Designing Run-time Evolution for Dependable and Resilient Cyber-Physical Systems Using Digital Twins
Bibliographic record
Abstract
The proliferation of Smart Cyber-Physical Systems (SCPS) is increasingly blurring the boundaries between physical and virtual entities. This trend is revolutionizing multiple application domains along the whole human activity spectrum, while pushing the growth of new businesses and innovations such as smart manufacturing, cities and transportation systems, as well as personalized healthcare. Technological advances in the Internet of Things, Big Data, Cloud Computing and Artificial Intelligence have effected tremendous progress toward the autonomic control of SCPS operations. However, the inherently dynamic nature of physical environments challenges SCPS’ ability to perform adequate control actions over managed physical assets in myriad of contexts. From a design perspective, this issue is related to the system states of operation that cannot be predicted entirely at design time, and the consequential need to define adequate capabilities for run-time self-adaptation and self-evolution. Nevertheless, adaptation and evolution actions must be assessed before realizing them in the managed system in order to ensure resiliency while minimizing the risks. Therefore, the design of SCPS must address not only dependable autonomy but also operational resiliency. In light of this, the contribution of this paper is threefold. First, we propose a reference architecture for designing dependable and resilient SCPS that integrates concepts from the research areas of Digital Twin, Adaptive Control and Autonomic Computing. Second, we propose a model identification mechanism for guiding self-evolution, based on continuous experimentation, evolutionary optimization and dynamic simulation, as the architecture’s first major component for dependable autonomy. Third, we propose an adjustment mechanism for self-adaptation, based on gradient descent, as the architecture’s second major component, addressing operational resiliency. Our contributions aim to further advance the research of reliable self-adaptation and self-evolution mechanisms and their inclusion in the design of SCPS. Finally, we evaluate our contributions by implementing prototypes and showing their viability using real data from a case study in the domain of intelligent transportation systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".