Latency Minimization in Wireless IoT Using Prioritized Channel Access and Data Aggregation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Future Internet of Things (IoT) networks are expected to support a massive number of heterogeneous devices/sensors in diverse applications ranging from eHealthcare to industrial control systems. In highly-dense deployment scenarios such as industrial IoT systems, providing reliable communication links with low-latency becomes challenging due to the involved system delay including data acquisition and processing latencies at the edge-side of IoT networks. In this regard, this paper proposes a priority-based channel access and data aggregation scheme at the Cluster Head (CH) to reduce channel access and queuing delays in a clustered industrial IoT network. First, a prioritized channel access mechanism is developed by assigning different Medium Access Control (MAC) layer attributes to the packets coming from two types of IoT nodes, namely, high-priority and low-priority nodes, based on the application-specific information provided from the cloud-center. Subsequently, a preemptive M/G/1 queuing model is employed by using separate low-priority and high- priority queues before sending aggregated data to the Cloud. Our results show that the proposed priority-based method significantly improves the system latency and reliability as compared to the non-prioritized 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 | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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