Proactive maintenance in RPL for 6LowPAN
Bibliographic record
Abstract
Maintenance is a core challenge in all routing protocols. The utilization of IPv6 for Low Power and Lossy Networks (6LowPAN) resulted in the recent standardization of a dedicated routing protocol called RPL (Routing Protocol for Low Power and Losy Neworks). In 6LowPAN, a critical challenge exists in decreasing packet loss under the stringent energy-efficiency mandate to increase network longevity. Moreover, the challenge of failed nodes/links, and operating in a lossy environment where connections require rapid maintenance, present significant challenges. Recent attempts at routing maintenance in RPL presented advancements in handling failures, yet under reactive mechanisms that respond to failures and attempt to reduce network down-time. In this paper we design and implement a proactive RPL (Pro-RPL) maintenance scheme that enables the network to selectively predict and mitigate failures before they impact network connectivity. In Pro-RPL we capitalize on a suffering index that is associated with RPL nodes, and monitors their tendency to result in a failure. This dynamic monitoring is decentralized in nature, and presents a conforming yardstick across RPL nodes, to eliminate overhead in implementation and potential control-traffic over the network. We evaluate the efficiency of Pro-RPL in reducing packet loss, energy consumption and extending network lifetime via extensive simulations with the Cooja Simulator over the Contiki OS.
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 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".