Topology Control for Internet of Underwater Things
Bibliographic record
Abstract
Internet of Underwater Things (IoUTs) has gained increased attention thanks to the recent developments in underwater communication and sensing technologies. IoUTs will employ heterogeneous underwater sensor nodes with diverse underwater communication technologies, for sensing their surrounds and improve ocean awareness. Despite the current advancements (e.g., energy harvesting, software-defined underwater networking, programmable underwater nodes), many critical challenges still need to be solved towards the large-scale, autonomous and efficient data collection from the oceans. In this regard, IoUTs' topology control (TC) can be explored to improve networking services. However, the classical solutions employed for TC will not be suitable for IoUTs, given the heterogeneity of nodes, underwater communication technologies, and the requirements of concurrent underwater monitoring applications. This paper discusses the challenges for the design of TC algorithms for IoUTs. We present recent advances in terms of physical and networking layers of IoUTs and highlight how such developments challenge the design of TC algorithms for IoUTs. Furthermore, we shed light on novel directions to be explored for TC in IoUTs, as well as we provide guidelines for the design of innovative TC algorithms. Finally, we present future research directions that need to be addressed.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".