Lifetime Maximization in UWB Sensor Networks for Event Detection
Bibliographic record
Abstract
The operational lifetime of a wireless sensor network (WSN) for event detection is determined by the maximum time that the network is able to meet given detection requirements (DRs), i.e., the probabilities of detection and false alarm demanded by the application. In this paper, we address the problem of maximizing lifetime of such WSNs through optimizing quantization and routing of event measurements. In particular, we consider the task of monitoring multiple events and reporting the observations to a sink, whereby sensor nodes adapt their data generation rate and the data flow distribution in the network for the purpose of lifetime maximization. We make use of ultra-wideband (UWB) signaling at the physical layer, which is well-suited for event-detection WSNs, because of the low-energy consumption for data transmission and relative robustness to multiuser interference. Expressing the DRs as convex constraints in the optimization variables, we present a convex-optimization framework for lifetime maximization of event detection UWB-based WSNs. Furthermore, based on the dual decomposition approach, we propose a decentralized algorithm, which makes it possible to solve the lifetime-maximization problem in a distributed manner and thus shares the computational complexity among network nodes and is robust to artifacts like node failures. Numerical results show that the proposed joint adaptation of quantization and routing leads to significant improvements in network operational lifetime compared to benchmark approaches known from literature.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".