Assessing Contiki-NG’s Reliability for RPL-based Trickle Algorithm in IoT Networks
Bibliographic record
Abstract
The IPv6 Routing Protocol for LLNs (RPL) is fundamental to the Internet of Things (IoT), providing essential routing capabilities for devices operating in constrained environments. At the heart of RPL’s efficiency is the Trickle Algorithm (TA), designed to optimize the dissemination of control messages across networks, balancing timely updates with the imperative to conserve bandwidth and energy. This paper focuses on validating the implementation of the TA within the Latest version of Contiki-OS (Contiki-NG), a leading experimental platform for IoT networks. By comparing the results of theoretical models with practical simulations of Contiki-NG, we aim to determine the reliability of the Contiki-NG representation of the TA behaviour. Our analysis reveals a high degree of agreement between theoretical expectations and simulated results, underscoring Contiki-NG’s robustness as a simulation tool for IoT research. The findings affirm the precision of Contiki-NG implementation and highlight the platform’s value in facilitating the development and testing of IoT protocols. By ensuring the reliability of these simulations, our work strengthens the confidence of the IoT research community in using Contiki-NG as a foundation for exploring innovative solutions to the unique challenges of IoT networks.
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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".