On the Impact of the RPL Decreased Rank Attack on 6TiSCH Networks
Bibliographic record
Abstract
The 6TiSCH protocol stack was designed to provide a reliable and time-bound multi-hop routing solution for the Industrial Internet of Things (1IoT), integrating the IPv6 Routing Protocol for Low Power and Lossy Networks (RPL) and Time-Slotted Channel Hopping (TSCH) protocols seamlessly. However, deviations from standard procedures by malicious nodes can severely affect network formation and operations, resulting in degraded performance. This study investigates the impact of the decreased rank attack (DRA) on RPL within the 6TiSCH network, examining its effects on network operations and per-formance. Experimental evaluation reveals that DRA can lead to an average delay of 21.4% in network formation and run-time disruptions, thereby affecting the 6TiSCH synchronization process. Consequently, node departures from the network and subsequent rejoining to maintain the topology increase by an average of 60.71 %. To deal with this disruption, the nodes need to send more keep-alive and RPL messages, resulting in an average increase of 69.70% and 20.08%, respectively, leading to a 49.9% increase in energy depletion. Furthermore, the D RA reduces the packet delivery ratio and increases the average end-to-end packet delay by averages of 5.13% and 20%, respectively. However, it is important to note that the impact of the attack can vary significantly depending on the specific configurations and setups used, such as network topology, network size, attacker position, neighboring density, and RPL/TSCH parameter settings.
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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 | 0.008 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".