Lifetime Maximization of UWB-Based Sensor Networks for Event Detection Applications
Bibliographic record
Abstract
Ultra wideband (UWB) technology is well suited for communication in wireless sensor networks, in which low power consumption for data transmission and the availability of precise ranging information are highly desirable. In this paper, we consider the application of UWB in an event-detection sensor network, and we are interested in maximizing the network operational lifetime while satisfying requirements on the detection and false alarm probabilities. Towards the goal of lifetime maximization we (i) jointly find the optimal routes from multiple events to the sink, to avoid the bottleneck-node phenomenon which often limits lifetime, and (ii) allow adjustment of the data rate that each sensor generates to contribute to event detection, in order to balance the energy consumption among the nodes in the network. Using the UWB signal characteristics, we present a convex optimization model to solve the lifetime maximization problem. The numerical results show that the proposed framework leads to significant improvements in network operational lifetime compared to benchmark approaches.
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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".