RPL Point-to-Point Communication Paths: Analysis and Enhancement
Bibliographic record
Abstract
Routing protocol for low-power and lossy networks (RPL) is a standard routing protocol for the Internet of Things (IoT). In RPL, point-to-point (P2P) communication is gaining importance as many emerging IoT applications require efficient P2P communications. In this work, we study the quality of the RPL’s P2P paths. In particular, we analyze how much RPL’s P2P paths “stretch” compared to the shortest paths. We prove that the average stretch is a factor of at least two in any RPL network. That is, the RPL’s P2P path between two randomly selected nodes in any network is expected to be at least twice as long as the shortest path between the two nodes. Furthermore, we show that RPL’s stretch factor can be considerably higher than two in some network topology, including linear networks and grid networks. To improve the quality of RPL’s P2P paths, we propose a solution which is simple to implement and fully compatible with RPL. Moreover, our solution does not require nodes to store any routing table; this is important as nodes in LLNs are typically highly resource constrained. We evaluate our proposed solution using the Contiki-NG operating system and show that our proposed solution can significantly improve the quality of RPL’s P2P paths and their end-to-end-delays with a modest overhead. In addition, we evaluate our solution in dynamic networks and show that, when the network’s mobility is moderate, our solution generates nearly the same amount of overhead as RPL yet it achieves lower end-to-end delay and power consumption than RPL.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".