An Efficient MAC Protocol with Correlated Connection Arrival and Variable Slot Assignment in Wireless Sensor Networks
Bibliographic record
Abstract
We present a novel analytical framework to investigate the performance of activity based TDMA slot scheduling under varying traffic conditions in cluster-based wireless sensor networks. Dynamic TDMA Slot Scheduling (DTSS) scheme is presented to analyze the channel utilization and packet dropping probability in bursty network environment under low and high traffic conditions with a large number of sensor nodes in a single cluster. DTSS 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 sensors nodes which have data to send, and requested number of TDMA slots is assigned to them dynamically. We have modeled 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 compared DTSS with traditionally proposed static TDMA model and proved substantial improvement in the bandwidth efficiency and decrease in connection blocking probability in low activity networks. The drawback of the proposed system is also investigated. Results show that the efficiency of the MAC protocol can be increased significantly using the proposed model with reasonably low packet dropping probability.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".