OSCAR: An Optimized Scheduling Cell Allocation Algorithm for Convergecast in IEEE 802.15.4e TSCH Networks
Bibliographic record
Abstract
Today's wireless sensor networks expect to receive increasingly more data from different sources. The Time Slotted Channel Hopping (TSCH) protocol defined in the IEEE 802.15.4-2015 version of the IEEE 802.15.4 standard plays a crucial role in reducing latency and minimizing energy consumption. In the case of convergecast traffic, nodes close to the root have consistently heavy traffic and suffer from severe network congestion problems. In this paper, we propose OSCAR, an novel autonomous scheduling TSCH cell allocation algorithm based on Orchestra. This new design differs from Orchestra by allocating slots according to the location of the node relative to the root. The goal of this algorithm is to allocate slots to nodes according to their needs. This algorithm manages the number of timeslots allocated to each node using the value of the rank described by the RPL routing protocol. The goal is that the closer the node is to the root, the more slots it gets in order to maximize the transmission opportunities. To avoid overconsumption, OSCAR sets up a mechanism to adjust the radio duty cycle of each node by reducing the slots allocated to inactive nodes regardless of their position in the network. We implement OSCAR on Contiki-ng and evaluate its performance by both simulations and experimentation. The performance assessment of OSCAR shows that it outperforms Orchestra on the average latency and reliability, without significantly increasing the average duty cycle, especially when the traffic load is high.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".