Efficient multi-receiver message aggregation for short message delivery in M2M networks
Why this work is in the frame
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Bibliographic record
Abstract
In wireless machine-to-machine (M2M) networks, how to efficiently and reliably deliver short, periodic message to a large number of receivers is an important, challenging issue. Automatic Repeat reQuest (ARQ) is a promising technique to provide the reliable communications. However, ARQ affects the transmission efficiency by retransmitting the whole packet even though partial packet has been received successfully. Such effect can be more serious when delivering messages to a large number of receivers. In this paper, we propose a new multireceiver message aggregation (MRMA) scheme and a busy-tone negative acknowledgement (BT-NACK) scheme to jointly improve the communication efficiency and reliability. To further optimize the performance, an integer programming problem is formulated to explore the optimal aggregation configuration. While it is NP-hard to find a global optimal solution, low complexity heuristic algorithms are developed. Simulation results show that our schemes significantly improve the communication efficiency and communication delay.
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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 it