Energy-Efficient Cooperative Routing in Wireless Sensor Networks: A Mixed-Integer Optimization Framework and Explicit Solution
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Bibliographic record
Abstract
This paper presents an optimization framework for a wireless sensor network whereby, in a given route, the optimal relay selection and power allocation are performed subject to signal-to-noise ratio constraints. The proposed approach determines whether a direct transmission is preferred for a given configuration of nodes, or a cooperative transmission. In the latter case, for each node, data transmission to the destination node is performed in two consecutive phases: broadcasting and relaying. The proposed strategy provides the best set of relays, the optimal broadcasting power and the optimal power values for the cooperative transmission phase. Once the minimum-energy transmission policy is obtained, the optimal routes from every node to a sink node are built-up using cooperative transmission blocks. We also present a low-complexity implementation approach of the proposed framework and provide an explicit solution to the optimization problem at hand by invoking the theory of multi-parametric programming. This technique provides the optimal solution as a function of measurable parameters in an off-line manner, and hence the on-line computational tasks are reduced to finding the parameters and evaluating simple functions. The proposed efficient approach has many potential applications in real-world problems and, to the best of the authors' knowledge, it has not been applied to communication problems before. Simulations are presented to demonstrate the efficacy of the approach.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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