Cyber-Physical-Social Systems: A State-of-the-Art Survey, Challenges and Opportunities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
It is the overriding trend of the present-day world that traditional systems and mobile devices are currently transforming into intelligent systems and smart devices. Against this backdrop, cyber-physical systems (CPSs) and Internet-of-Things (IoT) emerge as the times require. To achieve the parallel interactions between the human world and the computer network, IoT along with wireless mobile communication and computing open up some future opportunities as well as challenges for constructing a novel cyber-physical-social system (CPSS) that takes human factors into account during the system operation and management. In this article, a brief comprehensive survey is provided on some of the current research work that contributes to enabling CPSSs. Some crucial aspects of CPSSs are identified, including: the development from CPSs to CPSSs, architecture design, applications, standards, real-world case studies, enabling techniques and networks for CPSSs. To lay a foundation for the development of the upcoming smart world, we further propose a virtualization architecture and an integrated framework of caching, computing and networking for CPSSs. Simulations verify the performance improvement of the proposals. At last, some research issues with challenges and possible solutions are unearthed for researchers in the related research areas.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it