New Routing Protocol for Reliability to Intelligent Transportation Communication
Bibliographic record
Abstract
Internet of Things (IoT) a paradigm that brought several new communication technologies, allowing more ubiquity and real-time applications. This innovation sped up the implementation of intelligent transportation systems in smart cities. However, the use of these technologies needs the original routing protocols. The latters must meet real-time application requirements, such as reduced transmission delay, minimal packet loss, and less power consumption. This paper comes up with a novel solution LoRaWAN-based Geographic Routing Protocol (LGRP) using a multi-criteria metric taking into account delay, packet loss, distance, and relative velocity. The hybridization of LoRaWAN with 802.11p technologies is introduced to overcome challenges of urban scenarios in our protocol achievement. We carry out the routing protocol using the Network Simulator 3 (NS-3). Then, we assess its effectiveness in comparison with the greedy perimeter stateless routing (GPSR), the Ad hoc On-Demand Distance Vector (AODV), the Cross-Layer Weighted Position-based Routing (CLWPR), and the blended OpenFlow-Optimized Link State Routing (Centralized). The simulation results show that the proposed routing protocol outmatches the comparative ones in packet delivery and end-to-end delay.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".