An Efficient Routing Algorithm for Self-Organizing Networks in 5G-Based Intelligent Transportation Systems
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
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.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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