Scheduling for Scalable Energy-Efficient Localization in Mobile Ad Hoc Networks
Bibliographic record
Abstract
Existing localization schemes in wireless ad hoc networks rely on redundant measurements from multiple devices with known positions in order to reduce error. However, when node density is high this can result in excessive localization messages with minimal improvement on position accuracy. In this work we present a scheduling algorithm to select a subset of active reference nodes to be used in localization, which has the effect of reducing message overhead, increasing network lifetime, and improving localization accuracy in dense mobile networks. We investigate the Cramer-Rao Lower Bound (CRLB) and existing single-hop localization techniques to determine the optimal average node density to ensure sufficient estimation accuracy. The correctness and effectiveness of the proposed scheme is evaluated through extensive simulation results, which show that in dense networks localization messages are greatly reduced and network lifetimes are more than doubled, while maintaining high estimation accuracy. Furthermore, computational time of localization algorithms is reduced, which effectively decreases accumulated error due to computation latency when locating a mobile device.
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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 | 0.000 | 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".