A Time & Energy Efficient Topology Discovery and Scheduling Protocol for Wireless Sensor Networks
Bibliographic record
Abstract
TDMA-based MAC protocols are considered an energy efficient solution to prolong wireless sensor network lifetime. The topology learning and collection process, together with the used scheduling scheme, are essential parts in the design of such MAC protocols. Previous MAC and multihop scheduling protocols rely completely on CSMA to exchange topology scheduling information. However, for large or dense sensor networks, CSMA may lengthen the time it takes to reach a state in which enough information has been collected to build a highly conflict free multihop schedule. In addition, due to the nature of CSMA, collisions may occur during packet transmission. These factors cause energy waste in an environment where energy resources are scarce. In this paper, we propose PROGRESSIVE, a time and energy efficient topology discovery and multihop scheduling protocol that progressively schedules nodes as their topology information becomes available at the sink. The proposed protocol controls the time during which CSMA is used for control message transmission, and hence, energy consumption is reduced. Simulation results show that PROGRESSIVE is able to schedule a large number of nodes in less time and energy compared to DRAND.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".