Energy-Efficient Battery-Aware MAC protocol for Wireless Sensor Networks
Bibliographic record
Abstract
Wireless sensor networks suffer from limited power resources. Therefore, managing the energy constraints and exploring new ways to minimize the power consumption during the operation of the nodes are critical issues. Conventional MAC protocols deal with this problem without considering the internal properties of the sensor nodes’ batteries. However, recent studies about battery modeling and behaviour showed that the pulsed discharge mechanism and the charge recovery effect may have a significant impact on wireless communication in terms of power saving. In this thesis we propose two battery-aware MAC protocols that take benefit of these factors to save more energy and to prolong the lifetime of the nodes/network without affecting the throughput. In both protocols we measure the remaining battery capacity of the node and use that measurement in the back-off scheme. The first protocol gives the nodes with higher remaining battery capacity more priority to access the medium, while the other one provides more medium access priority to the nodes with lower remaining battery capacity. The objective is to investigate, through simulations, which protocol reduces the power consumption of the nodes, improve the lifetime of the network, and compare the results with the CSMA-CA protocol.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".