Special issue on “Ubiquitous clouds and communication networks”
Bibliographic record
Abstract
The recent technological enhancement in the communication and computing paradigm paves way for the development of next-generation ubiquitous IoT systems and applications. Moreover, the 5G and technology beyond are making huge differences on both computing and communication services. As the connectivity increases, the sheer volume of things connected through wireless mode for various real-time applications will not be efficiently handled by the existing technologies. The next-generation ubiquitous clouds and communication networks will gain the ability to leverage on-demand access through internet to utilize a shared pool of configurable communication resources. The ubiquitous cloud and communication platforms are widely used as services for data management, processing, and storage, whereas it can also enhance its reliability, flexibility, and efficiency by reducing the complexities. The research papers for this special issue were selected from among all the accepted papers by the special issue guest editors Dr. V. Suma, Dr. Ram Palanisamy, Dr. Xavier Fernando, and Dr. Robert Bestak based on the scope and objective of the journal. We appreciate the willingness of the authors to help in organizing this special issue. This special issue contains a collection state-of-the-art research articles with the objective to share research ideas and techniques in the areas of ubiquitous clouds and communication with the underlying technologies and applications, which include internet architecture and protocol, data analysis, information management, and its security. In particular, the articles included in the special issue analyzes the emerging state-of-the-art research areas like big data, cloud, cyber physical systems, and IoT to develop innovative solutions in order to overcome the existing challenges in computing and communication domains. This remains more essential to comply with the necessities of ubiquitous computing and communication applications. As one of the intended goals, the research articles aim to propose various network optimization algorithms, cloud/edge computing architectures, network routing protocols and task scheduling, self-organized networks, heterogeneous 5G networks, and Artificial Intelligence (AI) frameworks to propose the diverse range of features of reliability, flexibility, security, privacy, trust in ubiquitous clouds, and communication networks. Furthermore, this special issue also examines the significant network theories, formulates significant communication applications, and devises innovative methods to overcome the significant challenges that this research area poses.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.087 | 0.042 |
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".