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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".