Devices to Devices (Ds2Ds) Communication: Towards Energy Efficient IoT
Bibliographic record
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 part of future 5G 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 benefits of this technology. In this article we propose a new method for device centric communication in IoT system, where multiple source IoT devices can send data to multiple destination IoT devices using multiple interfaces. This method is called devices to devices (Ds2Ds) communication. A tree search algorithm is proposed to select the optimal source IoT devices, destination IoT devices and radio interfaces. The results of proposed Ds2Ds communication are benchmarked against Ds2D and MH- D2D. Extensive simulation has been carried out to compare energy efficiency per source device. The simulation results show the superiority of Ds2Ds over Ds2D in terms of energy efficiency, which, in turn implies better throughput. 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".