Device Grouping for Fast and Efficient Channel Access in IEEE 802.11ah Based IoT Networks
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Bibliographic record
Abstract
The recent advances in Internet of Things (IoT) have led to numerous emerging applications ranging from eHealthcare to industrial control, which often demand stringent Quality of Service (QoS) requirements such as low-latency and high system reliability. However, the ever-increasing number of connected devices in ultra-dense IoT networks and the dynamic traffic patterns increase the channel access delay and packet collision rate. In this regard, this paper proposes a sector-based device grouping scheme for fast and efficient channel access in IEEE 802.11ah based IoT networks such that the total number of the connected devices within each sector is dramatically reduced. In the proposed framework, the Access Point (AP) divides its coverage area into different sectors, and then each sector is further divided into distinct groups based on the number of devices and their location information available from the cloud-center. Subsequently, individual groups within a sector are assigned to specific Random Access Window (RAW) slots, and the devices within distinct groups in different sectors access the allocated RAW slots by employing a spatial orthogonal access mechanism. The performance of the proposed sectorized device grouping scheme has been analyzed in terms of system delay and network throughput. Our simulation results show that the proposed scheme can significantly enhance the network throughput while simultaneously decreasing the system delay as compared to the conventional Distributed Coordination Function (DCF) and IEEE 802.11ah grouping scheme.
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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