An energy‐efficient localized topology control algorithm for wireless<i>ad hoc</i>and sensor networks
Bibliographic record
Abstract
Abstract Topology control plays an important role in the design of wirelessad hocand sensor networks and has demonstrated its high capability in constructing networks with desirable characteristics such as sparser connectivity, lower transmission power, and smaller node degree. However, the enforcement of a topology control algorithm in a network may degrade the energy‐draining balancing capability of the network and thus reduce the network operational lifetime. For this reason, it is important to take into account energy efficiency in the design of a topology control algorithm in order to achieve prolonged network lifetime. In this paper, we propose a localized energy‐efficient topology control algorithm for wirelessad hocand sensor networks with power control capability in network nodes. To achieve prolonged network lifetime, we introduce a concept called energy criticality avoidance and propose an energy criticality avoidance strategy in topology control and energy‐efficient routing. Through theoretical analysis and simulation results, we prove that the proposed topology control algorithm can maintain the global network connectivity with low complexity and can significantly prolong the lifetime of a multi‐hop wireless network as compared with existing topology control algorithms with little additional protocol overhead. Copyright © 2008 John Wiley & Sons, Ltd.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".