Coexistence Analysis of H2H and M2M Traffic in FiWi Smart Grid Communications Infrastructures Based on Multi-Tier Business Models
Bibliographic record
Abstract
In this paper, we study the performance of multi-tier integrated fiber-wireless (FiWi) smart grid communications infrastructures based on low cost, simple, and reliable next-generation passive optical network (PON) with quality-of-service (QoS) enabled wireless local area networks (WLANs) in terms of capacity, latency, and reliability. We study the coexistence of human-to-human (H2H), e.g., triple-play traffic and machine-to-machine (M2M) traffic originating from wireless sensors operating on a wide range of possible configurations. Our analysis enables the quantification of the maximum achievable data rates of both event- and time-driven wireless sensors without violating given upper delay limits of H2H traffic. By using experimental measurements of real-world smart grid applications, we investigate the impact of variable H2H traffic loads on the sensor end-to-end delay performance. The obtained results show that a conventional Ethernet PON may cause a bottleneck and increase the delay for both H2H and M2M traffic. In contrast, by using a 10 G-EPON or wavelength division multiplexing (WDM) PON, the bottleneck arises in the wireless network. Furthermore, we study the interplay between time- and event-driven nodes and show that the theoretical upper bound of time-driven sensors decreases linearly as a function of the number of sensors, whereas with event-driven sensors, the upper bound decrease is nonlinear and more pronounced.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".