit from adaptive HEC. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/EDCC57035.2022.00015 | 2022 18th European Dependable Computing Conference (EDCC) |
Keywords | DocType | ISSN |
Cyber-Physical Systems,Error Control,Hybrid Error Coding,Deep Neural Networks | Conference | 2641-810X |
ISBN | Citations | PageRank |
978-1-6654-7403-0 | 0 | 0.34 |
References | Authors | |
11 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pablo Gil Pereira | 1 | 0 | 0.34 |
| Andreas Schmidt | 2 | 0 | 0.34 |
| Thorsten Herfet | 3 | 132 | 27.14 |