NoPSM: A Concurrent MAC Protocol over Low-Data-Rate Low-Power Wireless Channel without PRR-SINR Model
Bibliographic record
Abstract
Concurrent MAC protocols can improve channel usage of wireless sensor networks (WSNs), and provide a high-performance infrastructure for data intensive applications. Most of the existing concurrent MAC protocols are based on proactively constructed physical interference models, i.e., PRR-SINR models (PSM). However, it incurs relatively high bandwidth and energy overheads to construct PSM for WSNs. In this paper, we propose NoPSM, which does not take PSM as base to determine transmission concurrency. Instead, the base of NoPSM is reactively constructed interference relationships by passively analyzing overlapping relationships among time logs of block data transmissions and corresponding reception status of each packet in blocks. In this way, NoPSM has two salient features. First, NoPSM is able to construct interference relationships among nodes quickly and accurately along with block data transmissions without needs of network downtime. Second, based on the constructed interference relationships, NoPSM can make decisions of transmission concurrency with a comprehensive criterion, which not only estimates quality of any active links after initiating a new link, but also estimates throughput improvement gained from concurrent transmissions. NoPSM has been implemented in Tinyos-2.1 and extensively evaluated in TOSSIM. Experimental results show that NoPSM improves system throughput by up to 60 percent compared with a traditional CSMA protocol, which cannot exploit potential transmission concurrency. Moreover, NoPSM can gain up to 55 percent throughput improvement as compared to an existing reactive concurrent MAC.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".