EM-RPL: Enhanced RPL for Multigateway Internet-of-Things Environments
Bibliographic record
Abstract
The IPv6 routing protocol for low power and lossy networks (RPL) has some shortcomings, such as high packet loss rate and low network lifetime when used in Internet-of-Things (IoT) environments under heavy traffic. To overcome the RPL limitations, the current research tends to focus on a new paradigm of routing, referred to as Anycast Routing, in which a source node targets a set of destinations rather than a single one. In this article, we present a protocol that exploits the anycast perspective in the routing process since an important aspect of most IoT environments is to forward packets to a gateway, no matter which. Besides, we interconnect various instances of RPL to achieve better routing performance by offering the possibility of cooperation among various simultaneous instances of RPL within the network. Finally, to reach higher performance, we use a rank computation and parent selection mechanism that is different from those of the RPL. The evaluation results, which are obtained through simulation with the Cooja simulator, show that EM-RPL outperforms RPL in reducing the environmental footprint of the network, while it extends the network lifetime, decreases packet loss ratio, and better controls interpacket intervals and parent change overhead.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".