An Efficient Routing Algorithm for Self-Organizing Networks in 5G-Based Intelligent Transportation Systems
Bibliographic record
Abstract
The rapid growth of the consumer Internet of Things (CIoT) has resulted in substantial enhancements in networking and data analytics. The emergence of customer-centric communication technologies like mobile ad hoc networks (MANETs) and vehicular ad hoc networks (VANETs), also known as self-organizing networks (SONs), along with the integration of 5G Internet of Things and artificial intelligence, has paved the way for intelligent transportation systems (ITS). In SONs, each vehicle serves as a network node. Hence, it is essential for these network nodes to interact, communicate, and exchange data in a flexible, efficient, and convenient manner. SONs offer revolutionary communication capabilities by enabling self-configuration, self-setting, and autonomous data transmission. However, one significant drawback of SONs is their poor network performance. In this study, we have developed an efficient routing scheme that aims to enhance the performance and reduce energy consumption of SONs for ITS in smart cities with low mobility speeds. The simulation results clearly show that the proposed algorithm performs better than conventional routing protocols in low-speed mobility scenarios. It outperforms in terms of network lifetime, packet delivery ratio, and latency.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".