New Routing Protocol for Reliability to Intelligent Transportation Communication
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
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.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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