Virtualization of Wireless Sensor Networks Through MAC Layer Resource Scheduling
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we propose a joint throughput and time-resource allocation scheme for the virtualization of IEEE 802.15.4-based wireless sensor networks (WSNs). Virtualization is realized through utilization of the guaranteed time slot (GTS) mechanism of cluster-tree topology to schedule resources on a media access control (MAC) layer. We develop a scheduler that is located in the personal area network (PAN) coordinator and that virtualizes the network into an aggregate of independent profiles, assigning the available resources to each profile with end-to-end (ETE) delay guarantees. The scheduler solves the problem of managing resources available in the network in an optimization framework, taking into consideration the individual profile and sensor requirements. Moreover, it uses the proposed heuristic fair resource allocation (FRA) algorithm to derive the solution in polynomial time. We validate the scheduling performance via discrete event simulation (DES) and compare the proposed FRA algorithm with round robin (RR) and proportionally fair (PF) scheduling algorithms in several scenarios. The proposed scheme demonstrates efficient resource management while maintaining profile isolation in all cases, whereas other algorithms lead to increased latency and lower throughput in the network.
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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 | 0.001 | 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 | 0.001 |
| 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 it