Cooperative High-Rate and Low-Latency Transmission, Employing Two-Tier Narrow-Band Internet-of-Things and Bluetooth Low-Energy Networks
Bibliographic record
Abstract
Recently, narrowband Internet-of-Things (NB-IoT) networks have been on the rise in the IoT field due to their features like low power consumption and high penetration rate. However, NB-IoT’s main drawbacks are its high delays and low data rates. To address these problems, in this paper, we present a two-tier cooperative solution to improve network throughput. Two-tier networks generally consist of cellular and device-to-device (D2D) communications. In this work, we use NB-IoT for cellular networks and Bluetooth low energy (BLE) for D2D communications. By leveraging these communications technologies, we enable idle nodes in a group to assist target nodes download and upload data. In doing this, we aim to maximize throughput, minimize consumed energy, and maximize the total remaining capacity of the node group batteries. To tackle the faced multi-objective optimization problem, we used the non-dominated sorting genetic algorithm (NSGA-II). Only one group gets selected from the candidate groups by adjusting the level of node participation. Simulation results show a 7.7-fold growth of throughput against only an 8 percent increase in energy consumption compared to the baseline download scenario and a 7.6-fold growth of throughput against just a 2 percent increase in energy consumption compared to the baseline upload scenario.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".