Suitability of SDN and MEC to facilitate digital twin communication over LTE-A
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Haptic is the modality that complements traditional multimedia, i.e., audiovisual, to evolve the next wave of innovation at which the Internet data stream can be exchanged to enable remote skills and control applications. This will require ultra-low latency and ultra-high reliability to evolve the mobile experience into the era of Digital Twin and Tactile Internet. While the 5th generation of mobile networks are not yet widely deployed, Long-Term Evolution (LTE-A) latency remains much higher than the 1 ms requirement for the Tactile Internet and therefore the Digital Twin. This work investigates an interesting solution based on the incorporation of Software-defined networking (SDN) and Multi-access Mobile Edge Computing (MEC) technologies in an LTE-A network, to deliver future multimedia applications over the Tactile Internet while overcoming the QoS challenges. Several network scenarios were designed and simulated using Riverbed modeler and the performance was evaluated using several time-related Key Performance Indicators (KPIs) such as throughput, End-2-End (E2E) delay, and jitter. The best scenario possible is clearly the one integrating MEC and SDN approaches, where the overall delay, jitter, and throughput for haptics- attained 2 ms, 0.01 ms, and 1000 packets per second. The results obtained give clear evidence that the integration of, both SDN and MEC, in LTE-A indicates performance improvement, and fulfills the standard requirements in terms of the above KPIs, for realizing a Digital Twin/Tactile Internet-based system.
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.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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 it