Smart systems: A review of theory, applications, and recent advances
Bibliographic record
Abstract
Rapid technological advancements have permeated numerous professional fields, transforming mundane tasks and complex operations alike, with examples evident in smart cities, healthcare, and various industries. As a result, a significant surge in the literature concerning smart systems is observed, as is the rise of pragmatic implementations of such systems. In this comprehensive survey paper, we decompose the cumulative smart system architecture into five fundamental components, namely: control, perception, knowledge, communication, and security. Inspired by the underlying notions of cognitive dynamics theory, each component is discussed in detail and categorized, thoroughly detailing necessary concepts and functionality. To add, we discuss the state of the art with respect to each of these components and the most impactful applications of smart systems. From this, gaps in smart systems literature can be identified, where future work is proposed to rectify shortcomings in published methods. This work therefore has foremost utility to those investigating smart systems from an academic standpoint, with the goal of examining the smart system taxonomy and the most modern methods. In addition to further defining the smart system framework, our analysis concluded that the most increasingly researched, and most important components in advancing smart systems applications are knowledge and security. Primarily, this is motivated by aspirations towards safe, adaptive, and robust data-driven autonomy in large scale systems. We conclude that blockchain, IoT, and machine learning protocols and technologies are continuously developing topics that will be essential in smart system advancement.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".