An Efficient TDMA Scheme with Dynamic Slot Assignment in Clustered Wireless Sensor Networks
Bibliographic record
Abstract
In this paper, we present an efficient MAC layer scheme using Dynamic Slot Assignment (DSA) in TDMA-based MAC protocols for cluster-based wireless sensor networks. The DSA scheme is presented to analyze the energy efficiency and channel utilization in a bursty traffic environment under low traffic conditions with a large number of sensor nodes in a single cluster. The DSA scheme will allow the network to adapt to the changing traffic load. Based on the network activity, the connection is established between the cluster-head node and those sensor nodes which have data to send, and a TDMA slot is assigned to each of them dynamically. We present the complete system model and model the data traffic by a correlated stochastic process (i.e. Markov chain) for a network where the second and subsequent connection arrival rate is dependent on the first arrival rate. We numerically compare our DSA with a traditionally proposed static TDMA model, the BMA model, and a variant of the BMA model (EA-TDMA), and prove substantial improvement in the energy consumption and channel utilization in low activity sensor networks. Results show that the efficiency of the MAC protocol can be increased significantly using the presented model.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| 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 | 0.001 |
| 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 source (direct Gemma or distilled Codex), 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".