A distributed battery recovery aware topology control algorithm for wireless sensor networks
Bibliographic record
Abstract
Battery recovery effect is a phenomenon that the available capacity of a battery could increase if the battery can sleep for a certain period of time since its last discharging. Accordingly, the battery can work for a longer time when it takes some rests between consecutive discharging processes than when it works all the time. However, this effect has not been considered in the design of energy-efficient topology control algorithms for wireless sensor networks. In this paper, we propose a distributed battery recovery effect aware connected dominating set constructing algorithm (BRE-CDS) for wireless sensor networks. In BRE-CDS, each network node periodically decides to join the connected dominating set or not. Nodes that have slept in the preceding round have priority to join the connected dominating set in the current round while nodes that have worked in the preceding round are encouraged to take sleep in the current round for battery recovery. Detailed algorithm design is presented. The computational complexity of BRE-CDS is deduced to be O(D2), where D is node degree. Simulation results show that BRE-CDS can significantly prolong the network lifetime as compared with existing work. Copyright © 2016 John Wiley & Sons, Ltd.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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 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".