Relay node placement with energy and buffer constraints in wireless sensor networks using mobile data collector
Why this work is in the frame
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Bibliographic record
Abstract
Higher-powered relay nodes, used as cluster heads in hierarchical sensor networks, can improve network performances. Most existing relay node placement strategies consider only stationary nodes, where data are routed to the base station(s), possibly using multiple hops. We consider a relay node based network, where a Mobile Data Collector (MDC) collects data from each relay node and delivers the collected data to the base station. This reduces the energy dissipation of the relay nodes by relieving them of the burden of transmitting data over longer distances. The issue is to find the minimum number of relay nodes, along with their locations, such that network coverage and lifetime requirements are satisfied. We present an integrated Integer Linear Program (ILP) formulation that determines an optimal relay node placement scheme, which ensures that there is no data loss due to buffer overflow and the energy dissipation does not exceed a specified level.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it