MABO‐TSCH: Multihop and blacklist‐based optimized time synchronized channel hopping
Bibliographic record
Abstract
Abstract Emerging Industrial Internet of Things applications, such as smart factories, require reliable communication and robustness against interference from colocated wireless systems. To address these challenges, frequency‐hopping spread spectrum has been used by different protocols, including IEEE802.15.4‐2015 TSCH. Frequency‐hopping spread spectrum can be improved with the aid of blacklists to avoid bad frequencies. The quality of channels in most environments shows significant spatial‐temporal variation, which limits the effectiveness of simple blacklisting schemes. In this article, we propose an enhanced blacklisting solution to improve the TSCH protocol. The proposed algorithms work in a distributed fashion, where each pair of receiver/transmitter nodes negotiates a local blacklist, based on the estimation of packet delivery ratio. We model the channel quality estimation as a multiarmed bandit problem and show that it is possible to create blacklists that provide results close to optimal without any separate learning phase. The proposed algorithms are implemented in OpenWSN and evaluated through simulations in 2 different scenarios with about 40 motes and experiments using an indoor testbed with 40 TelosB motes.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".