Outage optimal node placement in ultra-wideband sensor networks
Bibliographic record
Abstract
Ultra-wideband (UWB) wireless technology provides a suitable platform for a variety of sensing applications, since it possesses many useful and unique properties, such as large available bandwidth, low power consumption, precise ranging capability, high signal penetration of UWB signals, etc. However, UWB also introduces new design challenges with regards to medium access control (MAC) in sensor networks, especially if the original benefits of UWB ought to be retained. In this paper, we consider UWB sensor networks with randomMAC which does not require overhead for collision avoidance. We first show that in such a scenario node placement can play a critical role to the performance of the UWB sensor network. Then, we investigate the problem of node placement in UWB sensor networks for achieving the most reliable communication between UWB sensors and the sink. Specifically, the tradeoff between the node density and the sensor-to-sink communication reliability is analyzed, and the optimal node density for the most reliable link is obtained. We present simulation results which confirm the correctness of the analytical method and illustrate the trade-off between energy consumption and performance for different MAC schemes.
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 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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".