Adaptive time slots control in wireless sensor networks for delay-aware applications
Bibliographic record
Abstract
Wireless Sensor Networks (WSNs) have been proposed for various monitoring applications including environmental, industrial, military and health care. The use of WSNs with cluster-tree topologies for such applications solves the limited coverage issue of the wireless sensor devices and allows them to be deployed in wider area. WSNs with cluster-tree topologies suffer from various problems including accurate synchronization of beacons used in the beacon enabled mode in the IEEE 802.15.4 standard and providing Quality of Service (QoS) to delay-aware applications. In this paper, we present a Time Slot Control (TSC) scheme that can adaptively manage the allocation of time slots in the beacon enabled mode of operation to provide QoS grantees to delay critical traffic. Our proposed scheme can improve the end-to-end delay and throughput of selected traffic types by managing the time slots between sensor devices in an optimum way.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".