Lightweight energy-cost-efficient RAT association for Internet of Things
Bibliographic record
Abstract
In an Internet of things (IoT) context, the existence of multiple technologies, that ensure communication between each node and the entire network, aims to improve the overall capacity to satisfy the growing number of connected devices. However, to meet a high performance, it is important to assign the right device to the right radio access technology (RAT). In this paper, we are interested in a radio access technology selection in an IoT context. We answer the question how to select the right RAT taking into account two main parameters: energy efficiency and cost to connect to a RAT, and using two different approaches. The first one is a fully distributed approach that allows to devices to autonomously select the best RAT that meets their preference requirement, but also satisfies the RATs access points utilities. The second approach relies on a centralized entity that ensures association between IoT devices and RATs using a binary knapsack optimization problem. The results obtained through simulations show that when it comes to energy efficiency and fairness among devices, the matching approach outperforms the knapsack based scheme.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".