Optimized relay repositioning for Wireless Sensor Networks applied in environmental applications
Bibliographic record
Abstract
Nowadays Wireless Sensor Networks (WSNs) are used to provide vast coverage areas in environmental applications, and thus relay nodes with wide transmission ranges are employed. However, these relays usually operate under harsh conditions with a very limited energy resources, making the network very prone to severe node failures and disconnectivities. In this paper, we propose a proactive Optimized Relay Repositioning (ORR) approach in which relays are regularly repositioned to maintain a specific level of fault-tolerance in addition to minimize the total network energy consumption. ORR is a grid-based approach, in which nodes are placed on grid vertices to limit the huge search space in large-scale environmental applications. This approach is formulated as a Mixed Integer Linear Program (MILP) for solid mathematical solutions. Extensive simulations and comparisons, assuming practical considerations of signal propagation and connectivity, show that our fault-tolerant approach can introduce a significant lifetime extension as compared to other heuristic and MILP-based 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 | 0.001 | 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".