Device‐centric communication in IoT: An energy efficiency perspective
Bibliographic record
Abstract
Abstract Emerging device‐centric communication technologies such as device‐to‐device (D2D) communication, devices‐to‐device (Ds2D) communication, and multi‐homing (MH) D2D have been considered as essential parts of future fifth‐generation networks as well as Internet of Things (IoT). The device‐centric communication offers enhanced cellular data rates, high spectral efficiency, reduced latency, improved fairness, better energy efficiency, and extended coverage; however, the battery life of end devices is crucial to fully reap the benefits of this technology. In this article, a new method for device‐centric communication in IoT system is introduced, where multiple source IoT devices (SIDs) can send data to multiple destination IoT devices (DIDs) using multiple radio resource blocks. This method is called devices‐to‐devices (Ds2Ds) communication. The objective is to select the optimal SIDs, DIDs, and radio resource blocks such that the total energy efficiency for all DIDs is maximized. A tree search algorithm is proposed to select the optimal SIDs, DIDs, and radio resource blocks. Extensive simulations have been carried out to compare energy efficiency per SID/DID for Ds2Ds, Ds2D, and MH‐D2D. The simulation results show the superiority of Ds2Ds over Ds2D in terms of energy efficiency, which, in turn, implies better throughput. Furthermore, Ds2Ds is superior to MH‐D2D in terms of energy consumption per source device, a very good and promising requirement for green communication.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".